Guiding SLEUTH land-use/land-cover change modeling using multicriteria evaluation: towards dynamic sustainable land-use planning

Abstract

Upgrading the SLEUTH urban-growth and land-use-change model, realizing its full capability in modeling change simultaneously in land-use and land-cover types, and using it as a self-organizing dynamic land-use planning tool have been the three main objectives of this study. In doing so, SLEUTH was applied to design a better plan for future and assess two scenarios concerning land-use and land-cover changes in Gorgan Township of the Golestan Province of Iran. Four land-use and land-cover maps were derived from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus imagery through a hybrid method including unsupervised, supervised, and on-screen classification for four years. To provide a more desirable forecast of future land-use and land-cover changes, SLEUTH's exclusion layer was combined with an urbanization-suitability layer from a multicriteria evaluation (MCE) using fifteen map layers that most influence land suitability for urban development. The layers used in the MCE process were related to landform, vegetation, soil and geology, and surrogate socioeconomic factors; hence, they portrayed the desirability of the urban growth. SLEUTH was used for forecasting with both this new and a standard exclusion layer. Using the new layer, the fragmentation of the future land-use pattern was controlled and urban development along roads was restrained, thereby safeguarding the remaining urban green space and remnant rural vegetation patches. The results were also compared with a separate site selection process for future urban development showing the desirability of MCE-guided SLEUTH modeling over original SLEUTH and the standalone urban MCE in terms of landform, surrogate socioeconomic factors, and landscape metrics such as patch size, shape, and proximity and fractal dimension. As SLEUTH derives change rules simultaneously for different land-use and land-cover types in a self-modifying self-organizing manner, we showed the approach can be regarded as a tool for dynamic land-use planning.

Evaluating the strategy of decentralized urban land-use planning in a developing region

“…The cumulative probability of urbanization image derived from the prediction mode implies land suitability for urbanization. In this case, SLEUTH's ability in identifying potential areas for urbanization can also be evaluated by assessing the urbanization capability of the allocated lands through an urbanization suitability layer (Mahiny and Clarke 2012). A detailed analysis of both scenarios is provided in the following sections for sustainability of urban areas from the standpoints of LULC suitability parameters and manageability of urban growth patterns.…”

Managing urban coastal areas through landscape metrics: An assessment of Mumbai's mangrove system

“…The long-term effect of these actions can support sustainable development at the regional level, providing a systemic control that optimises the use of available resources as drivers of sustainable decision-making practices (Nijkamp et al, 1992;McGee et al, 2012). Significant contributions in the field of Geographic Information Systems (GIS) that relate land use/land cover change (LULC) (Koomen et al, 2007) have allowed for a bridge thorough empirical analysis of the consequences of urban change on different scales (Mahiny and Clarke, 2012). This has been a result of the growing number of remote sensing techniques, which aid in more accurate satellite imagery (Wentz et al, 2008) that, when allied to spatial analysis, provide more accurate methods for understanding the spatial morphology of cities and the environment and can subsequently lead to more sustainable urban regions.…”

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

“…In unsupervised classification raster cells are grouped prior to classification, while in supervised classification an analyst assigns a subset of cells to train the classification algorithm [1]. Land cover classifications are versatile and often used in climate modeling [2], biodiversity monitoring [3], studies of landscape change [4] and land use planning [5]. In forest management, land cover classifications are frequently used to inform management activities such as timber harvest [6], forest restoration [7], fire risk mitigation [8], and preservation of rare habitats [9].…”