Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data

Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random...

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Bibliographic Details
Main Authors: Tianbao Huang, Guanglong Ou, Yong Wu, Xiaoli Zhang, Zihao Liu, Hui Xu, Xiongwei Xu, Zhenghui Wang, Can Xu
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Remote Sensing
Subjects:
environmental factors
forest AGB estimation
forest heterogeneity
forest types
machine learning algorithms
Yunnan Province of China
Online Access:https://www.mdpi.com/2072-4292/15/14/3550

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