Integration of Remote Sensing techniques to assess badlands susceptibility in the Basilicata region (Southern Italy)

Badlands are landscapes that develop on poorly consolidated bedrock under high erosion rates and their spatial distribution in Italy is related to marine clays outcrops. These landscapes are vulnerable to dynamic changes driven by geological processes, biological and anthropic factors at different spatial and temporal scales. This characteristic, along with the rapid transformation of landforms makes them ideal open-field laboratories, suitable for testing and improving means for predictive analysis. The study focuses on Badlands landscapes of the Basilicata region (Southern Italy) to explore the potential of the integration of different remotely-sensed data, from ground-based to satellite-based  technologies, in the definition of a probabilistic model for the evaluation of erosion trends. The integrative approach is essential for a comprehensive study of landscape dynamics, accounting for the complex interactions between top-down drivers (climatic and anthropogenic) and bottom-up drivers (biotic and geomorphological factors) of landforms evolution. In this perspective, Machine Learning (ML) techniques are effective tools for analysing the spatially heterogeneous responses of different morphologies in order to study their evolution and, therefore, their susceptibility to various processes (landslides, soil erosion). Maximum Entropy (MaxEnt) distribution models estimate a target probability distribution as a function of environmental predictors based on presence-only data. This approach has found several applications in the field of geomorphology, providing a more user-friendly and accessible way to perform studies based on statistical predictions. In contrast, MaxEnt has proven to be less robust in accuracy than other ML models. To overcome this issue while still guaranteeing accessibility, the study proposes the use of an advancement of the classical MaxEnt software called spatialMaxEnt, more sensible to the spatial distribution of data due to spatial cross-validation and a series of optimized functionalities to minimize overfitting. Presence data comprehends sites of occurrence of erosion processes identified through high-resolution topographic data within three study areas (Aliano, Tursi and Montalbano Jonico), externally grouped based on spatial autocorrelation, while environmental variables include topographic, climatic and anthropic attributes. A multicollinearity analysis using Pearson’s correlation coefficient is conducted prior to the MaxEnt modelling to identify and exclude highly correlated variables. This procedure ensures the selection of the most explanatory predictors and reduces the risk of model overfitting. The output data of the study is represented by Badlands susceptibility maps. This study further recommends comparisons with other AI-based models, as well as their possible combination, to enhance the robustness and significance of the results. Despite the challenges, the layers produced and the implementation of methodologies for modelling and identifying erosion-prone areas remains a fundamental tool for environmental monitoring and decision aimed at the conservation of the geological heritage.