1- Professor ، Department of Physical Geography ، Faculty of Social Sciences ، University of Mohaghegh Ardabili ، Ardabil ، Iran , s.asghari@uma.ac.ir
2- Master's Student ، Remote Sensing and Geographic Information Systems (GIS) ، Department of Physical Geography ، Faculty of Social Sciences ، University of Mohaghegh Ardabili ، Ardabil ، Iran.
3- Associate Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran.
2- Master's Student ، Remote Sensing and Geographic Information Systems (GIS) ، Department of Physical Geography ، Faculty of Social Sciences ، University of Mohaghegh Ardabili ، Ardabil ، Iran.
3- Associate Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran.
Abstract: (147 Views)
Objective: Land use/land cover (LULC) changes, as one of the main anthropogenic drivers, significantly influence runoff patterns and intensify flood hazards. This study aims to assess the impact of land use changes on flood hazard zonation over the period 2015 to 2024 in the Samian watershed, located in Ardabil Province, Iran.
Methodology: Satellite imagery from Landsat 7, Landsat 8, and Sentinel-2 was utilized to extract land use maps for the years 2015 and 2024 using the Google Earth Engine platform. LULC classification was performed using the Classification and Regression Trees (CART) algorithm. Subsequently, the Modified Flash Flood Potential Index (MFFPI) model was applied by integrating key environmental layers, including slope, flow accumulation, land use, geology, curvature, and soil texture, within the ArcMap environment to generate flood hazard zonation maps.
Findings: The results indicated substantial LULC changes between 2015 and 2024, including an 18.47% increase in irrigated agricultural lands, a 9.38% increase in residential areas, and a 25.85% rise in sparse rangelands. In contrast, dry farming lands decreased by 25.21%, dense rangelands by 9.14%, and snow-covered areas by 98.61%. These changes have led to a notable expansion of high-risk flood zones. The LULC classification achieved a high overall accuracy and Kappa coefficient exceeding 0.98, indicating reliable results.
Conclusion: The expansion of impervious surfaces and reduction in natural vegetation cover have increased surface runoff and, consequently, the extent of high-risk flood-prone areas. The MFFPI model, by incorporating both environmental and anthropogenic factors, proved to be an effective tool for flood hazard prediction and management.
Methodology: Satellite imagery from Landsat 7, Landsat 8, and Sentinel-2 was utilized to extract land use maps for the years 2015 and 2024 using the Google Earth Engine platform. LULC classification was performed using the Classification and Regression Trees (CART) algorithm. Subsequently, the Modified Flash Flood Potential Index (MFFPI) model was applied by integrating key environmental layers, including slope, flow accumulation, land use, geology, curvature, and soil texture, within the ArcMap environment to generate flood hazard zonation maps.
Findings: The results indicated substantial LULC changes between 2015 and 2024, including an 18.47% increase in irrigated agricultural lands, a 9.38% increase in residential areas, and a 25.85% rise in sparse rangelands. In contrast, dry farming lands decreased by 25.21%, dense rangelands by 9.14%, and snow-covered areas by 98.61%. These changes have led to a notable expansion of high-risk flood zones. The LULC classification achieved a high overall accuracy and Kappa coefficient exceeding 0.98, indicating reliable results.
Conclusion: The expansion of impervious surfaces and reduction in natural vegetation cover have increased surface runoff and, consequently, the extent of high-risk flood-prone areas. The MFFPI model, by incorporating both environmental and anthropogenic factors, proved to be an effective tool for flood hazard prediction and management.
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