气候变化领域集成服务门户(CCPortal): A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China

CCPortal

DOI	10.3390/rs11111286
	A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China
	Chen, Xiang; Wang, Tao; Liu, Shulin; Peng, Fei; Tsunekawa, Atsushi; Kang, Wenping; Guo, Zichen; Feng, Kun
发表日期	2019
ISSN	2072-4292
卷号	11 期号:11
英文摘要	Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approaches used to extract BSCs are multispectral indices, which lack accuracy, and hyperspectral indices, which have lower data availability and require a higher computational effort. This study employs random forest (RF) models to optimize the extraction of BSCs using band combinations similar to the two multispectral BSC indices (Crust Index-CI; Biological Soil Crust Index-BSCI), but covering all possible band combinations. Simulated multispectral datasets resampled from in-situ hyperspectral data were used to extract BSC information. Multispectral datasets (Landsat-8 and Sentinel-2 datasets) were then used to detect BSC coverage in Mu Us Sandy Land, located in northern China, where BSCs dominated by moss are widely distributed. The results show that (i) the spectral curves of moss-dominated BSCs are different from those of other typical land surfaces, (ii) the BSC coverage can be predicted using the simulated multispectral data (mean square error (MSE) < 0.01), (iii) Sentinel-2 satellite datasets with CI-based band combinations provided a reliable RF model for detecting moss-dominated BSCs (10-fold validation, R-2 = 0.947; ground validation, R-2 = 0.906). In conclusion, application of the RF algorithm to the Sentinel-2 dataset can precisely and effectively map BSCs dominated by moss. This new application can be used as a theoretical basis for detecting BSCs in other arid and semi-arid lands within desert ecosystems.
关键词	moss-dominated biological soil crusts (BSCs)random forest (RF) algorithm in-situ hyperspectral dataset multispectral remote sensing Mu Us Sandy Land
学科领域	Remote Sensing
语种	英语
WOS研究方向	Remote Sensing
来源期刊	REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/112051
作者单位	Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Desert & Desertificat, Lanzhou 730000, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Chen, Xiang,Wang, Tao,Liu, Shulin,et al. A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China[J]. 中国科学院西北生态环境资源研究院,2019,11(11).
APA	Chen, Xiang.,Wang, Tao.,Liu, Shulin.,Peng, Fei.,Tsunekawa, Atsushi.,...&Feng, Kun.(2019).A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China.REMOTE SENSING,11(11).
MLA	Chen, Xiang,et al."A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China".REMOTE SENSING 11.11(2019).