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Understanding urban expansion and shrinkage via green plastic cover mapping based on GEE cloud platform: A case study of Shandong, China
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2024-03-10 , DOI: 10.1016/j.jag.2024.103749 Jiantao Liu , Yan Zhang , Quanlong Feng , Gaofei Yin , Dong Zhang , Yi Li , Jianhua Gong , Yexiang Li , Jingxian Li
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2024-03-10 , DOI: 10.1016/j.jag.2024.103749 Jiantao Liu , Yan Zhang , Quanlong Feng , Gaofei Yin , Dong Zhang , Yi Li , Jianhua Gong , Yexiang Li , Jingxian Li
Green Plastic Cover (GPC) has been widely used in construction sites of China as the main measure for dust suppression. As a unique land cover in China, GPC could be viewed as a sign for evaluating the intensity of urban construction. Therefore, time series GPC maps could provide a new perspective to understand the pattern of urban development, including both urban expansion and urban shrinkage. The objective of this study is to present a novel framework for detection of urban development patterns based on high resolution GPC maps generated from Google Earth Engine (GEE) cloud platform. Specifically, we first generated the GPC maps of 137 districts and counties in Shandong Province of China from 2018 to 2022 by combining time series spectral features and textural features derived from Sentinel-1 and Sentinel-2 images provided by GEE cloud platform. Afterwards, we designed a GPC-based urban development vigor index (GPC-UDVI) to evaluate the vitality of each city, which offers a novel perspective to identify shrinking cities. Experimental results demonstrated that the average overall accuracy of GPC maps is 89.696 %, which is accurate enough for the following analysis. Besides, the proposed GPC-UDVI exhibits a consistent trend with statistical data and could reveal urban development patterns with a high performance. Above all, dynamic monitoring of urban GPC through remote sensing is significant for understanding urban expansion modes and aiding in urban reconstruction management.
更新日期：2024-03-10
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