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This study explores the dynamics of wetland and dryland transitions in Northwestern Riau, Indonesia, using Sentinel-2 satellite imagery combined with advanced geospatial indices. The analysis employs the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Moisture Index (NDMI) to evaluate changes in vegetation, water content, and soil moisture distribution over time. The findings highlight substantial shifts between wetland and dryland conditions, particularly in coastal and lowland areas, influenced by seasonal variations and human activities such as land conversion and agricultural expansion. The NDVI identifies dense vegetation in inland regions, while the MNDWI and NDMI reveal extensive wetland zones along coastal areas. By integrating geospatial indices with GIS-based mapping, this study provides valuable insights into environmental dynamics, offering a robust framework for monitoring and managing wetland ecosystems. This research underscores the potential of remote sensing technologies in supporting sustainable land use planning, environmental conservation, and effective ecosystem management in rapidly evolving landscapes.

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Assessing Wetland and Dryland Transitions in Northwestern Riau Using Sentinel-2 Imagery

  • 会议论文

  • 发表日期：

    2026年

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  • Husnul Kausarian,
  • Josaphat Tetuko Sri Sumantyo,
  • Batara Batara,
  • Muhammad Zainuddin Lubis,
  • Wenang Anurogo,
  • Pakhrur Razi,
  • Fitri Mairizki

摘要

This study explores the dynamics of wetland and dryland transitions in Northwestern Riau, Indonesia, using Sentinel-2 satellite imagery combined with advanced geospatial indices. The analysis employs the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Moisture Index (NDMI) to evaluate changes in vegetation, water content, and soil moisture distribution over time. The findings highlight substantial shifts between wetland and dryland conditions, particularly in coastal and lowland areas, influenced by seasonal variations and human activities such as land conversion and agricultural expansion. The NDVI identifies dense vegetation in inland regions, while the MNDWI and NDMI reveal extensive wetland zones along coastal areas. By integrating geospatial indices with GIS-based mapping, this study provides valuable insights into environmental dynamics, offering a robust framework for monitoring and managing wetland ecosystems. This research underscores the potential of remote sensing technologies in supporting sustainable land use planning, environmental conservation, and effective ecosystem management in rapidly evolving landscapes.