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A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR.

, , , , , and . Remote. Sens., 8 (11): 942 (2016)

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Separation of Wood and Foliage for Trees From Ground Point Clouds Using a Novel Least-Cost Path Model., , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2021)A Novel Framework for 2.5-D Building Contouring From Large-Scale Residential Scenes., , , , , , and . IEEE Trans. Geosci. Remote. Sens., 57 (6): 4121-4145 (2019)Synergizing a Deep Learning and Enhanced Graph-Partitioning Algorithm for Accurate Individual Rubber Tree-Crown Segmentation from Unmanned Aerial Vehicle Light-Detection and Ranging Data., , , , and . Remote. Sens., 16 (15): 2807 (August 2024)Improving the accuracy of canopy height mapping in rubber plantations based on stand age, multi-source satellite images, and random forest algorithm., , , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2024)Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data., , , , , and . Remote Sensing, 11 (1): 97 (2019)A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR., , , , , and . Remote. Sens., 8 (11): 942 (2016)Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances., , , , , , , and . Remote. Sens., 11 (8): 903 (2019)Status, advancements and prospects of deep learning methods applied in forest studies., , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2024)Correction: Chen et al. High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China. Remote Sens. 2020, 12, 3853., , , , , , , , , and 1 other author(s). Remote. Sens., 14 (19): 5044 (2022)Early identification of immature rubber plantations using Landsat and Sentinel satellite images., , , , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2024)