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Prioritizing Effective Factors and Badland Erosion Susceptibility Assessment Using Synthetic Model of Information Value and AHP
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Majid Mohammady *   , Mojtaba Amiri |
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Abstract: (897 Views) |
Introduction
Soil is an important component of geological, hydrological, biological, and environmental processes, and as such, it is always subject to change and transformation. Increasing demand for goods, services, and basic needs, especially in underdeveloped and developing countries, a huge stress has exerted on the top layers of soil. On a global scale, soil erosion and degradation is one of the most important natural phenomenon altering the relief of the landscape. Erosion is often capable of causing several on-site and off-site impacts. Soil degradation and erosion reduce the quality and quantity the soil and are important natural and anthropogenic processes that affect many countries. One of the most important kind of erosion is badland erosion. The term of badlands currently refers to areas of unconsolidated sediment with little or no vegetation, which are useless for agriculture because of their intensely dissected landscape. Badlands develop due to a complex interaction of conditioning factors, such as climatic, hydrologic, geologic and soil properties, topographic characteristics, and land use/land cover. Because soil erosion is a complicated process that is influenced by the properties of the land surface and the soil as well as by environmental factors, quantitatively accurate forecasts of soil erosion and susceptibility mapping are challenging. The main goal of this research is badland erosion susceptibility mapping in Firozkuh watershed using synthetic model of Information Value-AHP. Firozkuh watershed was selected as study area because in this watershed, badlands are the most important contributors to soil erosion because of the condition of climatic, hydrologic, topographic, and reduced vegetation conditions, and as well as presence of susceptible soil and geology formations in this region.
Materials and Methods
At first, distribution map of badland erosion in Firozkuh watershed was prepared using satellite images and field survey. Digital Elevation Model (DEM) map was prepared using the elevation points and lines in the topographic maps prepared by the National Cartography Center of Iran. Slope aspect, slope degree, plan curvature, TWI and elevation classes maps was created using DEM map in ArcGIS10.3 and SAGA-GIS environment. The land use of Firozkuh watershed was created from LANDSAT 8 images of year 2020 using a synthetic method. To map soil characteristics, 30 samples were taken and analyzed in the laboratory. The geology map of the watershed was extracted from the geologic map of Iran with the scale of 1:100000. The distance from river was created in ArcGIS10.3 environment. Average annual rainfall map was developed using rainfall data from meteorological stations. The weight of the conditioning factor classes were calculated using the information value model. AHP model was also used to prioritize conditioning factor. The weight obtained from both models was multiplied together and the final weight was calculated using synthetic model. By combining the weights, the final badland erosion susceptibility map was prepared. In the last step, ROC curve and area under the curve was used for accuracy assessment of the models.
Results and Discussion
To prioritize the conditioning factors of badland susceptibility in Firozkuh watershed, AHP model was used. The results indicated that slope degree, geology, elevation, and distance from rivers have the greatest impact on badland occurrence in the Firozkuh watershed. The weight of the conditioning factor classes were calculated using the information value model. Results showed that the highest weight of the information value is related to the slope more than 35, marl, limestone and shale formations, elevation class of 1712 to 2286 meters, northwest aspect, clay 25 to 33%, rangelands, and distance less than 1150 meters from drainage network.
Two information value models and synthetic model were used to prepare the badland susceptibility map. Accuracy assessment of the models was done using ROC and area under this curve. The area under the curve for the information value and synthetic model was 0.684 and 0.811, respectively. This results show that Information Value was moderate, and synthetic model was very good for badland erosion susceptibility mapping. To check the overall susceptibility of the watershed, the area of very high, high, moderate and low classes was calculated. The land area of the very high and high classes is 44% (about 640 square kilometers) of the Firozkuh watershed for the synthetic model.
Conclusion
In this research, the Information Vale and synthetic models were used for prioritizing the condition factors of badland occurrence and badland susceptibility mapping in the Firozkuh watershed. Eleven conditioning factors including elevation, slope angle, slope aspect, TWI, Plan Curvature, Precipitation, Land-use, geology, distance to river, clay percent, and soil pH were selected and input as spatial layers in a GIS environment. AHP method was used to investigate importance of conditioning factors and their prioritization. The results of conditioning factors importance explained that slope degree and geology have the greatest impact on badland erosion in the Firozkuh watershed. Also, Information Value and synthetic models showed acceptable efficiency to map badland susceptibility in the study area. Results of this research showed that about 44% of the study area has high to very high susceptibility to the badland erosion, so control and management of this phenomenon is essential in Firozkuh watershed. Badland erosion is one of the most important sources of sediment production in the study area and planners should pay attention to this issue in their decisions. Badland susceptibility map is a very important tool that can be used to improve managing future badland erosion in the Firozkuh watershed and other areas affected by badland erosion. |
Article number: 4 |
| Keywords: Badland erosion, Susceptibility map, Information Value, Synthetic model, ROC curve |
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Full-Text [PDF 2033 kb]
(344 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2024/02/26 | Accepted: 2024/07/6 | Published: 2024/09/15
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Mohammady M, Amiri M. Prioritizing Effective Factors and Badland Erosion Susceptibility Assessment Using Synthetic Model of Information Value and AHP. jwmseir 2024; 18 (66) : 4 URL: http://jwmsei.ir/article-1-1156-en.html
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