Landslide mapping using object-based image analysis and open source tools

Availability of high-resolution optical imagery and advances in image processing technologies have significantly improved our ability to map landslides. In recent years object-based image analysis (OBIA) has been gaining in popularity for landslide mapping due to its ability to incorporate spectral, textural, morphological and topographical properties. Many studies have been conducted based on commercial software. In this study, we create an open source Semi-Automatic Landslide Detection (SALaD) system utilizing OBIA and machine learning. Configured to run in Linux environment, it uses various open source Python packages and modules. This system was tested in 575 km(2) area along the Pasang Lhamu Highway, Nepal where large numbers of landslides were triggered by the 2015 Gorkha earthquake. Comparison with a manual inventory highlighted that this system was able to detect 70% of the landslide area. The speed and efficiency with which this system was able to detect landslides makes it a viable alternative to manual techniques for landslide mapping over large areas, when establishing approximate landslide locations is of prime importance.

Automated summary

The combination of high-resolution optical imagery and OBIA technology has significantly improved the efficiency of landslide mapping. The open-source SALaD system, utilizing OBIA and machine learning, successfully detected 70% of landslide areas in Nepal.