Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas

Abstract

Until now, few studies have used the mainstreaming models to simulate the land use changes in the cities of rapid urbanizing regions. Therefore, we aimed to develop a methodology to simulate the land use changes in rapid urbanizing regions that could reveal the land use change trend in the cities of the regions. Taking the urban areas of Wuhan, a typical rapid urbanizing region in China, as the study area, this study built a Markov chain–artificial neural network (ANN)–cellular automaton (CA) coupled model. The model used land use classification spatial data with a spatial resolution of 5 m in 2010 and 2020, obtained by remote sensing image interpretation, and data on natural and socio-economic driving forces for land use change simulation. Using the coupled model, the land use patterns of Wuhan urban areas in 2020 were simulated, which were validated in comparison with the actual land use data in 2020. Finally, the model was used to simulate the land uses in the study area in 2030. The model validation indicates that the land use change simulation has a high accuracy of 90.7% and a high kappa coefficient of 0.87. The simulated land uses of the urban areas of Wuhan show that artificial surfaces will continue to expand, with an area increase of approximately 7% from 2020 to 2030. Moreover, the area of urban green spaces will also increase by approximately 7%, while that of water bodies, grassland, cropland, and forests will decrease by 12.6%, 13.6%, 34.9%, and 1.3%, respectively, from 2020 to 2030. This study provides a method of simulating the land use changes in the cities of rapid urbanizing regions and helps to reveal the patterns and driving mechanisms of land use change in Wuhan urban areas.

Urban Ecological Quality Assessment Based on Google Earth Engine and Driving Factors Analysis: A Case Study of Wuhan City, China

“…As Wuhan has grown and developed, the central urban areas have experienced significant land cover changes, with the conversion of green spaces, wetlands, and agricultural lands into built-up areas [67]. This process has led to the degradation of natural habitats, reduction in biodiversity, and increased environmental pollution [68]. Moreover, the high concentration of population and industrial activities in the urban core has placed additional pressure on the local ecosystem, contributing to the lower EQ in these areas [69].…”

Considering Regional Connectivity and Policy Factors in the Simulation of Land Use Change in New Areas: A Case Study of Nansha New District, China

“…Additionally, scholars [41] proposed the factor of merit (FoM) as an alternative measure to evaluate simulation accuracy. FoM relies solely on the number of varying cells in the simulated process and offers a more robust assessment of simulation accuracy [13,16] . Building upon this, in this study, historical data was used for simulation, with the 2018 dataset serving as the initial input and the 2021 dataset used for verifying the validity of the CA model [1] .…”

“…The rapid and intense land use changes often observed in new areas within cities necessitate close monitoring and thorough understanding. When utilizing CA models for geographic simulation, researchers have found that the expansion of urban areas is influenced by a series of driving factors, including topographic conditions, transportation factors, and socio-economic development [14][15][16] .…”

“…Traditional drivers of land use change have been extensively studied, encompassing natural factors, transportation factors, and location factors [17] . Among these, particular attention has been given to natural factors, including terrain conditions represented by the digital elevation model (DEM) [6,7,9,13,[18][19][20][21] , elevation [10,16,22,23] , slope [6,7,10,12,13,16,18,19,[21][22][23] , and aspect [6,7,16,18] , among others. Notably, slope stands out as one of the most significant topographic factors influencing urban sprawl [7] .…”

“…Furthermore, economic and social development factors are widely employed in land use change and simulation studies. These factors consist of points of interest (POI) data [6,16,18,22] , nighttime lighting (NTL) data [18] , GDP [6,[18][19][20] , economic development indicators [18] , population statistics [6,19,23,25] , and population density metrics [13,16,18,20,23] .…”