Abstract
Abstract. The increasing availability of EO data from the Copernicus program through its Sentinel satellites of the medium spatial and spectral resolution has generated new applications for risk management and disaster management. The recent growth in the intensity and number of hurricanes and earthquakes has demanded an increase in the monitoring of landslides. It is necessary to monitor large areas at a detailed level, which has previously been unsatisfactory due to its reliance on the interpretation of aerial photographs and the cost of high-resolution images.Using the differential Bare Soil Index for optical imagery interpretation in combination with cloud-computing in Google Earth Engine is a novel approach. Applying this method on a recent landslide event in Oaxaca in Mexico around 62% of the landslides were detected automatically, however, there is a big potential for improvement. Including NDVI values and considering images with a higher spatial resolution could contribute to the enhancement of landslide detection, as the majority of missed events have a size smaller than half a pixel. Landslide detection in Google Earth Engine has become a promising approach for big data processing and landslide inventory creation.
Highlights
A landslide is a movements of rocks, earth or debris downhill categorized on the basis of material and type of movement (European Soil Data Centre (ESDAC))
In this study for landslide rapid mapping, we propose to base the response on the Bare Soil Index (BSI) (Roy et al, 1996; Rikimaru & Miyatake, 1997) for the detection of the traces of the soil movements
The results of the accuracy assessment of the Differential Bare Soil Index (dBSI) for landslide detection, were obtained for the total number of landslides detected by the decision tree based semi-automatic classification
Summary
A landslide is a movements of rocks, earth or debris downhill categorized on the basis of material and type of movement (European Soil Data Centre (ESDAC)). Several factors have an influence on the occurrence of landslides and are classified whether the trigger is natural like ground vibrations, groundwater pressure or wildfires or human activities being mining, pipe leakages, constructions and soil excavation (Mohan et al, 2020). The methods range from geomorphologic field survey and visual analysis of aerial images to remote sensing based approaches like satellite imagery (Mohan et al, 2020). The increasing availability of EO data from programs like Copernicus enhance the possibilities to develop new applications of change detection, landslide inventories or the use of Artificial Intelligence. Image classification and the automation of workflows is a great way to detect changes in the land cover
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