Aboveground forest biomass estimation at the individual tree level using terrestrial LiDAR
DOI:
https://doi.org/10.29298/rmcf.v16i89.1542Keywords:
Biomass, TLS, LiDAR, Point Cloud, DAPAbstract
Forest ecosystems play a key role in carbon storage, highlighting the importance of accurately estimating the tree biomass. The objective was to estimate the forest biomass using a laser scanner (LiDAR, Light Detection and Ranging), specifically a terrestrial device (TLS, Terrestrial Laser Scanner), at the individual tree level. Thirty-one trees were selected from a Pinus cooperi regular stand, whose diameter at breast height (DBH) and height (h) variables were measured in a traditional way. TLS data were collected with a model Focus M70 FARO® laser scanner and processed to three-dimensionally model the logs and calculate their biomass. These data were compared with estimates obtained by allometric equations and traditional measurements. Results indicate that the TLS is accurate in measuring diameters (R2=0.72 and RMSE=1.28 cm), compared to traditional methods. However, it underestimates the tree height (R2=0.79 and RMSE=1.68 m), affecting the accuracy of the biomass calculation. Although the TLS provided acceptable estimates, these were lower than those obtained using allometric equations. In conclusion, TLS is a promising tool for nondestructive biomass studies. Future work should consider in greater detail the influence of the characteristics of the studied area, the scanning methodology, and the algorithms applied in the estimation of biomass.
Downloads
References
Ashraf, M. I., Zhao, Z., Bourque, C. P.-A., MacLean, D. A., & Meng, F.-R. (2013). Integrating biophysical controls in forest growth and yield predictions with artificial intelligence technology. Canadian Journal of Forest Research, 43(12), 1162-1171. https://doi.org/10.1139/cjfr-2013-0090 DOI: https://doi.org/10.1139/cjfr-2013-0090
Bhandari, S. K., & Nandy, S. (2024). Forest aboveground biomass prediction by integrating terrestrial laser scanning data, Landsat 8 OLI-derived forest canopy density and spectral indices. Journal of the Indian Society of Remote Sensing, 52, 813-824. https://doi.org/10.1007/s12524-023-01687-z DOI: https://doi.org/10.1007/s12524-023-01687-z
Bornand, A., Rehush, N., Morsdorf, F., Thürig, E., & Abegg, M. (2023). Individual tree volume estimation with terrestrial laser scanning: Evaluating reconstructive and allometric approaches. Agricultural and Forest Meteorology, 341, Article 109654. https://doi.org/10.1016/j.agrformet.2023.109654 DOI: https://doi.org/10.1016/j.agrformet.2023.109654
Borsah, A. A., Nazeer, M., & Wong, M. S. (2023). LIDAR-based forest biomass remote sensing: A review of metrics, methods, and assessment criteria for the selection of allometric equations. Forests, 14(10), 2095. https://doi.org/10.3390/f14102095 DOI: https://doi.org/10.3390/f14102095
Cabo, C., Ordóñez, C., López-Sánchez, C. A., & Armesto, J. (2018). Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 69, 164-174. https://doi.org/10.1016/j.jag.2018.01.011 DOI: https://doi.org/10.1016/j.jag.2018.01.011
Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., Culvenor, D., Avitabile, V., Disney, M., Armston, J., & Kaasalainen, M. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6(2), 198-208. https://doi.org/10.1111/2041-210X.12301 DOI: https://doi.org/10.1111/2041-210X.12301
Chojnacky, D. C., Heath, L. S., & Jenkins, J. C. (2014). Updated generalized biomass equations for North American tree species. Forestry: An International Journal of Forest Research, 87(1), 129-151. https://doi.org/10.1093/forestry/cpt053 DOI: https://doi.org/10.1093/forestry/cpt053
CloudCompare. (2024). CloudCompare project (version 2.13) [Software]. CloudCompare. https://www.cloudcompare.org/
Compeán-Aguirre, J. L., & López-Serrano, P. M. (2024). Bibliometric insights into terrestrial laser scanning for forest biomass estimation. Ecologies, 5(3), 470-490. https://doi.org/10.3390/ecologies5030029 DOI: https://doi.org/10.3390/ecologies5030029
Compeán-Aguirre, J. L., López-Serrano, P. M., Silván-Cárdenas, J. L., Martínez-García-Moreno, C. A., Vega-Nieva, D. J., Corral-Rivas, J. J., & Pompa-García, M. (2024). Evaluation of two-dimensional DBH estimation algorithms using TLS. Forests, 15(11), 1964. https://doi.org/10.3390/f15111964 DOI: https://doi.org/10.3390/f15111964
De Petris, S., Sarvia, F., & Borgogno-Mondino, E. (2022). About tree height measurement: Theoretical and practical issues for uncertainty quantification and mapping. Forests, 13(7), 969. https://doi.org/10.3390/f13070969 DOI: https://doi.org/10.3390/f13070969
Dinno, A. (2024). dunn.test: Dunn's test of multiple comparisons using rank sums (version 1.3.6) [Software]. CRAN. https://doi.org/10.32614/CRAN.package.dunn.test DOI: https://doi.org/10.2307/1266041
Disney, M. I., Vicari, M. B., Burt, A., Calders, K., Lewis, S. L., Raumonen, P., & Wilkes, P. (2018). Weighing trees with lasers: Advances, challenges and opportunities. Interface Focus, 8, Article 20170048. https://doi.org/10.1098/rsfs.2017.0048 DOI: https://doi.org/10.1098/rsfs.2017.0048
FARO. (2019). FARO® SCENE Software (versión 5.5.3.16) [Software]. FARO. https://www.faro.com/es-MX/Products/Software/SCENE-Software
Galeote-Leyva, B., Valdez-Lazalde, J. R., Ángeles-Pérez, G., de los Santos-Posadas, H. M., y Romero-Padilla, J. M. (2022). Inventario forestal asistido por LIDAR: efecto de la densidad de retornos y el diseño de muestreo sobre la precisión. Madera y Bosques, 28(2), Artículo e2822330. https://doi.org/10.21829/myb.2022.2822330 DOI: https://doi.org/10.21829/myb.2022.2822330
García, E. (2004). Modificaciones al sistema de clasificación climática de Köppen. Instituto de Geografía de la Universidad Nacional Autónoma de México. https://publicaciones.geografia.unam.mx/index.php/ig/catalog/book/83
Haynes, W. (2013). Bonferroni correction. In W. Dubitzky, O. Wolkenhauer, K.-H. Cho & H. Yokota (Eds.), Encyclopedia of Systems Biology (p. 154). Springer. https://doi.org/10.1007/978-1-4419-9863-7_1213 DOI: https://doi.org/10.1007/978-1-4419-9863-7_1213
Hernández-Moreno, J. A., Pérez-Salicrup, D. R., y Velázquez-Martínez, A. (2025a). Medición de parámetros de inventario forestal en bosques plantados mediante tecnología LiDAR: Comparación de métodos. Revista Mexicana de Ciencias Forestales, 16(87), 72-99. https://doi.org/10.29298/rmcf.v16i87.1488 DOI: https://doi.org/10.29298/rmcf.v16i87.1488
Hernández-Moreno, J. A., Velázquez-Martínez, A., Pérez-Salicrup, D. R., Bravo, F., MacFarlane, D. W., & Reyes-Hernández, V. J. (2025b). Terrestrial laser scanning for estimating the volume and biomass of coniferous stems in the Mariposa Monarca Biosphere Reserve, Mexico. Forests, 16(2), 334. https://doi.org/10.3390/f16020334 DOI: https://doi.org/10.3390/f16020334
Hoover, C. M., & Smith, J. E. (2020). Selecting a minimum diameter for forest biomass and carbon estimation: How low should you go? (General Technical Report NRS-196). United States Department of Agriculture, Forest Service. https://www.fs.usda.gov/nrs/pubs/gtr/gtr_nrs196.pdf DOI: https://doi.org/10.2737/NRS-GTR-196
Hummel, F. C., Locke, G. M. L., Jeffers, J. N. R., & Christie, J. M. (1959). Code of sample plot procedure (Forestry Commission Bulletin No. 31). Forestry Commission. https://cdn.forestresearch.gov.uk/1959/04/fcbu031.pdf
Huy, B., Kralicek, K., Poudel, K. P., Phuong, V. T., Khoa, P. V., Hung, N. D., & Temesgen, H. (2016). Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam. Forest Ecology and Management, 382, 193-205. https://doi.org/10.1016/j.foreco.2016.10.021 DOI: https://doi.org/10.1016/j.foreco.2016.10.021
Islas-Gutiérrez, F., Cruz-Juárez, E., Buendía-Rodríguez, E., Guerra-De la Cruz, V., Pineda-Ojeda, T., Flores-Ayala, E., Carrillo-Anzures, F., y Acosta-Mireles, M. (2024). Ecuación alométrica para estimar biomasa aérea de árboles de Pinus hartwegii Lindl. a partir de datos LiDAR. Revista Fitotecnia Mexicana, 47(1), 70-79. https://doi.org/10.35196/rfm.2024.1.70 DOI: https://doi.org/10.35196/rfm.2024.1.70
Kaitaniemi, P., Lintunen, A., & Sievänen, R. (2020). Power-law estimation of branch growth. Ecological Modelling, 416, Article 108900. https://doi.org/10.1016/j.ecolmodel.2019.108900 DOI: https://doi.org/10.1016/j.ecolmodel.2019.108900
Kankare, V., Holopainen, M., Vastaranta, M., Puttonen, E., Yu, X., Hyyppä, J., Vaaja, M., Hyyppä, H., & Alho, P. (2013). 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 DOI: https://doi.org/10.1016/j.isprsjprs.2012.10.003
Krause, P., Forbes, B., Barajas-Ritchie, A., Clark, M., Disney, M., Wilkes, P., & Bentley, L. P. (2023). Using terrestrial laser scanning to evaluate non-destructive aboveground biomass allometries in diverse Northern California forests. Frontiers in Remote Sensing, 4, Article 1132208. https://doi.org/10.3389/frsen.2023.1132208 DOI: https://doi.org/10.3389/frsen.2023.1132208
Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., & Mathys, L. (2021). Above-ground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 128(6), 709-724. https://doi.org/10.1093/aob/mcab002 DOI: https://doi.org/10.1093/aob/mcab002
Liang, X., Hyyppä, J., Kaartinen, H., Lehtomäki, M., Pyörälä, J., Pfeifer, N., Holopainen, M., Brolly, G., Francesco, P., Hackenberg, J., Huang, H., Jo, H.-W., Katoh, M., Liu, L., Mokroš, M., Morel, J., Olofsson, K., Poveda-Lopez, J., Trochta, J., ... Wang, Y. (2018). International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 137-179. https://doi.org/10.1016/j.isprsjprs.2018.06.021 DOI: https://doi.org/10.1016/j.isprsjprs.2018.06.021
Liu, G., Wang, J., Dong, P., Chen, Y., & Liu, Z. (2018). Estimating individual tree height and Diameter at Breast Height (DBH) from Terrestrial Laser Scanning (TLS) data at plot level. Forests, 9(7), 398. https://doi.org/10.3390/f9070398 DOI: https://doi.org/10.3390/f9070398
Montoya, O., Icasio-Hernández, O., & Salas, J. (2021). TreeTool: A tool for detecting trees and estimating their DBH using forest point clouds. SoftwareX, 16, Article 100889. https://doi.org/10.1016/j.softx.2021.100889 DOI: https://doi.org/10.1016/j.softx.2021.100889
Návar, J. (2009). Allometric equations for tree species and carbon stocks for forests of Northwestern Mexico. Forest Ecology and Management, 257(2), 427-434. https://doi.org/10.1016/j.foreco.2008.09.028 DOI: https://doi.org/10.1016/j.foreco.2008.09.028
Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308-313. https://doi.org/10.1093/comjnl/7.4.308 DOI: https://doi.org/10.1093/comjnl/7.4.308
Ortiz-Reyes, A. D., Valdez-Lazalde, J. R., Ángeles-Pérez, G., De los Santos-Posadas, H. M., Schneider, L., Aguirre-Salado, C. A., y Peduzzi, A. (2019). Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales. Madera y Bosques, 25(3), Artículo e2531872. https://doi.org/10.21829/myb.2019.2531872 DOI: https://doi.org/10.21829/myb.2019.2531872
Ortiz-Reyes, A. D., Velasco-Bautista, E., Correa-Díaz, A., y Ángeles-Pérez, G. (2022). Predicción de variables dasométricas mediante modelos lineales mixtos y datos de LiDAR aerotransportado. e-CUCBA, 9(17), 88-95. https://www.researchgate.net/publication/360011682_Prediccion_de_variables_dasometricas_mediante_modelos_lineales_mixtos_y_datos_de_LiDAR_aerotransportado DOI: https://doi.org/10.32870/ecucba.vi17.213
Pitkänen, T. P., Raumonen, P., & Kangas, A. (2019). Measuring stem diameters with TLS in boreal forests by complementary fitting procedure. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 294-306. https://doi.org/10.1016/j.isprsjprs.2018.11.027 DOI: https://doi.org/10.1016/j.isprsjprs.2018.11.027
R Core Team. (2024). R: A Language and Environment for Statistical Computing (version 4.4.1) [Software]. R Foundation for Statistical Computing. https://www.R-project.org/
RStudio Team. (2024). RStudio (version 2023.12.1 Build 402) [Software]. Posit Software. https://posit.co/products/open-source/rstudio/
Segura, M., y Andrade, H. J. (2008). ¿Cómo construir modelos alométricos de volumen, biomasa o carbono de especies leñosas perennes? Agroforestería en las Américas, 46, 89-96. https://repositorio.catie.ac.cr/handle/11554/6935
Silva-Arredondo, F. M., y Návar-Cháidez, J. de J. (2012). Estimación de la densidad de madera en árboles de comunidades forestales templadas del norte del estado de Durango, México. Madera y Bosques, 18(1), 77-88. https://doi.org/10.21829/myb.2012.1811139 DOI: https://doi.org/10.21829/myb.2012.1811139
Tan, K., Zhang, W., Shen, F., & Cheng, X. (2018). Investigation of TLS intensity data and distance measurement errors from target specular reflections. Remote Sensing, 10(7), 1077. https://doi.org/10.3390/rs10071077 DOI: https://doi.org/10.3390/rs10071077
Umbach, D., & Jones, K. N. (2003). A few methods for fitting circles to data. IEEE Transactions on Instrumentation and Measurement, 52(6), 1881-1885. https://doi.org/10.1109/TIM.2003.820472 DOI: https://doi.org/10.1109/TIM.2003.820472
Vargas-Larreta, B., López-Sánchez, C. A., Corral-Rivas, J. J., López-Martínez, J. O., Aguirre-Calderón, C. G., & Álvarez-González, J. G. (2017). Allometric equations for estimating biomass and carbon stocks in the temperate forests of North-Western Mexico. Forests, 8(8), 269. https://doi.org/10.3390/f8080269 DOI: https://doi.org/10.3390/f8080269
Wang, F., Sun, Y., Jia, W., Zhu, W., Li, D., Zhang, X., Tang, Y., & Guo, H. (2023). Development of estimation models for individual tree aboveground biomass based on TLS-derived parameters. Forests, 14(2), 351. https://doi.org/10.3390/f14020351 DOI: https://doi.org/10.3390/f14020351
Wu, Y., Gan, X., Zhou, Y., & Yuan, X. (2024). Estimation of Diameter at Breast Height in tropical forests based on Terrestrial Laser Scanning and shape diameter function. Sustainability, 16(6), 2275. https://doi.org/10.3390/su16062275 DOI: https://doi.org/10.3390/su16062275
Ye, W. F., Qian, C., Tang, J., Liu, H., Fan, X. Y., Liang, X., & Zhang, H. J. (2020). Improved 3D stem mapping method and elliptic hypothesis-based DBH estimation from terrestrial laser scanning data. Remote Sensing, 12(3), 352. https://doi.org/10.3390/rs12030352 DOI: https://doi.org/10.3390/rs12030352

Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Revista Mexicana de Ciencias Forestales

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who publish in Revista Mexicana de Ciencias Forestales accept the following conditions:
In accordance with copyright laws, Revista Mexicana de Ciencias Forestales recognizes and respects the authors’ moral right and ownership of property rights which will be transferred to the journal for dissemination in open access.
All the texts published by Revista Mexicana de Ciencias Forestales –with no exception– are distributed under a Creative Commons License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which allows third parties to use the publication as long as the work’s authorship and its first publication in this journal are mentioned
The author(s) can enter into independent and additional contractual agreements for the nonexclusive distribution of the version of the article published in Revista Mexicana de Ciencias Forestales (for example, include it into an institutional repository or publish it in a book) as long as it is clearly and explicitly indicated that the work was published for the first time in Revista Mexicana de Ciencias Forestales.
For all the above, the authors shall send the form of Letter-transfer of Property Rights for the first publication duly filled in and signed by the author(s). This form must be sent as a PDF file to: ciencia.forestal2@inifap.gob.mx
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 International license.