Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data

Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data

Spaceborne LiDAR satellites, including GEDI and ICESat-2, have shown significant potential in estimating aboveground biomass (AGB) using machine learning (ML) methods. In contrast to advances focused on the refinement of ML algorithms, this study aims to enhance AGB estimation accuracy by integratin...

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Bibliographic Details
Main Authors: Biao Zhang, Zhichao Wang, Tiantian Ma, Zhihao Wang, Hao Li, Wenxu Ji, Mingyang He, Ao Jiao, Zhongke Feng
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:Ecological Informatics
Subjects:
Aboveground biomass
Canopy height
Bias correction
Uncertainty assessment
Spectral saturation
LiDAR
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954125000548
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