干旱区生态环境知识资源中心(Arid): Extraction of Rocky Desertification Information in the Karst Area Based on the Red-NIR-SWIR Spectral Feature Space

Arid

DOI	10.3390/rs15123056
	Extraction of Rocky Desertification Information in the Karst Area Based on the Red-NIR-SWIR Spectral Feature Space
	Cai, Jun; Yu, Wanyang; Fang, Qian; Zi, Ruyi; Fang, Fayong; Zhao, Longshan
通讯作者	Zhao, LS
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:12
英文摘要	The complex topography, severe surface fragmentation and landscape heterogeneity of the karst region of southwest China make it extremely difficult to extract information on rocky desertification in the region. In order to overcome the disadvantages of the surface parameter-based feature space approach, which is difficult to construct and apply, this study uses the reflectance of Landsat 8 Operational Land Imager (OLI) in the red (Red), near-infrared (NIR) and shortwave infrared (SWIR) bands as the feature variables, and establishes a two-dimensional SWIR-NIR, Red-NIR and SWIR-Red reflectance spectral feature space. The three models of perpendicular rocky desertification index 1 (PRDI1), perpendicular rocky desertification index 2 (PRDI2) and perpendicular rocky desertification index 3 (PRDI3) were also constructed based on the variation of the degree of rocky desertification in each spectral feature space. The accuracy of the rocky desertification extracted by these three index models was verified and compared with the karst rocky desertification index (KRDI) and rocky desertification difference index (RSDDI), which are constructed based on the surface parameter feature space. The results show that: (1) The waveband reflectance-based feature space model provides a new method for large-scale rocky desertification information extraction, characterized by easy data acquisition, simple index calculation and good stability, and is conducive to the monitoring and quantitative analysis of rocky desertification in karst areas. (2) The overall accuracy and Kappa coefficient of PRDI1 are 0.829 and 0.784, respectively, both higher than other index models, showing the best applicability, accuracy and effectiveness in rocky desertification information extraction. (3) According to the results extracted from PRDI1, the total area of rocky desertification in Huaxi District of Guizhou province is 320.44 km(2), with the more serious grades of rocky desertification, such as severe and moderate, mainly distributed in the southwestern, western and southeastern areas of Huaxi District. This study provides important information on the total area and spatial distribution of different degrees of rocky desertification in the study area, and these results can be used to support the local government's ecological and environmental management decisions. The method proposed in this study is a scientific and necessary complement to the characteristic spatial methods based on different surface parameters, and can provide important methodological support for the rapid and efficient monitoring of karstic rocky desertification over large areas.
英文关键词	karst rocky desertification remote sensing spectral feature space Red-NIR-SWIR
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001016020900001
WOS关键词	VEGETATION ; INDEXES ; MODEL ; EVOLUTION ; MOISTURE
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/2XILL650/398294
推荐引用方式 GB/T 7714	Cai, Jun,Yu, Wanyang,Fang, Qian,et al. Extraction of Rocky Desertification Information in the Karst Area Based on the Red-NIR-SWIR Spectral Feature Space[J],2023,15(12).
APA	Cai, Jun,Yu, Wanyang,Fang, Qian,Zi, Ruyi,Fang, Fayong,&Zhao, Longshan.(2023).Extraction of Rocky Desertification Information in the Karst Area Based on the Red-NIR-SWIR Spectral Feature Space.REMOTE SENSING,15(12).
MLA	Cai, Jun,et al."Extraction of Rocky Desertification Information in the Karst Area Based on the Red-NIR-SWIR Spectral Feature Space".REMOTE SENSING 15.12(2023).