打印

作者

Wanru He;Xuecao Li;Yuyu Zhou;Xiaoping Liu;Peng Gong;Tengyun Hu;Peiyi Yin;Jianxi Huang;Jianyu Yang;Shuangxi Miao;Xi Wang;Tinghai Wu

来源

CITIES,Vol.134,Issue1,Article 104146

语言

英文

关键字

作者单位

College of Land Science and Technology, China Agricultural University, Beijing 100083, China;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China;Department of Geology and Atmosphere Sciences, Iowa State University, IA 50014, USA;School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China;Department of Geography, The University of Hong Kong, Hong Kong, China;Beijing Municipal Institute of City Planning and Design, Beijing 100045, China;AI for Earth Lab, Cross-Strait Institute, Tsinghua University, Beijing 100084, China;School of Architecture, Tsinghua University, Beijing 100084, China;College of Land Science and Technology, China Agricultural University, Beijing 100083, China;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China;Department of Geology and Atmosphere Sciences, Iowa State University, IA 50014, USA;School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China;Department of Geography, The University of Hong Kong, Hong Kong, China;Beijing Municipal Institute of City Planning and Design, Beijing 100045, China;AI for Earth Lab, Cross-Strait Institute, Tsinghua University, Beijing 100084, China;School of Architecture, Tsinghua University, Beijing 100084, China;State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;National Tibetan Plateau Data Center, Beijing 100101, China;Roskilde University, Building 25.2, Universitetsvej 1, 4000 Roskilde, Denmark;State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China;School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China;School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;Department of Urban Planning and Management and Geo-information Management, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 127, 7500 AE Enschede, the Netherlands;C.K. Tedam University of Technology and Applied Sciences, Box 24, Navrongo, Ghana;Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan;LocationMind Inc., 3-5-2 Iwamotocho, Chiyoda-ku, Tokyo 101-0032, Japan;SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China;College of Management and Economics, Tianjin University, Tianjin, China;School of Urban Planning & Design, Peking University, Shenzhen, China;Technical University of Munich, Chair of Data Science in Earth Observation, Arcisstraße 21, Munich 80333, Germany;German Aerospace Center, Remote Sensing Technology Institute, Muenchener Straße 20, Weßling 82234, Germany

摘要

The cellular automata (CA) based models have been extensively used in urban sprawl modeling to support sustainable urban planning. However, in most existing urban CA models, only abrupt conversion (i.e., from non-urban to urban) was considered, whereas the difference in urbanization levels among different grids, as well as the nature of continuous urban evolution process with gradually increasing urban densities, were commonly ignored. Here, we proposed an impervious surface area (ISA) based urban CA model that can simulate the urban fractional change within each grid, using annual urban extent time series data from satellite observations. We implemented the developed ISA-based urban CA model in Beijing (China) and evaluated its performance through model comparison and scenario analyses. We found the ISA-based urban CA model can well capture the dynamics of urban sprawl with improved performance compared to the traditional urban CA model. The spatial pattern of modeled ISA is generally consistent with observed satellite data, with the root mean square error between the modeled and referred results of 0.14 across all changed pixels. Besides, results from our developed model agree well with the binary urban CA model, with notably reduced overestimation errors. The urban fractional change within each grid revealed in our model can provide more details than the traditional binary urban CA models, showing great potential in supporting sustainable urban development. Furthermore, the developed ISA-based urban CA model can be used for regional, even global scale urban sprawl modeling with urban fractional information in each grid, to support decision-making on the sustainable management and conservation of natural land resources.