High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

• Published in: International journal of applied earth observation and geoinformation Vol. 92; p. 102163
• Main Authors: Li, Wang, Niu, Zheng, Shang, Rong, Qin, Yuchu, Wang, Li, Chen, Hanyue
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2020; Elsevier
• Subjects: artificial intelligence; biodiversity; canopy height; carbon sequestration; Deep-learning; forest canopy; Forest canopy height; forests; ICESat-2; Landsat; Landsat-8; lidar; Machine-learning; mountains; prediction; Random forest; regression analysis; remote sensing; Sentinel-1; Sentinel-2; spatial data; Landsat-8; Forest canopy height; Deep-learning; ICESat-2; Sentinel-1; Sentinel-2; Random forest; Machine-learning

•A machine-learning based high-resolution mapping workflow was proposed.•Canopy height product from ICESat-2 satellite was validated by airborne LiDAR data.•Deep-learning and random forest models were used to upscale ICESat-2 canopy height.•Sentinel-2 and Landsat-8 data were compared in the predicti...