Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns

Eyelade, Damilola; Clarke, Keith; Ijagbone, Ighodalo

doi:10.1080/13658816.2021.2011292

Cited by 8 publications

(1 citation statement)

References 49 publications

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“…SLEUTH model overcomes such limitations and provides the prediction output spatially based on several factors like slope, road transportation network etc., including LULC (Alay et al 2021). The SLEUTH model works well even for the medium resolution images as it can achieve a good model of fit value on data of up to 30 m resolution (Eyelade et al 2021). Keith Clarke developed the SLEUTH model in Santa Barbara at the University of California (Clarke et al 1997).…”

Section: Introductionmentioning

confidence: 99%

Urban modelling and forecasting of landuse using SLEUTH model

Kumar

Agrawal

2022

Int. J. Environ. Sci. Technol.

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The present study performs landcover modelling using the SLEUTH model. The urban land use changing factors are calibrated to predict the Land Use Land Cover (LULC) for a densely populated and developing smart city, Prayagraj, India. This research aims to use the SLEUTH model for simulating the future urban growth with the help of historical LULC (1990LULC ( -2020, road network and elevation data. The influence of road gravity and slope resistant coefficients is very significant in this study's outcome. The built-up area of the region increased from 40.22 km 2 (5.10% of total area) in 1990 to 85.89 km 2 (10.89%) in 2020. According to prediction results, in the next 20 years, the built-up growth rate would be 1.9% and approximated built-up area would be 118.66 km 2 (14.98%) in the year 2040. The quality of the result has been quantified in terms of best fit value of Optimal SLEUTH Metric (OSM) and validated against the existing LULC. The study utilises a spatially explicit urban growth model with 30 m resolution remote sensing data and provides future landuse of Prayagraj city.

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Section: Introductionmentioning

confidence: 99%

Urban modelling and forecasting of landuse using SLEUTH model

Kumar

Agrawal

2022

Int. J. Environ. Sci. Technol.

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Future land use land cover changes in El-Fayoum governorate: a simulation study using satellite data and CA-Markov model

Atef,

Ahmed,

Abdel-Maguid

2023

Stoch Environ Res Risk Assess

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This study aims to monitor the changes in land use land cover (LULC) in El-Fayoum governorate over time (past, present, and future) to provide current information for stakeholders involved in land use planning. The study utilized Landsat satellite images and applied the Support Vector Machine algorithm using ArcGIS Pro 2.8.3 to classify the images into four major LULC classes: water, desert, built-up, and agricultural. To evaluate the accuracy of the LULC maps, the study used kappa statistical parameters, which ranged from 0.91 to 0.94, indicating acceptable results for further analysis. To predict spatio-temporal LULC changes, the study considered biophysical and socioeconomic factors such as distance to canals, distance to roads, distance to urban areas, a digital elevation model, and slope. A combination of Multi-Criteria Evaluation, a Fuzzy Membership Function, and the Analytic Hierarchy Process were employed to develop a land cover suitability map. The Hybrid CA-Markov model of the IDRISI-TerrSet software was used to simulate LULC changes, and the accuracy of the simulation was validated using 2020 imagery data. The values gained from the kappa indices for agreement (standard) = 0.9006, kappa for lack of information (no) = 0.916, and kappa for location at grid cell level (location) = 0.9572 demonstrate that the results of the simulation of the LULC changes were deemed satisfactory. The future scenarios modeled in LULC indicate a significant change in the LULC classes over time, specifically for 2030. The change rates of agriculture, desert, built-up, and water areas in El-Fayoum in 2030 compared to 2020 are estimated to be 9.68%, − 17.58%, 133.62%, and 6.06%, respectively. These findings establish both past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

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