Dynamic monitoring of land-use/land-cover change in cold and arid region based on deep learning: A case study of Mosuowan reclamation area in Xinjiang
Received date: 2020-12-08
Revised date: 2021-04-27
Online published: 2021-12-03
In this study, we conducted researchon the extraction of ground elements and the dynamic monitoring of land cover change in cold and arid areas with fragile ecological environments. The Mosuowan reclamation area in Xinjiang, China was selected as the target area, and the Landsat series satellite images from 2015 to 2019 were cut into subimages with sizes of 416×416 px. Specifically, the training set, validation set and testset are 306 subimages, 204 subimages and 120 subimages respectively. Three traditional methods were then evaluated: MLC,SVM, and RF. Five semantic segmentation models were also evaluated: DeepLabv3+(Xception), DeepLabv3+ (MobileNet), SegNet (ResNet50), U-Net (MobileNet), and PSPNet (MobileNet). In the evaluation experiments, DeepLabv3+ (Xception) was found to achieve the optimal segmentation effect and multiscale feature fusion using fewer parameters. The overall accuracy, Kappa coefficient, precision, recall, F1-score, and MIoU were 96.06%, 0.96, 87.69%, 83.78%, 0.86, and 0.77, respectively. The MIoU of the DeepLabv3+(Xception) model was significantly better than those of the other four models, improving from 0.03 to 0.39. On the basis of the land use classification results of the long-term timeseries remote sensing data from 1998 to 2020, we analyze spatial structure change in land use and the associated driving factors. Over the past 23 years, the total areas of desert, agricultural land, and construction land have been reduced by 15.00%, 12.68%, 2.53%, respectively. At the same time, the amount of water area has remained relatively stable. The overall transformation direction of land use is from desert to agricultural land and then from agricultural land to construction land. It can be seen from the results that the desertification control was effective, and urbanization rapidly developed. Consequently, this study can provide a reference for the application of deep learning in the field of medium-resolution remote sensing images, which can be used to realize the dynamic monitoring of land use and change.
YUAN Panli,WANG Chuanjian,ZHAO Qingzhan,WANG Xuewen,REN Yuanyuan,YANG Qiyuan . Dynamic monitoring of land-use/land-cover change in cold and arid region based on deep learning: A case study of Mosuowan reclamation area in Xinjiang[J]. Arid Land Geography, 2021 , 44(6) : 1717 -1728 . DOI: 10.12118/j.issn.1000–6060.2021.06.20
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