Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data
Yuling Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Haitao Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Zekun Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Qiuli Yang
College of Geography and Remote Sensing Science, Xinjiang University, Ürümqi 800017, China
Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Ürümqi 830017, China
Weiyan Liu
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Guoran Huang
College of Forestry, Southwest Forestry University, Kunming 650224, China
Yu Ren
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Kai Cheng
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Tianyu Xiang
College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Mengxi Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Danyang Lin
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Zhiyong Qi
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Jiachen Xu
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Yixuan Zhang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Guangcai Xu
Beijing GreenValley Technology Co., Ltd., Haidian, Beijing 100091, China
Qinghua Guo
CORRESPONDING AUTHOR
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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Cited articles
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, https://doi.org/10.1145/3292500.3330701, 2019.
Allard, D.: J.-P. Chilès, P. Delfiner: Geostatistics: Modeling Spatial Uncertainty, Math. Geosci., 45, 377–380, https://doi.org/10.1007/s11004-012-9429-y, 2013.
Bouvier, M., Durrieu, S., Fournier, R. A., and Renaud, J.-P.: Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data, Remote Sens. Environ., 156, 322–334, https://doi.org/10.1016/j.rse.2014.10.004, 2015.
Cheng, K., Chen, Y., Xiang, T., Yang, H., Liu, W., Ren, Y., Guan, H., Hu, T., Ma, Q., and Guo, Q.: A 2020 forest age map for China with 30 m resolution, Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, 2024a.
Cheng, K., Yang, H., Guan, H., Ren, Y., Chen, Y., Chen, M., Yang, Z., Lin, D., Liu, W., Xu, J., Xu, G., Ma, K., and Guo, Q.: Unveiling China's natural and planted forest spatial–temporal dynamics from 1990 to 2020, ISPRS J. Photogramm., 209, 37–50, https://doi.org/10.1016/j.isprsjprs.2024.01.024, 2024b.
Chen, Y., Yang, H., Yang, Z., Yang, Q., Liu, W., Huang, G., Ren, Y., Cheng, K., Xiang, T., Chen, M., Lin, D., Qi, Z., Xu, J., Zhang, Y., Xu, G., and Guo, Q.: Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.12697784, 2024.
Choi, S., McMaster, K. L., Kohli, N., Shanahan, E., Birinci, S., An, J., Duesenberg-Marshall, M., and Lembke, E. S.: Longitudinal effects of data-based instructional changes for students with intensive learning needs: A piecewise linear–linear mixed-effects modeling approach, J. Educ. Psychol., 116, 608–628, https://doi.org/10.1037/edu0000853, 2024.
Coops, N. C., Tompalski, P., Goodbody, T. R. H., Queinnec, M., Luther, J. E., Bolton, D. K., White, J. C., Wulder, M. A., van Lier, O. R., and Hermosilla, T.: Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends, Remote Sens. Environ., 260, 112477, https://doi.org/10.1016/j.rse.2021.112477, 2021.
Davies, A. B., Ancrenaz, M., Oram, F., and Asner, G. P.: Canopy structure drives orangutan habitat selection in disturbed Bornean forests, P. Natl. Acad. Sci. USA, 114, 8307–8312, https://doi.org/10.1073/pnas.1706780114, 2017.
Demidenko, E.: Mixed models: theory and applications with R, 2nd Edn., John Wiley & Sons, ISBN 978-1-118-09157-9, 2013.
Donoghue, D. N. M. and Watt, P. J.: Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests, Int. J. Remote Sens., 27, 2161–2175, https://doi.org/10.1080/01431160500396493, 2006.
Duncanson, L., Kellner, J. R., Armston, J., Dubayah, R., Minor, D. M., Hancock, S., Healey, S. P., Patterson, P. L., Saarela, S., Marselis, S., Silva, C. E., Bruening, J., Goetz, S. J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., Alonso, A., Andersen, H.-E., Aplin, P., Baker, T. R., Barbier, N., Bastin, J. F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Boucher, P. B., Boyd, D. S., Burslem, D. F. R. P., Calvo-Rodriguez, S., Chave, J., Chazdon, R. L., Clark, D. B., Clark, D. A., Cohen, W. B., Coomes, D. A., Corona, P., Cushman, K. C., Cutler, M. E. J., Dalling, J. W., Dalponte, M., Dash, J., de-Miguel, S., Deng, S., Ellis, P. W., Erasmus, B., Fekety, P. A., Fernandez-Landa, A., Ferraz, A., Fischer, R., Fisher, A. G., García-Abril, A., Gobakken, T., Hacker, J. M., Heurich, M., Hill, R. A., Hopkinson, C., Huang, H., Hubbell, S. P., Hudak, A. T., Huth, A., Imbach, B., Jeffery, K. J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, N., Král, K., Krůček, M., Labrière, N., Lewis, S. L., Longo, M., Lucas, R. M., Main, R., Manzanera, J. A., Martínez, R. V., Mathieu, R., Memiaghe, H., Meyer, V., Mendoza, A. M., Monerris, A., Montesano, P., Morsdorf, F., Næsset, E., Naidoo, L., Nilus, R., O'Brien, M., Orwig, D. A., Papathanassiou, K., Parker, G., Philipson, C., Phillips, O. L., Pisek, J., Poulsen, J. R., Pretzsch, H., Rüdiger, C., Saatchi, S., Sanchez-Azofeifa, A., Sanchez-Lopez, N., Scholes, R., Silva, C. A., Simard, M., Skidmore, A., Stereńczak, K., Tanase, M., Torresan, C., Valbuena, R., Verbeeck, H., Vrska, T., Wessels, K., White, J. C., White, L. J. T., Zahabu, E., and Zgraggen, C.: Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission, Remote Sens. Environ., 270, 112845, https://doi.org/10.1016/j.rse.2021.112845, 2022.
Fang, J., Chen, A., Peng, C., Zhao, S., and Ci, L.: Changes in Forest Biomass Carbon Storage in China Between 1949 and 1998, Science, 292, 2320–2322, https://doi.org/10.1126/science.1058629, 2001.
Fang, J., Brown, S., Tang, Y., Nabuurs, G.-J., Wang, X., and Shen, H.: Overestimated Biomass Carbon Pools of the Northern mid- and High Latitude Forests, Climatic Change, 74, 355–368, https://doi.org/10.1007/s10584-005-9028-8, 2006.
Fayad, I., Ciais, P., Schwartz, M., Wigneron, J.-P., Baghdadi, N., de Truchis, A., d'Aspremont, A., Frappart, F., Saatchi, S., Sean, E., Pellissier-Tanon, A., and Bazzi, H.: Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height, Remote Sens. Environ., 302, 113945, https://doi.org/10.1016/j.rse.2023.113945, 2024.
Guo, Q., Su, Y., Hu, T., Guan, H., Jin, S., Zhang, J., Zhao, X., Xu, K., Wei, D., Kelly, M., and Coops, N. C.: Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective, IEEE Geosci. Remote, 9, 232–257, https://doi.org/10.1109/MGRS.2020.3032713, 2021.
Guo, Q., Su, Y., Hu, T., Zhao, X., Wu, F., Li, Y., Liu, J., Chen, L., Xu, G., Lin, G., Zheng, Y., Lin, Y., Mi, X., Fei, L., and Wang, X.: An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China, Int. J. Remote Sens., 38, 2954–2972, https://doi.org/10.1080/01431161.2017.1285083, 2017.
Hall, R. J., Skakun, R. S., Arsenault, E. J., and Case, B. S.: Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume, Forest Ecol. Manag., 225, 378–390, https://doi.org/10.1016/j.foreco.2006.01.014, 2006.
Hu, J. and Szymczak, S.: A review on longitudinal data analysis with random forest, Brief Bioinform., 24, bbad002, https://doi.org/10.1093/bib/bbad002, 2023.
Hu, T., Sun, X., Su, Y., Guan, H., Sun, Q., Kelly, M., and Guo, Q.: Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications, Remote Sens., 13, 77, https://doi.org/10.3390/rs13010077, 2021.
Huang, H., Liu, C., Wang, X., Biging, G. S., Chen, Y., Yang, J., and Gong, P.: Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data, ISPRS J Photogramm., 129, 189–199, https://doi.org/10.1016/j.isprsjprs.2017.04.020, 2017.
Huo, L., Lindberg, E., and Holmgren, J.: Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD), Remote Sens. Environ., 270, 112857, https://doi.org/10.1016/j.rse.2021.112857, 2022.
Jensen, J. L. R. and Mathews, A. J.: Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem, Remote Sens., 8, 50, https://doi.org/10.3390/rs8010050, 2016.
Jucker, T., Hardwick, S. R., Both, S., Elias, D. M. O., Ewers, R. M., Milodowski, D. T., Swinfield, T., and Coomes, D. A.: Canopy structure and topography jointly constrain the microclimate of human-modified tropical landscapes, Glob. Chang Biol., 24, 5243–5258, https://doi.org/10.1111/gcb.14415, 2018.
Jurjević, L., Liang, X., Gašparović, M., and Balenović, I.: Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest, ISPRS J. Photogramm., 169, 227–241, https://doi.org/10.1016/j.isprsjprs.2020.09.014, 2020.
Kwong, I. H. Y. and Fung, T.: Tree height mapping and crown delineation using LiDAR, large format aerial photographs, and unmanned aerial vehicle photogrammetry in subtropical urban forest, Int. J. Remote Sens., 41, 5228–5256, https://doi.org/10.1080/01431161.2020.1731002, 2020.
Laar, A. v. and Akça, A. (Eds.): Measurement Of Stands, in: Forest Mensuration, Springer Netherlands, Dordrecht, 95–147, https://doi.org/10.1007/978-1-4020-5991-9_5, 2007.
Lang, N., Jetz, W., Schindler, K., and Wegner, J. D.: A high-resolution canopy height model of the Earth, Nat. Ecol. Evol., 7, 1778–1789, https://doi.org/10.1038/s41559-023-02206-6, 2023.
Lefsky, M. A.: A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System, Geophys. Res. Lett., 37, L15401, https://doi.org/10.1029/2010GL043622, 2010.
Lefsky, M. A., Harding, D. J., Keller, M., Cohen, W. B., Carabajal, C. C., Del Bom Espirito-Santo, F., Hunter, M. O., and de Oliveira Jr, R.: Estimates of forest canopy height and aboveground biomass using ICESat, Geophys. Res. Lett., 32, L22S02, https://doi.org/10.1029/2005GL023971, 2005.
Li, C., Chen, Z., Zhou, X., Zhou, M., and Li, Z.: Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics, GIsci. Remote Sens., 60, 2194601, https://doi.org/10.1080/15481603.2023.2194601, 2023.
Li, M., Liu, Q., Feng, Y., and Li, Z.: Analysis of estimation models of plantation stand heights using UAV LiDAR, National Remote Sensing Bulletin, 26, 2665–2678, https://doi.org/10.11834/jrs.20210246, 2022.
Li, W., Guo, Q., Jakubowski, M. K., and Kelly, M.: A new method for segmenting individual trees from the lidar point cloud, Photogramm Eng Remote Sens., 78, 75–84, https://doi.org/10.14358/PERS.78.1.75, 2012.
Li, W., Niu, Z., Shang, R., Qin, Y., Wang, L., and Chen, H.: High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data, Int. J. Appl. Earth Obs. Geoinf., 92, 102163, https://doi.org/10.1016/j.jag.2020.102163, 2020.
Liang, X., Kukko, A., Balenović, I., Saarinen, N., Junttila, S., Kankare, V., Holopainen, M., Mokroš, M., Surový, P., Kaartinen, H., Jurjević, L., Honkavaara, E., Näsi, R., Liu, J., Hollaus, M., Tian, J., Yu, X., Pan, J., Cai, S., Virtanen, J. P., Wang, Y., and Hyyppä, J.: Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions, IEEE Geosci. Remote Sens. Mag., 10, 32–71, https://doi.org/10.1109/MGRS.2022.3168135, 2022.
Liu, H., Zhang, Z., and Cao, L.: Estimating forest stand characteristics in a coastal plain forest plantation based on vertical structure profile parameters derived from ALS data, J. Remote Sens., 22, 872–888, 10.11834/jrs.20187465, 2018.
Liu, X., Su, Y., Hu, T., Yang, Q., Liu, B., Deng, Y., Tang, H., Tang, Z., Fang, J., and Guo, Q.: Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data, Remote Sens. Environ., 269, 112844, https://doi.org/10.1016/j.rse.2021.112844, 2022.
Lorey, T.: Die mittlere bestandeshöhe, J. Allgemeine Forst- und Jagdzeitung, 54, 149–155, 1878.
Lou, M., Zhang, H., Lei, X., Li, C., and Zang, H.: Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China, Forests, 7,43, https://doi.org/10.3390/f7020043, 2016.
Lu, D., Mausel, P., Brondıìzio, E., and Moran, E.: Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin, Forest Ecol. Manag., 198, 149–167, https://doi.org/10.1016/j.foreco.2004.03.048, 2004.
Ma, Q., Su, Y., Niu, C., Ma, Q., Hu, T., Luo, X., Tai, X., Qiu, T., Zhang, Y., Bales, R. C., Liu, L., Kelly, M., and Guo, Q.: Tree mortality during long-term droughts is lower in structurally complex forest stands, Nat. Commun., 14, 7467, 10.1038/s41467-023-43083-8, 2023.
Masaka, K., Sato, H., Torita, H., Kon, H., and Fukuchi, M.: Thinning effect on height and radial growth of Pinus thunbergii Parlat. trees with special reference to trunk slenderness in a matured coastal forest in Hokkaido, Japan, J. Forest Res., 18, 475–481, https://doi.org/10.1007/s10310-012-0373-y, 2013.
Matasci, G., Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W., and Zald, H. S. J.: Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots, Remote Sens. Environ., 209, 90–106, https://doi.org/10.1016/j.rse.2017.12.020, 2018.
McGregor, I. R., Helcoski, R., Kunert, N., Tepley, A. J., Gonzalez-Akre, E. B., Herrmann, V., Zailaa, J., Stovall, A. E. L., Bourg, N. A., McShea, W. J., Pederson, N., Sack, L., and Anderson-Teixeira, K. J.: Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest, New Phytol., 231, 601–616, https://doi.org/10.1111/nph.16996, 2021.
Mekruksavanich, S., Jantawong, P., Hnoohom, N., and Jitpattanakul, A.: Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data, 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), Bangkok, Thailand, 4–5 August 2022, 101–105, https://doi.org/10.1109/RI2C56397.2022.9910262, 2022.
Migliavacca, M., Musavi, T., Mahecha, M. D., Nelson, J. A., Knauer, J., Baldocchi, D. D., Perez-Priego, O., Christiansen, R., Peters, J., Anderson, K., Bahn, M., Black, T. A., Blanken, P. D., Bonal, D., Buchmann, N., Caldararu, S., Carrara, A., Carvalhais, N., Cescatti, A., Chen, J., Cleverly, J., Cremonese, E., Desai, A. R., El-Madany, T. S., Farella, M. M., Fernández-Martínez, M., Filippa, G., Forkel, M., Galvagno, M., Gomarasca, U., Gough, C. M., Göckede, M., Ibrom, A., Ikawa, H., Janssens, I. A., Jung, M., Kattge, J., Keenan, T. F., Knohl, A., Kobayashi, H., Kraemer, G., Law, B. E., Liddell, M. J., Ma, X., Mammarella, I., Martini, D., Macfarlane, C., Matteucci, G., Montagnani, L., Pabon-Moreno, D. E., Panigada, C., Papale, D., Pendall, E., Penuelas, J., Phillips, R. P., Reich, P. B., Rossini, M., Rotenberg, E., Scott, R. L., Stahl, C., Weber, U., Wohlfahrt, G., Wolf, S., Wright, I. J., Yakir, D., Zaehle, S., and Reichstein, M.: The three major axes of terrestrial ecosystem function, Nature, 598, 468–472, https://doi.org/10.1038/s41586-021-03939-9, 2021.
Nakai, T., Sumida, A., Kodama, Y., Hara, T., and Ohta, T.: A comparison between various definitions of forest stand height and aerodynamic canopy height, Agr. Forest Meteorol., 150 1225–1233, 2010.
Næsset, E.: Determination of mean tree height of forest stands using airborne laser scanner data, ISPRS J. Photogramm., 52, 49–56, https://doi.org/10.1016/S0924-2716(97)83000-6, 1997.
Næsset, E. and Økland, T.: Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve, Remote Sens. Environ., 79, 105–115, https://doi.org/10.1016/S0034-4257(01)00243-7, 2002.
Ni, X., Zhou, Y., Cao, C., Wang, X., Shi, Y., Park, T., Choi, S., and Myneni, R. B.: Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data, Remote Sens., 7, 8436–8452, https://doi.org/10.3390/rs70708436, 2015.
Ørka, H. O., Næsset, E., and Bollandsås, O. M.: Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data, Remote Sens. Environ., 113, 1163–1174, https://doi.org/10.1016/j.rse.2009.02.002, 2009.
Pang, Y., Zhao, F., and Li, Z.: Forest height inversion using airborne Lidar technology, J. Remote Sens., 12, 158, https://doi.org/10.11834/jrs.20080120, 2008.
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2021.
Qin, H., Zhou, W., Yao, Y., and Wang, W.: Individual tree segmentation and tree species classification in subtropical broadleaf forests using UAV-based LiDAR, hyperspectral, and ultrahigh-resolution RGB data, Remote Sens. Environ., 280, 113143, https://doi.org/10.1016/j.rse.2022.113143, 2022.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Su, Y., Ma, Q., and Guo, Q.: Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery, Int. J. Digit Earth, 10, 307–323, https://doi.org/10.1080/17538947.2016.1227380, 2017.
Su, Y., Guo, Q., Ma, Q., and Li, W.: SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery, Remote Sens., 7, 11202–11225, https://doi.org/10.3390/rs70911202, 2015.
Swayze, N. C., Tinkham, W. T., Vogeler, J. C., and Hudak, A. T.: Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density, Remote Sens. Environ., 263, 112540, https://doi.org/10.1016/j.rse.2021.112540, 2021.
Tang, J., Luyssaert, S., Richardson, A. D., Kutsch, W., and Janssens, I. A.: Steeper declines in forest photosynthesis than respiration explain age-driven decreases in forest growth, P. Natl. Acad. Sci. USA, 111, 8856–8860, https://doi.org/10.1073/pnas.1320761111, 2014.
Tao, S., Wu, F., Guo, Q., Wang, Y., Li, W., Xue, B., Hu, X., Li, P., Tian, D., Li, C., Yao, H., Li, Y., Xu, G., and Fang, J.: Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories, ISPRS J Photogramm., 110, 66–76, https://doi.org/10.1016/j.isprsjprs.2015.10.007, 2015.
Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., and Lantz, T. C.: Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data, Remote Sens. Environ., 305, 114097, https://doi.org/10.1016/j.rse.2024.114097, 2024.
Vaglio Laurin, G., Ding, J., Disney, M., Bartholomeus, H., Herold, M., Papale, D., and Valentini, R.: Tree height in tropical forest as measured by different ground, proximal, and remote sensing instruments, and impacts on above ground biomass estimates, Int. J. Appl. Earth Obs., 82, 101899, https://doi.org/10.1016/j.jag.2019.101899, 2019.
Vanclay, J. K.: Assessing site productivity in tropical moist forests: a review, Forest Ecol. Manag., 54, 257–287, https://doi.org/10.1016/0378-1127(92)90017-4, 1992.
Vatandaslar, C., Narin, O. G., and Abdikan, S.: Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat-2 missions for Crimean pine (Pinus nigra) stands, Trees, 37, 717–731, https://doi.org/10.1007/s00468-022-02378-x, 2023.
Wang, M., Kane, M. B., and Zhao, D.: Correlation-Regression Analysis for Understanding Dominant Height Projection Accuracy, Forest Sci., 69, e1–e10, https://doi.org/10.5849/fs-2016-092, 2023.
Wang, Y., Pyörälä, J., Liang, X., Lehtomäki, M., Kukko, A., Yu, X., Kaartinen, H., and Hyyppä, J.: In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest, Remote Sens. Environ., 232, 111309, https://doi.org/10.1016/j.rse.2019.111309, 2019a.
Wang, Y., Lehtomäki, M., Liang, X., Pyörälä, J., Kukko, A., Jaakkola, A., Liu, J., Feng, Z., Chen, R., and Hyyppä, J.: 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 J. Photogramm., 147, 132–145, https://doi.org/10.1016/j.isprsjprs.2018.11.008, 2019b.
Woods, M., Pitt, D., Penner, M., Lim, K., Nesbitt, D., Etheridge, D., and Treitz, P.: Operational implementation of a LiDAR inventory in Boreal Ontario, Forest. Chron., 87, 512–528, https://doi.org/10.5558/tfc2011-050, 2011.
Wu, Z. and Shi, F.: Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach, IEEE T. Geosci. Remote, 61, 1–16, https://doi.org/10.1109/TGRS.2022.3231926, 2023.
Xu, H., Yue, C., Zhang, Y., Liu, D., and Piao, S.: Forestation at the right time with the right species can generate persistent carbon benefits in China, P. Natl. Acad. Sci. USA, 120, e2304988120, https://doi.org/10.1073/pnas.2304988120, 2023.
Xu, Y., Li, C., Sun, Z., Jiang, L., and Fang, J.: Tree height explains stand volume of closed-canopy stands: Evidence from forest inventory data of China, Forest Ecol. Manag., 438, 51–56, https://doi.org/10.1016/j.foreco.2019.01.054, 2019.
Yang, J., Kang, Z., Cheng, S., Yang, Z., and Akwensi, P. H.: An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds, IEEE J. Sel. Top. Appl. Earth Obs., 13, 1055–1067, https://doi.org/10.1109/JSTARS.2020.2979369, 2020.
Yang, Q., Niu, C., Liu, X., Feng, Y., Ma, Q., Wang, X., Tang, H., and Guo, Q.: Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data, GIsci. Remote Sens., 60, 2203303, https://doi.org/10.1080/15481603.2023.2203303, 2023.
Yang, Z., Su, Y., Li, W., Cheng, K., Guan, H., Ren, Y., Hu, T., Xu, G., and Guo, Q.: Segmenting Individual Trees From Terrestrial LiDAR Data Using Tree Branch Directivity, IEEE J. Sel. Top. Appl., 17, 956–969, https://doi.org/10.1109/JSTARS.2023.3334014, 2024.
Yao, Y., Piao, S., and Wang, T.: Future biomass carbon sequestration capacity of Chinese forests, Sci. Bull., 63, 1108–1117, https://doi.org/10.1016/j.scib.2018.07.015, 2018.
Yin, D., Wang, L., Lu, Y., and Shi, C.: Mangrove tree height growth monitoring from multi-temporal UAV-LiDAR, Remote Sens. Environ., 303, 114002, https://doi.org/10.1016/j.rse.2024.114002, 2024.
Yun, T., Jiang, K., Li, G., Eichhorn, M. P., Fan, J., Liu, F., Chen, B., An, F., and Cao, L.: Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach, Remote Sens. Environ., 256, 112307, https://doi.org/10.1016/j.rse.2021.112307, 2021.
Zemp, D. C., Guerrero-Ramirez, N., Brambach, F., Darras, K., Grass, I., Potapov, A., Röll, A., Arimond, I., Ballauff, J., Behling, H., Berkelmann, D., Biagioni, S., Buchori, D., Craven, D., Daniel, R., Gailing, O., Ellsäßer, F., Fardiansah, R., Hennings, N., Irawan, B., Khokthong, W., Krashevska, V., Krause, A., Kückes, J., Li, K., Lorenz, H., Maraun, M., Merk, M. S., Moura, C. C. M., Mulyani, Y. A., Paterno, G. B., Pebrianti, H. D., Polle, A., Prameswari, D. A., Sachsenmaier, L., Scheu, S., Schneider, D., Setiajiati, F., Setyaningsih, C. A., Sundawati, L., Tscharntke, T., Wollni, M., Hölscher, D., and Kreft, H.: Tree islands enhance biodiversity and functioning in oil palm landscapes, Nature, 618, 316–321, https://doi.org/10.1038/s41586-023-06086-5, 2023.
Zhang, G., Ganguly, S., Nemani, R. R., White, M. A., Milesi, C., Hashimoto, H., Wang, W., Saatchi, S., Yu, Y., and Myneni, R. B.: Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data, Remote Sens. Environ., 151, 44–56, https://doi.org/10.1016/j.rse.2014.01.025, 2014.
Zhao, X., Su, Y., Hu, T., Cao, M., Liu, X., Yang, Q., Guan, H., Liu, L., and Guo, Q.: Analysis of UAV lidar information loss and its influence on the estimation accuracy of structural and functional traits in a meadow steppe, Ecol. Indic., 135, 108515, https://doi.org/10.1016/j.ecolind.2021.108515, 2022.
Short summary
The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
The national-scale continuous maps of arithmetic mean height and weighted mean height across...
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