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<p>Understanding Land Use and Land Cover (LULC) changes is crucial for effective environmental management and sustainable urban planning. This paper examines the LULC dynamics of Yancheng, Taizhou, and Nantong in Jiangsu, China, over 20&#xa0;years (2003–2023) using Landsat satellite imagery. Classification was performed using machine learning algorithms in Google Earth Engine (GEE) from Landsat images, with Random Forest (RF) and Support Vector Machine (SVM) as the primary techniques. The kappa coefficient and overall accuracy metrics were used to evaluate accuracy. Urban area expansion, especially the growth of cities at the expense of cropland, illustrates the high level of urbanization and meets the significant figure of 2,072.24&#xa0;km<sup>2</sup>. Wetland loss is also noticeable, with 309.78&#xa0;km<sup>2</sup> of wetlands lost to water bodies, indicating significant hydrological change. In addition, 381.31&#xa0;km<sup>2</sup> of cropland was flooded, converting the areas into wetlands, demonstrating land cover change driven by complex, interacting natural and anthropogenic processes. The analysis also reveals that net urban growth reached 3,047.31&#xa0;km<sup>2</sup>, driven by industrial development and other infrastructure expansion. Some reversals were also noted, such as 1,053.80&#xa0;km<sup>2</sup> of urban land converting to croplands, possibly due to urban farming and land reclamation efforts. The study underscores the role of remote sensing and GIS in monitoring LULC transitions and provides policymakers with insights into sustainable land management. Future research should integrate socio-economic data to understand the drivers of these changes better and support balanced urban growth strategies.</p>

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Spatio-temporal analysis of land use and land cover changes: assessing urban expansion and ecological transformations (2003–2023)

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Yanyan Dong,
  • Xiaogao Chang

摘要

Understanding Land Use and Land Cover (LULC) changes is crucial for effective environmental management and sustainable urban planning. This paper examines the LULC dynamics of Yancheng, Taizhou, and Nantong in Jiangsu, China, over 20 years (2003–2023) using Landsat satellite imagery. Classification was performed using machine learning algorithms in Google Earth Engine (GEE) from Landsat images, with Random Forest (RF) and Support Vector Machine (SVM) as the primary techniques. The kappa coefficient and overall accuracy metrics were used to evaluate accuracy. Urban area expansion, especially the growth of cities at the expense of cropland, illustrates the high level of urbanization and meets the significant figure of 2,072.24 km2. Wetland loss is also noticeable, with 309.78 km2 of wetlands lost to water bodies, indicating significant hydrological change. In addition, 381.31 km2 of cropland was flooded, converting the areas into wetlands, demonstrating land cover change driven by complex, interacting natural and anthropogenic processes. The analysis also reveals that net urban growth reached 3,047.31 km2, driven by industrial development and other infrastructure expansion. Some reversals were also noted, such as 1,053.80 km2 of urban land converting to croplands, possibly due to urban farming and land reclamation efforts. The study underscores the role of remote sensing and GIS in monitoring LULC transitions and provides policymakers with insights into sustainable land management. Future research should integrate socio-economic data to understand the drivers of these changes better and support balanced urban growth strategies.