Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics

References

• Asner, G. P., J. K. Clark, J. Mascaro, G. A. Galindo García, K. D. Chadwick, D. A. Navarrete Encinales, G. Paez-Acosta, et al. 2012. “High-Resolution Mapping of Forest Carbon Stocks in the Colombian Amazon.” Biogeosciences Discussions 9 (7): 2445–27. doi:10.5194/bg-9-2683-2012.
   Web of Science ®Google Scholar
• Asner, G. P., J. Mascaro, H. C. Muller-Landau, G. Vieilledent, R. Vaudry, M. Rasamoelina, J. S. Hall, and M. van Breugel. 2012. “A Universal Airborne LiDar Approach for Tropical Forest Carbon Mapping.” Oecologia 168 (4): 1147–1160. doi:10.1007/s00442-011-2165-z.
   PubMed Web of Science ®Google Scholar
• Bouvier, M., S. Durrieu, R. A. Fournier, and J. -P. Renaud. 2015. “Generalizing Predictive Models of Forest Inventory Attributes Using an Area-Based Approach with Airborne LiDar Data.” Remote Sensing of Environment 156: 322–334. doi:10.1016/j.rse.2014.10.004.
   Web of Science ®Google Scholar
• Cao, L., H. Liu, X. Fu, Z. Zhang, Z. Shen, and H. Ruan. 2019. “Comparison of UAV LiDar and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests.” Forests 10 (2): 145. doi:10.3390/f10020145.
   Web of Science ®Google Scholar
• Coomes, D. A., M. Dalponte, T. Jucker, G. P. Asner, L. F. Banin, D. F. R. P. Burslem, S. L. Lewis, et al. 2017. “Area-Based Vs Tree-Centric Approaches to Mapping Forest Carbon in Southeast Asian Forests from Airborne Laser Scanning Data.” Remote Sensing of Environment 194: 77–88. doi:10.1016/j.rse.2017.03.017.
   Web of Science ®Google Scholar
• Coops, N. C., P. Tompalski, T. R. H. Goodbody, M. Queinnec, J. E. Luther, D. K. Bolton, J. C. White, M. A. Wulder, O. R. van Lier, and T. Hermosilla. 2021. “Modelling Lidar-Derived Estimates of Forest Attributes Over Space and Time: A Review of Approaches and Future Trends.” Remote Sensing of Environment 260: 112477. doi:10.1016/j.rse.2021.112477.
   Web of Science ®Google Scholar
• Dalponte, M., C. Martinez, M. Rodeghiero, and D. Gianelle. 2011. “The Role of Ground Reference Data Collection in the Prediction of Stem Volume with LiDar Data in Mountain Areas.” Isprs Journal of Photogrammetry and Remote Sensing 66: 787–797. doi:10.1016/j.isprsjprs.2011.09.003.
   Web of Science ®Google Scholar
• da Silva, V. S., C. A. S. Ilva, M. Mohan, A. Cardil, F. E. Rex, G. H. Loureiro, D. R. A. de Almeida, et al. 2020. “Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus Spp. Forest Plantations Using Field and LiDar Data.” Remote Sensing 12 (9): 1438. doi:10.3390/rs12091438.
   Web of Science ®Google Scholar
• Detto, M., G. P. Asner, H. C. Muller-Landau, and O. Sonnentag. 2015. “Spatial Variability in Tropical Forest Leaf Area Density from Multireturn Lidar and Modeling.” Journal of Geophysical Research-Biogeoscience 120 (2): 294–309. doi:10.1002/2014JG002774.
   Web of Science ®Google Scholar
• Ene, L. T., E. Næsset, T. Gobakken, T. G. Gregoire, G. Ståhl, and R. Nelson. 2012. “Assessing the Accuracy of Regional LiDar-Based Biomass Estimation Using a Simulation Approach.” Remote Sensing of Environment 123: 579–592. doi:10.1016/j.rse.2012.04.017.
   Web of Science ®Google Scholar
• Fekety, P. A., M. J. Falkowski, and A. Hudak. 2015. “Temporal Transferability of LiDar-Based Imputation of Forest Inventory Attributes.” Canadian Journal of Forest Research 45: 422–435. doi:10.1139/cjfr-2014-0405.
   Web of Science ®Google Scholar
• Gajardo, J., M. García, and D. Riaño. 2014. “Chapter 22 Applications of Airborne Laser Scanning in Forest Fuel Assessment and Fire Prevention.” In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies, Managing Forest Ecosystems, edited by M. Maltamo, E. Næsset, and J. Vauhkonen, Vol. 27, 439–462. © Springer ScienceCBusiness Media Dordrecht. doi:10.1007/978-94-017-8663-81.
   Google Scholar
• Gao, Y., D. Lu, G. Li, G. Wang, Q. Chen, L. Liu, and D. Li. 2018. “Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region.” Remote Sensing 10 (4): 627. doi:10.3390/rs10040627.
   Web of Science ®Google Scholar
• García-Gutiérrez, J., E. Gonzalez-Ferreiro, J. C. Riquelme-Santos, D. Miranda, U. Dieguez-Aranda, and R. M. Navarro-Cerrillo. 2014. “Evolutionary Feature Selection to Estimate Forest Stand Variables Using LiDar.” International Journal of Applied Earth Observation and Geoinformation 26: 119–131. doi:10.1016/j.jag.2013.06.005.
   Web of Science ®Google Scholar
• García-Gutiérrez, J., F. Martínez-Álvarez, A. Troncoso, and J. C. Riquelme. 2015. “A Comparison of Machine Learning Regression Techniques for LiDar-Derived Estimation of Forest Variables.” Neurocomputing 167: 24–31. doi:10.1016/j.neucom.2014.09.091.
   Web of Science ®Google Scholar
• Getzin, S., R. Fischer, N. Knapp, and A. Huth. 2017. “Using Airborne LiDar to Assess Spatial Heterogeneity in Forest Structure on Mount Kilimanjaro.” Landscape Ecology 32: 1881–1894. doi:10.1007/s10980-017-0550-7.
   Web of Science ®Google Scholar
• Giannico, V., R. Lafortezza, R. John, G. Sanesi, L. Pesola, and J. Chen. 2016. “Estimating Stand Volume and Above-Ground Biomass of Urban Forests Using LiDar.” Remote Sensing 8: 339. doi:10.3390/rs8040339.
   Web of Science ®Google Scholar
• Gleason, C. J., and J. Im. 2012. “Forest Biomass Estimation from Airborne LiDar Data Using Machine Learning Approaches.” Remote Sensing of Environment 125: 80–91. doi:10.1016/j.rse.2012.07.006.
   Web of Science ®Google Scholar
• Gobakken, T., and E. Næsset. 2008. “Assessing Effects of Laser Point Density, Ground Sampling Intensity, and Field Sample Plot Size on Biophysical Stand Properties Derived from Airborne Laser Scanner Data.” Canadian Journal of Forest Research 38: 1095–1109. doi:10.1139/X07-219.
   Web of Science ®Google Scholar
• Görgens, E. B., P. Packalen, A. G. P. da Silva, C. A. Alvares, O. C. Campoe, J. L. Stape, and L. C. E. Rodriguez. 2015. “Stand Volume Models Based on Stable Metrics as from Multiple ALS Acquisitions in Eucalyptus Plantations.” Annals of Forest Science 72 (4): 489–498. doi:10.1007/s13595-015-0457-x.
   Web of Science ®Google Scholar
• Hamedianfar, A., C. Mohamedou, A. Kangas, and J. Vauhkonen. 2022. “Deep Learning for Forest Inventory and Planning: A critical Review on the Remote Sensing Approaches so Far and Prospects for Further Applications.” Forestry 95 (4): 451–465. doi:10.1093/forestry/cpac002.
   Google Scholar
• Hauglin, M., J. Rahlf, J. Schumacher, R. Astrup, and J. Breidenbach. 2021. “Large Scale Mapping of Forest Attributes Using Heterogeneous Sets of Airborne Laser Scanning and National Forest Inventory Data.” Forest Ecosystems 8: 65. doi:10.1186/s40663-021-00338-4.
   Web of Science ®Google Scholar
• Hawkins, D. M. 2004. “The Problem of Overfitting.” Journal of Chemical Information and Computer Sciences 44: 1–12. doi:10.1021/ci0342472.
   PubMedGoogle Scholar
• Hill, A., H. Buddenbaum, and D. Mandallaz. 2018. “Combining Canopy Height and Tree Species Map Information for Large‑scale Timber Volume Estimations Under Strong Heterogeneity of Auxiliary Data and Variable Sample Plot Sizes.” European Journal of Forest Research 137 (4): 489–505. doi:10.1007/s10342-018-1118-z.
   Web of Science ®Google Scholar
• Hollaus, M., W. Wagner, K. Schadauer, B. Maier, and K. Gabler. 2009. “Growing Stock Estimation for Alpine Forests in Austria: A Robust Lidar-Based Approach.” Canadian Journal of Forest Research 39: 1387–1400. doi:10.1139/X09-042.
   Web of Science ®Google Scholar
• Hopkinson, C., J. Lovell, L. Chasmer, D. Jupp, N. Kljun, and E. van Gorsel. 2013. “Integrating Terrestrial and Airborne Lidar to Calibrate a 3D Canopy Model of Effective Leaf Area Index.” Remote Sensing of Environment 136: 301–314. doi:10.1016/j.rse.2013.05.012.
   Web of Science ®Google Scholar
• Huang, Z., Z. Huang, C. Zeng, and Z. Huang. 1993. “The Annual Growth Rhythms of Tongmain Pine, a Geographic Seed Source of Pinus Massoniana.” Journal of Central-South Forestry College 13 (1): 98–102. in Chinese. doi:10.14067/j.cnki.1673-923x.1993.01.02.
   Google Scholar
• Huang, W., B. Zhou, J. Zhao, and C. Wang. 1996. “Study on Annual Growth Rhythm of Fir Stands with Different Silvicultural Density and Its Relationship with Meteorological Factors.” Forestry Science and Technology of Henan Province 2: 4–11. in Chinese.
   Google Scholar
• Hudak, A. T., N. L. Crookston, J. S. Evans, D. E. Hall, and M. J. Falkowski. 2008. “Nearest Neighbour Imputation of Species-Level, Plot-Scale Forest Structure Attributes from LiDar Data.” Remote Sensing of Environment 112 (5): 2232–2245. doi:10.1016/j.rse.2007.10.009.
   Web of Science ®Google Scholar
• Hu, R., G. Yan, F. Nerry, Y. Liu, Y. Jiang, S. Wang, Y. Chen, X. Mu, W. Zhang, and D. Xie. 2018. “Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index.” IEEE Transactions on Geoscience and Remote Sensing 56 (6): 3196–3209. doi:10.1109/TGRS.2018.2794504.
   Web of Science ®Google Scholar
• Kim, E., W. -K. Lee, M. Yoon, J. -Y. Lee, Y. Son, and K. A. Salim. 2016. “Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDar Data in an Intact Tropical Rain Forest, Brunei.” Forests 7: 259. doi:10.3390/f7110259.
   Web of Science ®Google Scholar
• Knapp, N., R. Fischer, V. Cazcarra-Bes, and A. Huth. 2020. “Structure Metrics to Generalize Biomass Estimation from Lidar Across Forest types from Different Continents.” Remote Sensing of Environment 237: 111597. doi:10.1016/j.rse.2019.111597.
   Web of Science ®Google Scholar
• Latifi, H., E. Fabian, F. E. Fassnacht, J. Müller, A. Tharani, S. Dech, and M. Heurich. 2015. “Forest Inventories by LiDar Data: A Comparison of Single Tree Segmentation and Metric-Based Methods for Inventories of a Heterogeneous Temperate Forest.” International Journal of Applied Earth Observation and Geoinformation 42: 162–174. doi:10.1016/j.jag.2015.06.008.
   Web of Science ®Google Scholar
• Lefsky, M. A., W. B. Cohen, and T. A. Spies. 2001. “An Evaluation of Alternate Remote Sensing Products for Forest Inventory, Monitoring, and Mapping of Douglas-Fir Forests in Western Oregon.” Canadian Journal of Forest Research 31 (1): 78–87. doi:10.1139/x00-142.
   Web of Science ®Google Scholar
• Liao, Z., and D. Huang. 1986. “Forest Inventory Handbook of Guangxi, China.” Pages 14-15. Forestry Department of Guangxi Zhuang Autonomous Region. Nanning, Guanxi, China. (in Chinese): Nanning.
   Google Scholar
• Li, C., and Z. Li. 2021. “Generalizing Predictive Models of Sub-Tropical Forest Inventory Attributes Using an Area-Based Approach with Airborne LiDar Data.” Scientia Silvae Sinicae 57: 23–35. In Chinese. doi:10.11707/j.1001-7488.20211003.
   Google Scholar
• Liu, H., X. Shen, L. Cao, T. Yun, Z. Zhang, X. Fu, X. Chen, and F. Liu. 2021. “Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDar Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14. doi:10.1109/JSTARS.2020.3046053.
   Web of Science ®Google Scholar
• Li, C., Z. Yu, X. Zhou, M. Zhou, and Z. Li. 2022. “Using the Error-In-Variable Simultaneous Equations Approach to Construct Compatible Estimation Models of Forest Inventory Attributes Based on Airborne LiDar.” Forests 14: 65. doi:10.3390/f14010065.
   Web of Science ®Google Scholar
• Lovell, J. L., D. L. Jupp, D. S. Culvenor, and N. C. Coops. 2003. “Using Airborne and Ground-Based Ranging Lidar to Measure Canopy Structure in Australian Forests.” Canadian Journal of Remote Sensing 29 (5): 607–622. doi:10.5589/m03-026.
   Web of Science ®Google Scholar
• Luo, S., J. M. Chen, C. Wang, A. Gonsamo, X. Xi, Y. Lin, M. Qian, et al. 2018. “Comparative Performances of Airborne LiDar Height and Intensity Data for Leaf Area Index Estimation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (1): 300–310. doi:10.1109/JSTARS.2017.2765890.
   Web of Science ®Google Scholar
• Matasci, G., T. Hermosilla, M. A. Wulder, J. C. White, N. G. Coops, G. W. Hobart, and H. S. J. Zald. 2018. “Large-Area Mapping of Canadian Boreal Forest Cover, Height, Biomass and Other Structural Attributes Using Landsat Composites and Lidar Plots.” Remote Sensing of Environment 209: 90–106. doi:10.1016/j.rse.2017.12.020.
   Web of Science ®Google Scholar
• Means, J. E., S. A. Acker, B. J. Fitt, M. Renslow, L. Emerson, and C. J. Hendrix. 2000. “Predicting Forest Stand Characteristics with Airborne Scanning Lidar.” Photogrammetric Engineering and Remote Sensing 66 (11): 1367–1371.
   Web of Science ®Google Scholar
• Millie, D. F., G. R. Weckman, W. A. Young, J. E. Ivey, H. J. Carrick, and G. L. Fahnenstiel. 2012. “Modeling Microalgal Abundance with Artificial Neural Networks: Demonstration of a Heuristic ‘Grey-Box’ to Deconvolve and Quantify Environmentalinfluences.” Environmental Modelling & Software 38: 27–39. doi:10.1016/j.envsoft.2012.04.009.
   Web of Science ®Google Scholar
• Montealegre, A. L., M. T. Lamelas, J. de la Riva, A. García-Martín, and F. Escribano. 2016. “Use of Low Point Density ALS Data to Estimate Stand-Level Structural Metrics in Mediterranean Aleppo Pine Forest.” Forestry 89 (4): 373–382. doi:10.1093/forestry/cpw008.
   Web of Science ®Google Scholar
• Næsset, E. 2014. “Chapter 11 Area-Based Based Inventory Inventory in Norway–From from Innovation Innovation to an Operational Operational Realityreality.” In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies, Managing Forest Ecosystems, edited by M. Maltamo, E. Næsset, and J. Vauhkonen, Vol. 27, 215–240. © Springer ScienceCBusiness Media Dordrecht. doi:10.1007/978-94-017-8663-81.
   Google Scholar
• Næsset, E., O. M. Bollandsas, T. Gobakken, and O. M. Bollandsá. 2005. “Comparing Regression Methods in Estimation of Biophysical Properties of Forest Stands from Two Different Inventories Using Laser Scanner Data.” Remote Sensing of Environment 94 (4): 541–553. doi:10.1016/j.rse.2004.11.010.
   Web of Science ®Google Scholar
• Næsset, E., T. Gobakken, J. Holmgren, H. Hyyppä, J. Hyyppä, M. Maltamo, M. Nilsson, H. Olsson, Å. Persson, and U. Söderman. 2004. “Laser Scanning of Forest Resources: The Nordic Experience.” Scandinavian Journal of Forest Research 19 (6): 482–499. doi:10.1080/02827580410019553.
   Web of Science ®Google Scholar
• Nilsson, M., K. Nordkvist, J. Jonzén, N. Lindgren, P. Axensten, J. Wallerman, M. Egberth, et al. 2017. “A Nationwide Forest Attribute Map of Sweden Predicted Using Airborne Laser Scanning Data and Field Data from the National Forest Inventory.” Remote Sensing of Environment 194: 447–454. doi:10.1016/j.rse.2016.10.022.
   Web of Science ®Google Scholar
• Nord-Larsen, T., and J. Schumacher. 2012. “Estimation of Forest Resources from a Country Wide Laser Scanning Survey and National Forest Inventory Data.” Remote Sensing of Environment 119: 148–157. doi:10.1016/j.rse.2011.12.022.
   Web of Science ®Google Scholar
• Novo-Fernández, A., M. Barrio-Anta, C. Recondo, A. Cámara-Obregón, and C. A. López-Sánchez. 2019. “Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain.” Remote Sensing 11: 1693. doi:10.3390/rs11141693.
   Web of Science ®Google Scholar
• Pearse, G. D., J. Morgenroth, M. S. Watt, and J. P. Dash. 2017. “Optimising Prediction of Forest Leaf Area Index from Discrete Airborne Lidar.” Remote Sensing of Environment 200: 220–239. doi:10.1016/j.rse.2017.08.002.
   Web of Science ®Google Scholar
• Penner, M., D. G. Pitt, and M. E. Woods. 2013. “Parametric Vs. Nonparametric LiDar Models for Operational Forest Inventory in Boreal Ontario.” Canadian Journal of Remote Sensing 39 (5): 426–443. doi:10.5589/m13-049.
   Web of Science ®Google Scholar
• Sheridan, R. D., S. C. Popescu, D. Gatziolis, C. L. S. Morgan, and N. -W. Ku. 2015. “Modeling Forest Aboveground Biomass and Volume Using Airborne LiDar Metrics and Forest Inventory and Analysis Data in the Pacific Northwest.” Remote Sensing 7 (1): 229–255. doi:10.3390/rs70100229.
   Web of Science ®Google Scholar
• Silva, C. A., A. T. Hudak, C. Klauberg, L. A. Vierling, C. Gonzalez‑benecke, S. de Padua Chaves Carvalho, L. C. E. Rodrihue, and A. Cardil. 2017. “Combined Effect of Pulse Density and Grid Cell Size on Predicting and Mapping Aboveground Carbon in Fast‑growing Eucalyptus Forest Plantation Using Airborne LiDar Data.” Carbon Balance and Management 12 (1): 13. doi:10.1186/s13021-017-0081-1.
   PubMedGoogle Scholar
• Silva, C. A., C. Klauberg, A. T. Hubdak, L. A. Vierling, S. J. Fennema, and P. D. Corte. 2017. “Modeling and Mapping Basal Area of Pinus Taeda L. Plantation Using Airborne LiDar Data.” Anais da Academia Brasileira de Ciencias 89 (3): 1895–1905. doi:10.1590/0001-3765201720160324.
   PubMed Web of Science ®Google Scholar
• Singh, K. K., G. Chen, J. B. McCarter, and R. K. Meentemeyer. 2015. “Effects of LiDar Point Density and Landscape Context on Estimates of Urban Forest Biomass.” Isprs Journal of Photogrammetry and Remote Sensing 101: 310–322. doi:10.1016/j.isprsjprs.2014.12.021.
   Web of Science ®Google Scholar
• Treitz, P., K. Lim, M. Woods, D. Pitt, D. Nesbitt, and D. Etheridge. 2012. “LiDar Sampling Density for Forest Resource Inventories in Ontario, Canada.” Remote Sensing 4: 830–848. doi:10.3390/rs4040830.
   Web of Science ®Google Scholar
• van Ewijk, K., P. Treitz, M. Woods, T. Jones, and J. Caspersen. 2019. “Forest Site and Type Variability in ALS-Based Forest Resource Inventory Attribute Predictions Over Three Ontario Forest Sites.” Forests 10: 226. doi:10.3390/f10030226.
   Web of Science ®Google Scholar
• Vauhkonen, J., M. Maltamo, R. E. McRoberts, and E. Næsset. 2014. “Introduction to Forestry Applications of Airborne Laser Scanning.” In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies, Managing Forest Ecosystems, edited by M. Maltamo, E. Næsset, and J. Vauhkonen, 27. © Springer ScienceCBusiness Media Dordrecht. doi:10.1007/978-94-017-8663-81.
   Google Scholar
• Watt, M. S., J. P. Dash, S. Bhandari, and P. Watt. 2015. “Comparing Parametric and Nonparametric Methods of Predicting Site Index for Radiata Pine Using Combinations of Data Derived from Environmental Surfaces, Satellite Imagery and Airborne Laser Scanning.” Forest Ecology and Management 357: 1–9. doi:10.1016/j.foreco.2015.08.001.
   Web of Science ®Google Scholar
• Watt, P., and M. S. Watt. 2013. “Development of a National Model of Pinus Radiate Stand Volume from Lidar Metrics for New Zealand.” International Journal of Remote Sensing 34 (15–16): 5892–5904. doi:10.1080/01431161.2013.798053.
   Web of Science ®Google Scholar
• White, J. C., P. Tompalski, M. Vastaranta, M. A. Wulder, N. Saarinen, C. Stepper, and N. C. Coops. 2017. A Model Development and Application Guide for Generating an Enhanced Forest Inventory Using Airborne Laser Scanning Data and an Area-Based Approach CWFC Information Report FI-X-018. Victoria, BC, Canada: Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre. doi:10.13140/rg.2.2.26770.96964.
   Google Scholar
• Woods, M., K. Lim, and P. Treitz. 2008. “Predicting Forest Stand Variables from LiDar Data in the Great Lakes – St. Lawrence Forest of Ontario.” Forestry Chronicle 84 (6): 827–839. doi:10.5558/tfc84827-6.
   Web of Science ®Google Scholar
• Xu, G., B. Manley, and J. Morgenroth. 2018. “Evaluation of Modelling Approaches in Predicting Forest Volume and Stand Age for Small-Scale Plantation Forests in New Zealand with RapidEye and LiDar.” International Journal of Applied Earth Observation and Geoinformation 73: 386–396. doi:10.1016/j.jag.2018.06.021.
   Web of Science ®Google Scholar
• Yu, Z., C. -G. Li, K. Su, X. -B. Zhou, and Z. -C. Chen. 2022. “Estimation of Forest Inventory Attributes Based on Stratification of Vertical Forest Structure Using Airborne Laser Scanning Data.” Journal of Guilin University of Technology (in Chinese). https://kns.cnki.net/kcms/detail/45.1375.n.20220429.1213.002.html.
   Google Scholar
• Zaffalon, M. 2005. “Credible Classification for Environmental Problems.” Environmental Modelling and Software 20 (8): 1003–1012. doi:10.1016/j.envsoft.2004.10.006.
   Web of Science ®Google Scholar
• Zhang, Z., L. Cao, and G. She. 2017. “Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDar Data in Subtropical Forests.” Remote Sensing 9: 940. doi:10.3390/rs9090940.
   Web of Science ®Google Scholar
• Zhao, K., J. C. Suarez, M. Garcia, T. Hu, C. Wang, and A. Londo. 2017. “Utility of Multitemporal Lidar for Forest and Carbon Monitoring: Tree Growth, Biomass Dynamics, and Carbon Flux.” Remote Sensing of Environment 204: 883–897. doi:10.1016/j.rse.2017.09.007.
   Web of Science ®Google Scholar
• Zhou, X., C. Li, Z. Yu, Z. Chen, and K. Su. 2022. “Classification of Vertical Forest Structure of Overstory in Subtropical Forest Using Airborne Lidar Data.” Forest Resources Management 1: 106–113. in Chinese. doi:10.13466/j.cnki.lyzygl.2022.01.013.
   Google Scholar
• Zolkos, S. G., S. J. Goetz, and R. Dubayah. 2013. “A Meta-Analysis of Terrestrial Aboveground Biomass Estimation Using Lidar Remote Sensing.” Remote Sensing of Environment 128: 128 289–298. doi:10.1016/j.rse.2012.10.017.
   Web of Science ®Google Scholar