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

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

Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling...

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Bibliographic Details
Main Authors: Wang Li, Zheng Niu, Rong Shang, Yuchu Qin, Li Wang, Hanyue Chen
Format: Article
Language:English
Published: Elsevier 2020-10-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Forest canopy height
ICESat-2
Sentinel-1
Sentinel-2
Landsat-8
Machine-learning
Online Access:http://www.sciencedirect.com/science/article/pii/S030324342030026X

Internet

http://www.sciencedirect.com/science/article/pii/S030324342030026X

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