Abstract
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.
Highlights
Land-use/land-cover (LULC) is usually defined as the human use of land, such as the economic and cultural activities that are practiced at a given place
The results show that agricultural land use has continued to decline, and that this enables forest recovery; an important land-cover transition has occurred, from a mode of regional forest-cover gain to one of forest-cover loss caused by timber cutting cycles, urbanization, and other land-use demands
In Google Earth Engine (GEE), the classification results of each remote sensing image and sample points are analyzed for the accuracy of the confusion matrix, and the learning accuracy is calculated to verify the classification effect of the random forest algorithm
Summary
Land-use/land-cover (LULC) is usually defined as the human use of land, such as the economic and cultural activities (e.g., agricultural, residential, industrial, mining, and recreational uses) that are practiced at a given place. Land-use change is the result of the interaction between human beings and nature and has become one of the main reasons for global change at present. Land-use/land-cover changes can be made very frequently in both public and private lands due to very different uses. It seriously affects various fields closely related to human life, such as the ecological environment, economic development, food production, and climate change [1,2,3,4,5]. Most waters contain dissolved salts and trace elements, many of which result from both the land use of human beings and the natural weathering of the Earth’s surface
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