Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil

Abstract

Landslides are among the most frequent hazards in Latin America and the world. In Brazil, they occur every year and cause economic and social loss. Landslide inventories are essential for assessing susceptibility, vulnerability, and risk. Over the decades, a variety of mapping approaches have been employed for the detection of landslides using Earth observation (EO) data. Object-based image analysis (OBIA) is a widely recognized method for mapping landslides and other morphological features. In Brazil, despite the high frequency of landslides, methods for inventory construction are poorly developed. The aim of this study is to semi-automatically recognize shallow landslides in Itaóca (Brazil) and evaluate the transferability of the approach within different areas in Brazil. RapidEye satellite images (5 m) and the derived normalized difference vegetation index (NDVI), as well as a digital elevation model (DEM) (12.5 m) and morphological data, were integrated into the classification. The results show that the method is suitable for the recognition of this type of hazard in Brazil. The overall accuracy was 89%. The main challenges were the identification of small landslides and the exact delineation of scars. The findings validate the applicability of the approach in Brazil, although additional adjustments to the primary rule set might lead to better results.

Keywords

• inventory
• mass movement
• semi-automated mapping
• expert knowledge integration
• remote sensing
• Serra do Mar