Research

Data citation and recent lidar research publications updated quarterly

Links to Publications

Data Citation and Acknowledgment
There are no restrictions on the use of IntELiMon products. Please cite the IntELiMon datasets using the information below.

U.S. Geological Survey, 2026, IntELiMon (Interagency Ecosystem Lidar Monitoring): U.S. Geological Survey database, https://doi.org/10.5066/P13T3UG3

FGDC metadata can be found through the USGS Science Data Catalog (SDC) at: USGS Science Data Catalog

Accessed: 

Title Authors Year Citation
Tree volume estimation with terrestrial laser scanning—Testing for bias in a 3D virtual environment Abegg, M., Bösch, R., Kükenbrink, D., Morsdorf, F. 2023
Full Citation
Abegg, M., Bösch, R., Kükenbrink, D., and Morsdorf, F., 2023, Tree volume estimation with terrestrial laser scanning—Testing for bias in a 3D virtual environment: Agricultural and Forest Meteorology, v. 331, article 109348, at https://doi.org/10.1016/j.agrformet.2023.109348..
Terrestrial laser scanning—A new standard of forest measuring and modelling? Åkerblom, M., Kaitaniemi, P. 2021
Full Citation
Åkerblom, M., and Kaitaniemi, P., 2021, Terrestrial laser scanning—A new standard of forest measuring and modelling?: Annals of Botany, v. 128, no. 6, p. 653–662, at https://doi.org/10.1093/aob/mcab111..
Evaluating spatial granularity in tree volume upscaling using LiDAR and orthophotos Ali, M., Lohani, B., Hollaus, M., Pfeifer, N. 2025
Full Citation
Ali, M., Lohani, B., Hollaus, M., and Pfeifer, N., 2025, Evaluating spatial granularity in tree volume upscaling using LiDAR and orthophotos, in India Geoscience and Remote Sensing Symposium, InGARSS 2025, Bhubaneswar, India, 10–13 December 2025, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 691–694, at https://doi.org/10.1109/InGARSS67683.2025.11583977..
Tree species classification from complex laser scanning data in Mediterranean forests using deep learning Allen, M. J., Grieve, S. W. D., Owen, H. J. F., Lines, E. R. 2023
Full Citation
Allen, M.J., Grieve, S.W.D., Owen, H.J.F., and Lines, E.R., 2023, Tree species classification from complex laser scanning data in Mediterranean forests using deep learning: Methods in Ecology and Evolution, v. 14, no. 7, p. 1657–1667, at https://doi.org/10.1111/2041-210X.13981..
An overview of tools and algorithms used to classify, detect, and monitor forest area using LiDAR data Amakhchan, W., El Kharki, O., Cherif, E. K., Wahbi, M., Yazidi Alaoui, O., Maatouk, M., Boulaassal, H. 2023
Full Citation
Amakhchan, W., El Kharki, O., Cherif, E.K., Wahbi, M., Yazidi Alaoui, O., Maatouk, M., and Boulaassal, H., 2023, An overview of tools and algorithms used to classify, detect, and monitor forest area using LiDAR data, in Pattern Recognition, Computer Vision, and Image Processing—ICPR 2022 International Workshops and Challenges, Montreal, Canada, 21–25 August 2022, Lecture Notes in Computer Science, v. 13644: New York, N.Y., Association for Computing Machinery, p. 171–182, at https://doi.org/10.1007/978-3-031-37742-6_14..
Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests Anderson, C. T., Dietz, S. L., Pokswinski, S. M., Jenkins, A. M., Kaeser, M. J., Hiers, J. K., Pelc, B. D. 2021
Full Citation
Anderson, C.T., Dietz, S.L., Pokswinski, S.M., Jenkins, A.M., Kaeser, M.J., Hiers, J.K., and Pelc, B.D., 2021, Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests: Forest Ecology and Management, v. 491, article 119118, at https://doi.org/10.1016/j.foreco.2021.119118..
Reliably mapping low-intensity forest disturbance using satellite radar data Aquino, C., Mitchard, E. T. A., McNicol, I. M., Carstairs, H., Burt, A., Puma Vilca, B. L., Obiang Ebanéga, M., Modinga Dikongo, A., Dassi, C., Mayta, S., Tamayo, M., Grijalba, P., Miranda, F., Disney, M. 2022
Full Citation
Aquino, C., Mitchard, E.T.A., McNicol, I.M., Carstairs, H., Burt, A., Puma Vilca, B.L., Obiang Ebanéga, M., Modinga Dikongo, A., Dassi, C., et al., 2022, Reliably mapping low-intensity forest disturbance using satellite radar data: Frontiers in Forests and Global Change, v. 5, article 1018762, at https://doi.org/10.3389/ffgc.2022.1018762..
Automatic object extraction from electrical substation point clouds Arastounia, M., Lichti, D. D. 2015
Full Citation
Arastounia, M., and Lichti, D.D., 2015, Automatic object extraction from electrical substation point clouds: Remote Sensing, v. 7, no. 11, p. 15605–15629, at https://doi.org/10.3390/rs71115605..
Terrestrial laser scanning (TLS) for tree structure studies—A review of methods for wood-leaf classifications from 3D point clouds Arrizza, S., Marras, S., Ferrara, R., Pellizzaro, G. 2024
Full Citation
Arrizza, S., Marras, S., Ferrara, R., and Pellizzaro, G., 2024, Terrestrial laser scanning (TLS) for tree structure studies—A review of methods for wood-leaf classifications from 3D point clouds: Remote Sensing Applications—Society and Environment, v. 36, article 101364, at https://doi.org/10.1016/j.rsase.2024.101364..
Dasometric analysis applying terrestrial laser scanner and conventional techniques for the estimation of aboveground biomass in a forest of the inter-Andean valley in Ecuador Basantes, J., Padilla-Almeida, O., Toulkeridis, T., Ordonez, E., Zapata-Vela, J., Zapata-Vela, A. C., Rojas, S. 2019
Full Citation
Basantes, J., Padilla-Almeida, O., Toulkeridis, T., Ordonez, E., Zapata-Vela, J., Zapata-Vela, A.C., and Rojas, S., 2019, Dasometric analysis applying terrestrial laser scanner and conventional techniques for the estimation of aboveground biomass in a forest of the inter-Andean valley in Ecuador, in 6th International Conference on eDemocracy and eGovernment (ICEDEG), Quito, Ecuador, 24–26 April 2019, Proceedings: Institute of Electrical and Electronics Engineers (IEEE), p. 334–337, at https://doi.org/10.1109/ICEDEG.2019.8734372..
Quantifying forest litter fuel moisture content with terrestrial laser scanning Batchelor, J. L., Rowell, E., Prichard, S., Nemens, D., Cronan, J., Kennedy, M. C., Moskal, L. M. 2023
Full Citation
Batchelor, J.L., Rowell, E., Prichard, S., Nemens, D., Cronan, J., Kennedy, M.C., and Moskal, L.M., 2023, Quantifying forest litter fuel moisture content with terrestrial laser scanning: Remote Sensing, v. 15, no. 6, article 1482, at https://doi.org/10.3390/rs15061482..
A 3D approach to model the taper of irregular tree stems—Making plots biomass estimates comparable in tropical forests Bauwens, S., Ploton, P., Fayolle, A., Ligot, G., Loumeto, J. J., Lejeune, P., Gourlet-Fleury, S. 2021
Full Citation
Bauwens, S., Ploton, P., Fayolle, A., Ligot, G., Loumeto, J.J., Lejeune, P., and Gourlet-Fleury, S., 2021, A 3D approach to model the taper of irregular tree stems—Making plots biomass estimates comparable in tropical forests: Ecological Applications, v. 31, no. 8, article e02451, at https://doi.org/10.1002/eap.2451..
Modeling the horizontal distribution of tree crown biomass from terrestrial laser scanning data Bazezew, M. N., Griese, N., Fehrmann, L., Kleinn, C., Nölke, N. 2024
Full Citation
Bazezew, M.N., Griese, N., Fehrmann, L., Kleinn, C., and Nölke, N., 2024, Modeling the horizontal distribution of tree crown biomass from terrestrial laser scanning data: Science of the Total Environment, v. 952, article 175377, at https://doi.org/10.1016/j.scitotenv.2024.175377..
Photogrammetric measurement of tree stems from vertical fisheye images Berveglieri, A., Tommaselli, A., Liang, X., Honkavaara, E. 2017
Full Citation
Berveglieri, A., Tommaselli, A., Liang, X., and Honkavaara, E., 2017, Photogrammetric measurement of tree stems from vertical fisheye images: Scandinavian Journal of Forest Research, v. 32, no. 8, p. 737–747, at https://doi.org/10.1080/02827581.2016.1273381..
Synthetic forest stands and point clouds for model selection and feature space comparison Bester, M. S., Maxwell, A. E., Nealey, I., Gallagher, M. R., Skowronski, N. S., McNeil, B. E. 2023
Full Citation
Bester, M.S., Maxwell, A.E., Nealey, I., Gallagher, M.R., Skowronski, N.S., and McNeil, B.E., 2023, Synthetic forest stands and point clouds for model selection and feature space comparison: Remote Sensing, v. 15, no. 18, article 4407, at https://doi.org/10.3390/rs15184407..
Estimation of forest variable and aboveground biomass using terrestrial laser scanning in the tropical rainforest Beyene, S. M. 2020
Full Citation
Beyene, S.M., 2020, Estimation of forest variable and aboveground biomass using terrestrial laser scanning in the tropical rainforest: Journal of the Indian Society of Remote Sensing, v. 48, no. 6, p. 853–863, at https://doi.org/10.1007/s12524-020-01119-2..
Forest inventory and aboveground biomass estimation with terrestrial LiDAR in the tropical forest of Malaysia Beyene, S. M., Hussin, Y. A., Kloosterman, H. E., Ismail, M. H. 2020
Full Citation
Beyene, S.M., Hussin, Y.A., Kloosterman, H.E., and Ismail, M.H., 2020, Forest inventory and aboveground biomass estimation with terrestrial LiDAR in the tropical forest of Malaysia: Canadian Journal of Remote Sensing, v. 46, no. 2, p. 130–145, at https://doi.org/10.1080/07038992.2020.1759036..
Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., Rehush, N. 2026
Full Citation
Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., and Rehush, N., 2026, Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning: Ecological Informatics, v. 97, article 103876, at https://doi.org/10.1016/j.ecoinf.2026.103876..
Forest cover buffers microclimate and increases fuel moisture in northern conifer forests—A terrestrial laser scanning approach Breigenzer, P. J., Fraver, S., Gallagher, M. R., Kenefic, L. S., Lane, E. D., Neely, J. M., Wason, J. W. 2026
Full Citation
Breigenzer, P.J., Fraver, S., Gallagher, M.R., Kenefic, L.S., Lane, E.D., Neely, J.M., and Wason, J.W., 2026, Forest cover buffers microclimate and increases fuel moisture in northern conifer forests—A terrestrial laser scanning approach: Fire Ecology, v. 22, no. 1, article 72, at https://doi.org/10.1186/s42408-026-00480-w..
Comparing biomass consumption estimated from point cloud data versus long-wave infrared imagery during prescribed growing season burns in pine woodlands of the southeastern United States Bright, B. C., Hudak, A. T., Sánchez-López, N., Loudermilk, E. L., Hawley, C. M., Rowell, E., O’Brien, J. J., Flanagan, S. A., Robertson, K., Kato, A., Hoffman, C., Weise, D. R., Hiers, J. K. 2025
Full Citation
Bright, B.C., Hudak, A.T., Sánchez-López, N., Loudermilk, E.L., Hawley, C.M., Rowell, E., O’Brien, J.J., Flanagan, S.A., Robertson, K., et al., 2025, Comparing biomass consumption estimated from point cloud data versus long-wave infrared imagery during prescribed growing season burns in pine woodlands of the southeastern United States: International Journal of Wildland Fire, v. 34, no. 7, article Wf24210, at https://doi.org/10.1071/WF24210..
Influence of ULS acquisition characteristics on tree stem parameter estimation Bruggisser, M., Hollaus, M., Otepka, J., Pfeifer, N. 2020
Full Citation
Bruggisser, M., Hollaus, M., Otepka, J., and Pfeifer, N., 2020, Influence of ULS acquisition characteristics on tree stem parameter estimation: ISPRS Journal of Photogrammetry and Remote Sensing, v. 168, p. 28–40, at https://doi.org/10.1016/j.isprsjprs.2020.08.002..
Terrestrial laser scanning in forest ecology—Expanding the horizon Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L. P., Chave, J., Danson, F. M., Demol, M., Disney, M., Gaulton, R., Krishna Moorthy, S. M., Levick, S. R., Saarinen, N., Schaaf, C., Stovall, A., Terryn, L., Wilkes, P., Verbeeck, H. 2020
Full Citation
Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L.P., Chave, J., Danson, F.M., Demol, M., et al., 2020, Terrestrial laser scanning in forest ecology—Expanding the horizon: Remote Sensing of Environment, v. 251, article 112102, at https://doi.org/10.1016/j.rse.2020.112102..
Remote sensing technology applications in forestry and REDD+ Calders, K., Jonckheere, I., Nightingale, J., Vastaranta, M. 2020
Full Citation
Calders, K., Jonckheere, I., Nightingale, J., and Vastaranta, M., 2020, Remote sensing technology applications in forestry and REDD+: Forests, v. 11, no. 2, article 188, at https://doi.org/10.3390/f11020188..
A terrestrial laser scanning measurement station to monitor long-term structural dynamics in a boreal forest Campos, M., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., Puttonen, E. 2020
Full Citation
Campos, M., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., and Puttonen, E., 2020, A terrestrial laser scanning measurement station to monitor long-term structural dynamics in a boreal forest, in XXIV ISPRS Congress, Commission I, Nice, France, 31 August–2 September 2020, ISPRS Archives XLIII-B1: International Society of Photogrammetry and Remote Sensing, p. 27–31, at https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-27-2020..
A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy Campos, M. B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., Puttonen, E. 2021
Full Citation
Campos, M.B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., and Puttonen, E., 2021, A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy: Frontiers in Plant Science, v. 11, article 606752, at https://doi.org/10.3389/fpls.2020.606752..
Terrestrial lidar scanning provides efficient measurements of fire-caused crown scorch in longleaf pine Cannon, J. B., Zampieri, N. E., Whelan, A. W., Shearman, T. M., Sánchez Meador, A. J., Varner, J. M. 2025
Full Citation
Cannon, J.B., Zampieri, N.E., Whelan, A.W., Shearman, T.M., Sánchez Meador, A.J., and Varner, J.M., 2025, Terrestrial lidar scanning provides efficient measurements of fire-caused crown scorch in longleaf pine: Fire Ecology, v. 21, no. 1, article 71, at https://doi.org/10.1186/s42408-025-00420-0..
Modality-specific feature design for species classification in forest inventories using TLS and UAS LiDAR Carpenter, J., Jung, M., Goel, A., Fei, S., Jung, J. 2025
Full Citation
Carpenter, J., Jung, M., Goel, A., Fei, S., and Jung, J., 2025, Modality-specific feature design for species classification in forest inventories using TLS and UAS LiDAR: Frontiers in Forests and Global Change, v. 8, article 1500178, at https://doi.org/10.3389/ffgc.2025.1500178..
Use of terrestrial laser scanner for aboveground biomass estimation in a seasonally dry tropical forest Carvalho, H. F. S., da Silva, T. G. F., Galvíncio, J. D., Antonino, A. C. D., Menezes, R. S. C., Dos Santos, C. V. B., Miranda, R. Q., Nóbrega, R. L. B., Domingues, T. F., Silva, E. A., de Moura, M. S. B. 2023
Full Citation
Carvalho, H.F.S., da Silva, T.G.F., Galvíncio, J.D., Antonino, A.C.D., Menezes, R.S.C., Dos Santos, C.V.B., Miranda, R.Q., Nóbrega, R.L.B., Domingues, T.F., et al., 2023, Use of terrestrial laser scanner for aboveground biomass estimation in a seasonally dry tropical forest: Revista Brasileira de Geografia Fisica, v. 16, no. 5, p. 2641–2657, at https://doi.org/10.26848/rbgf.v16.5.p2741-2657..
Learning neural radiance fields of forest structure for scalable and fine monitoring Castorena, J. 2024
Full Citation
Castorena, J., 2024, Learning neural radiance fields of forest structure for scalable and fine monitoring, in Advances in Soft Computing—22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, 13–18 November 2023, Lecture Notes in Computer Science, v. 14392: New York, N.Y., Association for Computing Machinery, p. 281–296, at https://doi.org/10.1007/978-3-031-47640-2_23..
ForestAlign—Automatic forest structure-based alignment for multi-view TLS and ALS point clouds Castorena, J., Dickman, L. T., Killebrew, A. J., Gattiker, J. R., Linn, R., Loudermilk, E. L. 2025
Full Citation
Castorena, J., Dickman, L.T., Killebrew, A.J., Gattiker, J.R., Linn, R., and Loudermilk, E.L., 2025, ForestAlign—Automatic forest structure-based alignment for multi-view TLS and ALS point clouds: Science of Remote Sensing, v. 11, article 100194, at https://doi.org/10.1016/j.srs.2024.100194..
Extraction of genetic test measurements from LiDAR cloud data Cavalheiro, R., Molina-Valero, J. A., Hodge, G., Howell, T., Acosta, J. J. 2026
Full Citation
Cavalheiro, R., Molina-Valero, J.A., Hodge, G., Howell, T., and Acosta, J.J., 2026, Extraction of genetic test measurements from LiDAR cloud data: Journal of Forestry Research, v. 37, no. 1, article 58, at https://doi.org/10.1007/s11676-026-02007-0..
Benchmarking tree instance segmentation of terrestrial laser scanning point clouds Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., Senf, C., Soenens, L., Terryn, L., Van den Broeck, W. A. J., Calders, K. 2026
Full Citation
Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., et al., 2026, Benchmarking tree instance segmentation of terrestrial laser scanning point clouds: ISPRS Journal of Photogrammetry and Remote Sensing, v. 231, p. 230–247, at https://doi.org/10.1016/j.isprsjprs.2025.10.033..
Ground level recovery from terrestrial laser scanning data with the variably randomized iterated hierarchical hough transform Chmielewski, L. J., Or 2015
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Chmielewski, L.J., and Or
Terrestrial LiDAR technology to evaluate the vertical structure of stands of Bertholletia excelsa Bonpl., a species symbol of conservation through sustainable use in the Brazilian Amazon Costa, F. F., Oliveira Júnior, R. C. D., de Almeida, D. R. A., Rosa, D. M., da Silva, K. E., Tonini, H., Beldini, T. P., Santos, D. B. D., Guedes, M. C. 2025
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Costa, F.F., Oliveira Júnior, R.C.D., de Almeida, D.R.A., Rosa, D.M., da Silva, K.E., Tonini, H., Beldini, T.P., Santos, D.B.D., and Guedes, M.C., 2025, Terrestrial LiDAR technology to evaluate the vertical structure of stands of Bertholletia excelsa Bonpl., a species symbol of conservation through sustainable use in the Brazilian Amazon: Sustainability, v. 17, no. 13, article 6049, at https://doi.org/10.3390/su17136049..
Biodiversity benefits of prescribed fire and mechanical thinning in Yosemite Valley Crumrine, S. A., Anderson, C. T., Hankin, L. E. 2026
Full Citation
Crumrine, S.A., Anderson, C.T., and Hankin, L.E., 2026, Biodiversity benefits of prescribed fire and mechanical thinning in Yosemite Valley: Fire Ecology, v. 22, no. 1, article 76, at https://doi.org/10.1186/s42408-026-00501-8..
Assessing the structural differences between tropical forest types using terrestrial laser scanning Decuyper, M., Mulatu, K. A., Brede, B., Calders, K., Armston, J., Rozendaal, D. M. A., Mora, B., Clevers, J. G. P. W., Kooistra, L., Herold, M., Bongers, F. 2018
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Decuyper, M., Mulatu, K.A., Brede, B., Calders, K., Armston, J., Rozendaal, D.M.A., Mora, B., Clevers, J.G.P.W., Kooistra, L., et al., 2018, Assessing the structural differences between tropical forest types using terrestrial laser scanning: Forest Ecology and Management, v. 429, p. 327–335, at https://doi.org/10.1016/j.foreco.2018.07.032..
Using terrestrial laser scanning (TLS) to estimate surface soil properties in a mixed conifer forest Dickman, L. T., Hutchings, G., Kroeger, M., Gutierrez, A., Killebrew, A. J., Guthrie, W., Atchley, A., Pokswinski, S., Gattiker, J., Linn, R. 2026
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Dickman, L.T., Hutchings, G., Kroeger, M., Gutierrez, A., Killebrew, A.J., Guthrie, W., Atchley, A., Pokswinski, S., Gattiker, J., et al., 2026, Using terrestrial laser scanning (TLS) to estimate surface soil properties in a mixed conifer forest: Ecological Indicators, v. 191, article 115625, at https://doi.org/10.1016/j.ecolind.2026.115625..
Calculation of Eucalyptus sp. timber volume from laser scanning do Nascimento Lima, J. V., Soares, Á A. V., da Silva Pacheco, M., Martins, G. D. 2025
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do Nascimento Lima, J.V., Soares, Á.A.V., da Silva Pacheco, M., and Martins, G.D., 2025, Calculation of Eucalyptus sp. timber volume from laser scanning: Revista Brasileira de Cartografia, v. 77, p. 1–14, at https://doi.org/10.14393/rbcv77n0a-75286..
Aerial-ground-space precision auto-measurement technology in stereo forest surveying Dong, B., Feng, Z. K., Yao, S., Yan, X. J., Sui, H. D., Wang, J., Guo, J. C. 2008
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Dong, B., Feng, Z.K., Yao, S., Yan, X.J., Sui, H.D., Wang, J., and Guo, J.C., 2008, Aerial-ground-space precision auto-measurement technology in stereo forest surveying: Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University, v. 30, no. SUPPL. 1, p. 173–177.
Advances in lidar remote sensing of forests Duncanson, L., Disney, M. 2025
Full Citation
Duncanson, L., and Disney, M., 2025, Advances in lidar remote sensing of forests, in Liang, S., ed., Comprehensive remote sensing, 2d ed.: Amsterdam, Netherlands, Elsevier, p. V3:212–V3:227, at https://doi.org/10.1016/B978-0-443-13220-9.00075-5..
Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques—A review Fang, H. 2021
Full Citation
Fang, H., 2021, Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques—A review: Kexue Tongbao/Chinese Science Bulletin, v. 66, no. 24, p. 3141–3153, at https://doi.org/10.1360/TB-2020-1057..
Interdisciplinary applications of LiDAR in forest studies—Advances in sensors, methods, and cross-domain metrics Fareed, N., Silva, C. A., Numata, I., Flores, J. P. 2026
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Fareed, N., Silva, C.A., Numata, I., and Flores, J.P., 2026, Interdisciplinary applications of LiDAR in forest studies—Advances in sensors, methods, and cross-domain metrics: Remote Sensing, v. 18, no. 2, article 219, at https://doi.org/10.3390/rs18020219..
Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths—A simulation study Forsman, M., Börlin, N., Olofsson, K., Reese, H., Holmgren, J. 2018
Full Citation
Forsman, M., Börlin, N., Olofsson, K., Reese, H., and Holmgren, J., 2018, Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths—A simulation study: ISPRS Journal of Photogrammetry and Remote Sensing, v. 135, p. 84–92, at https://doi.org/10.1016/j.isprsjprs.2017.11.013..
Estimation of plot-level burn severity using terrestrial laser scanning Gallagher, M. R., Maxwell, A. E., Guillén, L. A., Everland, A., Loudermilk, E. L., Skowronski, N. S. 2021
Full Citation
Gallagher, M.R., Maxwell, A.E., Guillén, L.A., Everland, A., Loudermilk, E.L., and Skowronski, N.S., 2021, Estimation of plot-level burn severity using terrestrial laser scanning: Remote Sensing, v. 13, no. 20, article 4168, at https://doi.org/10.3390/rs13204168..
Horizontal viewsheds of large herbivores as a function of woodland structure Gresham, A., Healey, J. R., Eichhorn, M. P., Barton, O., Smith, A. R., Shannon, G. 2023
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Gresham, A., Healey, J.R., Eichhorn, M.P., Barton, O., Smith, A.R., and Shannon, G., 2023, Horizontal viewsheds of large herbivores as a function of woodland structure: Ecology and Evolution, v. 13, no. 11, article e10699, at https://doi.org/10.1002/ece3.10699..
High terrestrial laser scanning—An accessible method for tree crown characterization Griese, N., Kleinn, C., Nölke, N. 2026
Full Citation
Griese, N., Kleinn, C., and Nölke, N., 2026, High terrestrial laser scanning—An accessible method for tree crown characterization: Ecological Informatics, v. 95, article 103707, at https://doi.org/10.1016/j.ecoinf.2026.103707..
A large dataset of labelled single tree point clouds, QSMs and tree graphs Griese, N., Ritzert, M., Nölke, N. 2025
Full Citation
Griese, N., Ritzert, M., and Nölke, N., 2025, A large dataset of labelled single tree point clouds, QSMs and tree graphs: Scientific Data, v. 12, no. 1, article 1953, at https://doi.org/10.1038/s41597-025-06421-7..
True leaf area index retrieval using terrestrial LiDAR for broadleaf trees via novel multiinclined-planes method Guo, Y., Li, S., Tang, H., He, Z. 2025
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Guo, Y., Li, S., Tang, H., and He, Z., 2025, True leaf area index retrieval using terrestrial LiDAR for broadleaf trees via novel multiinclined-planes method: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 18, p. 19025–19037, at https://doi.org/10.1109/JSTARS.2025.3591398..
Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments Guth, A., Dauner, M., Fowler, A., Hoehl, S., Hamlington, P., Hoffman, C., Hannigan, M. 2025
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Guth, A., Dauner, M., Fowler, A., Hoehl, S., Hamlington, P., Hoffman, C., and Hannigan, M., 2025, Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments: Frontiers in Forests and Global Change, v. 8, article 1663753, at https://doi.org/10.3389/ffgc.2025.1663753..
Complete workflow for detailed 3D forest reconstruction—From terrestrial laser scanning to complex 3D radiative transfer modelling Hanousek, T., Novotný, J., Navrátilová, B., Švik, M., Krejza, J., Janoutov 2025
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Hanousek, T., Novotný, J., Navrátilová, B.,
Extraction of individual tree skeletons from terrestrial LiDAR point clouds based on optimization of network graph structural model He, Y., Hui, Z., Cheng, X., Huang, F., Liu, H., Tu, L. 2026
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He, Y., Hui, Z., Cheng, X., Huang, F., Liu, H., and Tu, L., 2026, Extraction of individual tree skeletons from terrestrial LiDAR point clouds based on optimization of network graph structural model: Zhongguo Jiguang/Chinese Journal of Lasers, v. 53, no. 13, article 1310002, at https://doi.org/10.3788/CJL251225..
Integrating recreation into National Forest Inventories—Results from a forest visitor survey in winter and summer Hegetschweiler, K. T., Stride, C. B., Fischer, C., Ginzler, C., Hunziker, M. 2022
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Hegetschweiler, K.T., Stride, C.B., Fischer, C., Ginzler, C., and Hunziker, M., 2022, Integrating recreation into National Forest Inventories—Results from a forest visitor survey in winter and summer: Journal of Outdoor Recreation and Tourism, v. 39, article 100489, at https://doi.org/10.1016/j.jort.2022.100489..
Dynamic restoration and the impact of native versus invasive vegetation on coastal foredune morphodynamics, Lanphere Dunes, California, USA Hilgendorf, Z., Walker, I. J., Pickart, A. J., Turner, C. M. 2022
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Hilgendorf, Z., Walker, I.J., Pickart, A.J., and Turner, C.M., 2022, Dynamic restoration and the impact of native versus invasive vegetation on coastal foredune morphodynamics, Lanphere Dunes, California, USA: Earth Surface Processes and Landforms, v. 47, no. 13, p. 3083–3099, at https://doi.org/10.1002/esp.5445..
A method for validating the structural completeness of understory vegetation models captured with 3D remote sensing Hillman, S., Wallace, L., Reinke, K., Hally, B., Jones, S., Saldias, D. S. 2019
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Hillman, S., Wallace, L., Reinke, K., Hally, B., Jones, S., and Saldias, D.S., 2019, A method for validating the structural completeness of understory vegetation models captured with 3D remote sensing: Remote Sensing, v. 11, no. 18, article 2118, at https://doi.org/10.3390/rs11182118..
Robust single-image tree diameter estimation with mobile phones Holcomb, A., Tong, L., Keshav, S. 2023
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Holcomb, A., Tong, L., and Keshav, S., 2023, Robust single-image tree diameter estimation with mobile phones: Remote Sensing, v. 15, no. 3, article 772, at https://doi.org/10.3390/rs15030772..
Transferring instance and semantic segmentation across terrestrial laser scanning time series—Towards automated forest monitoring plot measurements Honkanen, E., Yrttimaa, T., Terryn, L., Kamula, T., Erkkilä, A., Liikonen, L., Hyyppä, J., Vastaranta, M. 2026
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Honkanen, E., Yrttimaa, T., Terryn, L., Kamula, T., Erkkilä, A., Liikonen, L., Hyyppä, J., and Vastaranta, M., 2026, Transferring instance and semantic segmentation across terrestrial laser scanning time series—Towards automated forest monitoring plot measurements: ISPRS Open Journal of Photogrammetry and Remote Sensing, v. 21, article 100147, at https://doi.org/10.1016/j.ophoto.2026.100147..
Towards spatially explicit quantification of pre- and postfire fuels and fuel consumption from traditional and point cloud measurements Hudak, A. T., Kato, A., Bright, B. C., Loudermilk, E. L., Hawley, C., Restaino, J. C., Ottmar, R. D., Prata, G. A., Cabo, C., Prichard, S. J., Rowell, E. M., Weise, D. R. 2020
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Hudak, A.T., Kato, A., Bright, B.C., Loudermilk, E.L., Hawley, C., Restaino, J.C., Ottmar, R.D., Prata, G.A., Cabo, C., et al., 2020, Towards spatially explicit quantification of pre- and postfire fuels and fuel consumption from traditional and point cloud measurements: Forest Science, v. 66, no. 4, p. 428–442, at https://doi.org/10.1093/forsci/fxz085..
Wildland fire mid-story—A generative modeling approach for representative fuels Hutchings, G., Gattiker, J., Scherting, B., Linn, R. R. 2024
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Hutchings, G., Gattiker, J., Scherting, B., and Linn, R.R., 2024, Wildland fire mid-story—A generative modeling approach for representative fuels: Environmental Modelling and Software, v. 171, article 105877, at https://doi.org/10.1016/j.envsoft.2023.105877..
Under-canopy UAV laser scanning for accurate forest field measurements Hyyppä, E., Hyyppä, J., Hakala, T., Kukko, A., Wulder, M. A., White, J. C., Pyörälä, J., Yu, X., Wang, Y., Virtanen, J. P., Pohjavirta, O., Liang, X., Holopainen, M., Kaartinen, H. 2020
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Hyyppä, E., Hyyppä, J., Hakala, T., Kukko, A., Wulder, M.A., White, J.C., Pyörälä, J., Yu, X., Wang, Y., et al., 2020, Under-canopy UAV laser scanning for accurate forest field measurements: ISPRS Journal of Photogrammetry and Remote Sensing, v. 164, p. 41–60, at https://doi.org/10.1016/j.isprsjprs.2020.03.021..
Comparison of terrestrial laser scanner and synthetic aperture radar data in the study of forest defoliation Kaasalainen, S., Hyyppä, J., Karjalainen, M., Krooks, A., Lyytikäinen-Saarenmaa, P., Holopainen, M., Jaakkola, A. 2010
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Kaasalainen, S., Hyyppä, J., Karjalainen, M., Krooks, A., Lyytikäinen-Saarenmaa, P., Holopainen, M., and Jaakkola, A., 2010, Comparison of terrestrial laser scanner and synthetic aperture radar data in the study of forest defoliation, in ISPRS Technical Commission VII Symposium, 100 Years ISPRS, Vienna, Austria, 5–7 July 2010, ISPRS Archives, XXXVIII, Pt. 7A: Lemmer, Netherlands, International Society for Photogrammetry and Remote Sensing, p. 82–87, at https://www.isprs.org/proceedings/XXXVIII/part7/a/pdf/82_XXXVIII-part7A.pdf..
Change detection of tree biomass with terrestrial laser scanning and quantitative structure modelling Kaasalainen, S., Krooks, A., Liski, J., Raumonen, P., Kaartinen, H., Kaasalainen, M., Puttonen, E., Anttila, K., Mäkipää, R. 2014
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Kaasalainen, S., Krooks, A., Liski, J., Raumonen, P., Kaartinen, H., Kaasalainen, M., Puttonen, E., Anttila, K., and Mäkipää, R., 2014, Change detection of tree biomass with terrestrial laser scanning and quantitative structure modelling: Remote Sensing, v. 6, no. 5, p. 3906–3922, at https://doi.org/10.3390/rs6053906..
Bi-temporal terrestrial laser scanning to measure secondary growth of trees—Toward non-destructive assessment of wood properties Kamula, T., Yrttimaa, T., Cimdins, R., Polvivaara, A., Kukko, A., Holopainen, M., Vastaranta, M. 2026
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Kamula, T., Yrttimaa, T., Cimdins, R., Polvivaara, A., Kukko, A., Holopainen, M., and Vastaranta, M., 2026, Bi-temporal terrestrial laser scanning to measure secondary growth of trees—Toward non-destructive assessment of wood properties: Scandinavian Journal of Forest Research, v. 41, no. 4, p. 363–378, at https://doi.org/10.1080/02827581.2026.2646458..
Ground truth measurement of trees using terrestrial laser for satellite remote sensing Kato, A., Morgenroth, J., Kelbe, D., Gomez, C., Van Aardt, J. 2013
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Kato, A., Morgenroth, J., Kelbe, D., Gomez, C., and Van Aardt, J., 2013, Ground truth measurement of trees using terrestrial laser for satellite remote sensing, in International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 21–26 July 2013, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2106–2109, at https://doi.org/10.1109/IGARSS.2013.6723228..
EcoViz—Co-designed environmental data visualizations to communicate ecosystem impacts, inform management, and envision solutions Kendall-Bar, J., Nealey, I., Costello, I., Lowrie, C., Nguyen, K. H., Ponganis, P. J., Beck, M. W., Altintas, I. 2024
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Kendall-Bar, J., Nealey, I., Costello, I., Lowrie, C., Nguyen, K.H., Ponganis, P.J., Beck, M.W., and Altintas, I., 2024, EcoViz—Co-designed environmental data visualizations to communicate ecosystem impacts, inform management, and envision solutions, in VIS Workshop on Visualization for Climate Action and Sustainability (Viz4Climate + Sustainability), St. Pete Beach, Fla., 14 October 2024, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 17–27, at https://doi.org/10.1109/Viz4Climate-Sustainability64680.2024.00007..
Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR Klockow, P. A., Putman, E. B., Vogel, J. G., Moore, G. W., Edgar, C. B., Popescu, S. C. 2020
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Klockow, P.A., Putman, E.B., Vogel, J.G., Moore, G.W., Edgar, C.B., and Popescu, S.C., 2020, Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR: Remote Sensing of Environment, v. 241, article 111729, at https://doi.org/10.1016/j.rse.2020.111729..
Fine root vitality decline results in reduced branch formation in mature beech stands after drought Koller, A., Azekenova, A., Feger, K. H., Kalbitz, K., von Oheimb, G. 2026
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Koller, A., Azekenova, A., Feger, K.H., Kalbitz, K., and von Oheimb, G., 2026, Fine root vitality decline results in reduced branch formation in mature beech stands after drought: Forest Ecology and Management, v. 605, article 123506, at https://doi.org/10.1016/j.foreco.2026.123506..
Aerial and terrestrial photogrammetric point cloud fusion for intensive forest monitoring Krause, S. 2019
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Krause, S., 2019, Aerial and terrestrial photogrammetric point cloud fusion for intensive forest monitoring: GI_Forum, v. 7, no. 2, p. 60–72, at https://doi.org/10.1553/GISCIENCE2019_02_S60..
Forest structural complexity tool—An open source, fully-automated tool for measuring forest point clouds Krisanski, S., Taskhiri, M. S., Aracil, S. G., Herries, D., Muneri, A., Gurung, M. B., Montgomery, J., Turner, P. 2021
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Krisanski, S., Taskhiri, M.S., Aracil, S.G., Herries, D., Muneri, A., Gurung, M.B., Montgomery, J., and Turner, P., 2021, Forest structural complexity tool—An open source, fully-automated tool for measuring forest point clouds: Remote Sensing, v. 13, no. 22, article 4677, at https://doi.org/10.3390/rs13224677..
Sensor agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning Krisanski, S., Taskhiri, M. S., Aracil, S. G., Herries, D., Turner, P. 2021
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Krisanski, S., Taskhiri, M.S., Aracil, S.G., Herries, D., and Turner, P., 2021, Sensor agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning: Remote Sensing, v. 13, no. 8, article 1413, at https://doi.org/10.3390/rs13081413..
Using stationary and mobile laser scanner to detect forest defoliation Krooks, A., Hyyppä, J., Lyytikäinen-Saarenmaa, P., Jaakkola, A., Kaasalainen, S., Holopainen, M. 2010
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Krooks, A., Hyyppä, J., Lyytikäinen-Saarenmaa, P., Jaakkola, A., Kaasalainen, S., and Holopainen, M., 2010, Using stationary and mobile laser scanner to detect forest defoliation, in ISPRS Commission V Mid-Term Symposium 'Close Range Image Measurement Techniques', Newcastle upon Tyne, UK, 21–24 June 2010, ISPRS Archives XXXVIII, Pt. 5: Lemmer, Netherlands, International Society for Photogrammetry and Remote Sensing, p. 372–374, at https://www.isprs.org/proceedings/xxxviii/part5/papers/84.pdf..
Artificial intelligence and terrestrial point clouds for forest monitoring Kulicki, M., Cabo, C., Trzci, Będkowski, J., Stereńczak, K. 2025
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Kulicki, M., Cabo, C., Trzci
Assessing the effects of large herbivores on the three-dimensional structure of temperate forests using terrestrial laser scanning Li, S., Hou, Z., Ge, J., Wang, T. 2022
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Li, S., Hou, Z., Ge, J., and Wang, T., 2022, Assessing the effects of large herbivores on the three-dimensional structure of temperate forests using terrestrial laser scanning: Forest Ecology and Management, v. 507, article 119985, at https://doi.org/10.1016/j.foreco.2021.119985..
Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests Li, S., Wang, T., Hou, Z., Gong, Y., Feng, L., Ge, J. 2021
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Li, S., Wang, T., Hou, Z., Gong, Y., Feng, L., and Ge, J., 2021, Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests: Ecological Indicators, v. 121, article 107011, at https://doi.org/10.1016/j.ecolind.2020.107011..
In-situ measurements from mobile platforms—An emerging approach to address the old challenges associated with forest inventories Liang, X., Kukko, A., Hyyppä, J., Lehtomäki, M., Pyörälä, J., Yu, X., Kaartinen, H., Jaakkola, A., Wang, Y. 2018
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Liang, X., Kukko, A., Hyyppä, J., Lehtomäki, M., Pyörälä, J., Yu, X., Kaartinen, H., Jaakkola, A., and Wang, Y., 2018, In-situ measurements from mobile platforms—An emerging approach to address the old challenges associated with forest inventories: ISPRS Journal of Photogrammetry and Remote Sensing, v. 143, p. 97–107, at https://doi.org/10.1016/j.isprsjprs.2018.04.019..
Combining airborne and terrestrial laser scanning technologies to measure forest understorey volume Liu, L., Pang, Y., Li, Z., Si, L., Liao, S. 2017
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Liu, L., Pang, Y., Li, Z., Si, L., and Liao, S., 2017, Combining airborne and terrestrial laser scanning technologies to measure forest understorey volume: Forests, v. 8, no. 4, article 111, at https://doi.org/10.3390/f8040111..
Remote sensing applications for prescribed burn research LoPresti, A., Hayden, M. T., Siegel, K., Poulter, B., Stavros, E. N., Dee, L. E. 2024
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LoPresti, A., Hayden, M.T., Siegel, K., Poulter, B., Stavros, E.N., and Dee, L.E., 2024, Remote sensing applications for prescribed burn research: International Journal of Wildland Fire, v. 33, no. 6, article WF23130, at https://doi.org/10.1071/wf23130..
Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern U.S. ecosystem Loudermilk, E. L., Pokswinski, S., Hawley, C. M., Maxwell, A., Gallagher, M. R., Skowronski, N. S., Hudak, A. T., Hoffman, C., Hiers, J. K. 2023
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Loudermilk, E.L., Pokswinski, S., Hawley, C.M., Maxwell, A., Gallagher, M.R., Skowronski, N.S., Hudak, A.T., Hoffman, C., and Hiers, J.K., 2023, Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern U.S. ecosystem: Fire, v. 6, no. 4, article 151, at https://doi.org/10.3390/fire6040151..
Revealing changes in the stem form and volume allocation in diverse boreal forests using two-date terrestrial laser scanning Luoma, V., Yrttimaa, T., Kankare, V., Saarinen, N., Pyörälä, J., Kukko, A., Kaartinen, H., Hyyppä, J., Holopainen, M., Vastaranta, M. 2021
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Luoma, V., Yrttimaa, T., Kankare, V., Saarinen, N., Pyörälä, J., Kukko, A., Kaartinen, H., Hyyppä, J., Holopainen, M., et al., 2021, Revealing changes in the stem form and volume allocation in diverse boreal forests using two-date terrestrial laser scanning: Forests, v. 12, no. 7, article 835, at https://doi.org/10.3390/f12070835..
TreeFlow—Conditional flow matching for 3D tree point cloud generation from inventory attributes Marcozzi, A., Tenny, J., Martin, D., Castorena, J., Crennen, Z., Wells, L., Hillman, S. 2026
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Marcozzi, A., Tenny, J., Martin, D., Castorena, J., Crennen, Z., Wells, L., and Hillman, S., 2026, TreeFlow—Conditional flow matching for 3D tree point cloud generation from inventory attributes: Remote Sensing, v. 18, no. 13, article 2197, at https://doi.org/10.3390/rs18132197..
Application of LiDAR derived fuel cells to wildfire modeling at laboratory scale Marcozzi, A. A., Johnson, J. V., Parsons, R. A., Flanary, S. J., Seielstad, C. A., Downs, J. Z. 2023
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Marcozzi, A.A., Johnson, J.V., Parsons, R.A., Flanary, S.J., Seielstad, C.A., and Downs, J.Z., 2023, Application of LiDAR derived fuel cells to wildfire modeling at laboratory scale: Fire, v. 6, no. 10, article 394, at https://doi.org/10.3390/fire6100394..
Repeat terrestrial laser scanning for documenting and monitoring prescribed fire efficacy Maxwell, A. E., Gallagher, M. R., Stockton, S. R., Wozniak, M. C., McNeil, B. E., Defeo, J. A., Everland, A., Skowronski, N. S. 2026
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Maxwell, A.E., Gallagher, M.R., Stockton, S.R., Wozniak, M.C., McNeil, B.E., Defeo, J.A., Everland, A., and Skowronski, N.S., 2026, Repeat terrestrial laser scanning for documenting and monitoring prescribed fire efficacy: Fire Ecology, v. 22, no. 1, article 91, at https://doi.org/10.1186/s42408-026-00498-0..
Operationalizing the use of TLS in forest inventories—The R package FORTLS Molina-Valero, J. A., Martínez-Calvo, A., Ginzo Villamayor, M. J., Novo Pérez, M. A., Álvarez-González, J. G., Montes, F., Pérez-Cruzado, C. 2022
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Molina-Valero, J.A., Martínez-Calvo, A., Ginzo Villamayor, M.J., Novo Pérez, M.A., Álvarez-González, J.G., Montes, F., and Pérez-Cruzado, C., 2022, Operationalizing the use of TLS in forest inventories—The R package FORTLS: Environmental Modelling and Software, v. 150, article 105337, at https://doi.org/10.1016/j.envsoft.2022.105337..
Use of close-range LiDAR devices and statistical inference approaches in operational stand-level forest inventories Molina-Valero, J. A., Martins-Neto, R. P., Martínez-Calvo, A., Rodríguez-Ruiz, J., Surový, P., Seppelt, A., Pérez-Cruzado, C. 2025
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Molina-Valero, J.A., Martins-Neto, R.P., Martínez-Calvo, A., Rodríguez-Ruiz, J., Surový, P., Seppelt, A., and Pérez-Cruzado, C., 2025, Use of close-range LiDAR devices and statistical inference approaches in operational stand-level forest inventories: Remote Sensing of Environment, v. 325, article 114773, at https://doi.org/10.1016/j.rse.2025.114773..
Visualization and labeling of terrestrial LiDAR data for three-dimensional fuel classification Moreno, I. G., Nealey, I., Roten, D., Nguyen, M. H., Crawl, D., O'Laughlin, K., Floca, M., Pokswinski, S., Altintas, I. 2023
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Moreno, I.G., Nealey, I., Roten, D., Nguyen, M.H., Crawl, D., O'Laughlin, K., Floca, M., Pokswinski, S., and Altintas, I., 2023, Visualization and labeling of terrestrial LiDAR data for three-dimensional fuel classification, in 19th International Conference on e-Science, Limassol, Cyprus, 9–13 October 2023, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 1–2, at https://doi.org/10.1109/e-Science58273.2023.10254841..
Tree value and log product yield determination in radiata pine (Pinus radiata) plantations in australia—Comparisons of terrestrial laser scanning with a forest inventory system and manual measurements Murphy, G. E., Acuna, M. A., Dumbrell, I. 2010
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Murphy, G.E., Acuna, M.A., and Dumbrell, I., 2010, Tree value and log product yield determination in radiata pine (Pinus radiata) plantations in australia—Comparisons of terrestrial laser scanning with a forest inventory system and manual measurements: Canadian Journal of Forest Research, v. 40, no. 11, p. 2223–2233, at https://doi.org/10.1139/X10-171..
Quantifying understory fuel structure and biomass using coupled terrestrial lidar scanning, close-range photogrammetry, and field sampling Nemens, D. G., Prichard, S. J., Cova, G. R., Drye, B., Eagle, P., Rowell, E., Thoreson, J. 2026
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Nemens, D.G., Prichard, S.J., Cova, G.R., Drye, B., Eagle, P., Rowell, E., and Thoreson, J., 2026, Quantifying understory fuel structure and biomass using coupled terrestrial lidar scanning, close-range photogrammetry, and field sampling: Fire Ecology, v. 22, no. 1, article 92, at https://doi.org/10.1186/s42408-026-00503-6..
Terrestrial laser scanning for plot-scale forest measurement Newnham, G. J., Armston, J. D., Calders, K., Disney, M. I., Lovell, J. L., Schaaf, C. B., Strahler, A. H., Danson, F. M. 2015
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Newnham, G.J., Armston, J.D., Calders, K., Disney, M.I., Lovell, J.L., Schaaf, C.B., Strahler, A.H., and Danson, F.M., 2015, Terrestrial laser scanning for plot-scale forest measurement: Current Forestry Reports, v. 1, no. 4, p. 239–251, at https://doi.org/10.1007/s40725-015-0025-5..
A LiDAR framework to characterise elevated and canopy firebrand fuels for fire prediction models Nguyen, H. N., Penman, T. D., Peace, M., Hinko-Najera, N., Penman, S., Filippi, J. B., Pinar, A. A., Filkov, A. I. 2026
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Nguyen, H.N., Penman, T.D., Peace, M., Hinko-Najera, N., Penman, S., Filippi, J.B., Pinar, A.A., and Filkov, A.I., 2026, A LiDAR framework to characterise elevated and canopy firebrand fuels for fire prediction models: Fire Ecology, v. 22, no. 1, article 78, at https://doi.org/10.1186/s42408-026-00524-1..
On the geometry and allometry of big-buttressed trees—A challenge for forest monitoring—New insights from 3D-modeling with terrestrial laser scanning Nölke, N., Fehrmann, L., Jaya, I. N. S., Tiryana, T., Seidel, D., Kleinn, C. 2015
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Nölke, N., Fehrmann, L., Jaya, I.N.S., Tiryana, T., Seidel, D., and Kleinn, C., 2015, On the geometry and allometry of big-buttressed trees—A challenge for forest monitoring—New insights from 3D-modeling with terrestrial laser scanning: IForest, v. 8, no. OCTOBER2015, p. 574–581, at https://doi.org/10.3832/ifor1449-007..
Estimating sagebrush biomass using terrestrial laser scanning Olsoy, P. J., Glenn, N. F., Clark, P. E. 2014
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Olsoy, P.J., Glenn, N.F., and Clark, P.E., 2014, Estimating sagebrush biomass using terrestrial laser scanning: Rangeland Ecology and Management, v. 67, no. 2, p. 224–228, at https://doi.org/10.2111/REM-D-12-00186.1..
Unmanned aerial systems measure structural habitat features for wildlife across multiple scales Olsoy, P. J., Shipley, L. A., Rachlow, J. L., Forbey, J. S., Glenn, N. F., Burgess, M. A., Thornton, D. H. 2018
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Olsoy, P.J., Shipley, L.A., Rachlow, J.L., Forbey, J.S., Glenn, N.F., Burgess, M.A., and Thornton, D.H., 2018, Unmanned aerial systems measure structural habitat features for wildlife across multiple scales: Methods in Ecology and Evolution, v. 9, no. 3, p. 594–604, at https://doi.org/10.1111/2041-210X.12919..
Common field measures and geometric assumptions of tree shape produce consistently biased estimates of tree and canopy structure in mixed Mediterranean forests Owen, H. J. F., Lines, E. R. 2024
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Owen, H.J.F., and Lines, E.R., 2024, Common field measures and geometric assumptions of tree shape produce consistently biased estimates of tree and canopy structure in mixed Mediterranean forests: Ecological Indicators, v. 165, article 112219, at https://doi.org/10.1016/j.ecolind.2024.112219..
An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies Penman, S., Lentini, P., Law, B., York, A. 2023
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Penman, S., Lentini, P., Law, B., and York, A., 2023, An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies: Forest Ecology and Management, v. 548, article 121405, at https://doi.org/10.1016/j.foreco.2023.121405..
The horizontal distribution of branch biomass in european beech—A model based on measurements and TLS based proxies Pérez‐Cruzado, C., Kleinn, C., Magdon, P., Álvarez‐González, J. G., Magnussen, S., Fehrmann, L., Nölke, N. 2021
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Pérez‐Cruzado, C., Kleinn, C., Magdon, P., Álvarez‐González, J.G., Magnussen, S., Fehrmann, L., and Nölke, N., 2021, The horizontal distribution of branch biomass in european beech—A model based on measurements and TLS based proxies: Remote Sensing, v. 13, no. 5, article 1041, at https://doi.org/10.3390/rs13051041..
Terrestrial structure from motion photogrammetry for deriving forest inventory data Piermattei, L., Karel, W., Wang, D., Wieser, M., Mokro, Surov, Kore, Tomašt, Pfeifer, N., Hollaus, M. 2019
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Piermattei, L., Karel, W., Wang, D., Wieser, M., Mokro
A simplified and affordable approach to forest monitoring using single terrestrial laser scans and transect sampling Pokswinski, S., Gallagher, M. R., Skowronski, N. S., Loudermilk, E. L., Hawley, C., Wallace, D., Everland, A., Wallace, J., Hiers, J. K. 2021
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Pokswinski, S., Gallagher, M.R., Skowronski, N.S., Loudermilk, E.L., Hawley, C., Wallace, D., Everland, A., Wallace, J., and Hiers, J.K., 2021, A simplified and affordable approach to forest monitoring using single terrestrial laser scans and transect sampling: MethodsX, v. 8, article 101484, at https://doi.org/10.1016/j.mex.2021.101484..
IntELiMon—An automated framework for standardized terrestrial lidar processing in multi-disciplinary ecosystem monitoring Pokswinski, S., Nelson, K., Maxwell, A., Link, E., Redford, H. E., Atkins, J. W., Sanchez Meador, A., Nealey, I., Crawl, D., Floca, M., Altintas, I., Loudermilk, E. L., Anderson, C. T., Oliveto, J., Linn, R. R., Wallace, J., Hiers, J. K. 2026
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Pokswinski, S., Nelson, K., Maxwell, A., Link, E., Redford, H.E., Atkins, J.W., Sanchez Meador, A., Nealey, I., Crawl, D., et al., 2026, IntELiMon—An automated framework for standardized terrestrial lidar processing in multi-disciplinary ecosystem monitoring: Forestry—An International Journal of Forest Research, v. 99, no. 5, article cpag083, at https://doi.org/10.1093/forestry/cpag083..
Urban tree detection using mobile laser scanning data Pratihast, A. K., Thakur, J. K. 2012
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Pratihast, A.K., and Thakur, J.K., 2012, Urban tree detection using mobile laser scanning data, in Thakur, J.K., Singh, S.K., Ramanathan, A., Prasad, M.B.K., and Gossel, W., eds., Geospatial techniques for managing environmental resources: Dordrecht, Netherlands, Springer, p. 188–200, at https://doi.org/10.1007/978-94-007-1858-6_12..
Hierarchically scaled remote sensing and field datasets for three-dimensional wildland fuel characterization Prichard, S. J., Nemens, D. G., Rowell, E., Batchelor, J. L., Bester, M. S., Bright, B. C., Cova, G. R., Cronan, J. B., Drye, B., Hudak, A. T., Eagle, P. C., Kennedy, M. C., Loudermilk, E. L., Redford, H. E., Parsons, R. A., Silva, C. A., Skowronski, N. S., Thoreson, J. 2026
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Prichard, S.J., Nemens, D.G., Rowell, E., Batchelor, J.L., Bester, M.S., Bright, B.C., Cova, G.R., Cronan, J.B., Drye, B., et al., 2026, Hierarchically scaled remote sensing and field datasets for three-dimensional wildland fuel characterization: Fire Ecology, v. 22, no. 1, article 130, at https://doi.org/10.1186/s42408-026-00507-2..
Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data Puletti, N., Galluzzi, M., Grotti, M., Ferrara, C. 2021
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Puletti, N., Galluzzi, M., Grotti, M., and Ferrara, C., 2021, Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data: Remote Sensing Applications—Society and Environment, v. 24, article 100620, at https://doi.org/10.1016/j.rsase.2021.100620..
Laser-scanned tree stem filtering for forest inventories measurements Ravaglia, J., Alexandra, B., Piboule, A. 2013
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Ravaglia, J., Alexandra, B., and Piboule, A., 2013, Laser-scanned tree stem filtering for forest inventories measurements, in DigitalHeritage 2013 - Federating the 19th Int'l VSMM, 10th Eurographics GCH, and 2nd UNESCO Memory of the World Conferences, plus special sessions from CAA, Arqueologica 2.0, Space2Place, ICOMOS ICIP & CIPA, EU projects, et al., Marseille, France, 28 October–1 November 2013, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 649–652, at https://doi.org/10.1109/DigitalHeritage.2013.6743809..
Identifying tree-related microhabitats in TLS point clouds using machine learning Rehush, N., Abegg, M., Waser, L. T., Brändli, U. B. 2018
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Rehush, N., Abegg, M., Waser, L.T., and Brändli, U.B., 2018, Identifying tree-related microhabitats in TLS point clouds using machine learning: Remote Sensing, v. 10, no. 11, article 1735, at https://doi.org/10.3390/rs10111735..
Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning Ritter, T., Schwarz, M., Tockner, A., Leisch, F., Nothdurft, A. 2017
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Ritter, T., Schwarz, M., Tockner, A., Leisch, F., and Nothdurft, A., 2017, Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning: Forests, v. 8, no. 8, article 265, at https://doi.org/10.3390/f8080265..
Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests Ro, Suomalainen, J., Bartholomeus, H., Herold, M. 2018
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Coupling terrestrial laser scanning with 3D fuel biomass sampling for advancing wildland fuels characterization Rowell, E., Loudermilk, E. L., Hawley, C., Pokswinski, S., Seielstad, C., Queen, L. L., O'Brien, J. J., Hudak, A. T., Goodrick, S., Hiers, J. K. 2020
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Rowell, E., Loudermilk, E.L., Hawley, C., Pokswinski, S., Seielstad, C., Queen, L.L., O'Brien, J.J., Hudak, A.T., Goodrick, S., et al., 2020, Coupling terrestrial laser scanning with 3D fuel biomass sampling for advancing wildland fuels characterization: Forest Ecology and Management, v. 462, article 117945, at https://doi.org/10.1016/j.foreco.2020.117945..
Development and validation of fuel height models for terrestrial lidar - RxCADRE 2012 Rowell, E. M., Seielstad, C. A., Ottmar, R. D. 2016
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Rowell, E.M., Seielstad, C.A., and Ottmar, R.D., 2016, Development and validation of fuel height models for terrestrial lidar - RxCADRE 2012: International Journal of Wildland Fire, v. 25, no. 1, p. 38–47, at https://doi.org/10.1071/WF14170..
Modeling tree crown-mediated accumulation of downed woody debris in fire-maintained longleaf pine forests of the southeastern US from laser scanning data Sánchez-López, N., Hudak, A. T., Xia, J., Robertson, K., Bright, B. C., Prichard, S., Nemens, D. G., Hawley, C., Loudermilk, E. L., Keane, R., Woodall, C. W., Mauro, F., Boschetti, L., Silva, C. A., Cabo, C. 2026
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Sánchez-López, N., Hudak, A.T., Xia, J., Robertson, K., Bright, B.C., Prichard, S., Nemens, D.G., Hawley, C., Loudermilk, E.L., et al., 2026, Modeling tree crown-mediated accumulation of downed woody debris in fire-maintained longleaf pine forests of the southeastern US from laser scanning data: Fire Ecology, v. 22, no. 1, article 136, at https://doi.org/10.1186/s42408-026-00548-7..
Quantifying plant-soil-nutrient dynamics in rangelands—Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland Sankey, J. B., Sankey, T. T., Li, J., Ravi, S., Wang, G., Caster, J., Kasprak, A. 2021
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Sankey, J.B., Sankey, T.T., Li, J., Ravi, S., Wang, G., Caster, J., and Kasprak, A., 2021, Quantifying plant-soil-nutrient dynamics in rangelands—Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland: Remote Sensing of Environment, v. 253, article 112223, at https://doi.org/10.1016/j.rse.2020.112223..
Experimental study of fire spread through discontinuous fuels without flame contact Schneider, L., Betting, B., Patterson, M., Skowronski, N., Simeoni, A. 2021
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Schneider, L., Betting, B., Patterson, M., Skowronski, N., and Simeoni, A., 2021, Experimental study of fire spread through discontinuous fuels without flame contact: Fire Safety Journal, v. 120, article 103066, at https://doi.org/10.1016/j.firesaf.2020.103066..
Monitoring trees outside forests—A review Schnell, S., Kleinn, C., Ståhl, G. 2015
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Schnell, S., Kleinn, C., and Ståhl, G., 2015, Monitoring trees outside forests—A review: Environmental Monitoring and Assessment, v. 187, no. 9, article 600, at https://doi.org/10.1007/s10661-015-4817-7..
TSS-Net—An efficient deep learning framework for individual tree segmentation in terrestrial laser scanning data Sha, A., Dong, B., Li, Z., Zhang, S., Liu, R., Li, S., Lu, H., Wang, H. 2026
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Sha, A., Dong, B., Li, Z., Zhang, S., Liu, R., Li, S., Lu, H., and Wang, H., 2026, TSS-Net—An efficient deep learning framework for individual tree segmentation in terrestrial laser scanning data: Results in Engineering, v. 29, article 109161, at https://doi.org/10.1016/j.rineng.2026.109161..
SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning Shao, J., Zhang, W., Mellado, N., Wang, N., Jin, S., Cai, S., Luo, L., Lejemble, T., Yan, G. 2020
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Shao, J., Zhang, W., Mellado, N., Wang, N., Jin, S., Cai, S., Luo, L., Lejemble, T., and Yan, G., 2020, SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning: ISPRS Journal of Photogrammetry and Remote Sensing, v. 163, p. 214–230, at https://doi.org/10.1016/j.isprsjprs.2020.03.008..
Assessing bat roosts using the LiDAR system at Wind Cave Nature Reserve in Sarawak, Malaysian Borneo Shazali, N., Chew, T. H., Shamsir, M. S., Tingga, R. C. T., Mohd-Ridwan, A. R., Khan, F. A. A. 2017
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Shazali, N., Chew, T.H., Shamsir, M.S., Tingga, R.C.T., Mohd-Ridwan, A.R., and Khan, F.A.A., 2017, Assessing bat roosts using the LiDAR system at Wind Cave Nature Reserve in Sarawak, Malaysian Borneo: Acta Chiropterologica, v. 19, no. 1, p. 199–210, at https://doi.org/10.3161/15081109ACC2017.19.1.016..
Comparison of TLS and ULS data for wildlife habitat assessments in temperate woodlands Shokirov, S., Levick, S. R., Jucker, T., Yeoh, P., Youngentob, K. 2020
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Shokirov, S., Levick, S.R., Jucker, T., Yeoh, P., and Youngentob, K., 2020, Comparison of TLS and ULS data for wildlife habitat assessments in temperate woodlands, in International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, 26 September–2 October 2020, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 6097–6100, at https://doi.org/10.1109/IGARSS39084.2020.9323451..
Spaceborne LiDAR and animal-environment relationships—An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem Smith, A. B., Vogeler, J. C., Bjornlie, N. L., Squires, J. R., Swayze, N. C., Holbrook, J. D. 2022
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Smith, A.B., Vogeler, J.C., Bjornlie, N.L., Squires, J.R., Swayze, N.C., and Holbrook, J.D., 2022, Spaceborne LiDAR and animal-environment relationships—An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem: Forest Ecology and Management, v. 520, article 120343, at https://doi.org/10.1016/j.foreco.2022.120343..
East African megafauna influence on vegetation structure permeates from landscape to tree level scales Sorokina, H. E., Nunes, M. H., Heiskanen, J., Munyao, M., Mwang'ombe, J., Pellikka, P., Raumonen, P., Maeda, E. E. 2024
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Sorokina, H.E., Nunes, M.H., Heiskanen, J., Munyao, M., Mwang'ombe, J., Pellikka, P., Raumonen, P., and Maeda, E.E., 2024, East African megafauna influence on vegetation structure permeates from landscape to tree level scales: Ecological Informatics, v. 79, article 102435, at https://doi.org/10.1016/j.ecoinf.2023.102435..
A fully automatic forest parameters extraction at single-tree level—A comparison of MLS and TLS applications Spadavecchia, C., Belcore, E., Grasso, N., Piras, M. 2023
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Spadavecchia, C., Belcore, E., Grasso, N., and Piras, M., 2023, A fully automatic forest parameters extraction at single-tree level—A comparison of MLS and TLS applications, in 12th International Symposium on Mobile Mapping Technology (MMT 2023), Padua, Italy, 24–26 May 2023, ISPRS Archives XLVIII-1/W1: International Society of Photogrammetry and Remote Sensing, p. 457–463, at https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-457-2023..
Beyond discrete visibility estimates—Single-scan LiDAR provides an efficient method for 3D viewshed estimation Stein, R. M., Allred, C. R., Lecigne, B., Eitel, J. U. H., Johnson, T. R., Rachlow, J. L. 2026
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Stein, R.M., Allred, C.R., Lecigne, B., Eitel, J.U.H., Johnson, T.R., and Rachlow, J.L., 2026, Beyond discrete visibility estimates—Single-scan LiDAR provides an efficient method for 3D viewshed estimation: Wildlife Society Bulletin, v. 50, no. 1, article e70014, at https://doi.org/10.1002/wsb.70014..
Enhancing tree species classification—Insights from YOLOv8 and explainable AI applied to TLS point cloud projections Straker, A., Magdon, P., Zullich, M., Freudenberg, M., Kleinn, C., Breidenbach, J., Puliti, S., Nölke, N. 2026
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Straker, A., Magdon, P., Zullich, M., Freudenberg, M., Kleinn, C., Breidenbach, J., Puliti, S., and Nölke, N., 2026, Enhancing tree species classification—Insights from YOLOv8 and explainable AI applied to TLS point cloud projections: Forestry, v. 99, no. 4, article cpag059, at https://doi.org/10.1093/forestry/cpag059..
Multi-station LiDAR scanning-based hierarchical features for generation of an allometric stem volume model Sun, Y., Lin, X., Gong, Y., Jiang, J., Zhang, Y., Wen, X. 2021
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Sun, Y., Lin, X., Gong, Y., Jiang, J., Zhang, Y., and Wen, X., 2021, Multi-station LiDAR scanning-based hierarchical features for generation of an allometric stem volume model: Journal of Applied Remote Sensing, v. 15, no. 2, article 028503, at https://doi.org/10.1117/1.JRS.15.028503..
Sentinel-1/2 time series for selective logging monitoring in temperate forests Tanase, M., Borlaf, I., Pascu, I., Pitar, D., Apostol, B., Petrila, M., Chivulescu, S., Leca, S., Pitar, D., Ciceu, A., Dobre, A., Popescu, F., Badea, O., Aponte, C. 2020
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Tanase, M., Borlaf, I., Pascu, I., Pitar, D., Apostol, B., Petrila, M., Chivulescu, S., Leca, S., Pitar, D., et al., 2020, Sentinel-1/2 time series for selective logging monitoring in temperate forests, in International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, 26 September–2 October 2020, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2902–2905, at https://doi.org/10.1109/IGARSS39084.2020.9323952..
Combining terrestrial lidar with single line transects to investigate geomorphic change—A case study on the Upper Verde River, Arizona Tango, L. L., Sankey, T. T., Leonard, J., Sankey, J. B., Kasprak, A. 2024
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Tango, L.L., Sankey, T.T., Leonard, J., Sankey, J.B., and Kasprak, A., 2024, Combining terrestrial lidar with single line transects to investigate geomorphic change—A case study on the Upper Verde River, Arizona: Geomorphology, v. 457, article 109231, at https://doi.org/10.1016/j.geomorph.2024.109231..
Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain Tupinambá-Simões, F., Pascual, A., Guerra-Hernández, J., Ordóñez, C., de Conto, T., Bravo, F. 2023
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Tupinambá-Simões, F., Pascual, A., Guerra-Hernández, J., Ordóñez, C., de Conto, T., and Bravo, F., 2023, Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain: Remote Sensing, v. 15, no. 5, article 1169, at https://doi.org/10.3390/rs15051169..
Representing 3-dimensional fuels for physics-based fire behavior models—A general framework and case study in a type-converted post-fire shrubfield Tutland, N. J., Wion, A. P., May, C. J., Hutchings, G. C., Nowak, H. A., Gattiker, J. R., Hiers, J. K., Linn, R. R., Pokswinski, S. M., Margolis, E. Q. 2025
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Tutland, N.J., Wion, A.P., May, C.J., Hutchings, G.C., Nowak, H.A., Gattiker, J.R., Hiers, J.K., Linn, R.R., Pokswinski, S.M., et al., 2025, Representing 3-dimensional fuels for physics-based fire behavior models—A general framework and case study in a type-converted post-fire shrubfield: Fire Ecology, v. 21, no. 1, article 43, at https://doi.org/10.1186/s42408-025-00383-2..
Automated change detection in photogrammetric 4D point clouds—Transferability and extension of 4D objects-by-change for monitoring riverbank dynamics using low-cost cameras Ulm, M., Elias, M., Eltner, A., Lotsari, E., Anders, K. 2025
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Ulm, M., Elias, M., Eltner, A., Lotsari, E., and Anders, K., 2025, Automated change detection in photogrammetric 4D point clouds—Transferability and extension of 4D objects-by-change for monitoring riverbank dynamics using low-cost cameras: Applied Geomatics, v. 17, no. 2, p. 367–378, at https://doi.org/10.1007/s12518-025-00623-9..
Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning Van den Broeck, W. A. J., Terryn, L., Chen, S., Cherlet, W., Cooper, Z. T., Calders, K. 2025
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Van den Broeck, W.A.J., Terryn, L., Chen, S., Cherlet, W., Cooper, Z.T., and Calders, K., 2025, Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning: ISPRS Journal of Photogrammetry and Remote Sensing, v. 227, p. 366–382, at https://doi.org/10.1016/j.isprsjprs.2025.06.023..
Three-dimensional deep learning for leaf-wood segmentation of tropical tree point clouds Van den Broeck, W. A. J., Terryn, L., Cherlet, W., Cooper, Z. T., Calders, K. 2023
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Van den Broeck, W.A.J., Terryn, L., Cherlet, W., Cooper, Z.T., and Calders, K., 2023, Three-dimensional deep learning for leaf-wood segmentation of tropical tree point clouds, in ISPRS Geospatial Week 2023, Cairo, Egypt, 2–7 September 2023, ISPRS Archives XLVIII-1/W2: International Society of Photogrammetry and Remote Sensing, p. 765–770, at https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-765-2023.N.
An open and novel low-cost terrestrial laser scanner prototype for forest monitoring Výbo, Chud, Tom, Gretsch, D., Toma, Pe, Bedkowski, J., Skladan, M., Mokroš, M. 2026
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An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data Wagner, B., Ginzler, C., Bürgi, A., Santini, S., Gärtner, H. 2018
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Wagner, B., Ginzler, C., Bürgi, A., Santini, S., and Gärtner, H., 2018, An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data: Trees - Structure and Function, v. 32, no. 1, p. 125–136, at https://doi.org/10.1007/s00468-017-1618-3..
A branch and leaf separation method based on near infrared band UAV-LiDAR data Wan, L., Li, S., Tian, Z., Shi, Y. 2023
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Wan, L., Li, S., Tian, Z., and Shi, Y., 2023, A branch and leaf separation method based on near infrared band UAV-LiDAR data, in International Geoscience and Remote Sensing Symposium, Pasadena, Calif., 16–21 July 2023, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 3284–3287, at https://doi.org/10.1109/IGARSS52108.2023.10282325..
Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning Wang, M., Baeten, L., Van Coillie, F., Calders, K., Verheyen, K., Ponette, Q., Blondeel, H., Muys, B., Armston, J., Verbeeck, H. 2024
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Wang, M., Baeten, L., Van Coillie, F., Calders, K., Verheyen, K., Ponette, Q., Blondeel, H., Muys, B., Armston, J., et al., 2024, Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning: Forest Ecosystems, v. 11, article 100196, at https://doi.org/10.1016/j.fecs.2024.100196..
A simple, interactive GIS tool for transforming assumed total station surveys to real world coordinates—The CHaMP transformation tool Wheaton, J. M., Garrard, C., Whitehead, K., Volk, C. J. 2012
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Wheaton, J.M., Garrard, C., Whitehead, K., and Volk, C.J., 2012, A simple, interactive GIS tool for transforming assumed total station surveys to real world coordinates—The CHaMP transformation tool: Computers and Geosciences, v. 42, p. 28–36, at https://doi.org/10.1016/j.cageo.2012.02.003..
Estimation of forest trees diameter from terrestrial laser scanning point clouds based on a circle fitting method Wu, R., Chen, Y., Wang, C., Li, J. 2018
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Wu, R., Chen, Y., Wang, C., and Li, J., 2018, Estimation of forest trees diameter from terrestrial laser scanning point clouds based on a circle fitting method, in International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2813–2816, at https://doi.org/10.1109/IGARSS.2018.8517303..
Delineating and reconstructing 3D forest fuel components and volumes with terrestrial laser scanning Xi, Z., Chasmer, L., Hopkinson, C. 2023
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Xi, Z., Chasmer, L., and Hopkinson, C., 2023, Delineating and reconstructing 3D forest fuel components and volumes with terrestrial laser scanning: Remote Sensing, v. 15, no. 19, article 4778, at https://doi.org/10.3390/rs15194778..
LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area Xie, B., Yang, H., Cao, J., Li, J., Wang, M., Zhang, W., Li, J., Xu, J. 2026
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Xie, B., Yang, H., Cao, J., Li, J., Wang, M., Zhang, W., Li, J., and Xu, J., 2026, LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area: Biodiversity Science, v. 34, no. 5, article 26004, at https://doi.org/10.17520/biods.2026004..
Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR Yang, C., Zhu, X., Yebra, M., Harris, S., Tapper, N. 2016
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Yang, C., Zhu, X., Yebra, M., Harris, S., and Tapper, N., 2016, Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR: Journal of Applied Remote Sensing, v. 10, no. 4, article 46025, at https://doi.org/10.1117/1.JRS.10.046025..
Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study—Guadalupe Island Yépez-Rincón, F. D., Luna-Mendoza, L., Ramírez-Serrato, N. L., Hinojosa-Corona, A., Ferriño-Fierro, A. L. 2021
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Yépez-Rincón, F.D., Luna-Mendoza, L., Ramírez-Serrato, N.L., Hinojosa-Corona, A., and Ferriño-Fierro, A.L., 2021, Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study—Guadalupe Island: Remote Sensing of Environment, v. 263, article 112563, at https://doi.org/10.1016/j.rse.2021.112563..
Efficient registration of terrestrial LiDAR scans using a coarse-to-fine strategy for forestry applications Zhang, W., Chen, Y., Wang, H., Chen, M., Wang, X., Yan, G. 2016
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Zhang, W., Chen, Y., Wang, H., Chen, M., Wang, X., and Yan, G., 2016, Efficient registration of terrestrial LiDAR scans using a coarse-to-fine strategy for forestry applications: Agricultural and Forest Meteorology, v. 225, p. 8–23, at https://doi.org/10.1016/j.agrformet.2016.05.005..
Detecting historic tar kilns and tar production sites using high-resolution, aerial LiDAR-derived digital elevation models: Introducing the Tar Kiln Feature Detection workflow (TKFD) using open-access R and FIJI software. Snitker, G., Moser, J.D., Southerlin, B., Stewart, C. 2022
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Snitker, G., Moser, J.D., Southerlin, B., Stewart, C., 2022, Detecting historic tar kilns and tar production sites using high-resolution, aerial LiDAR-derived digital elevation models: Introducing the Tar Kiln Feature Detection workflow (TKFD) using open-access R and FIJI software, at https://www.sciencedirect.com/science/article/abs/pii/S2352409X22000037?via%3Dihub..
Semantic segmentation of archaeological features on public lands – Case study of historical cotton terraces within the Piedmont National Wildlife Refuge, GA, USA. Gravel-Miguel, C., Snitker, G., Hirniak, J., Peck, K., Fetterhoff, A. 2024
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Gravel-Miguel, C., Snitker, G., Hirniak, J., Peck, K., Fetterhoff, A., 2024, Semantic segmentation of archaeological features on public lands – Case study of historical cotton terraces within the Piedmont National Wildlife Refuge, GA, USA, Manuscript in revision at Advances in Archaeological Practice.
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Tree volume estimation with terrestrial laser scanning—Testing for bias in a 3D virtual environment Abegg, M., Bösch, R., Kükenbrink, D., Morsdorf, F. 2023
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Abegg, M., Bösch, R., Kükenbrink, D., and Morsdorf, F., 2023, Tree volume estimation with terrestrial laser scanning—Testing for bias in a 3D virtual environment: Agricultural and Forest Meteorology, v. 331, article 109348, at https://doi.org/10.1016/j.agrformet.2023.109348..
Terrestrial laser scanning—A new standard of forest measuring and modelling? Åkerblom, M., Kaitaniemi, P. 2021
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Åkerblom, M., and Kaitaniemi, P., 2021, Terrestrial laser scanning—A new standard of forest measuring and modelling?: Annals of Botany, v. 128, no. 6, p. 653–662, at https://doi.org/10.1093/aob/mcab111..
Tree species classification from complex laser scanning data in Mediterranean forests using deep learning Allen, M. J., Grieve, S. W. D., Owen, H. J. F., Lines, E. R. 2023
Full Citation
Allen, M.J., Grieve, S.W.D., Owen, H.J.F., and Lines, E.R., 2023, Tree species classification from complex laser scanning data in Mediterranean forests using deep learning: Methods in Ecology and Evolution, v. 14, no. 7, p. 1657–1667, at https://doi.org/10.1111/2041-210X.13981..
Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests Anderson, C. T., Dietz, S. L., Pokswinski, S. M., Jenkins, A. M., Kaeser, M. J., Hiers, J. K., Pelc, B. D. 2021
Full Citation
Anderson, C.T., Dietz, S.L., Pokswinski, S.M., Jenkins, A.M., Kaeser, M.J., Hiers, J.K., and Pelc, B.D., 2021, Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests: Forest Ecology and Management, v. 491, article 119118, at https://doi.org/10.1016/j.foreco.2021.119118..
Reliably mapping low-intensity forest disturbance using satellite radar data Aquino, C., Mitchard, E. T. A., McNicol, I. M., Carstairs, H., Burt, A., Puma Vilca, B. L., Obiang Ebanéga, M., Modinga Dikongo, A., Dassi, C., Mayta, S., Tamayo, M., Grijalba, P., Miranda, F., Disney, M. 2022
Full Citation
Aquino, C., Mitchard, E.T.A., McNicol, I.M., Carstairs, H., Burt, A., Puma Vilca, B.L., Obiang Ebanéga, M., Modinga Dikongo, A., Dassi, C., et al., 2022, Reliably mapping low-intensity forest disturbance using satellite radar data: Frontiers in Forests and Global Change, v. 5, article 1018762, at https://doi.org/10.3389/ffgc.2022.1018762..
Automatic object extraction from electrical substation point clouds Arastounia, M., Lichti, D. D. 2015
Full Citation
Arastounia, M., and Lichti, D.D., 2015, Automatic object extraction from electrical substation point clouds: Remote Sensing, v. 7, no. 11, p. 15605–15629, at https://doi.org/10.3390/rs71115605..
Terrestrial laser scanning (TLS) for tree structure studies—A review of methods for wood-leaf classifications from 3D point clouds Arrizza, S., Marras, S., Ferrara, R., Pellizzaro, G. 2024
Full Citation
Arrizza, S., Marras, S., Ferrara, R., and Pellizzaro, G., 2024, Terrestrial laser scanning (TLS) for tree structure studies—A review of methods for wood-leaf classifications from 3D point clouds: Remote Sensing Applications—Society and Environment, v. 36, article 101364, at https://doi.org/10.1016/j.rsase.2024.101364..
Quantifying forest litter fuel moisture content with terrestrial laser scanning Batchelor, J. L., Rowell, E., Prichard, S., Nemens, D., Cronan, J., Kennedy, M. C., Moskal, L. M. 2023
Full Citation
Batchelor, J.L., Rowell, E., Prichard, S., Nemens, D., Cronan, J., Kennedy, M.C., and Moskal, L.M., 2023, Quantifying forest litter fuel moisture content with terrestrial laser scanning: Remote Sensing, v. 15, no. 6, article 1482, at https://doi.org/10.3390/rs15061482..
A 3D approach to model the taper of irregular tree stems—Making plots biomass estimates comparable in tropical forests Bauwens, S., Ploton, P., Fayolle, A., Ligot, G., Loumeto, J. J., Lejeune, P., Gourlet-Fleury, S. 2021
Full Citation
Bauwens, S., Ploton, P., Fayolle, A., Ligot, G., Loumeto, J.J., Lejeune, P., and Gourlet-Fleury, S., 2021, A 3D approach to model the taper of irregular tree stems—Making plots biomass estimates comparable in tropical forests: Ecological Applications, v. 31, no. 8, article e02451, at https://doi.org/10.1002/eap.2451..
Modeling the horizontal distribution of tree crown biomass from terrestrial laser scanning data Bazezew, M. N., Griese, N., Fehrmann, L., Kleinn, C., Nölke, N. 2024
Full Citation
Bazezew, M.N., Griese, N., Fehrmann, L., Kleinn, C., and Nölke, N., 2024, Modeling the horizontal distribution of tree crown biomass from terrestrial laser scanning data: Science of the Total Environment, v. 952, article 175377, at https://doi.org/10.1016/j.scitotenv.2024.175377..
Photogrammetric measurement of tree stems from vertical fisheye images Berveglieri, A., Tommaselli, A., Liang, X., Honkavaara, E. 2017
Full Citation
Berveglieri, A., Tommaselli, A., Liang, X., and Honkavaara, E., 2017, Photogrammetric measurement of tree stems from vertical fisheye images: Scandinavian Journal of Forest Research, v. 32, no. 8, p. 737–747, at https://doi.org/10.1080/02827581.2016.1273381..
Synthetic forest stands and point clouds for model selection and feature space comparison Bester, M. S., Maxwell, A. E., Nealey, I., Gallagher, M. R., Skowronski, N. S., McNeil, B. E. 2023
Full Citation
Bester, M.S., Maxwell, A.E., Nealey, I., Gallagher, M.R., Skowronski, N.S., and McNeil, B.E., 2023, Synthetic forest stands and point clouds for model selection and feature space comparison: Remote Sensing, v. 15, no. 18, article 4407, at https://doi.org/10.3390/rs15184407..
Estimation of forest variable and aboveground biomass using terrestrial laser scanning in the tropical rainforest Beyene, S. M. 2020
Full Citation
Beyene, S.M., 2020, Estimation of forest variable and aboveground biomass using terrestrial laser scanning in the tropical rainforest: Journal of the Indian Society of Remote Sensing, v. 48, no. 6, p. 853–863, at https://doi.org/10.1007/s12524-020-01119-2..
Forest inventory and aboveground biomass estimation with terrestrial LiDAR in the tropical forest of Malaysia Beyene, S. M., Hussin, Y. A., Kloosterman, H. E., Ismail, M. H. 2020
Full Citation
Beyene, S.M., Hussin, Y.A., Kloosterman, H.E., and Ismail, M.H., 2020, Forest inventory and aboveground biomass estimation with terrestrial LiDAR in the tropical forest of Malaysia: Canadian Journal of Remote Sensing, v. 46, no. 2, p. 130–145, at https://doi.org/10.1080/07038992.2020.1759036..
Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., Rehush, N. 2026
Full Citation
Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., and Rehush, N., 2026, Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning: Ecological Informatics, v. 97, article 103876, at https://doi.org/10.1016/j.ecoinf.2026.103876..
Forest cover buffers microclimate and increases fuel moisture in northern conifer forests—A terrestrial laser scanning approach Breigenzer, P. J., Fraver, S., Gallagher, M. R., Kenefic, L. S., Lane, E. D., Neely, J. M., Wason, J. W. 2026
Full Citation
Breigenzer, P.J., Fraver, S., Gallagher, M.R., Kenefic, L.S., Lane, E.D., Neely, J.M., and Wason, J.W., 2026, Forest cover buffers microclimate and increases fuel moisture in northern conifer forests—A terrestrial laser scanning approach: Fire Ecology, v. 22, no. 1, article 72, at https://doi.org/10.1186/s42408-026-00480-w..
Comparing biomass consumption estimated from point cloud data versus long-wave infrared imagery during prescribed growing season burns in pine woodlands of the southeastern United States Bright, B. C., Hudak, A. T., Sánchez-López, N., Loudermilk, E. L., Hawley, C. M., Rowell, E., O’Brien, J. J., Flanagan, S. A., Robertson, K., Kato, A., Hoffman, C., Weise, D. R., Hiers, J. K. 2025
Full Citation
Bright, B.C., Hudak, A.T., Sánchez-López, N., Loudermilk, E.L., Hawley, C.M., Rowell, E., O’Brien, J.J., Flanagan, S.A., Robertson, K., et al., 2025, Comparing biomass consumption estimated from point cloud data versus long-wave infrared imagery during prescribed growing season burns in pine woodlands of the southeastern United States: International Journal of Wildland Fire, v. 34, no. 7, article Wf24210, at https://doi.org/10.1071/WF24210..
Influence of ULS acquisition characteristics on tree stem parameter estimation Bruggisser, M., Hollaus, M., Otepka, J., Pfeifer, N. 2020
Full Citation
Bruggisser, M., Hollaus, M., Otepka, J., and Pfeifer, N., 2020, Influence of ULS acquisition characteristics on tree stem parameter estimation: ISPRS Journal of Photogrammetry and Remote Sensing, v. 168, p. 28–40, at https://doi.org/10.1016/j.isprsjprs.2020.08.002..
Terrestrial laser scanning in forest ecology—Expanding the horizon Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L. P., Chave, J., Danson, F. M., Demol, M., Disney, M., Gaulton, R., Krishna Moorthy, S. M., Levick, S. R., Saarinen, N., Schaaf, C., Stovall, A., Terryn, L., Wilkes, P., Verbeeck, H. 2020
Full Citation
Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L.P., Chave, J., Danson, F.M., Demol, M., et al., 2020, Terrestrial laser scanning in forest ecology—Expanding the horizon: Remote Sensing of Environment, v. 251, article 112102, at https://doi.org/10.1016/j.rse.2020.112102..
Remote sensing technology applications in forestry and REDD+ Calders, K., Jonckheere, I., Nightingale, J., Vastaranta, M. 2020
Full Citation
Calders, K., Jonckheere, I., Nightingale, J., and Vastaranta, M., 2020, Remote sensing technology applications in forestry and REDD+: Forests, v. 11, no. 2, article 188, at https://doi.org/10.3390/f11020188..
A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy Campos, M. B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., Puttonen, E. 2021
Full Citation
Campos, M.B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., and Puttonen, E., 2021, A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy: Frontiers in Plant Science, v. 11, article 606752, at https://doi.org/10.3389/fpls.2020.606752..
Terrestrial lidar scanning provides efficient measurements of fire-caused crown scorch in longleaf pine Cannon, J. B., Zampieri, N. E., Whelan, A. W., Shearman, T. M., Sánchez Meador, A. J., Varner, J. M. 2025
Full Citation
Cannon, J.B., Zampieri, N.E., Whelan, A.W., Shearman, T.M., Sánchez Meador, A.J., and Varner, J.M., 2025, Terrestrial lidar scanning provides efficient measurements of fire-caused crown scorch in longleaf pine: Fire Ecology, v. 21, no. 1, article 71, at https://doi.org/10.1186/s42408-025-00420-0..
Modality-specific feature design for species classification in forest inventories using TLS and UAS LiDAR Carpenter, J., Jung, M., Goel, A., Fei, S., Jung, J. 2025
Full Citation
Carpenter, J., Jung, M., Goel, A., Fei, S., and Jung, J., 2025, Modality-specific feature design for species classification in forest inventories using TLS and UAS LiDAR: Frontiers in Forests and Global Change, v. 8, article 1500178, at https://doi.org/10.3389/ffgc.2025.1500178..
Use of terrestrial laser scanner for aboveground biomass estimation in a seasonally dry tropical forest Carvalho, H. F. S., da Silva, T. G. F., Galvíncio, J. D., Antonino, A. C. D., Menezes, R. S. C., Dos Santos, C. V. B., Miranda, R. Q., Nóbrega, R. L. B., Domingues, T. F., Silva, E. A., de Moura, M. S. B. 2023
Full Citation
Carvalho, H.F.S., da Silva, T.G.F., Galvíncio, J.D., Antonino, A.C.D., Menezes, R.S.C., Dos Santos, C.V.B., Miranda, R.Q., Nóbrega, R.L.B., Domingues, T.F., et al., 2023, Use of terrestrial laser scanner for aboveground biomass estimation in a seasonally dry tropical forest: Revista Brasileira de Geografia Fisica, v. 16, no. 5, p. 2641–2657, at https://doi.org/10.26848/rbgf.v16.5.p2741-2657..
ForestAlign—Automatic forest structure-based alignment for multi-view TLS and ALS point clouds Castorena, J., Dickman, L. T., Killebrew, A. J., Gattiker, J. R., Linn, R., Loudermilk, E. L. 2025
Full Citation
Castorena, J., Dickman, L.T., Killebrew, A.J., Gattiker, J.R., Linn, R., and Loudermilk, E.L., 2025, ForestAlign—Automatic forest structure-based alignment for multi-view TLS and ALS point clouds: Science of Remote Sensing, v. 11, article 100194, at https://doi.org/10.1016/j.srs.2024.100194..
Extraction of genetic test measurements from LiDAR cloud data Cavalheiro, R., Molina-Valero, J. A., Hodge, G., Howell, T., Acosta, J. J. 2026
Full Citation
Cavalheiro, R., Molina-Valero, J.A., Hodge, G., Howell, T., and Acosta, J.J., 2026, Extraction of genetic test measurements from LiDAR cloud data: Journal of Forestry Research, v. 37, no. 1, article 58, at https://doi.org/10.1007/s11676-026-02007-0..
Benchmarking tree instance segmentation of terrestrial laser scanning point clouds Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., Senf, C., Soenens, L., Terryn, L., Van den Broeck, W. A. J., Calders, K. 2026
Full Citation
Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., et al., 2026, Benchmarking tree instance segmentation of terrestrial laser scanning point clouds: ISPRS Journal of Photogrammetry and Remote Sensing, v. 231, p. 230–247, at https://doi.org/10.1016/j.isprsjprs.2025.10.033..
Terrestrial LiDAR technology to evaluate the vertical structure of stands of Bertholletia excelsa Bonpl., a species symbol of conservation through sustainable use in the Brazilian Amazon Costa, F. F., Oliveira Júnior, R. C. D., de Almeida, D. R. A., Rosa, D. M., da Silva, K. E., Tonini, H., Beldini, T. P., Santos, D. B. D., Guedes, M. C. 2025
Full Citation
Costa, F.F., Oliveira Júnior, R.C.D., de Almeida, D.R.A., Rosa, D.M., da Silva, K.E., Tonini, H., Beldini, T.P., Santos, D.B.D., and Guedes, M.C., 2025, Terrestrial LiDAR technology to evaluate the vertical structure of stands of Bertholletia excelsa Bonpl., a species symbol of conservation through sustainable use in the Brazilian Amazon: Sustainability, v. 17, no. 13, article 6049, at https://doi.org/10.3390/su17136049..
Biodiversity benefits of prescribed fire and mechanical thinning in Yosemite Valley Crumrine, S. A., Anderson, C. T., Hankin, L. E. 2026
Full Citation
Crumrine, S.A., Anderson, C.T., and Hankin, L.E., 2026, Biodiversity benefits of prescribed fire and mechanical thinning in Yosemite Valley: Fire Ecology, v. 22, no. 1, article 76, at https://doi.org/10.1186/s42408-026-00501-8..
Assessing the structural differences between tropical forest types using terrestrial laser scanning Decuyper, M., Mulatu, K. A., Brede, B., Calders, K., Armston, J., Rozendaal, D. M. A., Mora, B., Clevers, J. G. P. W., Kooistra, L., Herold, M., Bongers, F. 2018
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Decuyper, M., Mulatu, K.A., Brede, B., Calders, K., Armston, J., Rozendaal, D.M.A., Mora, B., Clevers, J.G.P.W., Kooistra, L., et al., 2018, Assessing the structural differences between tropical forest types using terrestrial laser scanning: Forest Ecology and Management, v. 429, p. 327–335, at https://doi.org/10.1016/j.foreco.2018.07.032..
Using terrestrial laser scanning (TLS) to estimate surface soil properties in a mixed conifer forest Dickman, L. T., Hutchings, G., Kroeger, M., Gutierrez, A., Killebrew, A. J., Guthrie, W., Atchley, A., Pokswinski, S., Gattiker, J., Linn, R. 2026
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Dickman, L.T., Hutchings, G., Kroeger, M., Gutierrez, A., Killebrew, A.J., Guthrie, W., Atchley, A., Pokswinski, S., Gattiker, J., et al., 2026, Using terrestrial laser scanning (TLS) to estimate surface soil properties in a mixed conifer forest: Ecological Indicators, v. 191, article 115625, at https://doi.org/10.1016/j.ecolind.2026.115625..
Calculation of Eucalyptus sp. timber volume from laser scanning do Nascimento Lima, J. V., Soares, Á A. V., da Silva Pacheco, M., Martins, G. D. 2025
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do Nascimento Lima, J.V., Soares, Á.A.V., da Silva Pacheco, M., and Martins, G.D., 2025, Calculation of Eucalyptus sp. timber volume from laser scanning: Revista Brasileira de Cartografia, v. 77, p. 1–14, at https://doi.org/10.14393/rbcv77n0a-75286..
Aerial-ground-space precision auto-measurement technology in stereo forest surveying Dong, B., Feng, Z. K., Yao, S., Yan, X. J., Sui, H. D., Wang, J., Guo, J. C. 2008
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Dong, B., Feng, Z.K., Yao, S., Yan, X.J., Sui, H.D., Wang, J., and Guo, J.C., 2008, Aerial-ground-space precision auto-measurement technology in stereo forest surveying: Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University, v. 30, no. SUPPL. 1, p. 173–177.
Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques—A review Fang, H. 2021
Full Citation
Fang, H., 2021, Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques—A review: Kexue Tongbao/Chinese Science Bulletin, v. 66, no. 24, p. 3141–3153, at https://doi.org/10.1360/TB-2020-1057..
Interdisciplinary applications of LiDAR in forest studies—Advances in sensors, methods, and cross-domain metrics Fareed, N., Silva, C. A., Numata, I., Flores, J. P. 2026
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Fareed, N., Silva, C.A., Numata, I., and Flores, J.P., 2026, Interdisciplinary applications of LiDAR in forest studies—Advances in sensors, methods, and cross-domain metrics: Remote Sensing, v. 18, no. 2, article 219, at https://doi.org/10.3390/rs18020219..
Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths—A simulation study Forsman, M., Börlin, N., Olofsson, K., Reese, H., Holmgren, J. 2018
Full Citation
Forsman, M., Börlin, N., Olofsson, K., Reese, H., and Holmgren, J., 2018, Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths—A simulation study: ISPRS Journal of Photogrammetry and Remote Sensing, v. 135, p. 84–92, at https://doi.org/10.1016/j.isprsjprs.2017.11.013..
Estimation of plot-level burn severity using terrestrial laser scanning Gallagher, M. R., Maxwell, A. E., Guillén, L. A., Everland, A., Loudermilk, E. L., Skowronski, N. S. 2021
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Gallagher, M.R., Maxwell, A.E., Guillén, L.A., Everland, A., Loudermilk, E.L., and Skowronski, N.S., 2021, Estimation of plot-level burn severity using terrestrial laser scanning: Remote Sensing, v. 13, no. 20, article 4168, at https://doi.org/10.3390/rs13204168..
Horizontal viewsheds of large herbivores as a function of woodland structure Gresham, A., Healey, J. R., Eichhorn, M. P., Barton, O., Smith, A. R., Shannon, G. 2023
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Gresham, A., Healey, J.R., Eichhorn, M.P., Barton, O., Smith, A.R., and Shannon, G., 2023, Horizontal viewsheds of large herbivores as a function of woodland structure: Ecology and Evolution, v. 13, no. 11, article e10699, at https://doi.org/10.1002/ece3.10699..
High terrestrial laser scanning—An accessible method for tree crown characterization Griese, N., Kleinn, C., Nölke, N. 2026
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Griese, N., Kleinn, C., and Nölke, N., 2026, High terrestrial laser scanning—An accessible method for tree crown characterization: Ecological Informatics, v. 95, article 103707, at https://doi.org/10.1016/j.ecoinf.2026.103707..
A large dataset of labelled single tree point clouds, QSMs and tree graphs Griese, N., Ritzert, M., Nölke, N. 2025
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Griese, N., Ritzert, M., and Nölke, N., 2025, A large dataset of labelled single tree point clouds, QSMs and tree graphs: Scientific Data, v. 12, no. 1, article 1953, at https://doi.org/10.1038/s41597-025-06421-7..
True leaf area index retrieval using terrestrial LiDAR for broadleaf trees via novel multiinclined-planes method Guo, Y., Li, S., Tang, H., He, Z. 2025
Full Citation
Guo, Y., Li, S., Tang, H., and He, Z., 2025, True leaf area index retrieval using terrestrial LiDAR for broadleaf trees via novel multiinclined-planes method: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 18, p. 19025–19037, at https://doi.org/10.1109/JSTARS.2025.3591398..
Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments Guth, A., Dauner, M., Fowler, A., Hoehl, S., Hamlington, P., Hoffman, C., Hannigan, M. 2025
Full Citation
Guth, A., Dauner, M., Fowler, A., Hoehl, S., Hamlington, P., Hoffman, C., and Hannigan, M., 2025, Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments: Frontiers in Forests and Global Change, v. 8, article 1663753, at https://doi.org/10.3389/ffgc.2025.1663753..
Complete workflow for detailed 3D forest reconstruction—From terrestrial laser scanning to complex 3D radiative transfer modelling Hanousek, T., Novotný, J., Navrátilová, B., Švik, M., Krejza, J., Janoutov 2025
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Hanousek, T., Novotný, J., Navrátilová, B.,
Extraction of individual tree skeletons from terrestrial LiDAR point clouds based on optimization of network graph structural model He, Y., Hui, Z., Cheng, X., Huang, F., Liu, H., Tu, L. 2026
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He, Y., Hui, Z., Cheng, X., Huang, F., Liu, H., and Tu, L., 2026, Extraction of individual tree skeletons from terrestrial LiDAR point clouds based on optimization of network graph structural model: Zhongguo Jiguang/Chinese Journal of Lasers, v. 53, no. 13, article 1310002, at https://doi.org/10.3788/CJL251225..
Integrating recreation into National Forest Inventories—Results from a forest visitor survey in winter and summer Hegetschweiler, K. T., Stride, C. B., Fischer, C., Ginzler, C., Hunziker, M. 2022
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Hegetschweiler, K.T., Stride, C.B., Fischer, C., Ginzler, C., and Hunziker, M., 2022, Integrating recreation into National Forest Inventories—Results from a forest visitor survey in winter and summer: Journal of Outdoor Recreation and Tourism, v. 39, article 100489, at https://doi.org/10.1016/j.jort.2022.100489..
Dynamic restoration and the impact of native versus invasive vegetation on coastal foredune morphodynamics, Lanphere Dunes, California, USA Hilgendorf, Z., Walker, I. J., Pickart, A. J., Turner, C. M. 2022
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Hilgendorf, Z., Walker, I.J., Pickart, A.J., and Turner, C.M., 2022, Dynamic restoration and the impact of native versus invasive vegetation on coastal foredune morphodynamics, Lanphere Dunes, California, USA: Earth Surface Processes and Landforms, v. 47, no. 13, p. 3083–3099, at https://doi.org/10.1002/esp.5445..
A method for validating the structural completeness of understory vegetation models captured with 3D remote sensing Hillman, S., Wallace, L., Reinke, K., Hally, B., Jones, S., Saldias, D. S. 2019
Full Citation
Hillman, S., Wallace, L., Reinke, K., Hally, B., Jones, S., and Saldias, D.S., 2019, A method for validating the structural completeness of understory vegetation models captured with 3D remote sensing: Remote Sensing, v. 11, no. 18, article 2118, at https://doi.org/10.3390/rs11182118..
Robust single-image tree diameter estimation with mobile phones Holcomb, A., Tong, L., Keshav, S. 2023
Full Citation
Holcomb, A., Tong, L., and Keshav, S., 2023, Robust single-image tree diameter estimation with mobile phones: Remote Sensing, v. 15, no. 3, article 772, at https://doi.org/10.3390/rs15030772..
Transferring instance and semantic segmentation across terrestrial laser scanning time series—Towards automated forest monitoring plot measurements Honkanen, E., Yrttimaa, T., Terryn, L., Kamula, T., Erkkilä, A., Liikonen, L., Hyyppä, J., Vastaranta, M. 2026
Full Citation
Honkanen, E., Yrttimaa, T., Terryn, L., Kamula, T., Erkkilä, A., Liikonen, L., Hyyppä, J., and Vastaranta, M., 2026, Transferring instance and semantic segmentation across terrestrial laser scanning time series—Towards automated forest monitoring plot measurements: ISPRS Open Journal of Photogrammetry and Remote Sensing, v. 21, article 100147, at https://doi.org/10.1016/j.ophoto.2026.100147..
Towards spatially explicit quantification of pre- and postfire fuels and fuel consumption from traditional and point cloud measurements Hudak, A. T., Kato, A., Bright, B. C., Loudermilk, E. L., Hawley, C., Restaino, J. C., Ottmar, R. D., Prata, G. A., Cabo, C., Prichard, S. J., Rowell, E. M., Weise, D. R. 2020
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Hudak, A.T., Kato, A., Bright, B.C., Loudermilk, E.L., Hawley, C., Restaino, J.C., Ottmar, R.D., Prata, G.A., Cabo, C., et al., 2020, Towards spatially explicit quantification of pre- and postfire fuels and fuel consumption from traditional and point cloud measurements: Forest Science, v. 66, no. 4, p. 428–442, at https://doi.org/10.1093/forsci/fxz085..
Wildland fire mid-story—A generative modeling approach for representative fuels Hutchings, G., Gattiker, J., Scherting, B., Linn, R. R. 2024
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Hutchings, G., Gattiker, J., Scherting, B., and Linn, R.R., 2024, Wildland fire mid-story—A generative modeling approach for representative fuels: Environmental Modelling and Software, v. 171, article 105877, at https://doi.org/10.1016/j.envsoft.2023.105877..
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Change detection of tree biomass with terrestrial laser scanning and quantitative structure modelling Kaasalainen, S., Krooks, A., Liski, J., Raumonen, P., Kaartinen, H., Kaasalainen, M., Puttonen, E., Anttila, K., Mäkipää, R. 2014
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Bi-temporal terrestrial laser scanning to measure secondary growth of trees—Toward non-destructive assessment of wood properties Kamula, T., Yrttimaa, T., Cimdins, R., Polvivaara, A., Kukko, A., Holopainen, M., Vastaranta, M. 2026
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Kamula, T., Yrttimaa, T., Cimdins, R., Polvivaara, A., Kukko, A., Holopainen, M., and Vastaranta, M., 2026, Bi-temporal terrestrial laser scanning to measure secondary growth of trees—Toward non-destructive assessment of wood properties: Scandinavian Journal of Forest Research, v. 41, no. 4, p. 363–378, at https://doi.org/10.1080/02827581.2026.2646458..
Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR Klockow, P. A., Putman, E. B., Vogel, J. G., Moore, G. W., Edgar, C. B., Popescu, S. C. 2020
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Sensor agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning Krisanski, S., Taskhiri, M. S., Aracil, S. G., Herries, D., Turner, P. 2021
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Artificial intelligence and terrestrial point clouds for forest monitoring Kulicki, M., Cabo, C., Trzci, Będkowski, J., Stereńczak, K. 2025
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Kulicki, M., Cabo, C., Trzci
Assessing the effects of large herbivores on the three-dimensional structure of temperate forests using terrestrial laser scanning Li, S., Hou, Z., Ge, J., Wang, T. 2022
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Li, S., Hou, Z., Ge, J., and Wang, T., 2022, Assessing the effects of large herbivores on the three-dimensional structure of temperate forests using terrestrial laser scanning: Forest Ecology and Management, v. 507, article 119985, at https://doi.org/10.1016/j.foreco.2021.119985..
Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests Li, S., Wang, T., Hou, Z., Gong, Y., Feng, L., Ge, J. 2021
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Li, S., Wang, T., Hou, Z., Gong, Y., Feng, L., and Ge, J., 2021, Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests: Ecological Indicators, v. 121, article 107011, at https://doi.org/10.1016/j.ecolind.2020.107011..
In-situ measurements from mobile platforms—An emerging approach to address the old challenges associated with forest inventories Liang, X., Kukko, A., Hyyppä, J., Lehtomäki, M., Pyörälä, J., Yu, X., Kaartinen, H., Jaakkola, A., Wang, Y. 2018
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Liang, X., Kukko, A., Hyyppä, J., Lehtomäki, M., Pyörälä, J., Yu, X., Kaartinen, H., Jaakkola, A., and Wang, Y., 2018, In-situ measurements from mobile platforms—An emerging approach to address the old challenges associated with forest inventories: ISPRS Journal of Photogrammetry and Remote Sensing, v. 143, p. 97–107, at https://doi.org/10.1016/j.isprsjprs.2018.04.019..
Combining airborne and terrestrial laser scanning technologies to measure forest understorey volume Liu, L., Pang, Y., Li, Z., Si, L., Liao, S. 2017
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Liu, L., Pang, Y., Li, Z., Si, L., and Liao, S., 2017, Combining airborne and terrestrial laser scanning technologies to measure forest understorey volume: Forests, v. 8, no. 4, article 111, at https://doi.org/10.3390/f8040111..
Remote sensing applications for prescribed burn research LoPresti, A., Hayden, M. T., Siegel, K., Poulter, B., Stavros, E. N., Dee, L. E. 2024
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LoPresti, A., Hayden, M.T., Siegel, K., Poulter, B., Stavros, E.N., and Dee, L.E., 2024, Remote sensing applications for prescribed burn research: International Journal of Wildland Fire, v. 33, no. 6, article WF23130, at https://doi.org/10.1071/wf23130..
Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern U.S. ecosystem Loudermilk, E. L., Pokswinski, S., Hawley, C. M., Maxwell, A., Gallagher, M. R., Skowronski, N. S., Hudak, A. T., Hoffman, C., Hiers, J. K. 2023
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Loudermilk, E.L., Pokswinski, S., Hawley, C.M., Maxwell, A., Gallagher, M.R., Skowronski, N.S., Hudak, A.T., Hoffman, C., and Hiers, J.K., 2023, Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern U.S. ecosystem: Fire, v. 6, no. 4, article 151, at https://doi.org/10.3390/fire6040151..
Revealing changes in the stem form and volume allocation in diverse boreal forests using two-date terrestrial laser scanning Luoma, V., Yrttimaa, T., Kankare, V., Saarinen, N., Pyörälä, J., Kukko, A., Kaartinen, H., Hyyppä, J., Holopainen, M., Vastaranta, M. 2021
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TreeFlow—Conditional flow matching for 3D tree point cloud generation from inventory attributes Marcozzi, A., Tenny, J., Martin, D., Castorena, J., Crennen, Z., Wells, L., Hillman, S. 2026
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Application of LiDAR derived fuel cells to wildfire modeling at laboratory scale Marcozzi, A. A., Johnson, J. V., Parsons, R. A., Flanary, S. J., Seielstad, C. A., Downs, J. Z. 2023
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Marcozzi, A.A., Johnson, J.V., Parsons, R.A., Flanary, S.J., Seielstad, C.A., and Downs, J.Z., 2023, Application of LiDAR derived fuel cells to wildfire modeling at laboratory scale: Fire, v. 6, no. 10, article 394, at https://doi.org/10.3390/fire6100394..
Repeat terrestrial laser scanning for documenting and monitoring prescribed fire efficacy Maxwell, A. E., Gallagher, M. R., Stockton, S. R., Wozniak, M. C., McNeil, B. E., Defeo, J. A., Everland, A., Skowronski, N. S. 2026
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Maxwell, A.E., Gallagher, M.R., Stockton, S.R., Wozniak, M.C., McNeil, B.E., Defeo, J.A., Everland, A., and Skowronski, N.S., 2026, Repeat terrestrial laser scanning for documenting and monitoring prescribed fire efficacy: Fire Ecology, v. 22, no. 1, article 91, at https://doi.org/10.1186/s42408-026-00498-0..
Operationalizing the use of TLS in forest inventories—The R package FORTLS Molina-Valero, J. A., Martínez-Calvo, A., Ginzo Villamayor, M. J., Novo Pérez, M. A., Álvarez-González, J. G., Montes, F., Pérez-Cruzado, C. 2022
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Molina-Valero, J.A., Martínez-Calvo, A., Ginzo Villamayor, M.J., Novo Pérez, M.A., Álvarez-González, J.G., Montes, F., and Pérez-Cruzado, C., 2022, Operationalizing the use of TLS in forest inventories—The R package FORTLS: Environmental Modelling and Software, v. 150, article 105337, at https://doi.org/10.1016/j.envsoft.2022.105337..
Use of close-range LiDAR devices and statistical inference approaches in operational stand-level forest inventories Molina-Valero, J. A., Martins-Neto, R. P., Martínez-Calvo, A., Rodríguez-Ruiz, J., Surový, P., Seppelt, A., Pérez-Cruzado, C. 2025
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Molina-Valero, J.A., Martins-Neto, R.P., Martínez-Calvo, A., Rodríguez-Ruiz, J., Surový, P., Seppelt, A., and Pérez-Cruzado, C., 2025, Use of close-range LiDAR devices and statistical inference approaches in operational stand-level forest inventories: Remote Sensing of Environment, v. 325, article 114773, at https://doi.org/10.1016/j.rse.2025.114773..
Tree value and log product yield determination in radiata pine (Pinus radiata) plantations in australia—Comparisons of terrestrial laser scanning with a forest inventory system and manual measurements Murphy, G. E., Acuna, M. A., Dumbrell, I. 2010
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Murphy, G.E., Acuna, M.A., and Dumbrell, I., 2010, Tree value and log product yield determination in radiata pine (Pinus radiata) plantations in australia—Comparisons of terrestrial laser scanning with a forest inventory system and manual measurements: Canadian Journal of Forest Research, v. 40, no. 11, p. 2223–2233, at https://doi.org/10.1139/X10-171..
Quantifying understory fuel structure and biomass using coupled terrestrial lidar scanning, close-range photogrammetry, and field sampling Nemens, D. G., Prichard, S. J., Cova, G. R., Drye, B., Eagle, P., Rowell, E., Thoreson, J. 2026
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Nemens, D.G., Prichard, S.J., Cova, G.R., Drye, B., Eagle, P., Rowell, E., and Thoreson, J., 2026, Quantifying understory fuel structure and biomass using coupled terrestrial lidar scanning, close-range photogrammetry, and field sampling: Fire Ecology, v. 22, no. 1, article 92, at https://doi.org/10.1186/s42408-026-00503-6..
Terrestrial laser scanning for plot-scale forest measurement Newnham, G. J., Armston, J. D., Calders, K., Disney, M. I., Lovell, J. L., Schaaf, C. B., Strahler, A. H., Danson, F. M. 2015
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Newnham, G.J., Armston, J.D., Calders, K., Disney, M.I., Lovell, J.L., Schaaf, C.B., Strahler, A.H., and Danson, F.M., 2015, Terrestrial laser scanning for plot-scale forest measurement: Current Forestry Reports, v. 1, no. 4, p. 239–251, at https://doi.org/10.1007/s40725-015-0025-5..
A LiDAR framework to characterise elevated and canopy firebrand fuels for fire prediction models Nguyen, H. N., Penman, T. D., Peace, M., Hinko-Najera, N., Penman, S., Filippi, J. B., Pinar, A. A., Filkov, A. I. 2026
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Nguyen, H.N., Penman, T.D., Peace, M., Hinko-Najera, N., Penman, S., Filippi, J.B., Pinar, A.A., and Filkov, A.I., 2026, A LiDAR framework to characterise elevated and canopy firebrand fuels for fire prediction models: Fire Ecology, v. 22, no. 1, article 78, at https://doi.org/10.1186/s42408-026-00524-1..
On the geometry and allometry of big-buttressed trees—A challenge for forest monitoring—New insights from 3D-modeling with terrestrial laser scanning Nölke, N., Fehrmann, L., Jaya, I. N. S., Tiryana, T., Seidel, D., Kleinn, C. 2015
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Nölke, N., Fehrmann, L., Jaya, I.N.S., Tiryana, T., Seidel, D., and Kleinn, C., 2015, On the geometry and allometry of big-buttressed trees—A challenge for forest monitoring—New insights from 3D-modeling with terrestrial laser scanning: IForest, v. 8, no. OCTOBER2015, p. 574–581, at https://doi.org/10.3832/ifor1449-007..
Estimating sagebrush biomass using terrestrial laser scanning Olsoy, P. J., Glenn, N. F., Clark, P. E. 2014
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Olsoy, P.J., Glenn, N.F., and Clark, P.E., 2014, Estimating sagebrush biomass using terrestrial laser scanning: Rangeland Ecology and Management, v. 67, no. 2, p. 224–228, at https://doi.org/10.2111/REM-D-12-00186.1..
Unmanned aerial systems measure structural habitat features for wildlife across multiple scales Olsoy, P. J., Shipley, L. A., Rachlow, J. L., Forbey, J. S., Glenn, N. F., Burgess, M. A., Thornton, D. H. 2018
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Olsoy, P.J., Shipley, L.A., Rachlow, J.L., Forbey, J.S., Glenn, N.F., Burgess, M.A., and Thornton, D.H., 2018, Unmanned aerial systems measure structural habitat features for wildlife across multiple scales: Methods in Ecology and Evolution, v. 9, no. 3, p. 594–604, at https://doi.org/10.1111/2041-210X.12919..
Common field measures and geometric assumptions of tree shape produce consistently biased estimates of tree and canopy structure in mixed Mediterranean forests Owen, H. J. F., Lines, E. R. 2024
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Owen, H.J.F., and Lines, E.R., 2024, Common field measures and geometric assumptions of tree shape produce consistently biased estimates of tree and canopy structure in mixed Mediterranean forests: Ecological Indicators, v. 165, article 112219, at https://doi.org/10.1016/j.ecolind.2024.112219..
An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies Penman, S., Lentini, P., Law, B., York, A. 2023
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Penman, S., Lentini, P., Law, B., and York, A., 2023, An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies: Forest Ecology and Management, v. 548, article 121405, at https://doi.org/10.1016/j.foreco.2023.121405..
The horizontal distribution of branch biomass in european beech—A model based on measurements and TLS based proxies Pérez‐Cruzado, C., Kleinn, C., Magdon, P., Álvarez‐González, J. G., Magnussen, S., Fehrmann, L., Nölke, N. 2021
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Terrestrial structure from motion photogrammetry for deriving forest inventory data Piermattei, L., Karel, W., Wang, D., Wieser, M., Mokro, Surov, Kore, Tomašt, Pfeifer, N., Hollaus, M. 2019
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A simplified and affordable approach to forest monitoring using single terrestrial laser scans and transect sampling Pokswinski, S., Gallagher, M. R., Skowronski, N. S., Loudermilk, E. L., Hawley, C., Wallace, D., Everland, A., Wallace, J., Hiers, J. K. 2021
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Pokswinski, S., Gallagher, M.R., Skowronski, N.S., Loudermilk, E.L., Hawley, C., Wallace, D., Everland, A., Wallace, J., and Hiers, J.K., 2021, A simplified and affordable approach to forest monitoring using single terrestrial laser scans and transect sampling: MethodsX, v. 8, article 101484, at https://doi.org/10.1016/j.mex.2021.101484..
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Pokswinski, S., Nelson, K., Maxwell, A., Link, E., Redford, H.E., Atkins, J.W., Sanchez Meador, A., Nealey, I., Crawl, D., et al., 2026, IntELiMon—An automated framework for standardized terrestrial lidar processing in multi-disciplinary ecosystem monitoring: Forestry—An International Journal of Forest Research, v. 99, no. 5, article cpag083, at https://doi.org/10.1093/forestry/cpag083..
Hierarchically scaled remote sensing and field datasets for three-dimensional wildland fuel characterization Prichard, S. J., Nemens, D. G., Rowell, E., Batchelor, J. L., Bester, M. S., Bright, B. C., Cova, G. R., Cronan, J. B., Drye, B., Hudak, A. T., Eagle, P. C., Kennedy, M. C., Loudermilk, E. L., Redford, H. E., Parsons, R. A., Silva, C. A., Skowronski, N. S., Thoreson, J. 2026
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Prichard, S.J., Nemens, D.G., Rowell, E., Batchelor, J.L., Bester, M.S., Bright, B.C., Cova, G.R., Cronan, J.B., Drye, B., et al., 2026, Hierarchically scaled remote sensing and field datasets for three-dimensional wildland fuel characterization: Fire Ecology, v. 22, no. 1, article 130, at https://doi.org/10.1186/s42408-026-00507-2..
Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data Puletti, N., Galluzzi, M., Grotti, M., Ferrara, C. 2021
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Puletti, N., Galluzzi, M., Grotti, M., and Ferrara, C., 2021, Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data: Remote Sensing Applications—Society and Environment, v. 24, article 100620, at https://doi.org/10.1016/j.rsase.2021.100620..
Identifying tree-related microhabitats in TLS point clouds using machine learning Rehush, N., Abegg, M., Waser, L. T., Brändli, U. B. 2018
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Rehush, N., Abegg, M., Waser, L.T., and Brändli, U.B., 2018, Identifying tree-related microhabitats in TLS point clouds using machine learning: Remote Sensing, v. 10, no. 11, article 1735, at https://doi.org/10.3390/rs10111735..
Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning Ritter, T., Schwarz, M., Tockner, A., Leisch, F., Nothdurft, A. 2017
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Ritter, T., Schwarz, M., Tockner, A., Leisch, F., and Nothdurft, A., 2017, Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning: Forests, v. 8, no. 8, article 265, at https://doi.org/10.3390/f8080265..
Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests Ro, Suomalainen, J., Bartholomeus, H., Herold, M. 2018
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Ro
Coupling terrestrial laser scanning with 3D fuel biomass sampling for advancing wildland fuels characterization Rowell, E., Loudermilk, E. L., Hawley, C., Pokswinski, S., Seielstad, C., Queen, L. L., O'Brien, J. J., Hudak, A. T., Goodrick, S., Hiers, J. K. 2020
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Development and validation of fuel height models for terrestrial lidar - RxCADRE 2012 Rowell, E. M., Seielstad, C. A., Ottmar, R. D. 2016
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Rowell, E.M., Seielstad, C.A., and Ottmar, R.D., 2016, Development and validation of fuel height models for terrestrial lidar - RxCADRE 2012: International Journal of Wildland Fire, v. 25, no. 1, p. 38–47, at https://doi.org/10.1071/WF14170..
Modeling tree crown-mediated accumulation of downed woody debris in fire-maintained longleaf pine forests of the southeastern US from laser scanning data Sánchez-López, N., Hudak, A. T., Xia, J., Robertson, K., Bright, B. C., Prichard, S., Nemens, D. G., Hawley, C., Loudermilk, E. L., Keane, R., Woodall, C. W., Mauro, F., Boschetti, L., Silva, C. A., Cabo, C. 2026
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Sánchez-López, N., Hudak, A.T., Xia, J., Robertson, K., Bright, B.C., Prichard, S., Nemens, D.G., Hawley, C., Loudermilk, E.L., et al., 2026, Modeling tree crown-mediated accumulation of downed woody debris in fire-maintained longleaf pine forests of the southeastern US from laser scanning data: Fire Ecology, v. 22, no. 1, article 136, at https://doi.org/10.1186/s42408-026-00548-7..
Quantifying plant-soil-nutrient dynamics in rangelands—Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland Sankey, J. B., Sankey, T. T., Li, J., Ravi, S., Wang, G., Caster, J., Kasprak, A. 2021
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Sankey, J.B., Sankey, T.T., Li, J., Ravi, S., Wang, G., Caster, J., and Kasprak, A., 2021, Quantifying plant-soil-nutrient dynamics in rangelands—Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland: Remote Sensing of Environment, v. 253, article 112223, at https://doi.org/10.1016/j.rse.2020.112223..
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Schneider, L., Betting, B., Patterson, M., Skowronski, N., and Simeoni, A., 2021, Experimental study of fire spread through discontinuous fuels without flame contact: Fire Safety Journal, v. 120, article 103066, at https://doi.org/10.1016/j.firesaf.2020.103066..
Monitoring trees outside forests—A review Schnell, S., Kleinn, C., Ståhl, G. 2015
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Schnell, S., Kleinn, C., and Ståhl, G., 2015, Monitoring trees outside forests—A review: Environmental Monitoring and Assessment, v. 187, no. 9, article 600, at https://doi.org/10.1007/s10661-015-4817-7..
TSS-Net—An efficient deep learning framework for individual tree segmentation in terrestrial laser scanning data Sha, A., Dong, B., Li, Z., Zhang, S., Liu, R., Li, S., Lu, H., Wang, H. 2026
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Sha, A., Dong, B., Li, Z., Zhang, S., Liu, R., Li, S., Lu, H., and Wang, H., 2026, TSS-Net—An efficient deep learning framework for individual tree segmentation in terrestrial laser scanning data: Results in Engineering, v. 29, article 109161, at https://doi.org/10.1016/j.rineng.2026.109161..
SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning Shao, J., Zhang, W., Mellado, N., Wang, N., Jin, S., Cai, S., Luo, L., Lejemble, T., Yan, G. 2020
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Shao, J., Zhang, W., Mellado, N., Wang, N., Jin, S., Cai, S., Luo, L., Lejemble, T., and Yan, G., 2020, SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning: ISPRS Journal of Photogrammetry and Remote Sensing, v. 163, p. 214–230, at https://doi.org/10.1016/j.isprsjprs.2020.03.008..
Assessing bat roosts using the LiDAR system at Wind Cave Nature Reserve in Sarawak, Malaysian Borneo Shazali, N., Chew, T. H., Shamsir, M. S., Tingga, R. C. T., Mohd-Ridwan, A. R., Khan, F. A. A. 2017
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Shazali, N., Chew, T.H., Shamsir, M.S., Tingga, R.C.T., Mohd-Ridwan, A.R., and Khan, F.A.A., 2017, Assessing bat roosts using the LiDAR system at Wind Cave Nature Reserve in Sarawak, Malaysian Borneo: Acta Chiropterologica, v. 19, no. 1, p. 199–210, at https://doi.org/10.3161/15081109ACC2017.19.1.016..
Spaceborne LiDAR and animal-environment relationships—An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem Smith, A. B., Vogeler, J. C., Bjornlie, N. L., Squires, J. R., Swayze, N. C., Holbrook, J. D. 2022
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Smith, A.B., Vogeler, J.C., Bjornlie, N.L., Squires, J.R., Swayze, N.C., and Holbrook, J.D., 2022, Spaceborne LiDAR and animal-environment relationships—An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem: Forest Ecology and Management, v. 520, article 120343, at https://doi.org/10.1016/j.foreco.2022.120343..
East African megafauna influence on vegetation structure permeates from landscape to tree level scales Sorokina, H. E., Nunes, M. H., Heiskanen, J., Munyao, M., Mwang'ombe, J., Pellikka, P., Raumonen, P., Maeda, E. E. 2024
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Sorokina, H.E., Nunes, M.H., Heiskanen, J., Munyao, M., Mwang'ombe, J., Pellikka, P., Raumonen, P., and Maeda, E.E., 2024, East African megafauna influence on vegetation structure permeates from landscape to tree level scales: Ecological Informatics, v. 79, article 102435, at https://doi.org/10.1016/j.ecoinf.2023.102435..
Beyond discrete visibility estimates—Single-scan LiDAR provides an efficient method for 3D viewshed estimation Stein, R. M., Allred, C. R., Lecigne, B., Eitel, J. U. H., Johnson, T. R., Rachlow, J. L. 2026
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Stein, R.M., Allred, C.R., Lecigne, B., Eitel, J.U.H., Johnson, T.R., and Rachlow, J.L., 2026, Beyond discrete visibility estimates—Single-scan LiDAR provides an efficient method for 3D viewshed estimation: Wildlife Society Bulletin, v. 50, no. 1, article e70014, at https://doi.org/10.1002/wsb.70014..
Enhancing tree species classification—Insights from YOLOv8 and explainable AI applied to TLS point cloud projections Straker, A., Magdon, P., Zullich, M., Freudenberg, M., Kleinn, C., Breidenbach, J., Puliti, S., Nölke, N. 2026
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Straker, A., Magdon, P., Zullich, M., Freudenberg, M., Kleinn, C., Breidenbach, J., Puliti, S., and Nölke, N., 2026, Enhancing tree species classification—Insights from YOLOv8 and explainable AI applied to TLS point cloud projections: Forestry, v. 99, no. 4, article cpag059, at https://doi.org/10.1093/forestry/cpag059..
Multi-station LiDAR scanning-based hierarchical features for generation of an allometric stem volume model Sun, Y., Lin, X., Gong, Y., Jiang, J., Zhang, Y., Wen, X. 2021
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Sun, Y., Lin, X., Gong, Y., Jiang, J., Zhang, Y., and Wen, X., 2021, Multi-station LiDAR scanning-based hierarchical features for generation of an allometric stem volume model: Journal of Applied Remote Sensing, v. 15, no. 2, article 028503, at https://doi.org/10.1117/1.JRS.15.028503..
Combining terrestrial lidar with single line transects to investigate geomorphic change—A case study on the Upper Verde River, Arizona Tango, L. L., Sankey, T. T., Leonard, J., Sankey, J. B., Kasprak, A. 2024
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Tango, L.L., Sankey, T.T., Leonard, J., Sankey, J.B., and Kasprak, A., 2024, Combining terrestrial lidar with single line transects to investigate geomorphic change—A case study on the Upper Verde River, Arizona: Geomorphology, v. 457, article 109231, at https://doi.org/10.1016/j.geomorph.2024.109231..
Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain Tupinambá-Simões, F., Pascual, A., Guerra-Hernández, J., Ordóñez, C., de Conto, T., Bravo, F. 2023
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Tupinambá-Simões, F., Pascual, A., Guerra-Hernández, J., Ordóñez, C., de Conto, T., and Bravo, F., 2023, Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain: Remote Sensing, v. 15, no. 5, article 1169, at https://doi.org/10.3390/rs15051169..
Representing 3-dimensional fuels for physics-based fire behavior models—A general framework and case study in a type-converted post-fire shrubfield Tutland, N. J., Wion, A. P., May, C. J., Hutchings, G. C., Nowak, H. A., Gattiker, J. R., Hiers, J. K., Linn, R. R., Pokswinski, S. M., Margolis, E. Q. 2025
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Tutland, N.J., Wion, A.P., May, C.J., Hutchings, G.C., Nowak, H.A., Gattiker, J.R., Hiers, J.K., Linn, R.R., Pokswinski, S.M., et al., 2025, Representing 3-dimensional fuels for physics-based fire behavior models—A general framework and case study in a type-converted post-fire shrubfield: Fire Ecology, v. 21, no. 1, article 43, at https://doi.org/10.1186/s42408-025-00383-2..
Automated change detection in photogrammetric 4D point clouds—Transferability and extension of 4D objects-by-change for monitoring riverbank dynamics using low-cost cameras Ulm, M., Elias, M., Eltner, A., Lotsari, E., Anders, K. 2025
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Ulm, M., Elias, M., Eltner, A., Lotsari, E., and Anders, K., 2025, Automated change detection in photogrammetric 4D point clouds—Transferability and extension of 4D objects-by-change for monitoring riverbank dynamics using low-cost cameras: Applied Geomatics, v. 17, no. 2, p. 367–378, at https://doi.org/10.1007/s12518-025-00623-9..
Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning Van den Broeck, W. A. J., Terryn, L., Chen, S., Cherlet, W., Cooper, Z. T., Calders, K. 2025
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Van den Broeck, W.A.J., Terryn, L., Chen, S., Cherlet, W., Cooper, Z.T., and Calders, K., 2025, Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning: ISPRS Journal of Photogrammetry and Remote Sensing, v. 227, p. 366–382, at https://doi.org/10.1016/j.isprsjprs.2025.06.023..
An open and novel low-cost terrestrial laser scanner prototype for forest monitoring Výbo, Chud, Tom, Gretsch, D., Toma, Pe, Bedkowski, J., Skladan, M., Mokroš, M. 2026
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Výbo
An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data Wagner, B., Ginzler, C., Bürgi, A., Santini, S., Gärtner, H. 2018
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Wagner, B., Ginzler, C., Bürgi, A., Santini, S., and Gärtner, H., 2018, An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data: Trees - Structure and Function, v. 32, no. 1, p. 125–136, at https://doi.org/10.1007/s00468-017-1618-3..
Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning Wang, M., Baeten, L., Van Coillie, F., Calders, K., Verheyen, K., Ponette, Q., Blondeel, H., Muys, B., Armston, J., Verbeeck, H. 2024
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Wang, M., Baeten, L., Van Coillie, F., Calders, K., Verheyen, K., Ponette, Q., Blondeel, H., Muys, B., Armston, J., et al., 2024, Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning: Forest Ecosystems, v. 11, article 100196, at https://doi.org/10.1016/j.fecs.2024.100196..
A simple, interactive GIS tool for transforming assumed total station surveys to real world coordinates—The CHaMP transformation tool Wheaton, J. M., Garrard, C., Whitehead, K., Volk, C. J. 2012
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Wheaton, J.M., Garrard, C., Whitehead, K., and Volk, C.J., 2012, A simple, interactive GIS tool for transforming assumed total station surveys to real world coordinates—The CHaMP transformation tool: Computers and Geosciences, v. 42, p. 28–36, at https://doi.org/10.1016/j.cageo.2012.02.003..
Delineating and reconstructing 3D forest fuel components and volumes with terrestrial laser scanning Xi, Z., Chasmer, L., Hopkinson, C. 2023
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Xi, Z., Chasmer, L., and Hopkinson, C., 2023, Delineating and reconstructing 3D forest fuel components and volumes with terrestrial laser scanning: Remote Sensing, v. 15, no. 19, article 4778, at https://doi.org/10.3390/rs15194778..
LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area Xie, B., Yang, H., Cao, J., Li, J., Wang, M., Zhang, W., Li, J., Xu, J. 2026
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Xie, B., Yang, H., Cao, J., Li, J., Wang, M., Zhang, W., Li, J., and Xu, J., 2026, LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area: Biodiversity Science, v. 34, no. 5, article 26004, at https://doi.org/10.17520/biods.2026004..
Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR Yang, C., Zhu, X., Yebra, M., Harris, S., Tapper, N. 2016
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Yang, C., Zhu, X., Yebra, M., Harris, S., and Tapper, N., 2016, Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR: Journal of Applied Remote Sensing, v. 10, no. 4, article 46025, at https://doi.org/10.1117/1.JRS.10.046025..
Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study—Guadalupe Island Yépez-Rincón, F. D., Luna-Mendoza, L., Ramírez-Serrato, N. L., Hinojosa-Corona, A., Ferriño-Fierro, A. L. 2021
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Yépez-Rincón, F.D., Luna-Mendoza, L., Ramírez-Serrato, N.L., Hinojosa-Corona, A., and Ferriño-Fierro, A.L., 2021, Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study—Guadalupe Island: Remote Sensing of Environment, v. 263, article 112563, at https://doi.org/10.1016/j.rse.2021.112563..
Efficient registration of terrestrial LiDAR scans using a coarse-to-fine strategy for forestry applications Zhang, W., Chen, Y., Wang, H., Chen, M., Wang, X., Yan, G. 2016
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Zhang, W., Chen, Y., Wang, H., Chen, M., Wang, X., and Yan, G., 2016, Efficient registration of terrestrial LiDAR scans using a coarse-to-fine strategy for forestry applications: Agricultural and Forest Meteorology, v. 225, p. 8–23, at https://doi.org/10.1016/j.agrformet.2016.05.005..
Detecting historic tar kilns and tar production sites using high-resolution, aerial LiDAR-derived digital elevation models: Introducing the Tar Kiln Feature Detection workflow (TKFD) using open-access R and FIJI software. Snitker, G., Moser, J.D., Southerlin, B., Stewart, C. 2022
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Snitker, G., Moser, J.D., Southerlin, B., Stewart, C., 2022, Detecting historic tar kilns and tar production sites using high-resolution, aerial LiDAR-derived digital elevation models: Introducing the Tar Kiln Feature Detection workflow (TKFD) using open-access R and FIJI software, at https://www.sciencedirect.com/science/article/abs/pii/S2352409X22000037?via%3Dihub..
Title Authors Year Citation
Evaluating spatial granularity in tree volume upscaling using LiDAR and orthophotos Ali, M., Lohani, B., Hollaus, M., Pfeifer, N. 2025
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Ali, M., Lohani, B., Hollaus, M., and Pfeifer, N., 2025, Evaluating spatial granularity in tree volume upscaling using LiDAR and orthophotos, in India Geoscience and Remote Sensing Symposium, InGARSS 2025, Bhubaneswar, India, 10–13 December 2025, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 691–694, at https://doi.org/10.1109/InGARSS67683.2025.11583977..
An overview of tools and algorithms used to classify, detect, and monitor forest area using LiDAR data Amakhchan, W., El Kharki, O., Cherif, E. K., Wahbi, M., Yazidi Alaoui, O., Maatouk, M., Boulaassal, H. 2023
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Amakhchan, W., El Kharki, O., Cherif, E.K., Wahbi, M., Yazidi Alaoui, O., Maatouk, M., and Boulaassal, H., 2023, An overview of tools and algorithms used to classify, detect, and monitor forest area using LiDAR data, in Pattern Recognition, Computer Vision, and Image Processing—ICPR 2022 International Workshops and Challenges, Montreal, Canada, 21–25 August 2022, Lecture Notes in Computer Science, v. 13644: New York, N.Y., Association for Computing Machinery, p. 171–182, at https://doi.org/10.1007/978-3-031-37742-6_14..
Dasometric analysis applying terrestrial laser scanner and conventional techniques for the estimation of aboveground biomass in a forest of the inter-Andean valley in Ecuador Basantes, J., Padilla-Almeida, O., Toulkeridis, T., Ordonez, E., Zapata-Vela, J., Zapata-Vela, A. C., Rojas, S. 2019
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Basantes, J., Padilla-Almeida, O., Toulkeridis, T., Ordonez, E., Zapata-Vela, J., Zapata-Vela, A.C., and Rojas, S., 2019, Dasometric analysis applying terrestrial laser scanner and conventional techniques for the estimation of aboveground biomass in a forest of the inter-Andean valley in Ecuador, in 6th International Conference on eDemocracy and eGovernment (ICEDEG), Quito, Ecuador, 24–26 April 2019, Proceedings: Institute of Electrical and Electronics Engineers (IEEE), p. 334–337, at https://doi.org/10.1109/ICEDEG.2019.8734372..
A terrestrial laser scanning measurement station to monitor long-term structural dynamics in a boreal forest Campos, M., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., Puttonen, E. 2020
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Campos, M., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., and Puttonen, E., 2020, A terrestrial laser scanning measurement station to monitor long-term structural dynamics in a boreal forest, in XXIV ISPRS Congress, Commission I, Nice, France, 31 August–2 September 2020, ISPRS Archives XLIII-B1: International Society of Photogrammetry and Remote Sensing, p. 27–31, at https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-27-2020..
Learning neural radiance fields of forest structure for scalable and fine monitoring Castorena, J. 2024
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Castorena, J., 2024, Learning neural radiance fields of forest structure for scalable and fine monitoring, in Advances in Soft Computing—22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, 13–18 November 2023, Lecture Notes in Computer Science, v. 14392: New York, N.Y., Association for Computing Machinery, p. 281–296, at https://doi.org/10.1007/978-3-031-47640-2_23..
Ground level recovery from terrestrial laser scanning data with the variably randomized iterated hierarchical hough transform Chmielewski, L. J., Or 2015
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Comparison of terrestrial laser scanner and synthetic aperture radar data in the study of forest defoliation Kaasalainen, S., Hyyppä, J., Karjalainen, M., Krooks, A., Lyytikäinen-Saarenmaa, P., Holopainen, M., Jaakkola, A. 2010
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Kaasalainen, S., Hyyppä, J., Karjalainen, M., Krooks, A., Lyytikäinen-Saarenmaa, P., Holopainen, M., and Jaakkola, A., 2010, Comparison of terrestrial laser scanner and synthetic aperture radar data in the study of forest defoliation, in ISPRS Technical Commission VII Symposium, 100 Years ISPRS, Vienna, Austria, 5–7 July 2010, ISPRS Archives, XXXVIII, Pt. 7A: Lemmer, Netherlands, International Society for Photogrammetry and Remote Sensing, p. 82–87, at https://www.isprs.org/proceedings/XXXVIII/part7/a/pdf/82_XXXVIII-part7A.pdf..
Ground truth measurement of trees using terrestrial laser for satellite remote sensing Kato, A., Morgenroth, J., Kelbe, D., Gomez, C., Van Aardt, J. 2013
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Kato, A., Morgenroth, J., Kelbe, D., Gomez, C., and Van Aardt, J., 2013, Ground truth measurement of trees using terrestrial laser for satellite remote sensing, in International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 21–26 July 2013, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2106–2109, at https://doi.org/10.1109/IGARSS.2013.6723228..
EcoViz—Co-designed environmental data visualizations to communicate ecosystem impacts, inform management, and envision solutions Kendall-Bar, J., Nealey, I., Costello, I., Lowrie, C., Nguyen, K. H., Ponganis, P. J., Beck, M. W., Altintas, I. 2024
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Kendall-Bar, J., Nealey, I., Costello, I., Lowrie, C., Nguyen, K.H., Ponganis, P.J., Beck, M.W., and Altintas, I., 2024, EcoViz—Co-designed environmental data visualizations to communicate ecosystem impacts, inform management, and envision solutions, in VIS Workshop on Visualization for Climate Action and Sustainability (Viz4Climate + Sustainability), St. Pete Beach, Fla., 14 October 2024, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 17–27, at https://doi.org/10.1109/Viz4Climate-Sustainability64680.2024.00007..
Using stationary and mobile laser scanner to detect forest defoliation Krooks, A., Hyyppä, J., Lyytikäinen-Saarenmaa, P., Jaakkola, A., Kaasalainen, S., Holopainen, M. 2010
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Krooks, A., Hyyppä, J., Lyytikäinen-Saarenmaa, P., Jaakkola, A., Kaasalainen, S., and Holopainen, M., 2010, Using stationary and mobile laser scanner to detect forest defoliation, in ISPRS Commission V Mid-Term Symposium 'Close Range Image Measurement Techniques', Newcastle upon Tyne, UK, 21–24 June 2010, ISPRS Archives XXXVIII, Pt. 5: Lemmer, Netherlands, International Society for Photogrammetry and Remote Sensing, p. 372–374, at https://www.isprs.org/proceedings/xxxviii/part5/papers/84.pdf..
Visualization and labeling of terrestrial LiDAR data for three-dimensional fuel classification Moreno, I. G., Nealey, I., Roten, D., Nguyen, M. H., Crawl, D., O'Laughlin, K., Floca, M., Pokswinski, S., Altintas, I. 2023
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Moreno, I.G., Nealey, I., Roten, D., Nguyen, M.H., Crawl, D., O'Laughlin, K., Floca, M., Pokswinski, S., and Altintas, I., 2023, Visualization and labeling of terrestrial LiDAR data for three-dimensional fuel classification, in 19th International Conference on e-Science, Limassol, Cyprus, 9–13 October 2023, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 1–2, at https://doi.org/10.1109/e-Science58273.2023.10254841..
Laser-scanned tree stem filtering for forest inventories measurements Ravaglia, J., Alexandra, B., Piboule, A. 2013
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Ravaglia, J., Alexandra, B., and Piboule, A., 2013, Laser-scanned tree stem filtering for forest inventories measurements, in DigitalHeritage 2013 - Federating the 19th Int'l VSMM, 10th Eurographics GCH, and 2nd UNESCO Memory of the World Conferences, plus special sessions from CAA, Arqueologica 2.0, Space2Place, ICOMOS ICIP & CIPA, EU projects, et al., Marseille, France, 28 October–1 November 2013, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 649–652, at https://doi.org/10.1109/DigitalHeritage.2013.6743809..
Comparison of TLS and ULS data for wildlife habitat assessments in temperate woodlands Shokirov, S., Levick, S. R., Jucker, T., Yeoh, P., Youngentob, K. 2020
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Shokirov, S., Levick, S.R., Jucker, T., Yeoh, P., and Youngentob, K., 2020, Comparison of TLS and ULS data for wildlife habitat assessments in temperate woodlands, in International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, 26 September–2 October 2020, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 6097–6100, at https://doi.org/10.1109/IGARSS39084.2020.9323451..
A fully automatic forest parameters extraction at single-tree level—A comparison of MLS and TLS applications Spadavecchia, C., Belcore, E., Grasso, N., Piras, M. 2023
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Spadavecchia, C., Belcore, E., Grasso, N., and Piras, M., 2023, A fully automatic forest parameters extraction at single-tree level—A comparison of MLS and TLS applications, in 12th International Symposium on Mobile Mapping Technology (MMT 2023), Padua, Italy, 24–26 May 2023, ISPRS Archives XLVIII-1/W1: International Society of Photogrammetry and Remote Sensing, p. 457–463, at https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-457-2023..
Sentinel-1/2 time series for selective logging monitoring in temperate forests Tanase, M., Borlaf, I., Pascu, I., Pitar, D., Apostol, B., Petrila, M., Chivulescu, S., Leca, S., Pitar, D., Ciceu, A., Dobre, A., Popescu, F., Badea, O., Aponte, C. 2020
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Tanase, M., Borlaf, I., Pascu, I., Pitar, D., Apostol, B., Petrila, M., Chivulescu, S., Leca, S., Pitar, D., et al., 2020, Sentinel-1/2 time series for selective logging monitoring in temperate forests, in International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, 26 September–2 October 2020, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2902–2905, at https://doi.org/10.1109/IGARSS39084.2020.9323952..
Three-dimensional deep learning for leaf-wood segmentation of tropical tree point clouds Van den Broeck, W. A. J., Terryn, L., Cherlet, W., Cooper, Z. T., Calders, K. 2023
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Van den Broeck, W.A.J., Terryn, L., Cherlet, W., Cooper, Z.T., and Calders, K., 2023, Three-dimensional deep learning for leaf-wood segmentation of tropical tree point clouds, in ISPRS Geospatial Week 2023, Cairo, Egypt, 2–7 September 2023, ISPRS Archives XLVIII-1/W2: International Society of Photogrammetry and Remote Sensing, p. 765–770, at https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-765-2023.N.
A branch and leaf separation method based on near infrared band UAV-LiDAR data Wan, L., Li, S., Tian, Z., Shi, Y. 2023
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Wan, L., Li, S., Tian, Z., and Shi, Y., 2023, A branch and leaf separation method based on near infrared band UAV-LiDAR data, in International Geoscience and Remote Sensing Symposium, Pasadena, Calif., 16–21 July 2023, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 3284–3287, at https://doi.org/10.1109/IGARSS52108.2023.10282325..
Estimation of forest trees diameter from terrestrial laser scanning point clouds based on a circle fitting method Wu, R., Chen, Y., Wang, C., Li, J. 2018
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Wu, R., Chen, Y., Wang, C., and Li, J., 2018, Estimation of forest trees diameter from terrestrial laser scanning point clouds based on a circle fitting method, in International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2813–2816, at https://doi.org/10.1109/IGARSS.2018.8517303..
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Advances in lidar remote sensing of forests Duncanson, L., Disney, M. 2025
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Duncanson, L., and Disney, M., 2025, Advances in lidar remote sensing of forests, in Liang, S., ed., Comprehensive remote sensing, 2d ed.: Amsterdam, Netherlands, Elsevier, p. V3:212–V3:227, at https://doi.org/10.1016/B978-0-443-13220-9.00075-5..
Urban tree detection using mobile laser scanning data Pratihast, A. K., Thakur, J. K. 2012
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Pratihast, A.K., and Thakur, J.K., 2012, Urban tree detection using mobile laser scanning data, in Thakur, J.K., Singh, S.K., Ramanathan, A., Prasad, M.B.K., and Gossel, W., eds., Geospatial techniques for managing environmental resources: Dordrecht, Netherlands, Springer, p. 188–200, at https://doi.org/10.1007/978-94-007-1858-6_12..
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Semantic segmentation of archaeological features on public lands – Case study of historical cotton terraces within the Piedmont National Wildlife Refuge, GA, USA. Gravel-Miguel, C., Snitker, G., Hirniak, J., Peck, K., Fetterhoff, A. 2024
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Gravel-Miguel, C., Snitker, G., Hirniak, J., Peck, K., Fetterhoff, A., 2024, Semantic segmentation of archaeological features on public lands – Case study of historical cotton terraces within the Piedmont National Wildlife Refuge, GA, USA, Manuscript in revision at Advances in Archaeological Practice.
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Tree volume estimation with terrestrial laser scanning—Testing for bias in a 3D virtual environment Abegg, M., Bösch, R., Kükenbrink, D., Morsdorf, F. 2023
Full Citation
Abegg, M., Bösch, R., Kükenbrink, D., and Morsdorf, F., 2023, Tree volume estimation with terrestrial laser scanning—Testing for bias in a 3D virtual environment: Agricultural and Forest Meteorology, v. 331, article 109348, at https://doi.org/10.1016/j.agrformet.2023.109348..
Terrestrial laser scanning—A new standard of forest measuring and modelling? Åkerblom, M., Kaitaniemi, P. 2021
Full Citation
Åkerblom, M., and Kaitaniemi, P., 2021, Terrestrial laser scanning—A new standard of forest measuring and modelling?: Annals of Botany, v. 128, no. 6, p. 653–662, at https://doi.org/10.1093/aob/mcab111..
Evaluating spatial granularity in tree volume upscaling using LiDAR and orthophotos Ali, M., Lohani, B., Hollaus, M., Pfeifer, N. 2025
Full Citation
Ali, M., Lohani, B., Hollaus, M., and Pfeifer, N., 2025, Evaluating spatial granularity in tree volume upscaling using LiDAR and orthophotos, in India Geoscience and Remote Sensing Symposium, InGARSS 2025, Bhubaneswar, India, 10–13 December 2025, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 691–694, at https://doi.org/10.1109/InGARSS67683.2025.11583977..
Tree species classification from complex laser scanning data in Mediterranean forests using deep learning Allen, M. J., Grieve, S. W. D., Owen, H. J. F., Lines, E. R. 2023
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Allen, M.J., Grieve, S.W.D., Owen, H.J.F., and Lines, E.R., 2023, Tree species classification from complex laser scanning data in Mediterranean forests using deep learning: Methods in Ecology and Evolution, v. 14, no. 7, p. 1657–1667, at https://doi.org/10.1111/2041-210X.13981..
An overview of tools and algorithms used to classify, detect, and monitor forest area using LiDAR data Amakhchan, W., El Kharki, O., Cherif, E. K., Wahbi, M., Yazidi Alaoui, O., Maatouk, M., Boulaassal, H. 2023
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Amakhchan, W., El Kharki, O., Cherif, E.K., Wahbi, M., Yazidi Alaoui, O., Maatouk, M., and Boulaassal, H., 2023, An overview of tools and algorithms used to classify, detect, and monitor forest area using LiDAR data, in Pattern Recognition, Computer Vision, and Image Processing—ICPR 2022 International Workshops and Challenges, Montreal, Canada, 21–25 August 2022, Lecture Notes in Computer Science, v. 13644: New York, N.Y., Association for Computing Machinery, p. 171–182, at https://doi.org/10.1007/978-3-031-37742-6_14..
Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests Anderson, C. T., Dietz, S. L., Pokswinski, S. M., Jenkins, A. M., Kaeser, M. J., Hiers, J. K., Pelc, B. D. 2021
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Anderson, C.T., Dietz, S.L., Pokswinski, S.M., Jenkins, A.M., Kaeser, M.J., Hiers, J.K., and Pelc, B.D., 2021, Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests: Forest Ecology and Management, v. 491, article 119118, at https://doi.org/10.1016/j.foreco.2021.119118..
Reliably mapping low-intensity forest disturbance using satellite radar data Aquino, C., Mitchard, E. T. A., McNicol, I. M., Carstairs, H., Burt, A., Puma Vilca, B. L., Obiang Ebanéga, M., Modinga Dikongo, A., Dassi, C., Mayta, S., Tamayo, M., Grijalba, P., Miranda, F., Disney, M. 2022
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Aquino, C., Mitchard, E.T.A., McNicol, I.M., Carstairs, H., Burt, A., Puma Vilca, B.L., Obiang Ebanéga, M., Modinga Dikongo, A., Dassi, C., et al., 2022, Reliably mapping low-intensity forest disturbance using satellite radar data: Frontiers in Forests and Global Change, v. 5, article 1018762, at https://doi.org/10.3389/ffgc.2022.1018762..
Automatic object extraction from electrical substation point clouds Arastounia, M., Lichti, D. D. 2015
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Arastounia, M., and Lichti, D.D., 2015, Automatic object extraction from electrical substation point clouds: Remote Sensing, v. 7, no. 11, p. 15605–15629, at https://doi.org/10.3390/rs71115605..
Terrestrial laser scanning (TLS) for tree structure studies—A review of methods for wood-leaf classifications from 3D point clouds Arrizza, S., Marras, S., Ferrara, R., Pellizzaro, G. 2024
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Arrizza, S., Marras, S., Ferrara, R., and Pellizzaro, G., 2024, Terrestrial laser scanning (TLS) for tree structure studies—A review of methods for wood-leaf classifications from 3D point clouds: Remote Sensing Applications—Society and Environment, v. 36, article 101364, at https://doi.org/10.1016/j.rsase.2024.101364..
Dasometric analysis applying terrestrial laser scanner and conventional techniques for the estimation of aboveground biomass in a forest of the inter-Andean valley in Ecuador Basantes, J., Padilla-Almeida, O., Toulkeridis, T., Ordonez, E., Zapata-Vela, J., Zapata-Vela, A. C., Rojas, S. 2019
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Basantes, J., Padilla-Almeida, O., Toulkeridis, T., Ordonez, E., Zapata-Vela, J., Zapata-Vela, A.C., and Rojas, S., 2019, Dasometric analysis applying terrestrial laser scanner and conventional techniques for the estimation of aboveground biomass in a forest of the inter-Andean valley in Ecuador, in 6th International Conference on eDemocracy and eGovernment (ICEDEG), Quito, Ecuador, 24–26 April 2019, Proceedings: Institute of Electrical and Electronics Engineers (IEEE), p. 334–337, at https://doi.org/10.1109/ICEDEG.2019.8734372..
Quantifying forest litter fuel moisture content with terrestrial laser scanning Batchelor, J. L., Rowell, E., Prichard, S., Nemens, D., Cronan, J., Kennedy, M. C., Moskal, L. M. 2023
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Batchelor, J.L., Rowell, E., Prichard, S., Nemens, D., Cronan, J., Kennedy, M.C., and Moskal, L.M., 2023, Quantifying forest litter fuel moisture content with terrestrial laser scanning: Remote Sensing, v. 15, no. 6, article 1482, at https://doi.org/10.3390/rs15061482..
A 3D approach to model the taper of irregular tree stems—Making plots biomass estimates comparable in tropical forests Bauwens, S., Ploton, P., Fayolle, A., Ligot, G., Loumeto, J. J., Lejeune, P., Gourlet-Fleury, S. 2021
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Bauwens, S., Ploton, P., Fayolle, A., Ligot, G., Loumeto, J.J., Lejeune, P., and Gourlet-Fleury, S., 2021, A 3D approach to model the taper of irregular tree stems—Making plots biomass estimates comparable in tropical forests: Ecological Applications, v. 31, no. 8, article e02451, at https://doi.org/10.1002/eap.2451..
Modeling the horizontal distribution of tree crown biomass from terrestrial laser scanning data Bazezew, M. N., Griese, N., Fehrmann, L., Kleinn, C., Nölke, N. 2024
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Bazezew, M.N., Griese, N., Fehrmann, L., Kleinn, C., and Nölke, N., 2024, Modeling the horizontal distribution of tree crown biomass from terrestrial laser scanning data: Science of the Total Environment, v. 952, article 175377, at https://doi.org/10.1016/j.scitotenv.2024.175377..
Photogrammetric measurement of tree stems from vertical fisheye images Berveglieri, A., Tommaselli, A., Liang, X., Honkavaara, E. 2017
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Berveglieri, A., Tommaselli, A., Liang, X., and Honkavaara, E., 2017, Photogrammetric measurement of tree stems from vertical fisheye images: Scandinavian Journal of Forest Research, v. 32, no. 8, p. 737–747, at https://doi.org/10.1080/02827581.2016.1273381..
Synthetic forest stands and point clouds for model selection and feature space comparison Bester, M. S., Maxwell, A. E., Nealey, I., Gallagher, M. R., Skowronski, N. S., McNeil, B. E. 2023
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Bester, M.S., Maxwell, A.E., Nealey, I., Gallagher, M.R., Skowronski, N.S., and McNeil, B.E., 2023, Synthetic forest stands and point clouds for model selection and feature space comparison: Remote Sensing, v. 15, no. 18, article 4407, at https://doi.org/10.3390/rs15184407..
Estimation of forest variable and aboveground biomass using terrestrial laser scanning in the tropical rainforest Beyene, S. M. 2020
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Beyene, S.M., 2020, Estimation of forest variable and aboveground biomass using terrestrial laser scanning in the tropical rainforest: Journal of the Indian Society of Remote Sensing, v. 48, no. 6, p. 853–863, at https://doi.org/10.1007/s12524-020-01119-2..
Forest inventory and aboveground biomass estimation with terrestrial LiDAR in the tropical forest of Malaysia Beyene, S. M., Hussin, Y. A., Kloosterman, H. E., Ismail, M. H. 2020
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Beyene, S.M., Hussin, Y.A., Kloosterman, H.E., and Ismail, M.H., 2020, Forest inventory and aboveground biomass estimation with terrestrial LiDAR in the tropical forest of Malaysia: Canadian Journal of Remote Sensing, v. 46, no. 2, p. 130–145, at https://doi.org/10.1080/07038992.2020.1759036..
Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., Rehush, N. 2026
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Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., and Rehush, N., 2026, Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning: Ecological Informatics, v. 97, article 103876, at https://doi.org/10.1016/j.ecoinf.2026.103876..
Forest cover buffers microclimate and increases fuel moisture in northern conifer forests—A terrestrial laser scanning approach Breigenzer, P. J., Fraver, S., Gallagher, M. R., Kenefic, L. S., Lane, E. D., Neely, J. M., Wason, J. W. 2026
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Breigenzer, P.J., Fraver, S., Gallagher, M.R., Kenefic, L.S., Lane, E.D., Neely, J.M., and Wason, J.W., 2026, Forest cover buffers microclimate and increases fuel moisture in northern conifer forests—A terrestrial laser scanning approach: Fire Ecology, v. 22, no. 1, article 72, at https://doi.org/10.1186/s42408-026-00480-w..
Comparing biomass consumption estimated from point cloud data versus long-wave infrared imagery during prescribed growing season burns in pine woodlands of the southeastern United States Bright, B. C., Hudak, A. T., Sánchez-López, N., Loudermilk, E. L., Hawley, C. M., Rowell, E., O’Brien, J. J., Flanagan, S. A., Robertson, K., Kato, A., Hoffman, C., Weise, D. R., Hiers, J. K. 2025
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Bright, B.C., Hudak, A.T., Sánchez-López, N., Loudermilk, E.L., Hawley, C.M., Rowell, E., O’Brien, J.J., Flanagan, S.A., Robertson, K., et al., 2025, Comparing biomass consumption estimated from point cloud data versus long-wave infrared imagery during prescribed growing season burns in pine woodlands of the southeastern United States: International Journal of Wildland Fire, v. 34, no. 7, article Wf24210, at https://doi.org/10.1071/WF24210..
Influence of ULS acquisition characteristics on tree stem parameter estimation Bruggisser, M., Hollaus, M., Otepka, J., Pfeifer, N. 2020
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Bruggisser, M., Hollaus, M., Otepka, J., and Pfeifer, N., 2020, Influence of ULS acquisition characteristics on tree stem parameter estimation: ISPRS Journal of Photogrammetry and Remote Sensing, v. 168, p. 28–40, at https://doi.org/10.1016/j.isprsjprs.2020.08.002..
Terrestrial laser scanning in forest ecology—Expanding the horizon Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L. P., Chave, J., Danson, F. M., Demol, M., Disney, M., Gaulton, R., Krishna Moorthy, S. M., Levick, S. R., Saarinen, N., Schaaf, C., Stovall, A., Terryn, L., Wilkes, P., Verbeeck, H. 2020
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Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L.P., Chave, J., Danson, F.M., Demol, M., et al., 2020, Terrestrial laser scanning in forest ecology—Expanding the horizon: Remote Sensing of Environment, v. 251, article 112102, at https://doi.org/10.1016/j.rse.2020.112102..
Remote sensing technology applications in forestry and REDD+ Calders, K., Jonckheere, I., Nightingale, J., Vastaranta, M. 2020
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Calders, K., Jonckheere, I., Nightingale, J., and Vastaranta, M., 2020, Remote sensing technology applications in forestry and REDD+: Forests, v. 11, no. 2, article 188, at https://doi.org/10.3390/f11020188..
A terrestrial laser scanning measurement station to monitor long-term structural dynamics in a boreal forest Campos, M., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., Puttonen, E. 2020
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Campos, M., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., and Puttonen, E., 2020, A terrestrial laser scanning measurement station to monitor long-term structural dynamics in a boreal forest, in XXIV ISPRS Congress, Commission I, Nice, France, 31 August–2 September 2020, ISPRS Archives XLIII-B1: International Society of Photogrammetry and Remote Sensing, p. 27–31, at https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-27-2020..
A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy Campos, M. B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., Puttonen, E. 2021
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Campos, M.B., Litkey, P., Wang, Y., Chen, Y., Hyyti, H., Hyyppä, J., and Puttonen, E., 2021, A long-term terrestrial laser scanning measurement station to continuously monitor structural and phenological dynamics of boreal forest canopy: Frontiers in Plant Science, v. 11, article 606752, at https://doi.org/10.3389/fpls.2020.606752..
Terrestrial lidar scanning provides efficient measurements of fire-caused crown scorch in longleaf pine Cannon, J. B., Zampieri, N. E., Whelan, A. W., Shearman, T. M., Sánchez Meador, A. J., Varner, J. M. 2025
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Cannon, J.B., Zampieri, N.E., Whelan, A.W., Shearman, T.M., Sánchez Meador, A.J., and Varner, J.M., 2025, Terrestrial lidar scanning provides efficient measurements of fire-caused crown scorch in longleaf pine: Fire Ecology, v. 21, no. 1, article 71, at https://doi.org/10.1186/s42408-025-00420-0..
Modality-specific feature design for species classification in forest inventories using TLS and UAS LiDAR Carpenter, J., Jung, M., Goel, A., Fei, S., Jung, J. 2025
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Carpenter, J., Jung, M., Goel, A., Fei, S., and Jung, J., 2025, Modality-specific feature design for species classification in forest inventories using TLS and UAS LiDAR: Frontiers in Forests and Global Change, v. 8, article 1500178, at https://doi.org/10.3389/ffgc.2025.1500178..
Use of terrestrial laser scanner for aboveground biomass estimation in a seasonally dry tropical forest Carvalho, H. F. S., da Silva, T. G. F., Galvíncio, J. D., Antonino, A. C. D., Menezes, R. S. C., Dos Santos, C. V. B., Miranda, R. Q., Nóbrega, R. L. B., Domingues, T. F., Silva, E. A., de Moura, M. S. B. 2023
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Carvalho, H.F.S., da Silva, T.G.F., Galvíncio, J.D., Antonino, A.C.D., Menezes, R.S.C., Dos Santos, C.V.B., Miranda, R.Q., Nóbrega, R.L.B., Domingues, T.F., et al., 2023, Use of terrestrial laser scanner for aboveground biomass estimation in a seasonally dry tropical forest: Revista Brasileira de Geografia Fisica, v. 16, no. 5, p. 2641–2657, at https://doi.org/10.26848/rbgf.v16.5.p2741-2657..
Learning neural radiance fields of forest structure for scalable and fine monitoring Castorena, J. 2024
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Castorena, J., 2024, Learning neural radiance fields of forest structure for scalable and fine monitoring, in Advances in Soft Computing—22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, 13–18 November 2023, Lecture Notes in Computer Science, v. 14392: New York, N.Y., Association for Computing Machinery, p. 281–296, at https://doi.org/10.1007/978-3-031-47640-2_23..
ForestAlign—Automatic forest structure-based alignment for multi-view TLS and ALS point clouds Castorena, J., Dickman, L. T., Killebrew, A. J., Gattiker, J. R., Linn, R., Loudermilk, E. L. 2025
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Castorena, J., Dickman, L.T., Killebrew, A.J., Gattiker, J.R., Linn, R., and Loudermilk, E.L., 2025, ForestAlign—Automatic forest structure-based alignment for multi-view TLS and ALS point clouds: Science of Remote Sensing, v. 11, article 100194, at https://doi.org/10.1016/j.srs.2024.100194..
Extraction of genetic test measurements from LiDAR cloud data Cavalheiro, R., Molina-Valero, J. A., Hodge, G., Howell, T., Acosta, J. J. 2026
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Cavalheiro, R., Molina-Valero, J.A., Hodge, G., Howell, T., and Acosta, J.J., 2026, Extraction of genetic test measurements from LiDAR cloud data: Journal of Forestry Research, v. 37, no. 1, article 58, at https://doi.org/10.1007/s11676-026-02007-0..
Benchmarking tree instance segmentation of terrestrial laser scanning point clouds Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., Senf, C., Soenens, L., Terryn, L., Van den Broeck, W. A. J., Calders, K. 2026
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Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., et al., 2026, Benchmarking tree instance segmentation of terrestrial laser scanning point clouds: ISPRS Journal of Photogrammetry and Remote Sensing, v. 231, p. 230–247, at https://doi.org/10.1016/j.isprsjprs.2025.10.033..
Ground level recovery from terrestrial laser scanning data with the variably randomized iterated hierarchical hough transform Chmielewski, L. J., Or 2015
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Chmielewski, L.J., and Or
Terrestrial LiDAR technology to evaluate the vertical structure of stands of Bertholletia excelsa Bonpl., a species symbol of conservation through sustainable use in the Brazilian Amazon Costa, F. F., Oliveira Júnior, R. C. D., de Almeida, D. R. A., Rosa, D. M., da Silva, K. E., Tonini, H., Beldini, T. P., Santos, D. B. D., Guedes, M. C. 2025
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Costa, F.F., Oliveira Júnior, R.C.D., de Almeida, D.R.A., Rosa, D.M., da Silva, K.E., Tonini, H., Beldini, T.P., Santos, D.B.D., and Guedes, M.C., 2025, Terrestrial LiDAR technology to evaluate the vertical structure of stands of Bertholletia excelsa Bonpl., a species symbol of conservation through sustainable use in the Brazilian Amazon: Sustainability, v. 17, no. 13, article 6049, at https://doi.org/10.3390/su17136049..
Assessing the structural differences between tropical forest types using terrestrial laser scanning Decuyper, M., Mulatu, K. A., Brede, B., Calders, K., Armston, J., Rozendaal, D. M. A., Mora, B., Clevers, J. G. P. W., Kooistra, L., Herold, M., Bongers, F. 2018
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Decuyper, M., Mulatu, K.A., Brede, B., Calders, K., Armston, J., Rozendaal, D.M.A., Mora, B., Clevers, J.G.P.W., Kooistra, L., et al., 2018, Assessing the structural differences between tropical forest types using terrestrial laser scanning: Forest Ecology and Management, v. 429, p. 327–335, at https://doi.org/10.1016/j.foreco.2018.07.032..
Using terrestrial laser scanning (TLS) to estimate surface soil properties in a mixed conifer forest Dickman, L. T., Hutchings, G., Kroeger, M., Gutierrez, A., Killebrew, A. J., Guthrie, W., Atchley, A., Pokswinski, S., Gattiker, J., Linn, R. 2026
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Dickman, L.T., Hutchings, G., Kroeger, M., Gutierrez, A., Killebrew, A.J., Guthrie, W., Atchley, A., Pokswinski, S., Gattiker, J., et al., 2026, Using terrestrial laser scanning (TLS) to estimate surface soil properties in a mixed conifer forest: Ecological Indicators, v. 191, article 115625, at https://doi.org/10.1016/j.ecolind.2026.115625..
Calculation of Eucalyptus sp. timber volume from laser scanning do Nascimento Lima, J. V., Soares, Á A. V., da Silva Pacheco, M., Martins, G. D. 2025
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do Nascimento Lima, J.V., Soares, Á.A.V., da Silva Pacheco, M., and Martins, G.D., 2025, Calculation of Eucalyptus sp. timber volume from laser scanning: Revista Brasileira de Cartografia, v. 77, p. 1–14, at https://doi.org/10.14393/rbcv77n0a-75286..
Aerial-ground-space precision auto-measurement technology in stereo forest surveying Dong, B., Feng, Z. K., Yao, S., Yan, X. J., Sui, H. D., Wang, J., Guo, J. C. 2008
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Dong, B., Feng, Z.K., Yao, S., Yan, X.J., Sui, H.D., Wang, J., and Guo, J.C., 2008, Aerial-ground-space precision auto-measurement technology in stereo forest surveying: Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University, v. 30, no. SUPPL. 1, p. 173–177.
Advances in lidar remote sensing of forests Duncanson, L., Disney, M. 2025
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Duncanson, L., and Disney, M., 2025, Advances in lidar remote sensing of forests, in Liang, S., ed., Comprehensive remote sensing, 2d ed.: Amsterdam, Netherlands, Elsevier, p. V3:212–V3:227, at https://doi.org/10.1016/B978-0-443-13220-9.00075-5..
Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques—A review Fang, H. 2021
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Fang, H., 2021, Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques—A review: Kexue Tongbao/Chinese Science Bulletin, v. 66, no. 24, p. 3141–3153, at https://doi.org/10.1360/TB-2020-1057..
Interdisciplinary applications of LiDAR in forest studies—Advances in sensors, methods, and cross-domain metrics Fareed, N., Silva, C. A., Numata, I., Flores, J. P. 2026
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Fareed, N., Silva, C.A., Numata, I., and Flores, J.P., 2026, Interdisciplinary applications of LiDAR in forest studies—Advances in sensors, methods, and cross-domain metrics: Remote Sensing, v. 18, no. 2, article 219, at https://doi.org/10.3390/rs18020219..
Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths—A simulation study Forsman, M., Börlin, N., Olofsson, K., Reese, H., Holmgren, J. 2018
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Forsman, M., Börlin, N., Olofsson, K., Reese, H., and Holmgren, J., 2018, Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths—A simulation study: ISPRS Journal of Photogrammetry and Remote Sensing, v. 135, p. 84–92, at https://doi.org/10.1016/j.isprsjprs.2017.11.013..
Estimation of plot-level burn severity using terrestrial laser scanning Gallagher, M. R., Maxwell, A. E., Guillén, L. A., Everland, A., Loudermilk, E. L., Skowronski, N. S. 2021
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Gallagher, M.R., Maxwell, A.E., Guillén, L.A., Everland, A., Loudermilk, E.L., and Skowronski, N.S., 2021, Estimation of plot-level burn severity using terrestrial laser scanning: Remote Sensing, v. 13, no. 20, article 4168, at https://doi.org/10.3390/rs13204168..
Horizontal viewsheds of large herbivores as a function of woodland structure Gresham, A., Healey, J. R., Eichhorn, M. P., Barton, O., Smith, A. R., Shannon, G. 2023
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Gresham, A., Healey, J.R., Eichhorn, M.P., Barton, O., Smith, A.R., and Shannon, G., 2023, Horizontal viewsheds of large herbivores as a function of woodland structure: Ecology and Evolution, v. 13, no. 11, article e10699, at https://doi.org/10.1002/ece3.10699..
High terrestrial laser scanning—An accessible method for tree crown characterization Griese, N., Kleinn, C., Nölke, N. 2026
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Griese, N., Kleinn, C., and Nölke, N., 2026, High terrestrial laser scanning—An accessible method for tree crown characterization: Ecological Informatics, v. 95, article 103707, at https://doi.org/10.1016/j.ecoinf.2026.103707..
A large dataset of labelled single tree point clouds, QSMs and tree graphs Griese, N., Ritzert, M., Nölke, N. 2025
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Griese, N., Ritzert, M., and Nölke, N., 2025, A large dataset of labelled single tree point clouds, QSMs and tree graphs: Scientific Data, v. 12, no. 1, article 1953, at https://doi.org/10.1038/s41597-025-06421-7..
True leaf area index retrieval using terrestrial LiDAR for broadleaf trees via novel multiinclined-planes method Guo, Y., Li, S., Tang, H., He, Z. 2025
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Guo, Y., Li, S., Tang, H., and He, Z., 2025, True leaf area index retrieval using terrestrial LiDAR for broadleaf trees via novel multiinclined-planes method: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 18, p. 19025–19037, at https://doi.org/10.1109/JSTARS.2025.3591398..
Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments Guth, A., Dauner, M., Fowler, A., Hoehl, S., Hamlington, P., Hoffman, C., Hannigan, M. 2025
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Guth, A., Dauner, M., Fowler, A., Hoehl, S., Hamlington, P., Hoffman, C., and Hannigan, M., 2025, Using terrestrial laser scanning to estimate mass of hand-built slash piles following hazardous fuels treatments: Frontiers in Forests and Global Change, v. 8, article 1663753, at https://doi.org/10.3389/ffgc.2025.1663753..
Complete workflow for detailed 3D forest reconstruction—From terrestrial laser scanning to complex 3D radiative transfer modelling Hanousek, T., Novotný, J., Navrátilová, B., Švik, M., Krejza, J., Janoutov 2025
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Hanousek, T., Novotný, J., Navrátilová, B.,
Extraction of individual tree skeletons from terrestrial LiDAR point clouds based on optimization of network graph structural model He, Y., Hui, Z., Cheng, X., Huang, F., Liu, H., Tu, L. 2026
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He, Y., Hui, Z., Cheng, X., Huang, F., Liu, H., and Tu, L., 2026, Extraction of individual tree skeletons from terrestrial LiDAR point clouds based on optimization of network graph structural model: Zhongguo Jiguang/Chinese Journal of Lasers, v. 53, no. 13, article 1310002, at https://doi.org/10.3788/CJL251225..
Integrating recreation into National Forest Inventories—Results from a forest visitor survey in winter and summer Hegetschweiler, K. T., Stride, C. B., Fischer, C., Ginzler, C., Hunziker, M. 2022
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Hegetschweiler, K.T., Stride, C.B., Fischer, C., Ginzler, C., and Hunziker, M., 2022, Integrating recreation into National Forest Inventories—Results from a forest visitor survey in winter and summer: Journal of Outdoor Recreation and Tourism, v. 39, article 100489, at https://doi.org/10.1016/j.jort.2022.100489..
Dynamic restoration and the impact of native versus invasive vegetation on coastal foredune morphodynamics, Lanphere Dunes, California, USA Hilgendorf, Z., Walker, I. J., Pickart, A. J., Turner, C. M. 2022
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Hilgendorf, Z., Walker, I.J., Pickart, A.J., and Turner, C.M., 2022, Dynamic restoration and the impact of native versus invasive vegetation on coastal foredune morphodynamics, Lanphere Dunes, California, USA: Earth Surface Processes and Landforms, v. 47, no. 13, p. 3083–3099, at https://doi.org/10.1002/esp.5445..
A method for validating the structural completeness of understory vegetation models captured with 3D remote sensing Hillman, S., Wallace, L., Reinke, K., Hally, B., Jones, S., Saldias, D. S. 2019
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Hillman, S., Wallace, L., Reinke, K., Hally, B., Jones, S., and Saldias, D.S., 2019, A method for validating the structural completeness of understory vegetation models captured with 3D remote sensing: Remote Sensing, v. 11, no. 18, article 2118, at https://doi.org/10.3390/rs11182118..
Robust single-image tree diameter estimation with mobile phones Holcomb, A., Tong, L., Keshav, S. 2023
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Holcomb, A., Tong, L., and Keshav, S., 2023, Robust single-image tree diameter estimation with mobile phones: Remote Sensing, v. 15, no. 3, article 772, at https://doi.org/10.3390/rs15030772..
Transferring instance and semantic segmentation across terrestrial laser scanning time series—Towards automated forest monitoring plot measurements Honkanen, E., Yrttimaa, T., Terryn, L., Kamula, T., Erkkilä, A., Liikonen, L., Hyyppä, J., Vastaranta, M. 2026
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Towards spatially explicit quantification of pre- and postfire fuels and fuel consumption from traditional and point cloud measurements Hudak, A. T., Kato, A., Bright, B. C., Loudermilk, E. L., Hawley, C., Restaino, J. C., Ottmar, R. D., Prata, G. A., Cabo, C., Prichard, S. J., Rowell, E. M., Weise, D. R. 2020
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Wildland fire mid-story—A generative modeling approach for representative fuels Hutchings, G., Gattiker, J., Scherting, B., Linn, R. R. 2024
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Under-canopy UAV laser scanning for accurate forest field measurements Hyyppä, E., Hyyppä, J., Hakala, T., Kukko, A., Wulder, M. A., White, J. C., Pyörälä, J., Yu, X., Wang, Y., Virtanen, J. P., Pohjavirta, O., Liang, X., Holopainen, M., Kaartinen, H. 2020
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Comparison of terrestrial laser scanner and synthetic aperture radar data in the study of forest defoliation Kaasalainen, S., Hyyppä, J., Karjalainen, M., Krooks, A., Lyytikäinen-Saarenmaa, P., Holopainen, M., Jaakkola, A. 2010
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Kaasalainen, S., Hyyppä, J., Karjalainen, M., Krooks, A., Lyytikäinen-Saarenmaa, P., Holopainen, M., and Jaakkola, A., 2010, Comparison of terrestrial laser scanner and synthetic aperture radar data in the study of forest defoliation, in ISPRS Technical Commission VII Symposium, 100 Years ISPRS, Vienna, Austria, 5–7 July 2010, ISPRS Archives, XXXVIII, Pt. 7A: Lemmer, Netherlands, International Society for Photogrammetry and Remote Sensing, p. 82–87, at https://www.isprs.org/proceedings/XXXVIII/part7/a/pdf/82_XXXVIII-part7A.pdf..
Change detection of tree biomass with terrestrial laser scanning and quantitative structure modelling Kaasalainen, S., Krooks, A., Liski, J., Raumonen, P., Kaartinen, H., Kaasalainen, M., Puttonen, E., Anttila, K., Mäkipää, R. 2014
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Bi-temporal terrestrial laser scanning to measure secondary growth of trees—Toward non-destructive assessment of wood properties Kamula, T., Yrttimaa, T., Cimdins, R., Polvivaara, A., Kukko, A., Holopainen, M., Vastaranta, M. 2026
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Kamula, T., Yrttimaa, T., Cimdins, R., Polvivaara, A., Kukko, A., Holopainen, M., and Vastaranta, M., 2026, Bi-temporal terrestrial laser scanning to measure secondary growth of trees—Toward non-destructive assessment of wood properties: Scandinavian Journal of Forest Research, v. 41, no. 4, p. 363–378, at https://doi.org/10.1080/02827581.2026.2646458..
Ground truth measurement of trees using terrestrial laser for satellite remote sensing Kato, A., Morgenroth, J., Kelbe, D., Gomez, C., Van Aardt, J. 2013
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Kato, A., Morgenroth, J., Kelbe, D., Gomez, C., and Van Aardt, J., 2013, Ground truth measurement of trees using terrestrial laser for satellite remote sensing, in International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 21–26 July 2013, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2106–2109, at https://doi.org/10.1109/IGARSS.2013.6723228..
Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR Klockow, P. A., Putman, E. B., Vogel, J. G., Moore, G. W., Edgar, C. B., Popescu, S. C. 2020
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Klockow, P.A., Putman, E.B., Vogel, J.G., Moore, G.W., Edgar, C.B., and Popescu, S.C., 2020, Allometry and structural volume change of standing dead southern pine trees using non-destructive terrestrial LiDAR: Remote Sensing of Environment, v. 241, article 111729, at https://doi.org/10.1016/j.rse.2020.111729..
Fine root vitality decline results in reduced branch formation in mature beech stands after drought Koller, A., Azekenova, A., Feger, K. H., Kalbitz, K., von Oheimb, G. 2026
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Aerial and terrestrial photogrammetric point cloud fusion for intensive forest monitoring Krause, S. 2019
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Krause, S., 2019, Aerial and terrestrial photogrammetric point cloud fusion for intensive forest monitoring: GI_Forum, v. 7, no. 2, p. 60–72, at https://doi.org/10.1553/GISCIENCE2019_02_S60..
Forest structural complexity tool—An open source, fully-automated tool for measuring forest point clouds Krisanski, S., Taskhiri, M. S., Aracil, S. G., Herries, D., Muneri, A., Gurung, M. B., Montgomery, J., Turner, P. 2021
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Krisanski, S., Taskhiri, M.S., Aracil, S.G., Herries, D., Muneri, A., Gurung, M.B., Montgomery, J., and Turner, P., 2021, Forest structural complexity tool—An open source, fully-automated tool for measuring forest point clouds: Remote Sensing, v. 13, no. 22, article 4677, at https://doi.org/10.3390/rs13224677..
Sensor agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning Krisanski, S., Taskhiri, M. S., Aracil, S. G., Herries, D., Turner, P. 2021
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Krisanski, S., Taskhiri, M.S., Aracil, S.G., Herries, D., and Turner, P., 2021, Sensor agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning: Remote Sensing, v. 13, no. 8, article 1413, at https://doi.org/10.3390/rs13081413..
Using stationary and mobile laser scanner to detect forest defoliation Krooks, A., Hyyppä, J., Lyytikäinen-Saarenmaa, P., Jaakkola, A., Kaasalainen, S., Holopainen, M. 2010
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Krooks, A., Hyyppä, J., Lyytikäinen-Saarenmaa, P., Jaakkola, A., Kaasalainen, S., and Holopainen, M., 2010, Using stationary and mobile laser scanner to detect forest defoliation, in ISPRS Commission V Mid-Term Symposium 'Close Range Image Measurement Techniques', Newcastle upon Tyne, UK, 21–24 June 2010, ISPRS Archives XXXVIII, Pt. 5: Lemmer, Netherlands, International Society for Photogrammetry and Remote Sensing, p. 372–374, at https://www.isprs.org/proceedings/xxxviii/part5/papers/84.pdf..
Artificial intelligence and terrestrial point clouds for forest monitoring Kulicki, M., Cabo, C., Trzci, Będkowski, J., Stereńczak, K. 2025
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Kulicki, M., Cabo, C., Trzci
Assessing the effects of large herbivores on the three-dimensional structure of temperate forests using terrestrial laser scanning Li, S., Hou, Z., Ge, J., Wang, T. 2022
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Li, S., Hou, Z., Ge, J., and Wang, T., 2022, Assessing the effects of large herbivores on the three-dimensional structure of temperate forests using terrestrial laser scanning: Forest Ecology and Management, v. 507, article 119985, at https://doi.org/10.1016/j.foreco.2021.119985..
Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests Li, S., Wang, T., Hou, Z., Gong, Y., Feng, L., Ge, J. 2021
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Li, S., Wang, T., Hou, Z., Gong, Y., Feng, L., and Ge, J., 2021, Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests: Ecological Indicators, v. 121, article 107011, at https://doi.org/10.1016/j.ecolind.2020.107011..
In-situ measurements from mobile platforms—An emerging approach to address the old challenges associated with forest inventories Liang, X., Kukko, A., Hyyppä, J., Lehtomäki, M., Pyörälä, J., Yu, X., Kaartinen, H., Jaakkola, A., Wang, Y. 2018
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Liang, X., Kukko, A., Hyyppä, J., Lehtomäki, M., Pyörälä, J., Yu, X., Kaartinen, H., Jaakkola, A., and Wang, Y., 2018, In-situ measurements from mobile platforms—An emerging approach to address the old challenges associated with forest inventories: ISPRS Journal of Photogrammetry and Remote Sensing, v. 143, p. 97–107, at https://doi.org/10.1016/j.isprsjprs.2018.04.019..
Combining airborne and terrestrial laser scanning technologies to measure forest understorey volume Liu, L., Pang, Y., Li, Z., Si, L., Liao, S. 2017
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Liu, L., Pang, Y., Li, Z., Si, L., and Liao, S., 2017, Combining airborne and terrestrial laser scanning technologies to measure forest understorey volume: Forests, v. 8, no. 4, article 111, at https://doi.org/10.3390/f8040111..
Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern U.S. ecosystem Loudermilk, E. L., Pokswinski, S., Hawley, C. M., Maxwell, A., Gallagher, M. R., Skowronski, N. S., Hudak, A. T., Hoffman, C., Hiers, J. K. 2023
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Loudermilk, E.L., Pokswinski, S., Hawley, C.M., Maxwell, A., Gallagher, M.R., Skowronski, N.S., Hudak, A.T., Hoffman, C., and Hiers, J.K., 2023, Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern U.S. ecosystem: Fire, v. 6, no. 4, article 151, at https://doi.org/10.3390/fire6040151..
Revealing changes in the stem form and volume allocation in diverse boreal forests using two-date terrestrial laser scanning Luoma, V., Yrttimaa, T., Kankare, V., Saarinen, N., Pyörälä, J., Kukko, A., Kaartinen, H., Hyyppä, J., Holopainen, M., Vastaranta, M. 2021
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Luoma, V., Yrttimaa, T., Kankare, V., Saarinen, N., Pyörälä, J., Kukko, A., Kaartinen, H., Hyyppä, J., Holopainen, M., et al., 2021, Revealing changes in the stem form and volume allocation in diverse boreal forests using two-date terrestrial laser scanning: Forests, v. 12, no. 7, article 835, at https://doi.org/10.3390/f12070835..
TreeFlow—Conditional flow matching for 3D tree point cloud generation from inventory attributes Marcozzi, A., Tenny, J., Martin, D., Castorena, J., Crennen, Z., Wells, L., Hillman, S. 2026
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Marcozzi, A., Tenny, J., Martin, D., Castorena, J., Crennen, Z., Wells, L., and Hillman, S., 2026, TreeFlow—Conditional flow matching for 3D tree point cloud generation from inventory attributes: Remote Sensing, v. 18, no. 13, article 2197, at https://doi.org/10.3390/rs18132197..
Application of LiDAR derived fuel cells to wildfire modeling at laboratory scale Marcozzi, A. A., Johnson, J. V., Parsons, R. A., Flanary, S. J., Seielstad, C. A., Downs, J. Z. 2023
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Marcozzi, A.A., Johnson, J.V., Parsons, R.A., Flanary, S.J., Seielstad, C.A., and Downs, J.Z., 2023, Application of LiDAR derived fuel cells to wildfire modeling at laboratory scale: Fire, v. 6, no. 10, article 394, at https://doi.org/10.3390/fire6100394..
Operationalizing the use of TLS in forest inventories—The R package FORTLS Molina-Valero, J. A., Martínez-Calvo, A., Ginzo Villamayor, M. J., Novo Pérez, M. A., Álvarez-González, J. G., Montes, F., Pérez-Cruzado, C. 2022
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Molina-Valero, J.A., Martínez-Calvo, A., Ginzo Villamayor, M.J., Novo Pérez, M.A., Álvarez-González, J.G., Montes, F., and Pérez-Cruzado, C., 2022, Operationalizing the use of TLS in forest inventories—The R package FORTLS: Environmental Modelling and Software, v. 150, article 105337, at https://doi.org/10.1016/j.envsoft.2022.105337..
Use of close-range LiDAR devices and statistical inference approaches in operational stand-level forest inventories Molina-Valero, J. A., Martins-Neto, R. P., Martínez-Calvo, A., Rodríguez-Ruiz, J., Surový, P., Seppelt, A., Pérez-Cruzado, C. 2025
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Molina-Valero, J.A., Martins-Neto, R.P., Martínez-Calvo, A., Rodríguez-Ruiz, J., Surový, P., Seppelt, A., and Pérez-Cruzado, C., 2025, Use of close-range LiDAR devices and statistical inference approaches in operational stand-level forest inventories: Remote Sensing of Environment, v. 325, article 114773, at https://doi.org/10.1016/j.rse.2025.114773..
Tree value and log product yield determination in radiata pine (Pinus radiata) plantations in australia—Comparisons of terrestrial laser scanning with a forest inventory system and manual measurements Murphy, G. E., Acuna, M. A., Dumbrell, I. 2010
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Murphy, G.E., Acuna, M.A., and Dumbrell, I., 2010, Tree value and log product yield determination in radiata pine (Pinus radiata) plantations in australia—Comparisons of terrestrial laser scanning with a forest inventory system and manual measurements: Canadian Journal of Forest Research, v. 40, no. 11, p. 2223–2233, at https://doi.org/10.1139/X10-171..
Quantifying understory fuel structure and biomass using coupled terrestrial lidar scanning, close-range photogrammetry, and field sampling Nemens, D. G., Prichard, S. J., Cova, G. R., Drye, B., Eagle, P., Rowell, E., Thoreson, J. 2026
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Nemens, D.G., Prichard, S.J., Cova, G.R., Drye, B., Eagle, P., Rowell, E., and Thoreson, J., 2026, Quantifying understory fuel structure and biomass using coupled terrestrial lidar scanning, close-range photogrammetry, and field sampling: Fire Ecology, v. 22, no. 1, article 92, at https://doi.org/10.1186/s42408-026-00503-6..
Terrestrial laser scanning for plot-scale forest measurement Newnham, G. J., Armston, J. D., Calders, K., Disney, M. I., Lovell, J. L., Schaaf, C. B., Strahler, A. H., Danson, F. M. 2015
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Newnham, G.J., Armston, J.D., Calders, K., Disney, M.I., Lovell, J.L., Schaaf, C.B., Strahler, A.H., and Danson, F.M., 2015, Terrestrial laser scanning for plot-scale forest measurement: Current Forestry Reports, v. 1, no. 4, p. 239–251, at https://doi.org/10.1007/s40725-015-0025-5..
A LiDAR framework to characterise elevated and canopy firebrand fuels for fire prediction models Nguyen, H. N., Penman, T. D., Peace, M., Hinko-Najera, N., Penman, S., Filippi, J. B., Pinar, A. A., Filkov, A. I. 2026
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Nguyen, H.N., Penman, T.D., Peace, M., Hinko-Najera, N., Penman, S., Filippi, J.B., Pinar, A.A., and Filkov, A.I., 2026, A LiDAR framework to characterise elevated and canopy firebrand fuels for fire prediction models: Fire Ecology, v. 22, no. 1, article 78, at https://doi.org/10.1186/s42408-026-00524-1..
On the geometry and allometry of big-buttressed trees—A challenge for forest monitoring—New insights from 3D-modeling with terrestrial laser scanning Nölke, N., Fehrmann, L., Jaya, I. N. S., Tiryana, T., Seidel, D., Kleinn, C. 2015
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Nölke, N., Fehrmann, L., Jaya, I.N.S., Tiryana, T., Seidel, D., and Kleinn, C., 2015, On the geometry and allometry of big-buttressed trees—A challenge for forest monitoring—New insights from 3D-modeling with terrestrial laser scanning: IForest, v. 8, no. OCTOBER2015, p. 574–581, at https://doi.org/10.3832/ifor1449-007..
Estimating sagebrush biomass using terrestrial laser scanning Olsoy, P. J., Glenn, N. F., Clark, P. E. 2014
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Olsoy, P.J., Glenn, N.F., and Clark, P.E., 2014, Estimating sagebrush biomass using terrestrial laser scanning: Rangeland Ecology and Management, v. 67, no. 2, p. 224–228, at https://doi.org/10.2111/REM-D-12-00186.1..
Unmanned aerial systems measure structural habitat features for wildlife across multiple scales Olsoy, P. J., Shipley, L. A., Rachlow, J. L., Forbey, J. S., Glenn, N. F., Burgess, M. A., Thornton, D. H. 2018
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Common field measures and geometric assumptions of tree shape produce consistently biased estimates of tree and canopy structure in mixed Mediterranean forests Owen, H. J. F., Lines, E. R. 2024
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Owen, H.J.F., and Lines, E.R., 2024, Common field measures and geometric assumptions of tree shape produce consistently biased estimates of tree and canopy structure in mixed Mediterranean forests: Ecological Indicators, v. 165, article 112219, at https://doi.org/10.1016/j.ecolind.2024.112219..
An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies Penman, S., Lentini, P., Law, B., York, A. 2023
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Penman, S., Lentini, P., Law, B., and York, A., 2023, An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies: Forest Ecology and Management, v. 548, article 121405, at https://doi.org/10.1016/j.foreco.2023.121405..
The horizontal distribution of branch biomass in european beech—A model based on measurements and TLS based proxies Pérez‐Cruzado, C., Kleinn, C., Magdon, P., Álvarez‐González, J. G., Magnussen, S., Fehrmann, L., Nölke, N. 2021
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Pérez‐Cruzado, C., Kleinn, C., Magdon, P., Álvarez‐González, J.G., Magnussen, S., Fehrmann, L., and Nölke, N., 2021, The horizontal distribution of branch biomass in european beech—A model based on measurements and TLS based proxies: Remote Sensing, v. 13, no. 5, article 1041, at https://doi.org/10.3390/rs13051041..
Terrestrial structure from motion photogrammetry for deriving forest inventory data Piermattei, L., Karel, W., Wang, D., Wieser, M., Mokro, Surov, Kore, Tomašt, Pfeifer, N., Hollaus, M. 2019
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A simplified and affordable approach to forest monitoring using single terrestrial laser scans and transect sampling Pokswinski, S., Gallagher, M. R., Skowronski, N. S., Loudermilk, E. L., Hawley, C., Wallace, D., Everland, A., Wallace, J., Hiers, J. K. 2021
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Pokswinski, S., Gallagher, M.R., Skowronski, N.S., Loudermilk, E.L., Hawley, C., Wallace, D., Everland, A., Wallace, J., and Hiers, J.K., 2021, A simplified and affordable approach to forest monitoring using single terrestrial laser scans and transect sampling: MethodsX, v. 8, article 101484, at https://doi.org/10.1016/j.mex.2021.101484..
Urban tree detection using mobile laser scanning data Pratihast, A. K., Thakur, J. K. 2012
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Pratihast, A.K., and Thakur, J.K., 2012, Urban tree detection using mobile laser scanning data, in Thakur, J.K., Singh, S.K., Ramanathan, A., Prasad, M.B.K., and Gossel, W., eds., Geospatial techniques for managing environmental resources: Dordrecht, Netherlands, Springer, p. 188–200, at https://doi.org/10.1007/978-94-007-1858-6_12..
Hierarchically scaled remote sensing and field datasets for three-dimensional wildland fuel characterization Prichard, S. J., Nemens, D. G., Rowell, E., Batchelor, J. L., Bester, M. S., Bright, B. C., Cova, G. R., Cronan, J. B., Drye, B., Hudak, A. T., Eagle, P. C., Kennedy, M. C., Loudermilk, E. L., Redford, H. E., Parsons, R. A., Silva, C. A., Skowronski, N. S., Thoreson, J. 2026
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Prichard, S.J., Nemens, D.G., Rowell, E., Batchelor, J.L., Bester, M.S., Bright, B.C., Cova, G.R., Cronan, J.B., Drye, B., et al., 2026, Hierarchically scaled remote sensing and field datasets for three-dimensional wildland fuel characterization: Fire Ecology, v. 22, no. 1, article 130, at https://doi.org/10.1186/s42408-026-00507-2..
Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data Puletti, N., Galluzzi, M., Grotti, M., Ferrara, C. 2021
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Puletti, N., Galluzzi, M., Grotti, M., and Ferrara, C., 2021, Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data: Remote Sensing Applications—Society and Environment, v. 24, article 100620, at https://doi.org/10.1016/j.rsase.2021.100620..
Laser-scanned tree stem filtering for forest inventories measurements Ravaglia, J., Alexandra, B., Piboule, A. 2013
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Ravaglia, J., Alexandra, B., and Piboule, A., 2013, Laser-scanned tree stem filtering for forest inventories measurements, in DigitalHeritage 2013 - Federating the 19th Int'l VSMM, 10th Eurographics GCH, and 2nd UNESCO Memory of the World Conferences, plus special sessions from CAA, Arqueologica 2.0, Space2Place, ICOMOS ICIP & CIPA, EU projects, et al., Marseille, France, 28 October–1 November 2013, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 649–652, at https://doi.org/10.1109/DigitalHeritage.2013.6743809..
Identifying tree-related microhabitats in TLS point clouds using machine learning Rehush, N., Abegg, M., Waser, L. T., Brändli, U. B. 2018
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Rehush, N., Abegg, M., Waser, L.T., and Brändli, U.B., 2018, Identifying tree-related microhabitats in TLS point clouds using machine learning: Remote Sensing, v. 10, no. 11, article 1735, at https://doi.org/10.3390/rs10111735..
Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning Ritter, T., Schwarz, M., Tockner, A., Leisch, F., Nothdurft, A. 2017
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Ritter, T., Schwarz, M., Tockner, A., Leisch, F., and Nothdurft, A., 2017, Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning: Forests, v. 8, no. 8, article 265, at https://doi.org/10.3390/f8080265..
Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests Ro, Suomalainen, J., Bartholomeus, H., Herold, M. 2018
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Coupling terrestrial laser scanning with 3D fuel biomass sampling for advancing wildland fuels characterization Rowell, E., Loudermilk, E. L., Hawley, C., Pokswinski, S., Seielstad, C., Queen, L. L., O'Brien, J. J., Hudak, A. T., Goodrick, S., Hiers, J. K. 2020
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Development and validation of fuel height models for terrestrial lidar - RxCADRE 2012 Rowell, E. M., Seielstad, C. A., Ottmar, R. D. 2016
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Rowell, E.M., Seielstad, C.A., and Ottmar, R.D., 2016, Development and validation of fuel height models for terrestrial lidar - RxCADRE 2012: International Journal of Wildland Fire, v. 25, no. 1, p. 38–47, at https://doi.org/10.1071/WF14170..
Modeling tree crown-mediated accumulation of downed woody debris in fire-maintained longleaf pine forests of the southeastern US from laser scanning data Sánchez-López, N., Hudak, A. T., Xia, J., Robertson, K., Bright, B. C., Prichard, S., Nemens, D. G., Hawley, C., Loudermilk, E. L., Keane, R., Woodall, C. W., Mauro, F., Boschetti, L., Silva, C. A., Cabo, C. 2026
Full Citation
Sánchez-López, N., Hudak, A.T., Xia, J., Robertson, K., Bright, B.C., Prichard, S., Nemens, D.G., Hawley, C., Loudermilk, E.L., et al., 2026, Modeling tree crown-mediated accumulation of downed woody debris in fire-maintained longleaf pine forests of the southeastern US from laser scanning data: Fire Ecology, v. 22, no. 1, article 136, at https://doi.org/10.1186/s42408-026-00548-7..
Quantifying plant-soil-nutrient dynamics in rangelands—Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland Sankey, J. B., Sankey, T. T., Li, J., Ravi, S., Wang, G., Caster, J., Kasprak, A. 2021
Full Citation
Sankey, J.B., Sankey, T.T., Li, J., Ravi, S., Wang, G., Caster, J., and Kasprak, A., 2021, Quantifying plant-soil-nutrient dynamics in rangelands—Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland: Remote Sensing of Environment, v. 253, article 112223, at https://doi.org/10.1016/j.rse.2020.112223..
Experimental study of fire spread through discontinuous fuels without flame contact Schneider, L., Betting, B., Patterson, M., Skowronski, N., Simeoni, A. 2021
Full Citation
Schneider, L., Betting, B., Patterson, M., Skowronski, N., and Simeoni, A., 2021, Experimental study of fire spread through discontinuous fuels without flame contact: Fire Safety Journal, v. 120, article 103066, at https://doi.org/10.1016/j.firesaf.2020.103066..
Monitoring trees outside forests—A review Schnell, S., Kleinn, C., Ståhl, G. 2015
Full Citation
Schnell, S., Kleinn, C., and Ståhl, G., 2015, Monitoring trees outside forests—A review: Environmental Monitoring and Assessment, v. 187, no. 9, article 600, at https://doi.org/10.1007/s10661-015-4817-7..
TSS-Net—An efficient deep learning framework for individual tree segmentation in terrestrial laser scanning data Sha, A., Dong, B., Li, Z., Zhang, S., Liu, R., Li, S., Lu, H., Wang, H. 2026
Full Citation
Sha, A., Dong, B., Li, Z., Zhang, S., Liu, R., Li, S., Lu, H., and Wang, H., 2026, TSS-Net—An efficient deep learning framework for individual tree segmentation in terrestrial laser scanning data: Results in Engineering, v. 29, article 109161, at https://doi.org/10.1016/j.rineng.2026.109161..
SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning Shao, J., Zhang, W., Mellado, N., Wang, N., Jin, S., Cai, S., Luo, L., Lejemble, T., Yan, G. 2020
Full Citation
Shao, J., Zhang, W., Mellado, N., Wang, N., Jin, S., Cai, S., Luo, L., Lejemble, T., and Yan, G., 2020, SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning: ISPRS Journal of Photogrammetry and Remote Sensing, v. 163, p. 214–230, at https://doi.org/10.1016/j.isprsjprs.2020.03.008..
Assessing bat roosts using the LiDAR system at Wind Cave Nature Reserve in Sarawak, Malaysian Borneo Shazali, N., Chew, T. H., Shamsir, M. S., Tingga, R. C. T., Mohd-Ridwan, A. R., Khan, F. A. A. 2017
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Shazali, N., Chew, T.H., Shamsir, M.S., Tingga, R.C.T., Mohd-Ridwan, A.R., and Khan, F.A.A., 2017, Assessing bat roosts using the LiDAR system at Wind Cave Nature Reserve in Sarawak, Malaysian Borneo: Acta Chiropterologica, v. 19, no. 1, p. 199–210, at https://doi.org/10.3161/15081109ACC2017.19.1.016..
Comparison of TLS and ULS data for wildlife habitat assessments in temperate woodlands Shokirov, S., Levick, S. R., Jucker, T., Yeoh, P., Youngentob, K. 2020
Full Citation
Shokirov, S., Levick, S.R., Jucker, T., Yeoh, P., and Youngentob, K., 2020, Comparison of TLS and ULS data for wildlife habitat assessments in temperate woodlands, in International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, 26 September–2 October 2020, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 6097–6100, at https://doi.org/10.1109/IGARSS39084.2020.9323451..
Spaceborne LiDAR and animal-environment relationships—An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem Smith, A. B., Vogeler, J. C., Bjornlie, N. L., Squires, J. R., Swayze, N. C., Holbrook, J. D. 2022
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Smith, A.B., Vogeler, J.C., Bjornlie, N.L., Squires, J.R., Swayze, N.C., and Holbrook, J.D., 2022, Spaceborne LiDAR and animal-environment relationships—An assessment for forest carnivores and their prey in the Greater Yellowstone Ecosystem: Forest Ecology and Management, v. 520, article 120343, at https://doi.org/10.1016/j.foreco.2022.120343..
East African megafauna influence on vegetation structure permeates from landscape to tree level scales Sorokina, H. E., Nunes, M. H., Heiskanen, J., Munyao, M., Mwang'ombe, J., Pellikka, P., Raumonen, P., Maeda, E. E. 2024
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Sorokina, H.E., Nunes, M.H., Heiskanen, J., Munyao, M., Mwang'ombe, J., Pellikka, P., Raumonen, P., and Maeda, E.E., 2024, East African megafauna influence on vegetation structure permeates from landscape to tree level scales: Ecological Informatics, v. 79, article 102435, at https://doi.org/10.1016/j.ecoinf.2023.102435..
A fully automatic forest parameters extraction at single-tree level—A comparison of MLS and TLS applications Spadavecchia, C., Belcore, E., Grasso, N., Piras, M. 2023
Full Citation
Spadavecchia, C., Belcore, E., Grasso, N., and Piras, M., 2023, A fully automatic forest parameters extraction at single-tree level—A comparison of MLS and TLS applications, in 12th International Symposium on Mobile Mapping Technology (MMT 2023), Padua, Italy, 24–26 May 2023, ISPRS Archives XLVIII-1/W1: International Society of Photogrammetry and Remote Sensing, p. 457–463, at https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-457-2023..
Beyond discrete visibility estimates—Single-scan LiDAR provides an efficient method for 3D viewshed estimation Stein, R. M., Allred, C. R., Lecigne, B., Eitel, J. U. H., Johnson, T. R., Rachlow, J. L. 2026
Full Citation
Stein, R.M., Allred, C.R., Lecigne, B., Eitel, J.U.H., Johnson, T.R., and Rachlow, J.L., 2026, Beyond discrete visibility estimates—Single-scan LiDAR provides an efficient method for 3D viewshed estimation: Wildlife Society Bulletin, v. 50, no. 1, article e70014, at https://doi.org/10.1002/wsb.70014..
Enhancing tree species classification—Insights from YOLOv8 and explainable AI applied to TLS point cloud projections Straker, A., Magdon, P., Zullich, M., Freudenberg, M., Kleinn, C., Breidenbach, J., Puliti, S., Nölke, N. 2026
Full Citation
Straker, A., Magdon, P., Zullich, M., Freudenberg, M., Kleinn, C., Breidenbach, J., Puliti, S., and Nölke, N., 2026, Enhancing tree species classification—Insights from YOLOv8 and explainable AI applied to TLS point cloud projections: Forestry, v. 99, no. 4, article cpag059, at https://doi.org/10.1093/forestry/cpag059..
Multi-station LiDAR scanning-based hierarchical features for generation of an allometric stem volume model Sun, Y., Lin, X., Gong, Y., Jiang, J., Zhang, Y., Wen, X. 2021
Full Citation
Sun, Y., Lin, X., Gong, Y., Jiang, J., Zhang, Y., and Wen, X., 2021, Multi-station LiDAR scanning-based hierarchical features for generation of an allometric stem volume model: Journal of Applied Remote Sensing, v. 15, no. 2, article 028503, at https://doi.org/10.1117/1.JRS.15.028503..
Sentinel-1/2 time series for selective logging monitoring in temperate forests Tanase, M., Borlaf, I., Pascu, I., Pitar, D., Apostol, B., Petrila, M., Chivulescu, S., Leca, S., Pitar, D., Ciceu, A., Dobre, A., Popescu, F., Badea, O., Aponte, C. 2020
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Tanase, M., Borlaf, I., Pascu, I., Pitar, D., Apostol, B., Petrila, M., Chivulescu, S., Leca, S., Pitar, D., et al., 2020, Sentinel-1/2 time series for selective logging monitoring in temperate forests, in International Geoscience and Remote Sensing Symposium, Waikoloa, Hawaii, 26 September–2 October 2020, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2902–2905, at https://doi.org/10.1109/IGARSS39084.2020.9323952..
Combining terrestrial lidar with single line transects to investigate geomorphic change—A case study on the Upper Verde River, Arizona Tango, L. L., Sankey, T. T., Leonard, J., Sankey, J. B., Kasprak, A. 2024
Full Citation
Tango, L.L., Sankey, T.T., Leonard, J., Sankey, J.B., and Kasprak, A., 2024, Combining terrestrial lidar with single line transects to investigate geomorphic change—A case study on the Upper Verde River, Arizona: Geomorphology, v. 457, article 109231, at https://doi.org/10.1016/j.geomorph.2024.109231..
Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain Tupinambá-Simões, F., Pascual, A., Guerra-Hernández, J., Ordóñez, C., de Conto, T., Bravo, F. 2023
Full Citation
Tupinambá-Simões, F., Pascual, A., Guerra-Hernández, J., Ordóñez, C., de Conto, T., and Bravo, F., 2023, Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain: Remote Sensing, v. 15, no. 5, article 1169, at https://doi.org/10.3390/rs15051169..
Representing 3-dimensional fuels for physics-based fire behavior models—A general framework and case study in a type-converted post-fire shrubfield Tutland, N. J., Wion, A. P., May, C. J., Hutchings, G. C., Nowak, H. A., Gattiker, J. R., Hiers, J. K., Linn, R. R., Pokswinski, S. M., Margolis, E. Q. 2025
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Tutland, N.J., Wion, A.P., May, C.J., Hutchings, G.C., Nowak, H.A., Gattiker, J.R., Hiers, J.K., Linn, R.R., Pokswinski, S.M., et al., 2025, Representing 3-dimensional fuels for physics-based fire behavior models—A general framework and case study in a type-converted post-fire shrubfield: Fire Ecology, v. 21, no. 1, article 43, at https://doi.org/10.1186/s42408-025-00383-2..
Automated change detection in photogrammetric 4D point clouds—Transferability and extension of 4D objects-by-change for monitoring riverbank dynamics using low-cost cameras Ulm, M., Elias, M., Eltner, A., Lotsari, E., Anders, K. 2025
Full Citation
Ulm, M., Elias, M., Eltner, A., Lotsari, E., and Anders, K., 2025, Automated change detection in photogrammetric 4D point clouds—Transferability and extension of 4D objects-by-change for monitoring riverbank dynamics using low-cost cameras: Applied Geomatics, v. 17, no. 2, p. 367–378, at https://doi.org/10.1007/s12518-025-00623-9..
Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning Van den Broeck, W. A. J., Terryn, L., Chen, S., Cherlet, W., Cooper, Z. T., Calders, K. 2025
Full Citation
Van den Broeck, W.A.J., Terryn, L., Chen, S., Cherlet, W., Cooper, Z.T., and Calders, K., 2025, Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning: ISPRS Journal of Photogrammetry and Remote Sensing, v. 227, p. 366–382, at https://doi.org/10.1016/j.isprsjprs.2025.06.023..
Three-dimensional deep learning for leaf-wood segmentation of tropical tree point clouds Van den Broeck, W. A. J., Terryn, L., Cherlet, W., Cooper, Z. T., Calders, K. 2023
Full Citation
Van den Broeck, W.A.J., Terryn, L., Cherlet, W., Cooper, Z.T., and Calders, K., 2023, Three-dimensional deep learning for leaf-wood segmentation of tropical tree point clouds, in ISPRS Geospatial Week 2023, Cairo, Egypt, 2–7 September 2023, ISPRS Archives XLVIII-1/W2: International Society of Photogrammetry and Remote Sensing, p. 765–770, at https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-765-2023.N.
An open and novel low-cost terrestrial laser scanner prototype for forest monitoring Výbo, Chud, Tom, Gretsch, D., Toma, Pe, Bedkowski, J., Skladan, M., Mokroš, M. 2026
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Výbo
An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data Wagner, B., Ginzler, C., Bürgi, A., Santini, S., Gärtner, H. 2018
Full Citation
Wagner, B., Ginzler, C., Bürgi, A., Santini, S., and Gärtner, H., 2018, An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data: Trees - Structure and Function, v. 32, no. 1, p. 125–136, at https://doi.org/10.1007/s00468-017-1618-3..
A branch and leaf separation method based on near infrared band UAV-LiDAR data Wan, L., Li, S., Tian, Z., Shi, Y. 2023
Full Citation
Wan, L., Li, S., Tian, Z., and Shi, Y., 2023, A branch and leaf separation method based on near infrared band UAV-LiDAR data, in International Geoscience and Remote Sensing Symposium, Pasadena, Calif., 16–21 July 2023, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 3284–3287, at https://doi.org/10.1109/IGARSS52108.2023.10282325..
Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning Wang, M., Baeten, L., Van Coillie, F., Calders, K., Verheyen, K., Ponette, Q., Blondeel, H., Muys, B., Armston, J., Verbeeck, H. 2024
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Wang, M., Baeten, L., Van Coillie, F., Calders, K., Verheyen, K., Ponette, Q., Blondeel, H., Muys, B., Armston, J., et al., 2024, Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning: Forest Ecosystems, v. 11, article 100196, at https://doi.org/10.1016/j.fecs.2024.100196..
A simple, interactive GIS tool for transforming assumed total station surveys to real world coordinates—The CHaMP transformation tool Wheaton, J. M., Garrard, C., Whitehead, K., Volk, C. J. 2012
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Wheaton, J.M., Garrard, C., Whitehead, K., and Volk, C.J., 2012, A simple, interactive GIS tool for transforming assumed total station surveys to real world coordinates—The CHaMP transformation tool: Computers and Geosciences, v. 42, p. 28–36, at https://doi.org/10.1016/j.cageo.2012.02.003..
Estimation of forest trees diameter from terrestrial laser scanning point clouds based on a circle fitting method Wu, R., Chen, Y., Wang, C., Li, J. 2018
Full Citation
Wu, R., Chen, Y., Wang, C., and Li, J., 2018, Estimation of forest trees diameter from terrestrial laser scanning point clouds based on a circle fitting method, in International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 2813–2816, at https://doi.org/10.1109/IGARSS.2018.8517303..
Delineating and reconstructing 3D forest fuel components and volumes with terrestrial laser scanning Xi, Z., Chasmer, L., Hopkinson, C. 2023
Full Citation
Xi, Z., Chasmer, L., and Hopkinson, C., 2023, Delineating and reconstructing 3D forest fuel components and volumes with terrestrial laser scanning: Remote Sensing, v. 15, no. 19, article 4778, at https://doi.org/10.3390/rs15194778..
LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area Xie, B., Yang, H., Cao, J., Li, J., Wang, M., Zhang, W., Li, J., Xu, J. 2026
Full Citation
Xie, B., Yang, H., Cao, J., Li, J., Wang, M., Zhang, W., Li, J., and Xu, J., 2026, LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area: Biodiversity Science, v. 34, no. 5, article 26004, at https://doi.org/10.17520/biods.2026004..
Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR Yang, C., Zhu, X., Yebra, M., Harris, S., Tapper, N. 2016
Full Citation
Yang, C., Zhu, X., Yebra, M., Harris, S., and Tapper, N., 2016, Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR: Journal of Applied Remote Sensing, v. 10, no. 4, article 46025, at https://doi.org/10.1117/1.JRS.10.046025..
Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study—Guadalupe Island Yépez-Rincón, F. D., Luna-Mendoza, L., Ramírez-Serrato, N. L., Hinojosa-Corona, A., Ferriño-Fierro, A. L. 2021
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Yépez-Rincón, F.D., Luna-Mendoza, L., Ramírez-Serrato, N.L., Hinojosa-Corona, A., and Ferriño-Fierro, A.L., 2021, Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study—Guadalupe Island: Remote Sensing of Environment, v. 263, article 112563, at https://doi.org/10.1016/j.rse.2021.112563..
Efficient registration of terrestrial LiDAR scans using a coarse-to-fine strategy for forestry applications Zhang, W., Chen, Y., Wang, H., Chen, M., Wang, X., Yan, G. 2016
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Zhang, W., Chen, Y., Wang, H., Chen, M., Wang, X., and Yan, G., 2016, Efficient registration of terrestrial LiDAR scans using a coarse-to-fine strategy for forestry applications: Agricultural and Forest Meteorology, v. 225, p. 8–23, at https://doi.org/10.1016/j.agrformet.2016.05.005..
Title Authors Year Citation
Biodiversity benefits of prescribed fire and mechanical thinning in Yosemite Valley Crumrine, S. A., Anderson, C. T., Hankin, L. E. 2026
Full Citation
Crumrine, S.A., Anderson, C.T., and Hankin, L.E., 2026, Biodiversity benefits of prescribed fire and mechanical thinning in Yosemite Valley: Fire Ecology, v. 22, no. 1, article 76, at https://doi.org/10.1186/s42408-026-00501-8..
EcoViz—Co-designed environmental data visualizations to communicate ecosystem impacts, inform management, and envision solutions Kendall-Bar, J., Nealey, I., Costello, I., Lowrie, C., Nguyen, K. H., Ponganis, P. J., Beck, M. W., Altintas, I. 2024
Full Citation
Kendall-Bar, J., Nealey, I., Costello, I., Lowrie, C., Nguyen, K.H., Ponganis, P.J., Beck, M.W., and Altintas, I., 2024, EcoViz—Co-designed environmental data visualizations to communicate ecosystem impacts, inform management, and envision solutions, in VIS Workshop on Visualization for Climate Action and Sustainability (Viz4Climate + Sustainability), St. Pete Beach, Fla., 14 October 2024, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 17–27, at https://doi.org/10.1109/Viz4Climate-Sustainability64680.2024.00007..
Remote sensing applications for prescribed burn research LoPresti, A., Hayden, M. T., Siegel, K., Poulter, B., Stavros, E. N., Dee, L. E. 2024
Full Citation
LoPresti, A., Hayden, M.T., Siegel, K., Poulter, B., Stavros, E.N., and Dee, L.E., 2024, Remote sensing applications for prescribed burn research: International Journal of Wildland Fire, v. 33, no. 6, article WF23130, at https://doi.org/10.1071/wf23130..
Repeat terrestrial laser scanning for documenting and monitoring prescribed fire efficacy Maxwell, A. E., Gallagher, M. R., Stockton, S. R., Wozniak, M. C., McNeil, B. E., Defeo, J. A., Everland, A., Skowronski, N. S. 2026
Full Citation
Maxwell, A.E., Gallagher, M.R., Stockton, S.R., Wozniak, M.C., McNeil, B.E., Defeo, J.A., Everland, A., and Skowronski, N.S., 2026, Repeat terrestrial laser scanning for documenting and monitoring prescribed fire efficacy: Fire Ecology, v. 22, no. 1, article 91, at https://doi.org/10.1186/s42408-026-00498-0..
Visualization and labeling of terrestrial LiDAR data for three-dimensional fuel classification Moreno, I. G., Nealey, I., Roten, D., Nguyen, M. H., Crawl, D., O'Laughlin, K., Floca, M., Pokswinski, S., Altintas, I. 2023
Full Citation
Moreno, I.G., Nealey, I., Roten, D., Nguyen, M.H., Crawl, D., O'Laughlin, K., Floca, M., Pokswinski, S., and Altintas, I., 2023, Visualization and labeling of terrestrial LiDAR data for three-dimensional fuel classification, in 19th International Conference on e-Science, Limassol, Cyprus, 9–13 October 2023, Proceedings: Piscataway, N.J., Institute of Electrical and Electronics Engineers (IEEE), p. 1–2, at https://doi.org/10.1109/e-Science58273.2023.10254841..
IntELiMon—An automated framework for standardized terrestrial lidar processing in multi-disciplinary ecosystem monitoring Pokswinski, S., Nelson, K., Maxwell, A., Link, E., Redford, H. E., Atkins, J. W., Sanchez Meador, A., Nealey, I., Crawl, D., Floca, M., Altintas, I., Loudermilk, E. L., Anderson, C. T., Oliveto, J., Linn, R. R., Wallace, J., Hiers, J. K. 2026
Full Citation
Pokswinski, S., Nelson, K., Maxwell, A., Link, E., Redford, H.E., Atkins, J.W., Sanchez Meador, A., Nealey, I., Crawl, D., et al., 2026, IntELiMon—An automated framework for standardized terrestrial lidar processing in multi-disciplinary ecosystem monitoring: Forestry—An International Journal of Forest Research, v. 99, no. 5, article cpag083, at https://doi.org/10.1093/forestry/cpag083..


 

Researchers and Contributors

Chad Hoffman
Colorado State University

Ben Helms
Colorado State University

Adam Atchley
Los Alamos National Laboratory

Juan Castorena
Los Alamos National Laboratory

Rod Linn
Los Alamos National Laboratory

Turin Dickman
Los Alamos National Laboratory

Chad Anderson
National Park Service

Jennifer Barnes
National Park Service

Lacey Hankin
National Park Service

Sarah Crumrine
National Park Service

Nate Beine
Northern Arizona University

Andrew Sanchez Meador
Northern Arizona University, Ecological Restoration Institute

Meag Dreher
Northern Arizona University, Ecological Restoration Institute

Julia DeFeo
The Ohio State University

Derek Young
University of California Davis

Ilkay Altintas
University of California San Diego, San Diego Supercomputer Center

Leticia Lee
University of California San Diego, San Diego Supercomputer Center

Melissa Floca
University of California San Diego, San Diego Supercomputer Center

Susan Prichard
University of Washington

Russ Parsons
USDA Forest Service Rocky Mountain Research Station

Sarah Flannary
USDA Forest Service Rocky Mountain Research Station

Kevin Brown
USDA Forest Service, Northern Research Station

Michael Gallagher
USDA Forest Service, Northern Research Station

E. Louise Loudermilk
USDA Forest Service, Southern Research Station

Birgit Peterson
USGS

Ellis Margolis
USGS

Katelyn Woolfrey
USGS

Kurtis Nelson
USGS

Matt Germino
USGS

Samual Jake Price
USGS

Seth Munson
USGS

Adam Coates
Virginia Tech

Aaron Maxwell
West Virginia University