Revista Brasileira de Geomorfologia

Sandy soil spots in northwestern Paraná: approaches for identification and quantification

2024-10-21T16:50:07-03:00

The predominance of sandy soils in the Northwest region of the state of Paraná, associated with the removal of natural vegetation, favored the development of patches of white sand on the surface without any aggregation. These spots are associated with the different types of land use existing in the region and the process of lateral soil transformation. As they constitute material without aggregation, they can be easily transported by wind and water and deposited in water bodies, causing severe environmental impacts. Thus, using thermal infrared satellite images, the objective of the present study was to map, determine the formation process of the spots and estimate the predominant particle size fractions. The model used and validated with field and laboratory information, allowed the identification of surfaces with patches of white sand present in the study region and estimating their percentage in different classes. After filtering the samples with vegetation cover during the product calculation period, an R² of 0.78 was obtained. The removal of natural vegetation has contributed to the formation and expansion of sandy patches and erosion in the northwest region of Paraná.

Lithostructural control of the relief in the eastern sector of the Parnaíba sedimentary basin (Ibiapaba Plateau), Northeast Brazil

2024-08-15T11:27:05-03:00

Cartographic updates of the geological area between the Parnaíba and Borborema Provinces made it possible to provide details in the interpretation of the lithostructural control of the Ibiapaba Plateau and surroundings. The article aims to interpret regional morphostructural aspects based on bibliographic review, fieldwork and GIS interpretations. The area is characterized by an asymmetrical plateau with a continuous glint-type escarpment (≈800 m), supported by Paleozoic sandstones overlying the Precambrian basement. In the surroundings, flat surfaces predominate (≈200m), whose morphologies vary according to the lithologies. From a structural point of view, the Transbrasilian Lineament (LT) constitutes the main regional tectonic divide, influencing the behavior of the escarpment and the reverse. In lithological terms, the sandstones and conglomerates of the Serra Grande Group stand out, responsible for maintaining the top of the plateau, the Cabeças Formation and lateritic coverings, which support small tabular plateaus within the sedimentary basin, in addition to the granitoids, quartzites and orthogneisses that support residual reliefs in the basement. Considering a regional geomorphological evolution associated with Cretaceous uplifts of rift and post-rift phases, the detailing of lithostructural aspects is fundamental to explain the relationship of differential erosion in the current regional morphology.

Landslide susceptibility mapping using logistic regression, random forests, and artificial neural networks: a case study in Mariana/MG, Brazil

2024-08-28T17:50:16-03:00

The landslide susceptibility mapping (LSM) plays an important role in risk management. This study evaluated the predictive capabilities of three machine learning (ML) approaches applied to LSM: logistic regression (LR), random forests (RF), and artificial neural networks (ANN). The study was conducted in a mountainous region of Mariana/MG, Brazil. Initially, a point inventory with 364 landslides and 364 stable regions was randomly partitioned in a 70% training and 30% testing ratio for the models. Nine landslide conditioning factors (LCF), ranked by information gain (IG), were considered: slope angle (IG=0.486), geomorphology (IG=0.235), topographic wetness index - TWI (IG=0.138), lithology (IG=0.077), slope orientation (IG=0.067), topographic position index - TPI (IG=0.052), distance from drainage (IG=0.032), slope curvature (IG=0.029) and the distance from roads (IG=0.024). The evaluation of the area under the curve (AUC-ROC) and the classification efficiency rates in high () and low () susceptibility were used to compare the results of the approaches. The results demonstrated that although RF (AUC-ROC=0,947, =6,808, =0,030) slightly outperformed LR (AUC-ROC=0,936, =5,695, =0,050) and ANN (AUC-ROC=0,934, =6,495, =0,060), all the approaches exhibited high predictive capability in identifying areas susceptible to landslides.