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This study investigates the spatiotemporal dynamics of land use and land cover (LULC) changes in the Kamrup Metropolitan District, Assam, India, over a period of 22 years from 2000 to 2022. The primary objective is to analyze the extent of urban expansion and its impacts on agricultural land, vegetation, barren land, and water bodies. Using satellite imagery from Landsat 5 TM and Landsat 8 OLI, and incorporating spatial variables such as Digital Elevation Model (DEM), slope, and proximity to infrastructure, these elements were integrated into the Land Change Modeler (LCM) within the TerrSet software. This integration enhances predictive accuracy and provides a comprehensive understanding of the factors driving LULC changes. The methodological framework employed includes the maximum likelihood classification (MLC) technique, which was validated through several accuracy metrics, achieving a high Kappa coefficient of 0.8773. This high level of accuracy confirms the model's reliability and applicability in similar studies. The findings indicate significant urban expansion primarily encroaching on agricultural land and vegetation, transforming tunused lands. Areas in close proximity to urban centers and infrastructure were more prone to development, influenced by topographical features such as DEM and slope. Furthermore, the study identifies potential habitat risk zones using a multi-criteria evaluation approach and the Analytical Hierarchy Process (AHP) weightage algorithm. With a consistency ratio of 0.7, the AHP method effectively mapped areas at risk of habitat loss, providing a valuable tool for conservation efforts. This analysis is crucial for urban planning, offering predictive models to manage urban growth sustainably while integrating biophysical and socioeconomic factors to balance development with environmental conservation. The implications of this study are significant for achieving Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 15 (Life on Land). By understanding the dynamics of LULC changes and identifying areas at risk, policymakers and urban planners can make informed decisions to promote sustainable development in Kamrup Metropolitan District. This research provides a robust framework for assessing LULC changes and offers insights that are applicable to other regions facing similar challenges.

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Spatio-Temporal Change Assessment of Land Use Land Cover Change Dynamics and Identification of Potential Habitat Risk Zones to Achieve Sustainable Development, Using AHP and Markov Chain in Kamrup Metropolitan, Assam, India

  • 章节

  • 发表日期：

    2025年

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  • Puja Chutia,
  • Shruti Kanga

摘要

This study investigates the spatiotemporal dynamics of land use and land cover (LULC) changes in the Kamrup Metropolitan District, Assam, India, over a period of 22 years from 2000 to 2022. The primary objective is to analyze the extent of urban expansion and its impacts on agricultural land, vegetation, barren land, and water bodies. Using satellite imagery from Landsat 5 TM and Landsat 8 OLI, and incorporating spatial variables such as Digital Elevation Model (DEM), slope, and proximity to infrastructure, these elements were integrated into the Land Change Modeler (LCM) within the TerrSet software. This integration enhances predictive accuracy and provides a comprehensive understanding of the factors driving LULC changes. The methodological framework employed includes the maximum likelihood classification (MLC) technique, which was validated through several accuracy metrics, achieving a high Kappa coefficient of 0.8773. This high level of accuracy confirms the model's reliability and applicability in similar studies. The findings indicate significant urban expansion primarily encroaching on agricultural land and vegetation, transforming tunused lands. Areas in close proximity to urban centers and infrastructure were more prone to development, influenced by topographical features such as DEM and slope. Furthermore, the study identifies potential habitat risk zones using a multi-criteria evaluation approach and the Analytical Hierarchy Process (AHP) weightage algorithm. With a consistency ratio of 0.7, the AHP method effectively mapped areas at risk of habitat loss, providing a valuable tool for conservation efforts. This analysis is crucial for urban planning, offering predictive models to manage urban growth sustainably while integrating biophysical and socioeconomic factors to balance development with environmental conservation. The implications of this study are significant for achieving Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 15 (Life on Land). By understanding the dynamics of LULC changes and identifying areas at risk, policymakers and urban planners can make informed decisions to promote sustainable development in Kamrup Metropolitan District. This research provides a robust framework for assessing LULC changes and offers insights that are applicable to other regions facing similar challenges.