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

Bosino, Alberto; Giordani, Paolo; Quénéhervé, Geraldine; Maerker, Michael

doi:10.1002/esp.4949

Cited by 11 publications

(5 citation statements)

References 77 publications

(98 reference statements)

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“…From the end of the nineteenth century and later for most of the twentieth century, soil erosion forms and features were only studied through extensive field investigations and using aerial photographs [68]. More recently, the development of GIS systems allowed the characterization and modeling of the land surface based on remote sensing image analysis techniques and digital terrain analysis, conducted using digital elevation models [69].…”

Section: Topographic Indices and Environmental Parametersmentioning

confidence: 99%

“…MaxEnt is a machine learning method based on statistical mechanics with a simple and precise mathematical formulation [57] that predicts species distributions using detailed climatic and environmental datasets [82]. We decided to utilize this model because it was applied worldwide for different types of erosion landforms, particularly rill-interrill, gully and badland erosion [7,68,72,73,83,84], and generally produced high-quality data.…”

Section: Modelingmentioning

confidence: 99%

“…The MaxEnt model generally works better than other statistical algorithms in terms of model performance (e.g., [13,37]). In addition, the presence-only dataset makes the model more robust to spatial error [68]. The MaxEnt model could estimate a variable's probability distribution (π) on the X set of sites in the study area.…”

Section: Modelingmentioning

confidence: 99%

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Evaluation of Gully Erosion Susceptibility Using a Maximum Entropy Model in the Upper Mkhomazi River Basin in South Africa

Bernini

Bosino

Botha

et al. 2021

IJGI

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Soil erosion is one of the most challenging environmental issues in the world, causing unsustainable soil loss every year. In South Africa, several episodes of gully erosion have been documented and clearly linked to the presence of Quaternary colluvial deposits on the Drakensberg Mountain footslopes. The aim of this study was to identify and assess the triggering factors of gully erosion in the upper Mkhomazi River basin in KwaZulu-Natal, South Africa. We compiled a gully inventory map and applied remote sensing techniques as well as field surveys to validate the gully inventory. The gullies were subdivided into slope gullies and fluvial gullies. We derived susceptibility maps based on the spatial distribution of gully types to assess the most important driving factors. A stochastic modeling approach (MaxEnt) was applied, and the results showed two susceptibility maps within the spatial distribution of the gully erosion probability. To validate the MaxEnt model results, a subset of the existing inventory map was used. Additionally, by using areas with high susceptibilities, we were able to delineate previously unmapped colluvial deposits in the region. This predictive mapping tool can be applied to provide a theoretical basis for highlighting erosion-sensitive substrates to reduce the risk of expanding gully erosion.

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Section: Topographic Indices and Environmental Parametersmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

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Evaluation of Gully Erosion Susceptibility Using a Maximum Entropy Model in the Upper Mkhomazi River Basin in South Africa

Bernini

Bosino

Botha

et al. 2021

IJGI

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“…To explore the spatial probability distribution of the solar features of the climate in terms of heliotherapy, we used the Maximum Entropy Method (MaxEnt) [74,75]. Max-Ent using a presence-only algorithm is suitability for research on habitat suitability and environmental modeling [76,77]. The preprocessing analysis was performed using the SAGA.…”

Section: Maximum Entropy Modelmentioning

confidence: 99%

Solar Climate Features Taking into Account the Morphometric Conditions of the Area and the Possibility of Using Them in Heliotherapy on the Example of the Cieplice and Kołobrzeg Health Resorts (Poland)

Szatten

Więcław

2021

Atmosphere

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Global solar radiation is an important atmospheric stimulus affecting the human body and has been used in heliotherapy for years. In addition to environmental factors, the effectiveness of global solar radiation is increasingly influenced by human activity. This research was based on the use of heliographic and actinometric data (1996–2015) and the model distribution of global solar radiation to determine the possibility of heliotherapy with the example of two health resorts: Cieplice and Kołobrzeg (Poland). The solar features of health resorts (sunshine duration and global solar radiation) were characterized, and they were correlated with the spatial distribution of global solar radiation data obtained with the use of remote sensing techniques (System for Automated Geoscientific Analyzes-SAGA), including COoRdination and INformation on the Environment (CORINE) land cover (CLC) data. Using the maximum entropy model (MaxEnt), a qualitative and quantitative relationship between morphometric parameters and solar climate features was demonstrated for individual land cover types. Studies have shown that the period of late spring and summer, due to the climate’s solar features, is advisable for the use of heliotherapy. The human activity that determines the land cover is the main element influencing the spatial differentiation of the possibilities of using this form of health treatment. It also affects topographic indicators shown as significant in the MaxEnt predictive model. In general, areas with high openness were shown as predisposed for health treatment using global solar radiation, which is not consistent with areas commonly used for heliotherapy. The conducted research has shown the need for an interdisciplinary approach to the issue of heliotherapy, which will contribute to the optimization of the use of this form of health treatment from the perspective of climate change and human pressure.

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“…NDVI before landslides (unitless): NDVI is the most common index for evaluating the surface vegetation status in an area [40][41][42][43]. The NDVI value before the flood season can be used as a quantitative value for the vegetation status before landslides.…”

mentioning

confidence: 99%

Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan

et al. 2020

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Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change.

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

Cited by 11 publications

References 77 publications

Evaluation of Gully Erosion Susceptibility Using a Maximum Entropy Model in the Upper Mkhomazi River Basin in South Africa

Evaluation of Gully Erosion Susceptibility Using a Maximum Entropy Model in the Upper Mkhomazi River Basin in South Africa

Solar Climate Features Taking into Account the Morphometric Conditions of the Area and the Possibility of Using Them in Heliotherapy on the Example of the Cieplice and Kołobrzeg Health Resorts (Poland)

Application of High-Resolution Radar Rain Data to the Predictive Analysis of Landslide Susceptibility under Climate Change in the Laonong Watershed, Taiwan

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