Evaluation of gully development over several years using GIS and fractal analysis: a case study of the Palmital watershed, Minas Gerais (Brazil)

Real, Ligia Sampaio Corte; Crestana, Sı́lvio; Ferreira, Rogério Resende Martins; Rodrigues, Valéria Guimarães Silvestre

doi:10.1007/s10661-020-08362-7

Cited by 7 publications

(7 citation statements)

References 68 publications

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“…In order to take into account a wide spectrum of geo-environmental factors that may influence the development of gullies [4,24,28,29], and more specifically under humid tropical climate, thirteen parameters were selected. These are elevation, slope, exposure, landform curvature, topographic wetness index (TWI), topographic position index (TPI), land cover, geology, drainage density, distance to rivers, distance to roads, soil type, and rainfall.…”

Section: Selection Of Environmental Parametersmentioning

confidence: 99%

“…These methods have demonstrated their superiority over traditional statistical-based techniques by having the ability to model highly dimensional and nonlinear data sets, allowing complex environmental interactions to be evaluated [9,21,22]. Taking advantage of these recent tools, there are studies analyzing gully morphometry based on their fractal dimension [23,24], but to our knowledge, such databases have only been slightly developed to better understand erosive systems, specifically in the case of gullying in Brazil [25].…”

Section: Introductionmentioning

confidence: 99%

“…Landform curvature is a factor in stormwater runoff [15,19,24]. Three categories were distinguished from the 30 m resolution DEM: concave, convex, or flat surfaces (Figure 3d).…”

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confidence: 99%

“…The TPI is positive when the cell is higher than its surroundings (ridges and hilltops), and negative for depressed features such as valleys. • Land cover and use can directly affect erosion [24]. The development of gullies is sometimes analyzed as an ancient phenomenon that has had its share of contribution to the morphology of Brazilian landscapes [3], but there are many studies that attest to the role of anthropogenic activities in contributing to and accelerating erosive processes [37].…”

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confidence: 99%

“…Geology is a critical parameter influencing erosive processes due to the strength of the rocks and soil formations that develop there, and the presence of lithological disconti-nuities [19,24]. Geological data of the study area were obtained from CRPM [38].…”

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confidence: 99%

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Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

et al. 2022

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In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.

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Section: Selection Of Environmental Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Landform curvature is a factor in stormwater runoff [15,19,24]. Three categories were distinguished from the 30 m resolution DEM: concave, convex, or flat surfaces (Figure 3d).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

et al. 2022

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Soil erosion resistance of gully system under different plant communities on the Loess Plateau of China

Zeng,

Zhang,

et al. 2024

Earth Surf Processes Landf

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Gully erosion, considered as a type of intensive erosion, is the dominant source of sediment at small watershed scales in certain environments. Variation of vegetation may result in the changes in near soil surface characteristics, which likely further affect the resistance of gully systems to erosion. However, the potential effects of near soil surface characteristics of plant communities on resistance to erosion are still unclear in gully systems. This study was performed to investigate the variations in resistance of gully systems to erosion under different plant communities and to identify the dominant influencing factors leading to these variations on the Loess Plateau. Five typical plant communities (two grasses, two shrubs and one forest) that distributed on different gully systems were selected. Six hundred undisturbed soil samples collected from different sites of gully systems were subjected to detach by overland flow under six different shear stresses (6.66 to 15.02 Pa). The results showed that the mean soil detachment capacity of gully systems covered by grass and shrub communities was 0.15 and 0.37 times to that of gully system covered by forest. Compared to forest gully systems, rill erodibility reduced by 29.8% to 85.6% for the other four plant communities. The relative rill erodibility of different vegetation communities generally increased from grass to shrub and forest communities. The critical shear stress of forest gully system was 58.7% and 63.8% of gully systems covered by grass (6.22 Pa) and shrub (5.73 Pa) communities. Bulk density, soil cohesion, water stable aggregate, root mass density, and the thickness of biological soil crust were the dominant factors affecting the resistance of gully systems to soil erosion. Rill erodibility decreased logarithmically with increasing soil cohesion, water stable aggregate and root mass density. Critical shear stress increased with the increase of soil cohesion and root mass density as a power function, and linearly with the increase of water stable aggregate. These results show how vegetation can mitigate against the erosion induced by concentrated flow in relatively stable gully systems.

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Assessment of the impact of conservation measures by modeling soil loss in Minas Gerais, Brazil

Soares,

Crestana,

Ferreira

et al. 2024

Environ Monit Assess

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Evaluation of gully development over several years using GIS and fractal analysis: a case study of the Palmital watershed, Minas Gerais (Brazil)

Cited by 7 publications

References 68 publications

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

Soil erosion resistance of gully system under different plant communities on the Loess Plateau of China

Assessment of the impact of conservation measures by modeling soil loss in Minas Gerais, Brazil

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