Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine

Land-use classification is fundamental for environmental and water resource evaluation in coastal plain areas. However, comprehensive remote sensing image-based land-use analysis is challenged by the lack of massive remote sensing images and the massive computing power of large-scale server systems. In this paper, the spatial-temporal land-use change characteristics of the Hangzhou Bay area coastal plain are investigated on the Google Earth Engine platform. The proposed model uses a random forest algorithm to assist the land-use classification. The dataset is selected from the year 2009 to 2020 and classified with an average classification accuracy of 89% and Kappa coefficient of 88%. The results show that the land use in the selected region is affected by urbanization, the balance of cultivated land occupation and compensation, construction of economic development zone, and other activities. The investigation also shows that in the past 12 years, land use has changed rapidly, and each land-use type maintains the dynamic balance of occupation and compensation. Although the overall land-use distribution is stable, the information entropy fluctuates at a high level, with an average value of 1.15, and the multi-year average value of equilibrium is as high as 0.83. The driving force of land-use change is analyzed and accounted as demographics and human population dynamics, social-economic development, urbanization, and coupling effects of the above-mentioned factors.