Environment and Interdisciplinary Development

Land Use Change Analysis in the Meighan Wetland Basin: A Remote Sensing and GIS Approach

Document Type : Original Article

Authors

Azadeh Kazemi 1
Mohammadreza Gili 2
Mohammad Hossein Mohammadi Ashnani 1

1 Department of Environmental Science and Engineering, Faculty of Agriculture and Environment, Arak University, Arak, Iran Environmental Sciences Research Institute, Arak University, Arak, Iran

2 Department of Environmental Science and Engineering Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

https://doi.org/10.22034/envj.2025.515037.1490
Abstract
Introduction:  Land use changes fundamentally reshape the sustainability of natural ecosystems, with wetlands being among the most vulnerable biomes due to their critical role in providing ecosystem services. The Meighan Wetland, one of Iran's key habitats for migratory birds, has undergone substantial land use transformations in recent decades due to anthropogenic and climatic changes. Understanding the dynamics of land use/land cover changes and quantifying the spatiotemporal patterns of wetland loss are essential for addressing environmental challenges and risks, understanding trends, identifying sensitive and vulnerable zones, restoring degraded areas, sustainably managing wetland resources, and developing informed policies and future planning strategies.  
Materials and Methods:  This study aimed to identify and analyze land use changes in the Meighan Wetland basin over more than two decades (1998–2020) using remote sensing data and Geographic Information Systems (GIS). Landsat 5 TM and Landsat 8 OLI_TIRS satellite images were employed, with radiometric and geometric corrections applied to the images, achieving an RMSE of 0.45. Image processing was conducted using the Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM) methods. To evaluate classification accuracy, an error matrix was generated, and accuracy assessment was performed using overall accuracy, Kappa coefficient, producer’s accuracy, and user’s accuracy. Post-classification change detection was applied to identify changes over time. The extracted land use maps for each study period were analyzed for the different sub-basins within the Meighan watershed, comparing the areas of major land use classes, including water bodies, residential-industrial zones, rainfed croplands, rocky outcrops, and rangelands. The land use change detection and analysis were conducted using classification models in ArcGIS.  
Results:  The accuracy assessment validated the high reliability of the generated land use maps. For SVM and MLC classification methods, Kappa coefficients of 0.68 and 0.91, and overall accuracies of 71.57% and 93.12% were obtained for 1998, respectively. For 2020, Kappa coefficients of 0.90 and 0.92, and overall accuracies of 93.11% and 94.56% were achieved. The MLC method outperformed SVM in classification accuracy. During the investigation period, rangeland cover declined by 16.93%, with the highest reduction observed in the Ebrahimabad sub-basin (61% decrease). Agricultural land, particularly irrigated fields and orchards, increased by 12.35%, while rainfed croplands expanded in all sub-basins except Karharood. Residential-industrial land use exhibited an increasing trend across all sub-basins except Ebrahimabad.  
Discussion:  The 22-year analysis reveals that the Meighan Wetland basin, as a sensitive and fragile ecosystem, has experienced extensive land use and cover changes. Climate change, human activities, and excessive resource exploitation have significantly contributed to wetland shrinkage. These factors, interacting in a complex manner, have led to an unsustainable development cycle that threatens water and soil resources while altering the region’s ecological functions. Failure to implement strategic management could shift the basin from an ecosystem service provider to a primary source of saline dust, posing severe public health risks to surrounding communities. This research highlights the critical role of remote sensing data and GIS-based analyses in detecting environmental change patterns in wetlands and providing precise spatiotemporal data to support decision-making and the development of effective environmental management strategies.  

Keywords

Land Use Change
Meighan Wetland
Remote Sensing
Geographic Information System
Environmental Management
Satellite Imagery

Subjects

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Environment and Interdisciplinary Development
Volume 10, Issue 89
Summer 2025
Pages 109-135

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