Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods
The degradation of land (LD) is a major concern for the health and sustainability of natural resources. It is primarily caused by the deterioration of vegetation and soil. In this study, we focus on assessing the risk of LD in the Bakhtegan basin in Iran, one of the arid and semi-arid ecosystems. Ou...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014043 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846121345613561856 |
|---|---|
| author | Ehsan Moradi Hassan Khosravi Pouyan Dehghan Rahimabadi Bahram Choubin Zlatica Muchová |
| author_facet | Ehsan Moradi Hassan Khosravi Pouyan Dehghan Rahimabadi Bahram Choubin Zlatica Muchová |
| author_sort | Ehsan Moradi |
| collection | DOAJ |
| description | The degradation of land (LD) is a major concern for the health and sustainability of natural resources. It is primarily caused by the deterioration of vegetation and soil. In this study, we focus on assessing the risk of LD in the Bakhtegan basin in Iran, one of the arid and semi-arid ecosystems. Our objective is to predict LD hazard and vulnerability maps, and then combine them to identify areas at high risk. To predict LD hazard, the Support Vector Machine (SVM) algorithm was used with 179 LD locations and twelve variables, including land use, lithology, rainfall, temperature, distance to the stream, elevation, aspect, slope, curvature, distance to the road, Normalized Difference Moisture Index (NDMI), and population density. The LD hazard map was evaluated using five error statistics extracted from the contingency table. For LD vulnerability mapping, eight criteria were deployed, including land use, population density, Normalized Difference Vegetation Index (NDVI), livestock density, groundwater quantity and quality, Salinity Index (SI), and migration. These criteria were weighted using the integrating eDPSIR framework and Analytic Network Process (ANP) methods. The results show that low-altitude areas which have low rainfall and high temperatures face the highest LD hazard. Additionally, the western and northwestern regions of the basin are more vulnerable compared to other areas due to factors such as land use and vegetation cover. Lastly, the LD risk map reveals that some 7.56 % of the region falls into the high-risk classification, totaling 2413.37 km2. Notably, salt lands emerge as the most at-risk land use, with 77 % under high risk, with rain-fed agricultural land following as the second-highest risk class. These findings underscore the importance of considering LD risk in land management strategies. |
| format | Article |
| id | doaj-art-beec1dd8c53842079848868a34c0137d |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-beec1dd8c53842079848868a34c0137d2024-12-16T05:35:39ZengElsevierEcological Indicators1470-160X2024-12-01169112947Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methodsEhsan Moradi0Hassan Khosravi1Pouyan Dehghan Rahimabadi2Bahram Choubin3Zlatica Muchová4Institute of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture in Nitra, Hospodárska 7, 949 76 Nitra, SlovakiaDepartment of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj 31585-3314, Iran; Corresponding authors at: Institute of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture in Nitra, Hospodárska 7, 949 76 Nitra, Slovakia.Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj 31585-3314, IranSoil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, IranInstitute of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture in Nitra, Hospodárska 7, 949 76 Nitra, Slovakia; Corresponding authors at: Institute of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture in Nitra, Hospodárska 7, 949 76 Nitra, Slovakia.The degradation of land (LD) is a major concern for the health and sustainability of natural resources. It is primarily caused by the deterioration of vegetation and soil. In this study, we focus on assessing the risk of LD in the Bakhtegan basin in Iran, one of the arid and semi-arid ecosystems. Our objective is to predict LD hazard and vulnerability maps, and then combine them to identify areas at high risk. To predict LD hazard, the Support Vector Machine (SVM) algorithm was used with 179 LD locations and twelve variables, including land use, lithology, rainfall, temperature, distance to the stream, elevation, aspect, slope, curvature, distance to the road, Normalized Difference Moisture Index (NDMI), and population density. The LD hazard map was evaluated using five error statistics extracted from the contingency table. For LD vulnerability mapping, eight criteria were deployed, including land use, population density, Normalized Difference Vegetation Index (NDVI), livestock density, groundwater quantity and quality, Salinity Index (SI), and migration. These criteria were weighted using the integrating eDPSIR framework and Analytic Network Process (ANP) methods. The results show that low-altitude areas which have low rainfall and high temperatures face the highest LD hazard. Additionally, the western and northwestern regions of the basin are more vulnerable compared to other areas due to factors such as land use and vegetation cover. Lastly, the LD risk map reveals that some 7.56 % of the region falls into the high-risk classification, totaling 2413.37 km2. Notably, salt lands emerge as the most at-risk land use, with 77 % under high risk, with rain-fed agricultural land following as the second-highest risk class. These findings underscore the importance of considering LD risk in land management strategies.http://www.sciencedirect.com/science/article/pii/S1470160X24014043Hazard predictionVulnerability mappingAnalytic network process (ANP)Land managementSupport vector machine (SVM) |
| spellingShingle | Ehsan Moradi Hassan Khosravi Pouyan Dehghan Rahimabadi Bahram Choubin Zlatica Muchová Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods Ecological Indicators Hazard prediction Vulnerability mapping Analytic network process (ANP) Land management Support vector machine (SVM) |
| title | Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods |
| title_full | Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods |
| title_fullStr | Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods |
| title_full_unstemmed | Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods |
| title_short | Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods |
| title_sort | integrated approach to land degradation risk assessment in arid and semi arid ecosystems applying svm and edpsir anp methods |
| topic | Hazard prediction Vulnerability mapping Analytic network process (ANP) Land management Support vector machine (SVM) |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014043 |
| work_keys_str_mv | AT ehsanmoradi integratedapproachtolanddegradationriskassessmentinaridandsemiaridecosystemsapplyingsvmandedpsiranpmethods AT hassankhosravi integratedapproachtolanddegradationriskassessmentinaridandsemiaridecosystemsapplyingsvmandedpsiranpmethods AT pouyandehghanrahimabadi integratedapproachtolanddegradationriskassessmentinaridandsemiaridecosystemsapplyingsvmandedpsiranpmethods AT bahramchoubin integratedapproachtolanddegradationriskassessmentinaridandsemiaridecosystemsapplyingsvmandedpsiranpmethods AT zlaticamuchova integratedapproachtolanddegradationriskassessmentinaridandsemiaridecosystemsapplyingsvmandedpsiranpmethods |