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Land use and land cover (LULC) substantially impact rising LST, considerably affecting human health, the outdoor environment, and urban biodiversity. This study aims to assess the changing LULC and urban fragmentation (FI) impacts on seasonal LST (summer and winter) in a semi-arid climate city. For decadal observations, Landsat satellite imagery from 2003 to 2023 was utilized. The Support Vector Machine (SVM) technique was employed for LULC classification and the mono-window method was used to estimate the LST. LULC distribution and seasonal LST characteristics were projected for Chhatrapati Sambhajinagar, India, in 2033 using the QGIS software’s Cellular Automata-Artificial Neural Network (CA-ANN) model. The CA-ANN prediction of LULC shows the changes from 2023 to 2033, that there is a projected decline of 2.03% in barren land and 2.75% in vegetation. While the built-up area is projected to increase by 4.89%. By 2033 suggested that the maximum LST will increase by 4.65 °C in winter and 7.87 °C in summer. Built-up fragmentation between 5.5 ≤ FI ≥ 30 can reduce LST between 6.04 and 10.12 °C in winter and 2.74–4.58 °C in summer in the selected city for the studied period. This study’s conclusions will help city officials, policymakers, and urban planners create comprehensive micro-level urban development plans to ensure efficient land use planning, sustainable urban development, and increased outdoor thermal comfort in semi-arid cities.

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CA-ANN Based Predictive Modeling of Land Use Land Cover, Urban Fragmentation, and Seasonal Land Surface Temperature: Insights from Semi-arid Climate City

  • 会议论文

  • 发表日期：

    2026年

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  • Sheewani Patle,
  • Vidya V. Ghuge

摘要

Land use and land cover (LULC) substantially impact rising LST, considerably affecting human health, the outdoor environment, and urban biodiversity. This study aims to assess the changing LULC and urban fragmentation (FI) impacts on seasonal LST (summer and winter) in a semi-arid climate city. For decadal observations, Landsat satellite imagery from 2003 to 2023 was utilized. The Support Vector Machine (SVM) technique was employed for LULC classification and the mono-window method was used to estimate the LST. LULC distribution and seasonal LST characteristics were projected for Chhatrapati Sambhajinagar, India, in 2033 using the QGIS software’s Cellular Automata-Artificial Neural Network (CA-ANN) model. The CA-ANN prediction of LULC shows the changes from 2023 to 2033, that there is a projected decline of 2.03% in barren land and 2.75% in vegetation. While the built-up area is projected to increase by 4.89%. By 2033 suggested that the maximum LST will increase by 4.65 °C in winter and 7.87 °C in summer. Built-up fragmentation between 5.5 ≤ FI ≥ 30 can reduce LST between 6.04 and 10.12 °C in winter and 2.74–4.58 °C in summer in the selected city for the studied period. This study’s conclusions will help city officials, policymakers, and urban planners create comprehensive micro-level urban development plans to ensure efficient land use planning, sustainable urban development, and increased outdoor thermal comfort in semi-arid cities.