Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework

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• 作者：Omid Rahmati^a ; ^{Omid_rahmati@ut.ac.ir} ; Naser Tahmasebipour^a ; ^{Tahmasebi.n@lu.ac.ir} ; Ali Haghizadeh^a ; Hamid Reza Pourghasemi^b ; Bakhtiar Feizizadeh^c
• 关键词：Gully erosion ; Geo-environmental factors ; GEOBIA ; Conditional probability ; Iran
• 刊名：Science of The Total Environment
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：579
• 期：Complete
• 页码：913-927
• 全文大小：3236 K
• 卷排序：579

文摘

Despite the importance of soil erosion in sustainable development goals in arid and semi-arid areas, the study of the geo-environmental conditions and factors influencing gully erosion occurrence is rarely undertaken. As effort to this challenge, the main objective of this study is to apply an integrated approach of Geographic Object-Based Image Analysis (GEOBIA) together with high-spatial resolution imagery (SPOT-5) for detecting gully erosion features at the Kashkan-Poldokhtar watershed, Iran. We also aimed to apply a Conditional Probability (CP) model for establishing the spatial relationship between gullies and the Geo-Environmental Factors (GEFs). The gully erosion inventory map prepared using GEOBIA and field surveying was randomly partitioned into two subsets: (1) part 1 that contains 70% was used in the training phase of the CP model; (2) part 2 is a validation dataset (30%) for validation of the model and to confirm its accuracy. Prediction performances of the GEOBIA and CP model were checked by overall accuracy and Receiver Operating Characteristics (ROC) curve methods, respectively. In addition, the influence of all GEFs on gully erosion was evaluated by performing a sensitivity analysis model. The validation findings illustrated that overall accuracy for GEOBIA approach and the area under the ROC curve for the CP model were 92.4% and 89.9%, respectively. Also, based on sensitivity analysis, soil texture, drainage density, and lithology represent significantly effects on the gully erosion occurrence. This study has shown that the integrated framework can be successfully used for modeling gully erosion occurrence in a data-poor environment.