干旱区生态环境知识资源中心(Arid): Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land

Arid

DOI	10.3390/rs14194736
	Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land
	Chen, Zhanzhuo; Huang, Min; Xiao, Changjiang; Qi, Shuhua; Du, Wenying; Zhu, Daoye; Altan, Orhan
通讯作者	Huang, M
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:19
英文摘要	One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km(2)/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability.
英文关键词	desert transformation artificial vegetation restoration remote sensing spatiotemporal analysis suitability mapping
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:000868071600001
WOS关键词	NORTHERN CHINA ; HABITAT SUITABILITY ; CLIMATE-CHANGE ; DESERTIFICATION ; COVER ; PERSPECTIVE ; TRENDS
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/2XILL650/394209
推荐引用方式 GB/T 7714	Chen, Zhanzhuo,Huang, Min,Xiao, Changjiang,et al. Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land[J],2022,14(19).
APA	Chen, Zhanzhuo.,Huang, Min.,Xiao, Changjiang.,Qi, Shuhua.,Du, Wenying.,...&Altan, Orhan.(2022).Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land.REMOTE SENSING,14(19).
MLA	Chen, Zhanzhuo,et al."Integrating Remote Sensing and Spatiotemporal Analysis to Characterize Artificial Vegetation Restoration Suitability in Desert Areas: A Case Study of Mu Us Sandy Land".REMOTE SENSING 14.19(2022).