“…The accuracy assessment of the classified land cover maps is presented in Table 2 . The results revealed overall accuracy (OA) and kappa coefficients ( ) above 85% and found to be satisfactory for further analysis (Saadani et al, 2020 ). Fig.…”
In recent decades, medium-sized Indian cities have experienced accelerated urban growth due to the saturation of large cities. Such rapid urban growth combined with inadequate urban planning has triggered urban sprawl in medium-sized Indian cities. In this context, the present study focuses on the geospatial measurement of urban sprawl in four rapidly expanding Indian medium-sized cities located in diverse physiographic regions, such as Lucknow urban agglomeration (UA), Bhubaneswar UA, Raipur UA, and Dehradun UA. Multi-temporal Landsat imageries from 1991 to 2021 were downloaded for land cover classification through the maximum likelihood classification tool in ArcGIS 10.3. Thereafter, spatiotemporal land cover change detection was performed based on the classified land cover maps. The presence of urban sprawl was detected using the relative entropy index while the urban expansion index quantified the urban sprawl typologies such as edge expansion, leapfrog development, and ribbon development. The results exhibited a rapid rise in built-up land cover from 1991 to 2021. The prevalence of urban sprawl was detected in all four cities as per the relative entropy index. Edge expansion typology of urban sprawl was dominant compared to leapfrog development and ribbon development. Such urban growth phenomenon creates a hindrance in promoting sustainable urban development in medium-sized Indian cities. The results obtained from this paper would assist urban planners and policymakers in developing strategies to encourage planned urban growth. This paper exhibits the potential of geoinformatics to monitor and analyze urban sprawl.
“…The accuracy assessment of the classified land cover maps is presented in Table 2 . The results revealed overall accuracy (OA) and kappa coefficients ( ) above 85% and found to be satisfactory for further analysis (Saadani et al, 2020 ). Fig.…”
In recent decades, medium-sized Indian cities have experienced accelerated urban growth due to the saturation of large cities. Such rapid urban growth combined with inadequate urban planning has triggered urban sprawl in medium-sized Indian cities. In this context, the present study focuses on the geospatial measurement of urban sprawl in four rapidly expanding Indian medium-sized cities located in diverse physiographic regions, such as Lucknow urban agglomeration (UA), Bhubaneswar UA, Raipur UA, and Dehradun UA. Multi-temporal Landsat imageries from 1991 to 2021 were downloaded for land cover classification through the maximum likelihood classification tool in ArcGIS 10.3. Thereafter, spatiotemporal land cover change detection was performed based on the classified land cover maps. The presence of urban sprawl was detected using the relative entropy index while the urban expansion index quantified the urban sprawl typologies such as edge expansion, leapfrog development, and ribbon development. The results exhibited a rapid rise in built-up land cover from 1991 to 2021. The prevalence of urban sprawl was detected in all four cities as per the relative entropy index. Edge expansion typology of urban sprawl was dominant compared to leapfrog development and ribbon development. Such urban growth phenomenon creates a hindrance in promoting sustainable urban development in medium-sized Indian cities. The results obtained from this paper would assist urban planners and policymakers in developing strategies to encourage planned urban growth. This paper exhibits the potential of geoinformatics to monitor and analyze urban sprawl.
“…Simulation models produced in the literature often use low-resolution data (Akin et al, 2014;Bihamta et al, 2014;Jantz et al, 2004;Saadani et al, 2020;Sandamali et al, 2018). To improve model accuracy, high-resolution cadastral data were used in this study.…”
Uncontrolled urban growth is one of the most prominent problems in modern urbanism and planning. Rapid urbanization and population growth cause changes in land cover. In addition, determining the effects of these changes is essential in terms of sustainable urban management policies. Urban growth is a complex, dynamic structure that initiates changes in land cover. For this reason, simulation models are used extensively in planning studies. In this study, land cover simulation of the Sancaktepe district in Istanbul was carried out with the SLEUTH model based on cellular automata (CA). The study aims to identify the damage caused by uncontrolled urbanization. In this context, a scenario was created based on the assumption that forests will be protected based on the changes in land cover that occurred between 1961-2014. The data used in the model were generated from cadastral maps on a parcel basis. For this purpose, four-period data sets (1961-1992-2001-2014) were prepared between 1961-2014. According to the simulation results, 82% of agricultural land, 2% of forest land and 84% of open land will probably be converted into residential use. According to the results, it has been determined that almost all of the open and agricultural land in the towns of Pasakoy and Samandira in the district of Sancaktepe have been converted into residential areas. According to the prediction that the changes in current land cover will continue, no change is expected in forests, while it has been determined that the potential to transform agricultural land and open land into settlement areas is quite high.
“…The crucial concept is learning and producing knowledge from the neurons [57]. Consequently, it is a model approach applied in recent research [56][57][58][59][60][61][62][63][64], determining the influence of variables on LULC change and urban expansion. In research by Losiri et al [63], the authors modeled urban expansion in the Bangkok metropolitan area for 2035 employing the MLP model and using proximity factors of LULC and socioeconomic variables.…”
Section: Introductionmentioning
confidence: 99%
“…In research by Losiri et al [63], the authors modeled urban expansion in the Bangkok metropolitan area for 2035 employing the MLP model and using proximity factors of LULC and socioeconomic variables. Saadani et al [61] applied the MLP model to simulate the spatiotemporal urban expansion in Moroccan metropolitan, exploring the influence of topography and proximity driving factors. Moreover, a comparison of spatial modeling approaches by Mondal et al [62] showed the good performance of the MLP model in representing the urban expansion employing five proximity and LULC likelihood factors.…”
Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.
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