干旱区生态环境知识资源中心(Arid): A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China

Arid

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 1,2,3; Wang, Tao1,2 ; Liu, Shulin 1; Peng, Fei 1,4; Tsunekawa, Atsushi 3; Kang, Wenping 1,2; Guo, Zichen 1,2; Feng, Kun 1,2
通讯作者	Liu, Shulin
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
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.
英文关键词	moss-dominated biological soil crusts (BSCs) random forest (RF) algorithm in-situ hyperspectral dataset multispectral remote sensing Mu Us Sandy Land
类型	Article
语种	英语
国家	Peoples R China ; Japan
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000472648000029
WOS关键词	VEGETATION ; NITROGEN ; DESERT ; INDEX ; REFLECTANCE ; DISTURBANCE ; PLATEAU ; BIOMASS ; CARBON
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218379
作者单位	1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Desert & Desertificat, Lanzhou 730000, Gansu, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.Tottori Univ, Arid Land Res Ctr, Tottori 6800001, Japan; 4.Tottori Univ, Int Platform Dryland Res & Educ, Tottori 6800001, Japan
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],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).