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• W4382372328 endingPage "5854" @default.
• W4382372328 startingPage "5854" @default.
• W4382372328 abstract "The availability of geospatial data infrastructure and earth observation technology can play an essential role in facilitating the monitoring of sustainable urban development. However, in most developing countries, a spatiotemporal evaluation of urban growth is still lacking. Maseru, Lesotho’s capital and largest city, is growing rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in developing countries has been characterized by entangled structures and trends exacerbating numerous negative consequences such as ecological degradation, the loss of green space, and pollution. Understanding the urban land use and land cover (LULC) dynamic is essential to mitigate such adverse impacts. This study focused on mapping and quantifying the urban extension in Maseru, using Landsat imagery from 1988 to 2019, based on the Support Vector Machines (SVM) classifier. We also simulated and predicted LULC changes for the year 2050 using the cellular automata model of an artificial neural network (ANN-CA). Our results showed a notable increase in the built-up area from 15.3% in 1988 to 48% in 2019 and bare soil from 12.3% to 35.3%, while decreased agricultural land (21.7 to 1.7%), grassland (43.3 to 10.5%) and forest vegetation (5.5 to 3.2%) were observed over the study period. The classified maps have high accuracy, between 88% and 95%. The ANN-CA projections for 2050 show that built-up areas will continue to increase with a decrease in agricultural fields, bare soil, grasslands, water bodies and woody vegetation. To our knowledge, this is the first detailed, long-term study to provide insights on urban growth to planners and other stakeholders in Maseru in order to improve the implementation of the Maseru 2050 urban plan." @default.
• W4382372328 created "2023-06-29" @default.
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• W4382372328 date "2023-05-09" @default.
• W4382372328 modified "2023-09-25" @default.
• W4382372328 title "Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data" @default.
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• W4382372328 doi "https://doi.org/10.3390/app13105854" @default.
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