Satellite Image-Based Methods of Spatiotemporal Analysis on Sustainable Urban Land Use Change and the Driving Factors: A Case Study in Caofeidian and the Suburbs, China

• Main Authors: Guang Yang, Sara Chao, Jin Yeu Tsou, Yuanzhi Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2019-05-01
• Series: Sustainability
• Subjects: urban land use change; driving factors; phenological phenomena; satellite images; Caofeidian
• Online Access: https://www.mdpi.com/2071-1050/11/10/2927

As a typical rapid-development seaport area in coastal cities, such as Caofeidian, the study on the spatiotemporal changes of urban land use and its surrounding rural areas is valuable and significant in reference to the future urban planning and land policies in similar coastal areas of China or other countries. Based on satellite images, this research processes images in different years for summarizing the changes of vegetation, urban areas, and water areas in Caofeidian and the suburbs. This research aims to summarize the experience of the coastal city in the process of sustainable development by analyzing the dynamic trends and driving factors of land use spatial and temporal changes in the target area so that it provides a reference for the long-term development of the city. Meanwhile, it also hopes to give support for refining and improving the spatiotemporal analysis method for sustainable urban land use through the experiment. Due to the appearance of the results of the abnormal data, in the experiment process, this article adopts a comparative experiment to avoid the error of the analysis result and to find out the reason. The results show that the urban area for construction increased rapidly in the past twenty years, which is mainly affected by factors, such as economic development, policy guidance, environmental awareness, and environmental protection measures, especially guided by policies. Thus, coastal cities should stretch the planning of sustainable development from the three aspects combining with local characteristics. Besides, phenological phenomena and crops harvest time tremendously affect the images and calculation. The selection of remotely-sensed images should fully consider the characteristics of urban and rural locations, especially the impact of local phenological phenomena. The results of the analysis provide reference value and support for sustainable urban land management and development in the study area and other coastal cities.