干旱区生态环境知识资源中心(Arid): Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Arid

DOI	10.3390/su14127470
	Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning
	Li, Peixian; Chen, Peng; Shen, Jiaqi; Deng, Weinan; Kang, Xinliang; Wang, Guorui; Zhou, Shoubao
通讯作者	Chen, P
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2022
卷号	14 期号:12
英文摘要	The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003-2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.
英文关键词	Landsat desertification machine learning Ningdong coal base dynamic monitoring driving factors analysis
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:000816799800001
WOS关键词	LOGISTIC-REGRESSION ; RANDOM FOREST ; CHINA ; SENSITIVITY ; DERIVATION ; EVOLUTION ; PATTERN ; MODEL ; INDEX ; BASIN
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/2XILL650/394594
推荐引用方式 GB/T 7714	Li, Peixian,Chen, Peng,Shen, Jiaqi,et al. Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning[J],2022,14(12).
APA	Li, Peixian.,Chen, Peng.,Shen, Jiaqi.,Deng, Weinan.,Kang, Xinliang.,...&Zhou, Shoubao.(2022).Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning.SUSTAINABILITY,14(12).
MLA	Li, Peixian,et al."Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning".SUSTAINABILITY 14.12(2022).