The Effect of Synergistic Approaches of Features and Ensemble Learning Algorithms on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8
Date:2025-06-02 clicks:
Impact Factor:3.576
DOI number:10.3390/s21175974
Journal:Sensors
Key Words:ensemble learning; machine learning; feature extraction; AGB; NSFs
Abstract:Although the combination of Airborne Laser Scanning (ALS) data and optical imagery and machine learning algorithms were proved to improve the estimation of aboveground biomass (AGB), the synergistic approaches of different data and ensemble learning algorithms have not been fully investigated, especially for natural secondary forests (NSFs) with complex structures. This study aimed to explore the effects of the two factors on AGB estimation of NSFs based on ALS data and Landsat 8 imagery. The synergistic method of extracting novel features (i.e., COLI1 and COLI2) using optimal Landsat 8 features and the best-performing ALS feature (i.e., elevation mean) yielded higher accuracy of AGB estimation than either optical-only or ALS-only features. However, both of them failed to improve the accuracy compared to the simple combination of the untransformed features that generated them. The convolutional neural networks (CNN) model was much superior to other classic machine learning algorithms no matter of features. The stacked generalization (SG) algorithms, a kind of ensemble learning algorithms, greatly improved the accuracies compared to the corresponding base model, and the SG with the CNN meta-model performed best. This study provides technical support for a wall-to-wall AGB mapping of NSFs of northeastern China using efficient features and algorithms.
Co-author:Hung-Il Jin,Ye Ma,Wenyi Fan
First Author:Q1, Chunyu Du
Indexed by:Journal paper
Correspondence Author:Zhen Zhen*
Volume:21
Issue:17
Page Number:5974
ISSN No.:1424-8220
Translation or Not:no
Date of Publication:2022-01-01
Included Journals:SCI