Spatiotemporal evolution of urban agglomerations in four major bay areas of US, China and Japan from 1987 to 2017: Evidence from remote sensing images.

As major urban agglomerations with strong urbanization, global bay areas are seldom detected and compared in detail regarding the spatiotemporal evolution of their urban expansion. In this work, a framework was applied for detecting and comparing the spatiotemporal evolution of urban agglomerations in four major bay areas: the San Francisco Bay Area and the New York Bay Area in the US, the Tokyo Bay Area in Japan, and the Guangdong-Hong Kong-Macau (GHM) Bay Area in China. Landsat images from 1987, 1997, 2007 and 2017 were employed to derive the four urban bay areas using the object-oriented support vector machine (O-SVM) classification method, and a multi-scale spatial analysis method was applied to detect the landscape characteristics and types of growth in the urban expansions. The results showed that: (1) the O-SVM classification method exhibited a high accuracy in urban area extraction, especially for classifying large-scale images; (2) the urban areas of the San Francisco Bay Area, the New York Bay Area, the Tokyo Bay Area and the GHM Bay Area from 1987 to 2017 expanded from 1686.82, 5315.93, 3765.09 and 605.71 km to 2714.7, 8359.18, 5351.06 and 7568.19 km, respectively, with a corresponding annual average increase of 1.60%, 1.52%, 1.18% and 8.82%; (3) the GHM Bay Area had the largest expansion area and rate among the four bay areas; (4) both the San Francisco Bay Area and the New York Bay Area successively formed a multi-nuclei ribbon model, and the Tokyo Bay Area and the GHM Bay Area formed a multinuclear fan-shaped model and a triangle zonal expansion pattern, respectively; and (5) the spatial patterns of urban expansions in these bay areas shifted from outlying to edge-expansion and infilling, in which the Tokyo Bay Area and the New York Bay Area experienced the largest infilling growth, and the San Francisco Bay Area followed closely thereafter; all were ahead of the GHM Bay Area. These results will be helpful for the understanding and sustainable development of these bay areas.