干旱区生态环境知识资源中心(Arid): A Spatial and Temporal Evolution Analysis of Desert Land Changes in Inner Mongolia by Combining a Structural Equation Model and Deep Learning

Arid

DOI	10.3390/rs15143617
	A Spatial and Temporal Evolution Analysis of Desert Land Changes in Inner Mongolia by Combining a Structural Equation Model and Deep Learning
	Chang, Xinyue; Zhang, Bing; Zhu, Hongbo; Song, Weidong; Ren, Dongfeng; Dai, Jiguang
通讯作者	Zhang, B
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:14
英文摘要	With the wide application of remote sensing technology, target detection based on deep learning has become a research hotspot in the field of remote sensing. In this paper, aimed at the problems of the existing deep-learning-based desert land intelligent extraction methods, such as the spectral similarity of features and unclear texture features, we propose a multispectral remote sensing image desert land intelligent extraction method that takes into account band information. Firstly, we built a desert land intelligent interpretation dataset based on band weighting to enhance the desert land foreground features of the images. On this basis, we introduced the deformable convolution adaptive feature extraction capability to U-Net and developed the Y-Net model to extract desert land from Landsat remote sensing images covering the Inner Mongolia Autonomous Region. Finally, in order to analyze the spatial and temporal trends of the desert land in the study area, we used a structural equation model (SEM) to evaluate the direct and indirect effects of natural conditions and human activities, i.e., population density (PD), livestock volume (LS), evaporation (Evp), temperature (T), days of sandy wind conditions (LD), humidity (RH), precipitation (P), anthropogenic disturbance index (Adi), and cultivated land (CL). The results show that the F1-score of the Y-Net model proposed in this paper is 95.6%, which is 11.5% more than that of U-Net. Based on the Landsat satellite images, the area of desert land in the study area for six periods from 1990 to 2020 was extracted. The results show that the area of desert land in the study area first increased and then decreased. The main influencing factors have been precipitation, humidity, and anthropogenic disturbance, for which the path coefficients are 0.646, 0.615, and 0.367, respectively. This study will be of great significance in obtaining large-scale and long-term time series of desert land cover and revealing the inner mechanism of desert land area change.
英文关键词	desert land Y-Net model multispectral images structural equation model driving factors
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:001036469600001
WOS关键词	AEOLIAN DESERTIFICATION ; SANDY LAND ; DEGRADATION ; CLIMATE ; CHINA ; DUNES
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/2XILL650/398302
推荐引用方式 GB/T 7714	Chang, Xinyue,Zhang, Bing,Zhu, Hongbo,et al. A Spatial and Temporal Evolution Analysis of Desert Land Changes in Inner Mongolia by Combining a Structural Equation Model and Deep Learning[J],2023,15(14).
APA	Chang, Xinyue,Zhang, Bing,Zhu, Hongbo,Song, Weidong,Ren, Dongfeng,&Dai, Jiguang.(2023).A Spatial and Temporal Evolution Analysis of Desert Land Changes in Inner Mongolia by Combining a Structural Equation Model and Deep Learning.REMOTE SENSING,15(14).
MLA	Chang, Xinyue,et al."A Spatial and Temporal Evolution Analysis of Desert Land Changes in Inner Mongolia by Combining a Structural Equation Model and Deep Learning".REMOTE SENSING 15.14(2023).