A contribution to an understanding of landscape development through three-dimensional morphological analysis of a pedological cover (Paulinia, State of Sao Paulo, Brazil)/Contribution de l'analyse morphologique tridimensionnelle de la couverture pédologique à la reconstitution de l'évolution du modelé (Paulinia, Etat de Sao Paulo, Brésil)

Boulet, René; Curmi, Pierre; Queiroz-Neto, José Pereira de; Pellerin, Jöel

doi:10.3406/morfo.1995.858

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, several detailed soil studies carried out on sandstone formations, mainly used for livestock production, have shown the development of a Bt horizon progressing up the slopes, shifting the drainage from vertical to lateral and sub-surface, favoring a piping phenomenon that eventually leads to the formation of gullies [73]. The development and functioning of typical soil covers, representative of large regions, constitute crucial information, much more useful than a soil map, and should be taken into consideration in future multiparameter analysis of erosion patterns [74][75][76]. In the western part of the basin, gullies assigned to negative values on PC3 are associated with the presence of field crops and high TWI indexes.…”

Section: Processes Associated With the Diversity Of Gully Conditionsmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Processes Associated With the Diversity Of Gully Conditionsmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

The assessment of soil chemical, physical, and structural properties as landslide predisposing factors in the Serra do Mar mountain range (Caraguatatuba, Brazil)

Cerri

Rosolen

Reis

et al. 2020

Bull Eng Geol Environ

View full text Add to dashboard Cite

Organisation of the soil mantle in tropical southeastern Brazil (Serra do Mar) in relation to landslides processes

Furian¹,

Barbiéro²,

Boulet³

1999

CATENA

Self Cite

View full text Add to dashboard Cite

The Serra do Mar complex in the southeastern Brazil is subject to frequent landslides especially on the Atlantic coast. The three dimensional organisation of the soil was studied in a 56 ha representative catchment. Upslope in situ soil material has developed from the parent rock, and downslope the soil has resulted from landslide processes. The soil mantle upslope is mainly characterised by slowly permeable kaolinitic horizons within a thick gibbsitic weathering horizon. The field and microscopic relationships between the two suggest that the kaolinitic horizon has evolved at the expense of the gibbsitic material. Resilication of gibbsite to form kaolinite is postulated in this environment. The kaolinitic horizons are overlain by a microaggregated horizon. The difference in porosity and water movement between these two horizons was estimated by density measurements, mercury porosimetry, image processing and calculation of water retention and shrinkage curves. The results indicate that water is retained briefly within the microaggregated horizon during intensive rainfall. Because of the inclination of the kaolinitic horizon, any excess water within it flows laterally downslope and accumulates in the lowest part of the slope. This leads to landslipping, the main process of landform development in the region.

show abstract

Cited by 4 publications

References 0 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

The assessment of soil chemical, physical, and structural properties as landslide predisposing factors in the Serra do Mar mountain range (Caraguatatuba, Brazil)

Organisation of the soil mantle in tropical southeastern Brazil (Serra do Mar) in relation to landslides processes

Contact Info

Product

Resources

About