Scenario Prediction and Analysis of Urban Growth Using SLEUTH Model

Feng, Huihui; Liu, Huiping; Lu, Yawei

doi:10.1016/s1002-0160(12)60007-1

Cited by 54 publications

(20 citation statements)

References 42 publications

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“…Few scholars around the world are used the SLEUTH model to simulate urban growth of a city under different scenarios viz., historical trend, managed growth, ecologically sustainable growth scenarios (Jantz et al, 2004;Feng et al, 2012;Osman et al, 2016), diffuse or more compact growth (Solecki and Oliveri, 2004;Osman et al, 2016;Goodarzi et al, 2017). However, the assessment of the future simulations based on urban growth models with the city development plans are still missing.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

2018

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Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions (GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority (UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.

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Section: Introductionmentioning

confidence: 99%

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

2018

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“…It implements three consecutive phases on different data scales: coarse (100 m), fine (50 m), and final (30 m) calibration, where each stage produces 13 measures for the "goodness of fit" [19]. The Lee-Sallee index is the principal measure used due to its ability to assess the spatial fit [21]. The metric is the ratio of the intersection and the union of the simulated and actual urban areas in the control years [21].…”

Section: Sleuth Calibrationmentioning

confidence: 99%

Integrating Data-Driven and Participatory Modeling to Simulate Future Urban Growth Scenarios: Findings from Monastir, Tunisia

et al. 2020

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Current rapid urbanization trends in developing countries present considerable challenges to local governments, potentially hindering efforts towards sustainable urban development. To effectively anticipate the challenges posed by urbanization, participatory modeling techniques can help to stimulate future-oriented decision-making by exploring alternative development scenarios. With the example of the coastal city of Monastir, we present the results of an integrated urban growth analysis that combines the SLEUTH (slope, land use, exclusion, urban extent, transportation, and hill shade) cellular automata model with qualitative inputs from relevant local stakeholders to simulate urban growth until 2030. While historical time-series of Landsat data fed a business-as-usual prediction, the quantification of narrative storylines derived from participatory scenario workshops enabled the creation of four additional urban growth scenarios. Results show that the growth of the city will occur at different rates under all scenarios. Both the “business-as-usual” (BaU) prediction and the four scenarios revealed that urban expansion is expected to further encroach on agricultural land by 2030. The various scenarios suggest that Monastir will expand between 127–149 hectares. The information provided here goes beyond simply projecting past trends, giving decision-makers the necessary support for both understanding possible future urban expansion pathways and proactively managing the future growth of the city.

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“…Urbanization in this region is characterized by a "bottom-up" mode, which worsens the air pollution [57]. By contrast, afforestation over the region would be an effective way to improve the air quality.…”

Section: In Situ Scalementioning

confidence: 99%

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

2017

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Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM 2.5 ) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon's diversity index (SHDI). Then, the correlation between PM 2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM 2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM 2.5 at a relatively large scale. Furthermore, regional PM 2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM 2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM 2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM 2.5 pollution for each urban agglomeration.

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Scenario Prediction and Analysis of Urban Growth Using SLEUTH Model

Cited by 54 publications

References 42 publications

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

Integrating Data-Driven and Participatory Modeling to Simulate Future Urban Growth Scenarios: Findings from Monastir, Tunisia

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

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