Abstract
Annotated datasets are essential for training and evaluating machine learning models in forest ecology. This dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species. Data were acquired using Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS), include field-measured attributes such as species identity and Diameter at Breast Height (DBH), and terrestrial and aerial RGB imagery. TLS point clouds were georeferenced and co-registered with centimetre-level accuracy, enabling precise integration with ALS data. The dataset includes segmented individual trees and wood–leaf classifications, suitable for applications such as tree morphology analysis, biomass estimation, and species classification. To support benchmarking, outputs from established classification algorithms (LeWoS, TLSeparation, CANUPO, and Random Forest) are included. As one of the first open-access LiDAR datasets from Indian tropical forests, it provides critical reference data for developing and validating forest structure models. It can also aid biomass mapping efforts in support of large-scale missions such as NASA-ISRO’s NISAR and ESA’s BIOMASS.
Similar content being viewed by others
Data availability
The full dataset generated in this study is openly available via the Zenodo repository (https://doi.org/10.5281/zenodo.15362444). For long-term discoverability, the dataset is also mirrored on the LiDAR point cloud repository, LiDAVerse (www.LiDAVerse.com). Both repositories provide identical versions of the data, ensuring transparency, reproducibility, and ease of reuse.
Code availability
All code used for data processing, wood-leaf classification, feature extraction, and tree volume estimation is openly available on GitHub at https://github.com/moonis-ali/Dataset.
References
Pan, Y. et al. A Large and Persistent Carbon Sink in the World’s Forests. Science (1979) 333, 988–993, https://doi.org/10.1126/science.1201609 (2011).
Global Forest Resource Assessment 2020. https://www.fao.org/interactive/forest-resources-assessment/2020/en/.
Masson-Delmotte, V. et al. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems. www.ipcc.ch (2019).
Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol 20, 3177–3190, https://doi.org/10.1111/gcb.12629 (2014).
Clark, D. A. et al. Measuring Net Primary Production in Forests: Concepts and Field Methods. Ecological Applications 11, 356, https://doi.org/10.2307/3060894 (2001).
Dubayah, R. O. & Drake, J. B. Lidar Remote Sensing for Forestry. J For 98, 44–46, https://doi.org/10.1093/jof/98.6.44 (2000).
Calders, K. et al. Nondestructive estimates of above‐ground biomass using terrestrial laser scanning. Methods Ecol Evol 6, 198–208, https://doi.org/10.1111/2041-210X.12301 (2015).
Hackenberg, J., Wassenberg, M., Spiecker, H. & Sun, D. Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density. Forests 6, 1274–1300, https://doi.org/10.3390/f6041274 (2015).
Demol, M. et al. Volumetric overestimation of small branches in 3D reconstructions of Fraxinus excelsior. Silva Fennica 56, 10550, https://doi.org/10.14214/sf.10550 (2022).
Demol, M. et al. Estimating forest above‐ground biomass with terrestrial laser scanning: Current status and future directions. Methods Ecol Evol 13, 1628–1639, https://doi.org/10.1111/2041-210X.13906 (2022).
Calders, K. et al. Terrestrial laser scanning in forest ecology: Expanding the horizon. Remote Sens Environ 251, 112102, https://doi.org/10.1016/j.rse.2020.112102 (2020).
Ali, M., Lohani, B., Hollaus, M. & Pfeifer, N. A hybrid approach for enhanced tree volume estimation of complex trees using terrestrial LiDAR. GIsci Remote Sens 62, 2474836, https://doi.org/10.1080/15481603.2025.2474836 (2025).
Ali, M., Lohani, B., Hollaus, M., Fotedar, P. & Pfeifer, N. Object-Based Upscaling of Growing Stock Volume Using Tls, Als, and Orthophoto Data in the Shivalik Forests of India. Preprint at https://doi.org/10.2139/ssrn.5334528 (2025).
Fan, G. et al. A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds. Remote Sens (Basel) 12, 1779, https://doi.org/10.3390/rs12111779 (2020).
Ali, M., Lohani, B., Hollaus, M. & Pfeifer, N. Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners. Remote Sens (Basel) 16, 1021, https://doi.org/10.3390/rs16061021 (2024).
Wang, D., Momo Takoudjou, S. & Casella, E. LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR. Methods Ecol Evol 11, 376–389, https://doi.org/10.1111/2041-210X.13342 (2020).
Momo Takoudjou, S. et al. Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods Ecol Evol 9, 905–916, https://doi.org/10.1111/2041-210X.12933 (2018).
Zolkos, S. G., Goetz, S. J. & Dubayah, R. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens Environ 128, 289–298, https://doi.org/10.1016/j.rse.2012.10.017 (2013).
Asner, G. P. et al. High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Sciences 107, 16738–16742, https://doi.org/10.1073/pnas.1004875107 (2010).
Goetz, S. J. et al. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environmental Research Letters 10, 123001, https://doi.org/10.1088/1748-9326/10/12/123001 (2015).
Herold, M. & Johns, T. Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process. Environmental Research Letters 2, 045025, https://doi.org/10.1088/1748-9326/2/4/045025 (2007).
Wang, Y. et al. Is field-measured tree height as reliable as believed – A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest. ISPRS Journal of Photogrammetry and Remote Sensing 147, 132–145, https://doi.org/10.1016/j.isprsjprs.2018.11.008 (2019).
Pearse, G. D., Watt, M. S., Dash, J. P., Stone, C. & Caccamo, G. Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities. International Journal of Applied Earth Observation and Geoinformation 78, 341–351, https://doi.org/10.1016/j.jag.2018.10.008 (2019).
Beyene, S. M., Hussin, Y. A., Kloosterman, H. E. & Ismail, M. H. Forest Inventory and Aboveground Biomass Estimation with Terrestrial LiDAR in the Tropical Forest of Malaysia. Canadian Journal of Remote Sensing 46, 130–145, https://doi.org/10.1016/j.jag.2018.10.008 (2020).
Hauglin, M., Gobakken, T., Astrup, R., Ene, L. & Næsset, E. Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data. Forests 5, 384–403, https://doi.org/10.3390/f5030384 (2014).
Abd Rahman, M. et al. Non-Destructive, Laser-Based Individual Tree Aboveground Biomass Estimation in a Tropical Rainforest. Forests 8, 86, https://doi.org/10.3390/f8030086 (2017).
Kankare, V. et al. Individual tree biomass estimation using terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 75, 64–75, https://doi.org/10.1016/j.isprsjprs.2012.10.003 (2013).
Hyyppä, J., Holopainen, M. & Olsson, H. Laser Scanning in Forests. Remote Sens (Basel) 4, 2919–2922, https://doi.org/10.3390/rs4102919 (2012).
Ramirez, F., Armitage, R. & Danson, F. Testing the Application of Terrestrial Laser Scanning to Measure Forest Canopy Gap Fraction. Remote Sens (Basel) 5, 3037–3056, https://doi.org/10.3390/rs5063037 (2013).
Ferraz, A., Saatchi, S., Mallet, C. & Meyer, V. Lidar detection of individual tree size in tropical forests. Remote Sens Environ 183, 318–333, https://doi.org/10.1016/j.rse.2016.05.028 (2016).
Arumäe, T. & Lang, M. Estimation of canopy cover in dense mixed-species forests using airborne lidar data. Eur J Remote Sens 51, 132–141, https://doi.org/10.1080/22797254.2017.1411169 (2018).
Smith, A. M. S. et al. A cross-comparison of field, spectral, and lidar estimates of forest canopy cover. Canadian Journal of Remote Sensing 35, 447–459, https://doi.org/10.5589/m09-038 (2009).
Disney, M. I. et al. Weighing trees with lasers: advances, challenges and opportunities. Interface Focus 8, 20170048, https://doi.org/10.1098/rsfs.2017.0048 (2018).
Næsset, E. et al. Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area. Remote Sens Environ 115, 3599–3614, https://doi.org/10.1016/j.rse.2011.08.021 (2011).
Calders, K. et al. Estimating above ground biomass from terrestrial laser scanning in Australian Eucalypt open forest. In Proceedings SilviLaser 2013, 9-11 October, Beijing, China (pp. 90–97) https://edepot.wur.nl/309912 (2013).
Wagers, S., Castilla, G., Filiatrault, M. & Sanchez-Azofeifa, G. A. Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees. Forests 12, 1521, https://doi.org/10.3390/f12111521 (2021).
Dassot, M., Colin, A., Santenoise, P., Fournier, M. & Constant, T. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comput Electron Agric 89, 86–93, https://doi.org/10.1016/j.compag.2012.08.005 (2012).
Popescu, S. C., Randolph, H. W. & Nelson, R. F. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing 29, 564–577, https://doi.org/10.5589/m03-027 (2003).
Dassot, M., Constant, T. & Fournier, M. The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Ann For Sci 68, 959–974, https://doi.org/10.1007/s13595-011-0102-2 (2011).
Lefsky, M. A., Cohen, W. B., Parker, G. G. & Harding, D. J. Lidar Remote Sensing for Ecosystem Studies: Lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. Bioscience 52, 19–30 (2002). 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2.
RIEGL Laser Measurement Systems: RIEGL VZ-2000i, Data Sheet. http://www.riegl.com/uploads/tx_pxpriegldownloads/RIEGL_VZ-2000i_Datasheet_2024-09-02.pdf (2024).
RIEGL Laser Measurement Systems: Operating & Processing Software RiSCAN PRO for RIEGL 3D Laser Scanners. http://www.riegl.com/uploads/tx_pxpriegldownloads/RiSCAN-PRO_DataSheet_2022-10-07.pdf (2022).
Weiser, H. et al. Terrestrial, UAV-borne, and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements. PANGAEA https://doi.org/10.1594/PANGAEA.942856 (2022).
Trochta, J., Krůček, M., Vrška, T. & Král, K. 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS One 12, e0176871, https://doi.org/10.1371/journal.pone.0176871 (2017).
GreenValley LiDAR360: LiDAR360, version 7.2. https://www.greenvalleyintl.com/LiDAR360 (2024).
CloudCompare: CloudCompare, version 2.12.4. https://www.cloudcompare.org/ (2025).
Coomes, D. A. et al. Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data. Remote Sens Environ 194, 77–88, https://doi.org/10.1016/j.rse.2017.03.017 (2017).
Raumonen, P. et al. Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data. Remote Sens (Basel) 5, 491–520, https://doi.org/10.3390/rs5020491 (2013).
Calders, K. et al. Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs). Zenodo https://doi.org/10.5281/zenodo.7307956 (2022).
Gonzalez de Tanago, J. et al. Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR. Methods Ecol Evol 9, 223–234, https://doi.org/10.1111/2041-210X.12904 (2018).
Ali, M. Terrestrial and Airborne Laser Scanning Dataset of Trees in the Shivalik Range, India with Field Measurements and Leaf–Wood Classifications. Zenodo https://doi.org/10.5281/zenodo.15362444 (2025).
Fugro: FugroViewer, 2024, version 3.5. https://www.fugro.com/expertise/other-expertise/fugroviewer (2024).
Rapidlasso GmbH: LAStools – Efficient LiDAR Processing Software (Version 241017, Unlicensed) https://rapidlasso.de/ (2024).
Pfeifer, N., Mandlburger, G., Otepka, J. & Karel, W. OPALS – A framework for Airborne Laser Scanning data analysis. Comput Environ Urban Syst 45, 125–136, https://doi.org/10.1016/j.compenvurbsys.2013.11.002 (2014).
Computree Group: Computree. https://computree.onf.fr/?page_id=42%2F (2023).
DendroCloud: DendroCloud, 2022, version 1.53. https://gis.tuzvo.sk/dendrocloud/ (2024).
Roussel, J.-R. et al. lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sens Environ 251, 112061, https://doi.org/10.1016/j.rse.2020.112061 (2020).
Vicari, M. B. et al. Leaf and wood classification framework for terrestrial LiDAR point clouds. Methods Ecol Evol 10, 680–694, https://doi.org/10.1111/2041-210X.13144 (2019).
Krisanski, S. et al. Forest Structural Complexity Tool—An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds. Remote Sens (Basel) 13, 4677, https://doi.org/10.3390/rs13224677 (2021).
Du, S., Lindenbergh, R., Ledoux, H., Stoter, J. & Nan, L. AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees. Remote Sens (Basel) 11, 2074, https://doi.org/10.3390/rs11182074 (2019).
Delagrange, S., Jauvin, C. & Rochon, P. PypeTree: A Tool for Reconstructing Tree Perennial Tissues from Point Clouds. Sensors 14, 4271–4289, https://doi.org/10.3390/s140304271 (2014).
Hackenberg, J., Spiecker, H., Calders, K., Disney, M. & Raumonen, P. SimpleTree - An efficient open source tool to build tree models from TLS clouds. Forests 6, 4245–4294, https://doi.org/10.3390/f6114245 (2015).
Boudon, F. et al. Quantitative assessment of automatic reconstructions of branching systems obtained from laser scanning. Ann Bot 114, 853–862, https://doi.org/10.1093/aob/mcu062 (2014).
NASA-ISRO SAR (NISAR) satellite. Indian Space Research Organisation https://www.isro.gov.in/NISARSatellite.html (2024).
Biomass. European Space Agency https://www.esa.int/Applications/Observing_the_Earth/FutureEO/Biomass (2024).
Owen, H., Lines, E. & Flynn, W. Terrestrial laser scanning (TLS) data on tree crown morphology and neighbourhood competition from both Cuellar and Alto Tajo, Spain. Dryad https://doi.org/10.5061/dryad.0k6djhb0m (2021).
Hollaus, M. & Chen, Y.-C. SilviLaser 2021 Benchmark Dataset - Terrestrial Challenge. TU Wien https://doi.org/10.48436/afdjq-ce434 (2022).
Krishna Moorthy, S. M. et al. Datasets to quantify the impact of lianas on 3D tree structure and biomass. Zenodo https://doi.org/10.5281/zenodo.6981485 (2022).
Disney, M. et al. Data for: UK redwoods terrestrial laser scanner point clouds. Dryad https://doi.org/10.5061/dryad.ttdz08m3n (2023).
Wan, P., Zhang, W. & Jin, S. Plot-level wood-leaf separation for terrestrial laser scanning point clouds. Dryad https://doi.org/10.5061/dryad.rfj6q5799 (2021).
Sanne, V. D. B. et al. Terrestrial laser scanning - RIEGL VZ-1000, individual tree point clouds and cylinder models, Belgian hedgerows and tree rows. Zenodo https://doi.org/10.5281/zenodo.4487116 (2021).
Demol, M., Gielen, B. & Verbeeck, H. QSMs, point cloud and harvest data from a destructive forest biomass experiment in Belgium using terrestrial laser scanning. Zenodo https://doi.org/10.5281/zenodo.4557401 (2021).
Calders, K. Terrestrial laser scans - Riegl VZ400, individual tree point clouds and cylinder models, Rushworth Forest. Version 1. Terrestrial Ecosystem Research Network https://doi.org/10.4227/05/542B766D5D00D (2014).
Momo Takoudjou, S. et al. Data from: Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: a comparison with traditional destructive approach. Zenodo https://doi.org/10.5061/dryad.10hq7 (2018).
Gonzales de Tanago Menaca, J. et al. Data underlying the publication: ‘Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR’. 4TU.ResearchData https://doi.org/10.4121/21552084 (2022).
Acknowledgements
We gratefully acknowledge the Department of Science and Technology (DST) for their funding support, which made this research possible. We also extend our heartfelt thanks to the Haryana Forest Department for their invaluable assistance in facilitating fieldwork and logistical support. Additionally, we acknowledge the contributions of numerous individuals who provided field support and assistance during data collection. Their collective efforts were instrumental in the successful completion of this project. Geokno India Pvt. Ltd. captured the aerial LiDAR and photographic data and processed these. SimDaaS Autonomy Pvt. Ltd. supported the development of LiDAVerse. The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme.
Author information
Authors and Affiliations
Contributions
Conceptualization: M.A. and B.L.; Methodology: M.A.; Data Curation: M.A., A.B., A.I., S.K.; Supervision: B.L., V.K., M.H., N.P.; Writing – Original Draft: M.A.; Writing – Review and Editing: All authors.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Ali, M., Biswas, A., Iglseder, A. et al. Terrestrial and Airborne Laser Scanning Dataset of Trees in the Shivalik Range, India with Field Measurements and Leaf–Wood Classifications. Sci Data (2026). https://doi.org/10.1038/s41597-026-06674-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41597-026-06674-w
