Literature DB >> 27887837

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

Omid Rahmati1, Naser Tahmasebipour2, Ali Haghizadeh3, Hamid Reza Pourghasemi4, Bakhtiar Feizizadeh5.   

Abstract

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.
Copyright © 2016 Elsevier B.V. All rights reserved.

Keywords:  Conditional probability; GEOBIA; Geo-environmental factors; Gully erosion; Iran

Year:  2016        PMID: 27887837     DOI: 10.1016/j.scitotenv.2016.10.176

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  2 in total

1.  Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study.

Authors:  Alireza Arabameri; Thomas Blaschke; Biswajeet Pradhan; Hamid Reza Pourghasemi; John P Tiefenbacher; Dieu Tien Bui
Journal:  Sensors (Basel)       Date:  2020-01-07       Impact factor: 3.576

2.  A machine learning framework for multi-hazards modeling and mapping in a mountainous area.

Authors:  Saleh Yousefi; Hamid Reza Pourghasemi; Sayed Naeim Emami; Soheila Pouyan; Saeedeh Eskandari; John P Tiefenbacher
Journal:  Sci Rep       Date:  2020-07-22       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.