干旱区生态环境知识资源中心(Arid): Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data

Arid

DOI	10.3390/su142013385
	Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data
	Cao, Jiaju; Wen, Xingping; Zhang, Meimei; Luo, Dayou; Tan, Yinlong
通讯作者	Wen, XP
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2022
卷号	14 期号:20
英文摘要	Rock desertification has become the third most serious ecological problem in western China after desertification and soil erosion. It is also the primary environmental problem to be solved in the karst region of southwest China. Karst landscapes in China are mainly distributed in southwest China, and the area centered on the Guizhou plateau is the center of karst landscape development in southern China. It has a fragile ecological environment, and natural factors and human activities have influenced the development of stone desertification in the karst areas to different degrees. In this paper, Dafang County, Guizhou Province, was selected as the study area to analyze the effect of the decision tree and multiple linear regression model on stone desertification and to analyze the evolution characteristics of stone desertification in Dafang County from 2005 to 2020. The FLUS model was applied to predict and validate the stone desertification information. The results show that the overall accuracy of multiple linear regression extraction of stone desertification is 70%, and the Kappa coefficient is 0.69; the overall accuracy of decision tree extraction of stone desertification is 60%, and the Kappa coefficient is 0.521. The multiple linear regression stone desertification extraction model is more accurate than the traditional decision tree classification. The overlay analysis of stone desertification and slope, elevation, slope direction and vegetation cover showed that stone desertification was more distributed between 1300-1900 m in elevation; stone desertification decreased gradually with the increase in slope; each grade of stone desertification was mainly distributed in the range of 5 to 25 degrees in slope, which might be related to human activities. The FLUS model was used to predict the accuracy of 2015 data in the region and project the changes in stone desertification area in 2035 under a conventional scenario and an ecological protection scenario in the region to provide a new reference for predicting stone desertification.
英文关键词	karst scenery decision tree multiple regression model FLUS model
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000875216200001
WOS关键词	VEGETATION RESTORATION ; KARST ; RUNOFF ; AREAS ; CHINA
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394664
推荐引用方式 GB/T 7714	Cao, Jiaju,Wen, Xingping,Zhang, Meimei,et al. Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data[J],2022,14(20).
APA	Cao, Jiaju,Wen, Xingping,Zhang, Meimei,Luo, Dayou,&Tan, Yinlong.(2022).Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data.SUSTAINABILITY,14(20).
MLA	Cao, Jiaju,et al."Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data".SUSTAINABILITY 14.20(2022).