干旱区生态环境知识资源中心(Arid): Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China

Arid

DOI	10.3390/rs14112663
	Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China
	Feng, Kun; Wang, Tao; Liu, Shulin; Kang, Wenping; Chen, Xiang; Guo, Zichen; Zhi, Ying
通讯作者	Liu, SL
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:11
英文摘要	Mu Us Sandy Land is a typical semi-arid vulnerable ecological zone, characterized by vegetation degradation and severe desertification. Effectively identifying desertification changes has been a topical environmental issue in China. However, most previous studies have used a single method or remote sensing index to monitor desertification, and lacked an efficient and high-precision monitoring system. In this study, an optimal monitoring scheme that considers multiple indicators combination and different machine learning methods (Classification and Regression Tree-Decision Tree, CART-DT; Random Forest, RF; Convolutional Neural Networks, CNN) was developed and used to analyze the spatial-temporal patterns of desertification from 2000 to 2018 in Mu Us Sandy Land. The results showed that: (a) The random forest model performed best for monitoring desertification based on medium and low-resolution remote sensing images, and the four-index combination (Albedo, NDVI, LST and TGSI) obtained the highest classification accuracy (OA = 87.67%) in Mu Us Sandy Land. Surprisingly, the model accuracy of the three-index combination (NDVI, LST and TGSI) (OA = 85.74%) is comparable to the four-index combination. (b) The TGSI index used to characterize soil information performs well, while the LST is not conducive to the extraction of desertified land in several desertification monitoring indicators. (c) Since 2000, the area of extremely severe desertified land has shown a reversal trend; however, there is significant interannual fluctuation in the total and light desertification land area affected by extreme climate. This research provides a novel approach and a valuable reference for monitoring the evolution of desertification in regional studies, and the results improve the research system of desertification and provide a data basis for desertification cause analysis and prevention.
英文关键词	desertification CART-DT RF CNN image classification remote sensing index
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:000809129400001
WOS关键词	RANDOM FOREST ; AEOLIAN DESERTIFICATION ; QUANTITATIVE ASSESSMENT ; PLATEAU ; NDVI ; CLASSIFICATION ; METAANALYSIS ; DEGRADATION ; DYNAMICS ; PATTERNS
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/2XILL650/394150
推荐引用方式 GB/T 7714	Feng, Kun,Wang, Tao,Liu, Shulin,et al. Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China[J],2022,14(11).
APA	Feng, Kun.,Wang, Tao.,Liu, Shulin.,Kang, Wenping.,Chen, Xiang.,...&Zhi, Ying.(2022).Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China.REMOTE SENSING,14(11).
MLA	Feng, Kun,et al."Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China".REMOTE SENSING 14.11(2022).