Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Zaheer Abbas, Guang Yang, Yuanjun Zhong and Yaolong Zhao
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Zaheer Abbas: School of Geography, South China Normal University, Guangzhou 510631, China
Guang Yang: School of Geography, South China Normal University, Guangzhou 510631, China
Yuanjun Zhong: Lands and Resource Department of Guangdong Province, Surveying and Mapping Institute, Guangzhou 510500, China
Yaolong Zhao: School of Geography, South China Normal University, Guangzhou 510631, China

Land, 2021, vol. 10, issue 6, 1-26

Abstract: Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland.

Keywords: change analysis; transition potential modeling; CA-ANN; prediction; intensity analysis; Greater Bay Area (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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