Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model

Pournader, Maryam; Hasan, Ahmadi; Feiznia, Sadat; Karimi, Haji; Peirovan, Hamid Reza

doi:10.1007/s12145-018-0338-6

Cited by 33 publications

(14 citation statements)

References 69 publications

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“…Susceptibility maps of GE are essential for conservation of natural resources, and for evaluating the relationship among gully occurrence and relevant GEVs [12]. Several models have been applied to assess soil erosion and GE rate in a quantitative and qualitative way, such as the Universal Soil Loss Equation (USLE) [1,15], Erosion Potential Method, Modified Pacific Southwest Interagency Committee Model (MPSIAC) [16], Water Erosion Prediction Project (WEEP) [17], European Soil Erosion Model (EUROSEM) [18], Ephemeral Gully Erosion Model (EGEM) [19], and Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS) [20].…”

Section: Introductionmentioning

confidence: 99%

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

et al. 2018

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Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

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Section: Introductionmentioning

confidence: 99%

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

et al. 2018

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“…One example where gully erosion is a big concern for rural and urban areas is Iran [40][41][42]. Thus, in this study, we tried to estimate gully head advancement through digitizing the minimum number of aerial photos between 1968-1994 and 2009, in combination with field observations in the Samal watershed in Iran.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Sustainability of the Territories Affected by Gully Head Advancements through Aerial Photography and Modeling Estimations: A Case Study on Samal Watershed, Iran

et al. 2018

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Gully erosion is considered one of the major issues of land sustainability because it can remove considerable volumes of sediment and productive soils. Once started, gullies can continue to move by headcut retreat, or slumping of the side walls. Studies of gully development require constant monitoring activities which are not possible in not-well-explored areas, such as the arduous region of Iran, due to costs and a lack of geoinformation. Thus, the present research attempts to assess gully evolution using only two digital aerial photographs of different periods (1968 and 1994) and field assessment (2009) to estimate the gully head advancement based on frames geometry and rigorous procedure in southwestern Iran. Also, the gully head advancement was estimated and compared among them by different empirical equations. The results indicated that the mean of gully head advancement was 1.4 m year−1 and 1.2 m year−1 during 1968–1994 and 1994–2009, respectively, and the annual average of sediment mobilization was 26.8 m3 ha−1 in 2009. The model assessment indexes indicated that SCS (Soil Conservation Service) II was the best model for gully head advancement estimations in this study area. The main reasons for this can be associated with the Rp factor (previous gully head advancement) and the local environmental conditions. We conclude that the sustainability of the territory has been greatly affected due to this advancement. We also hypothesize that gully head changes could be related to the susceptibility of geological formations, climate, soil properties, and the coincidence of other gullies’ formation with common drainage networks in the study area. Based on the obtained results, land managers can use the results to distinguish the gullies in this region with a higher environmental risk, and to decide an effective implementation of soil conservation measures in order to include them in the land management plans.

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“…As reported by other authors, soil type also plays a role in explaining the spatial distribution of the three erosional landforms (see e.g. Pournader et al, 2018). Unfortunately, there is no detailed soil information available in the Oltrepo Pavese study area, and thus the soil type was not considered in this study.…”

Section: Discussionmentioning

confidence: 84%

“…Conforti et al, 2011;Magliulo, 2012;Conoscenti et al, 2013Conoscenti et al, , 2014Gomez-Gutierrez et al, 2015;Angileri et al, 2016;Rahmati et al, 2017;Al-Abadi and Al-Alii, 2018;Zakerinejad et al, 2018;Javidan et al, 2020) or rill-interrill (sheet) erosion (e.g. Conoscenti et al, 2008;Maerker et al, 2011;Magliulo, 2012;Angileri et al, 2016;Pournader et al, 2018;Cama et al, 2020). Instead, very few studies have been conducted on the susceptibility modelling of calanchi processes at local and/or basin scale (e.g.…”

Section: Introductionmentioning

confidence: 99%

Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese, Northern Apennines, Italy

Bosino

Giordani

Quénéhervé

et al. 2020

Earth Surf Processes Landf

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Soil erosion is one of the most important environmental problems distributed worldwide. In the last decades, numerous studies have been published on the assessment of soil erosion and the related processes and forms using empirical, conceptual and physically based models. For the prediction of the spatial distribution, more and more sophisticated stochastic modelling approaches have been proposed – especially on smaller spatial scales such as river basins. In this work, we apply a maximum entropy model (MaxEnt) to evaluate badlands (calanchi) and rill–interrill (sheet erosion) areas in the Oltrepo Pavese (Northern Apennines, Italy). The aim of the work is to assess the important environmental predictors that influence calanchi and rill–interrill erosion at the regional scale. We used 13 topographic parameters derived from a 12 m digital elevation model (TanDEM‐X) and data on the lithology and land use. Additional information about the vegetation is introduced through the normalized difference vegetation index based on remotely sensed data (ASTER images). The results are presented in the form of susceptibility maps showing the spatial distribution of the occurrence probability for calanchi and rill–interrill erosion. For the validation of the MaxEnt model results, a support vector machine approach was applied. The models show reliable results and highlight several locations of the study area that are potentially prone to future soil erosion. Thus, coping and mitigation strategies may be developed to prevent or fight the soil erosion phenomenon under consideration. © 2020 John Wiley & Sons, Ltd.

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Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model

Cited by 33 publications

References 69 publications

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Assessment of the Sustainability of the Territories Affected by Gully Head Advancements through Aerial Photography and Modeling Estimations: A Case Study on Samal Watershed, Iran

Assessment of calanchi and rill–interrill erosion susceptibilities using terrain analysis and geostochastics: A case study in the Oltrepo Pavese, Northern Apennines, Italy

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