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影响因子：3.576

DOI码：10.3390/s21175974

发表刊物：Sensors

关键字：ensemble learning; machine learning; feature extraction; AGB; NSFs

摘要：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.

合写作者：Hung-Il Jin,Ye Ma,Wenyi Fan

第一作者：Q1, Chunyu Du

论文类型：期刊论文

通讯作者：Zhen Zhen*

卷号：21

期号：17

页面范围：5974

ISSN号：1424-8220

是否译文：否

发表时间：2022-01-01

收录刊物：SCI