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

Rahmati, Omid; Tahmasebipour, Naser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

doi:10.1016/j.scitotenv.2016.10.176

Cited by 168 publications

(94 citation statements)

References 99 publications

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“…These soils, especially Vertisols, are characterized by crusting and sealing of the surface layer and low infiltration rate, and consequently, they often generate high annual runoff under dry soil conditions [34]. Some parts of the study area have high soil erosion sensitivity due to the lack of vegetation cover, massive rainfall events, and topographical characteristics combined with heavy livestock grazing, leading to high surface runoff and sediment transport [35]. The watershed also experiences flash floods with severe damages to the environment, buildings, and infrastructure; Figure 2 shows two field photographs of such a deadly flood event on April 1, 2019, causing the deaths of 13 people, and heavy damage to infrastructures and buildings.…”

Section: Study Areamentioning

confidence: 99%

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

et al. 2019

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Check dams are widely used watershed management measures for reducing flood peak discharge and sediment transport, and increasing lag time and groundwater recharge throughout the world. However, identifying the best suitable sites for check dams within the stream networks of various watersheds remains challenging. This study aimed to develop an open-source software with user-friendly interface for screening the stream network possibilities and identifying and guiding the selection of suitable sites for check dams within watersheds. In this developed site selection software (SSS), multi-criteria decision analysis (MCDA) was integrated into geographic information systems (GIS), which allowed for numerous spatial data of the multiple criteria to be relatively simply and visually processed. Different geomorphometric and topo-hydrological factors were considered and accounted for to enhance the SSS identification of the best locations for check dams. The factors included topographic wetness index (TWI), terrain ruggedness index (TRI), topographic position index (TPI), sediment transport index (STI), stream power index (SPI), slope, drainage density (DD), and stream order (SO). The site identification performance of the SSS was assessed using the receiver operating characteristic (ROC) curve method, with results for the case study example of the Poldokhtar watershed in Iran showing excellent performance and identifying 327 potential sites for efficient check dam construction in this watershed. The SSS tool is not site-specific but is rather general, adaptive, and comprehensive, such that it can and should be further applied and tested across different watersheds and parts of the world.

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Section: Study Areamentioning

confidence: 99%

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

et al. 2019

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“…Statistical models provide a general advantage of working with diverse types of independent variables, like continues, binary, and categorical data [5]. The most successful models may be: information value (IV) [5], conditional probability [11], frequency ratio (FR) [12], evidential belief function [3], index of entropy (IoE) [13], certainty factor [14], weights of evidence (WOE) [15], and logistic regression [16]. The performance of statistical models is low, however.…”

Section: Introductionmentioning

confidence: 99%

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

Arabameri

Blaschke

Pradhan

et al. 2020

Sensors

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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

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“…Satellite-based spectral indices, for example, Normalized Difference Vegetation Index (NDVI), Normalized Difference Soil Index (NDSI), Tasselled Cap Transformation (TCT), along with Linear Spectral Unmixing Analysis (LSMA) have been frequently employed to estimate soil erosion process [21,22], investigate soil exposure intensity [23], measure soil reflectance [24,25], evaluate soil erosion status [5,26] and assess soil properties and bare soil fractions [27]. In addition, object-oriented analysis has been used to aid image classification and gully erosion identification [28,29]. Recently, machine-learning approaches have been applied to reveal statistical relationships between gully erosion and its controlling factors [30] and to retrieve key factors for soil erosion risk detection [31].…”

Section: Introductionmentioning

confidence: 99%

A Remote Sensing Based Method to Detect Soil Erosion in Forests

Xiu-juan

Guan

et al. 2019

Remote Sensing

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Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.

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

Cited by 168 publications

References 99 publications

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

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

A Remote Sensing Based Method to Detect Soil Erosion in Forests

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