气候变化领域集成服务门户(CCPortal): Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms

CCPortal

DOI	10.3390/rs14041039
	Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms
	Wang, Xiaoyi; Liu, Caixia; Lv, Guanting; Xu, Jinfeng; Cui, Guishan
通讯作者	Cui, GS (通讯作者)
发表日期	2022
EISSN	2072-4292
卷号	14 期号:4
英文摘要	Forest aboveground biomass (AGB) is of great significance since it represents large carbon storage and may reduce global climate change. However, there are still considerable uncertainties in forest AGB estimates, especially in rugged regions, due to the lack of effective algorithms to remove the effects of topography and the lack of comprehensive comparisons of methods used for estimation. Here, we systematically compare the performance of three sources of remote sensing data used in forest AGB estimation, along with three machine-learning algorithms using extensive field measurements (N = 1058) made in the Khingan Mountains of north-eastern China in 2008. The datasets used were obtained from the LiDAR-based Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation satellite (ICESat/GLAS), the optical-based Moderate Resolution Imaging Spectroradiometer (MODIS), and the SAR-based Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR). We show that terrain correction is effective for this mountainous study region and that the combination of terrain-corrected GLAS and PALSAR features with Random Forest regression produces the best results at the plot scale. Including further MODIS-based features added little power for prediction. Based upon the parsimonious data source combination, we created a map of AGB circa 2008 and its uncertainty, which yields a coefficient of determination (R-2) of 0.82 and a root mean squared error of 16.84 Mg ha(-1) when validated with field data. Forest AGB values in our study area were within the range 79.81 +/- 16.00 Mg ha(-1), ~25% larger than a previous, SAR-based, analysis. Our result provides a historic benchmark for regional carbon budget estimation.
关键词	MULTIPLE SPATIAL SCALES NORTHEASTERN CHINA VEGETATION HEIGHT NATIONAL FOREST SAR BACKSCATTER CARBON-DENSITY CANOPY HEIGHT LIDAR MODIS VOLUME
英文关键词	benchmark mapping; AGB; machine learning; carbon sink; forest monitoring
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000762792200001
来源期刊	REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260568
推荐引用方式 GB/T 7714	Wang, Xiaoyi,Liu, Caixia,Lv, Guanting,et al. Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms[J]. 中国科学院青藏高原研究所,2022,14(4).
APA	Wang, Xiaoyi,Liu, Caixia,Lv, Guanting,Xu, Jinfeng,&Cui, Guishan.(2022).Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms.REMOTE SENSING,14(4).
MLA	Wang, Xiaoyi,et al."Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms".REMOTE SENSING 14.4(2022).