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<p>Coastal cities undergoing rapid urban expansion often experience sustained conversion of agricultural and vegetated land into built-up areas under increasing development pressure. The Puducherry region represents a rapidly transforming coastal landscape where recent land-use dynamics remain insufficiently quantified. This study analysed land use and land cover (LULC) changes for 2004, 2014 and 2024 using multi-sensor satellite data. Supervised classification using a support vector machine (SVM) algorithm achieved overall accuracies of 96–97% with kappa values between 0.93 and 0.94. The results indicate substantial declines in agriculture/fallow land from 121.92 to 93.90 km<sup>2</sup> and vegetation from 122.05 to 82.97 km<sup>2</sup>, accompanied by a nearly fourfold expansion of built-up area from 25.00 to 90.94 km<sup>2</sup> over two decades. Further, a cellular automata-artificial neural network (CA-ANN) model within the modules for land use change evaluation (MOLUSCE) plugin was calibrated with elevation, slope, distance to roads and distance to built-up areas as driver variables. Validation of the simulated 2024 achieved an overall correctness of 82.39% (Koverall = 0.75, Khisto = 0.91 and Kloc = 0.82). Projections for 2034 indicate continued expansion of built-up land, which is expected to reach 136.31 km<sup>2</sup>. These findings provide spatially explicit evidence of ongoing land conversion and establish a quantitative basis for assessing future land-use change in coastal regions.</p>

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Spatiotemporal analysis and prediction of land use and land cover change in a rapidly urbanizing coastal region of India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月25日

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  • Sethumadhavan Parthasarathi Akila,
  • Lingassamy Arul Pragasan

摘要

Coastal cities undergoing rapid urban expansion often experience sustained conversion of agricultural and vegetated land into built-up areas under increasing development pressure. The Puducherry region represents a rapidly transforming coastal landscape where recent land-use dynamics remain insufficiently quantified. This study analysed land use and land cover (LULC) changes for 2004, 2014 and 2024 using multi-sensor satellite data. Supervised classification using a support vector machine (SVM) algorithm achieved overall accuracies of 96–97% with kappa values between 0.93 and 0.94. The results indicate substantial declines in agriculture/fallow land from 121.92 to 93.90 km2 and vegetation from 122.05 to 82.97 km2, accompanied by a nearly fourfold expansion of built-up area from 25.00 to 90.94 km2 over two decades. Further, a cellular automata-artificial neural network (CA-ANN) model within the modules for land use change evaluation (MOLUSCE) plugin was calibrated with elevation, slope, distance to roads and distance to built-up areas as driver variables. Validation of the simulated 2024 achieved an overall correctness of 82.39% (Koverall = 0.75, Khisto = 0.91 and Kloc = 0.82). Projections for 2034 indicate continued expansion of built-up land, which is expected to reach 136.31 km2. These findings provide spatially explicit evidence of ongoing land conversion and establish a quantitative basis for assessing future land-use change in coastal regions.