Automatic mapping of multitemporal landslide inventories by using open-access Synthetic Aperture Radar and NDVI imagery in Google Earth Engine: a case study of the “Via al Llano” highway (Colombia)

Steep slopes, deforestation, unconsolidated deposits, high annual rainfall, and a highly dissected landscape facilitate the occurrence of landslides in one of the most important Colombian highways “Via al Llano”, frequently causing traffic interruptions. Prior to a susceptibility assessment of the area, a multitemporal inventory is required. Usually, landslides are identified and mapped by visual interpretation of satellite optical and/or aerial images. However, in study areas located in tropical areas such as that of Via al Llano, due to the frequent presence of clouds, a number of images are needed to identify the landslides and estimate the period of their occurrence. Therefore, an automatic detection procedure is indispensable for large tropical areas and multitemporal event inventories. The cloud-based Google Earth Engine (GEE) allows geospatial processing of freely available multi-temporal data. In this work, we perform automatic detection of landslides using the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 (optical images) and the SAR-backscatter change from Sentinel-1 (radar images) over a sector of the Buenavista area, extending for 53km² in the south portion of the “Via al Llano”. Considering a period during which the occurrence of some landslides blocked the highway, images before and after this event were selected for automatic detection, and the results were compared with landslide inventory previously prepared by an expert operator by visual analysis of images available on Google Earth (optical-natural color images). To assess the ability of each method to discriminate between landslides and stable slopes, confusion matrices were calculated. The NDVI-based approach demonstrated an acceptable ability to identify the landslides, although generating a high number of false positives. On the other hand, the SAR-based method exhibits a lower ability to correctly detect the landslide polygons, even if generating a lower number of false positives. This is maybe due to the pattern of predicted positives which mostly consists of isolated pixels; conversely, the NDVI-based approach provides groups of adjacent pixels predicted as positives which better reproduce the shapes of the landslide polygons. Finally, by combining the two approaches and using topographic masks, better accuracy in the automatic mapping of our multitemporal landslide inventories was achieved.