The unsuPervised shAllow laNdslide rapiD mApping: PANDA method applied to severe rainfalls in northeastern appenine (Italy)

Abstract

Shallow landslides, frequently triggered by extreme events such as heavy rainfall, snowmelt, or earthquakes, affect vast areas with remarkable density. In the immediate aftermath of such events, it becomes crucial to rapidly assess landslides distribution and pinpoint the most severely affected areas to prioritize damage assessments and guide field survey operations effectively. Once the emergency phase subsides, the attention can shift to enhancing the accuracy of landslide inventory. In this work, we introduce the two-phase methodology “PANDA”, the unsuPervised shAllow laNdslide rapiD mApping, for the low-cost mapping of the potential landslides, firstly in the emergency phase and then, with an improved version, in the post-emergency one. This approach utilizes variations in NDVI derived from Sentinel-2 satellite imagery and geomorphological filters. We applied PANDA to rainfall events in the northeastern Apennine range, Italy, occurred in May 2023, causing dramatic social and economic consequences for this mountain territory. Within just five days of obtaining Sentinel-2 post-event imagery, we produced a reliable, ready-to-use map covering a vast area (∼4000 km2). The map tested during emergency field mapping shows positive feedback. In the post-emergency phase, accuracy was enhanced using completely cloud-free imagery, a filter to identify false positives associated with land use changes, a higher resolution digital terrain model (DTM), and an iterative approach to optimize NDVI and slope thresholds. Potential landslide density related with rainfall, indicating that the most severely affected region attained a density of approximately 50 landslides/km2. Validation against an independent manual inventory based on high-resolution imagery demonstrated encouraging accuracy results from both inventories, with a noticeable increase in the F1 score for the post-emergency version.

Keywords

• Semi-automatic processing
• Sentinel-2
• Extreme event
• Emergency management
• Residual risk
• Change detection