Analysis of the Effectiveness of Urban Land-Use-Change Models Based on the Measurement of Spatio-Temporal, Dynamic Urban Growth: A Cellular Automata Case Study

China is undergoing rapid urbanization, which has caused undesirable urban sprawl and ecological deterioration. Urban growth boundaries (UGBs) are an effective measure to restrict the irrational urban sprawl and protect the green space. However, the delimiting method and control measures of the UGBs is at the exploratory stage in China. In this paper, a cellular automata model based on multi-criteria evaluation (MCE-CA) was proposed to delimit the UGBs. The MCE-CA model considers influencing factors related to urban growth and generates UGBs based on spatiotemporally dynamic simulations. The MCE-CA model was applied to generate the UGBs of Jiayuguan City in 2020 and 2030, the results show that the simulation accuracy is higher than 0.8 and the compactness increases to 0.23, which demonstrates that the MCE-CA model is an effective model for delimiting UGBs. Moreover, the MCE-CA model can corporate the contradiction between environmental protection and urban development, promoting urban smart growth and sustainable development. UGBs is an effective tool for China to realize ecological civilization construction and improve the spatial governance ability, and the MCE-CA model can be used to assist planners in delimiting future UGBs, this study provides a methodological reference for future research of UGBs in Chinese cities.

Section: Kappa Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban Expansion and Growth Boundaries in an Oasis City in an Arid Region: A Case Study of Jiayuguan City, China

Ren

Liu

et al. 2019

“…Many studies, based on historical LULC data, have simulated future land use change and urban expansion. Numerous methods and models such as cellular automata, Markovian chain, and agent-based and CLUE-S models are widely used in the study of urban expansion prediction [14,15]. A generic framework for path-dependent industrial land transition and even commercial software such as Metronamica and Land Use Scanner have also been developed to analyze LULC change [16].…”

Section: Introductionmentioning

confidence: 99%

Accelerating Cities in an Unsustainable Landscape: Urban Expansion and Cropland Occupation in China, 1990–2030

Cui

Liu

et al. 2019

It is crucial to assess the effects of urban expansion on croplands to allow sustainable urbanization and cropland supply. However, owing to the complexity of land conversion and various land policies in China, it is difficult to quantify the cropland dynamics and implications of urban expansion throughout the whole accelerated stage of urbanization. This study was based on land use data from 1990 to 2015 and urban expansion data from 2000 to 2030, analyzing urban expansion and predicting its impact on croplands. We found that urban area would continue to increase and croplands would contribute more than 70% of the urban expansion area. The urban area in China will likely reach 71.6–87.0 thousand km2 or more by 2030. Although the overall area of croplands may remain at a similar magnitude in future decades, our findings imply that croplands will tend to shift northward, resulting in some potential challenges owing to resource limitations in northern regions. Our study provides a new perspective in terms of assessing future cropland dynamics and the effects of urban expansion and highlights the significance of ensuring a realistic land policy in the future.

“…Associated research topics are focused on remote sensing monitoring methods and simulation models, spatio-temporal characteristics and evolution laws, impacting factors and driving mechanisms, change effect assessment, and landscape structure optimization [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The simulation models employed for urban expansion and land use change include the Markov, CA (Cellular Automata)-Markov, ANN (Artificial Neural Network)-Markov, LTM (Land Transformation Model), ABM (agent-based model), CLUE-S, and SLEUTH models [18,19].…”

Section: Introductionmentioning

confidence: 99%

Study of the Simulated Expansion Boundary of Construction Land in Shanghai Based on a SLEUTH Model

Shi

2017

Abstract:The SLEUTH model is one of the recent methodological advances, but its data sources are mainly interpreted from remote sensing images. It doesn't cover the whole urban area and exhibits technical errors. Moreover, the relationships between land use and land cover change (LUCC) and sustainability have seldom been discussed. Aimed at addressing these shortcomings, we focus on a wider range and use different data sources to simulate and predict the boundary of urban construction land expansion, and analyze the relationship between the expansion style of urban land and the change of ecological service value. Based on Shanghai land use map data, which covers the whole city scope and eliminates the error in data processing, we use a SLEUTH model to simulate the urban growth mode in Shanghai in 2020 under two scenarios, i.e., the natural growth mode and ecological priority mode, which are different from the previous scenario modes such as unprotected, light protected, moderately protected, and heavily protected modes. The results are as follows:(1) The development of urban land in Shanghai is most reasonable under the ecological priority mode as its ecological service value is higher than that of the natural growth mode; (2) Construction land expansion in Shanghai is very slow under the ecological priority mode, wherein construction land growth mainly occurs in the northwest, southwest, and southeast of Shanghai, and the northwest part of Chongming Island; (3) The surrounding area of the central city is given priority over edge growth, where the growth range is small. The outer suburbs are given priority for widespread growth, which is synchronous with the slow growth of roads.