An Assessment of Landscape Evolution Through Pedo-Geomorphological Mapping and Predictive Classification Using Random Forest: A Case Study of the Statherian Natividade Basin, Central Brazil

Understanding the relationship between geological and geomorphological processes is essential for reconstructing landscape evolution. This study examines how geology and geomorphology shape landscape development in central Brazil, focusing on the Natividade Group area. Sentinel-2 and SRTM data were integrated with geospatial analyses to produce two key maps: (i) a pedo-geomorphological map, classifying landforms and soil–landscape relationships, and (ii) a predictive geological–geomorphological map, based on a machine learning-based prediction of geomorphic units, which employed a Random Forest classifier trained with 15 environmental predictors from remote sensing datasets. The predictive model classified the landscape into six classes, revealing the ongoing interactions between geology, geomorphology, and surface processes. The pedo-geomorphological map identified nine pedoforms, grouped into three slope classes, each reflecting distinct lithology–relief–soil relationships. Resistant lithologies, such as quartzite-rich metasedimentary rocks, are associated with shallow, poorly developed soils, particularly in the Natividade Group. In contrast, phyllite, schist, and Paleoproterozoic basement rocks from the Almas and Aurumina Terranes support deeper, more weathered soils. These findings highlight soil formation as a critical indicator of landscape evolution in tropical climates. Although the model captured geological and geomorphological patterns, its moderate accuracy suggests that incorporating geophysical data could enhance the results. The landscape bears the imprint of several tectonic events, including the Rhyacian amalgamation (~2.2 Ga), Statherian taphrogenesis (~1.6 Ga), Neoproterozoic orogeny (~600 Ma), and the development of the Sanfranciscana Basin (~100 Ma). The results confirm that the interplay between geology and geomorphology significantly influences landscape evolution, though other factors, such as climate and vegetation, also play crucial roles in landscape development. Overall, the integration of remote sensing, geospatial analysis, and machine learning offers a robust framework for interpreting landscape evolution. These insights are valuable for applications in land-use planning, environmental management, and geohazard assessment in geologically complex regions.