After the collapse of the San Rafael waterfall in Northeast Ecuador on 2 February 2020, a regressive erosion started along the River Coca putting national infrastructure, the environment and indigenous communities at risk. A fast monitoring of areas exposed to landslides on local scales therefore is necessary to provide adequate risk management for the region. The study area, located in the Andean tropics close to the volcano Reventador, is characterized by steep slopes, seismic activity and high rainfall throughout the year. Sentinel-1 SAR data provide a solution for time-series monitoring in the region as imagery is available day and night and not affected by cloud cover. Landslide monitoring with Sentinel-1 SAR data was implemented using a bi-temporal change detection (BCD) with SNAP and a sequential change detection (SCD) with EESA Docker and the Google Earth Engine (GEE) aiming at the identification of a suited approach for fast disaster monitoring and management. The SCD showed an overall accuracy of 0.91 compared to 0.88 using the BCD approach validated with high-resolution imagery. Based on the landslide detection, hazard variables could be further identified to support future hazard and risk assessment. Fast processing of Sentinel-1 time-series data in a cloud-based environment allows for near real-time monitoring of ongoing erosion and provides a potential for pro-active measures to protect the national economy, the environment and the society.
The failure of tailings dams causes ecological damage and economic loss and can cause casualties. The simulation of the tailings’ spill path in the event of tailings dam failures (TDFs) can mitigate the risk by the provision of spatial information for disaster prevention and preparedness. In order to close the gap between basic one-dimensional spill-path routing models and complex numerical models, this paper examines an empirical model based on the freely available Laharz model. The model incorporates a tailings-specific planimetric area regression from the literature to describe the spatial extent of tailings flows based on the released volume. By providing information about affected residents and infrastructure, such a model can be used for preliminary risk evaluation. The model was validated against the TDF in Brumadinho (2019) and reached hit rates of over 80%, critical success indices of approximately 60% and false alarm ratios of roughly 30%. The latter is particularly evident in the overestimation of the lower part of the tailings flow. The risk assessment identified 120 affected residents, 117 destroyed buildings (109 reported) and several kilometres of affected roads (1.9 km) and railway (2.75 km). However, the OpenStreetMap-based part of the risk assessment inherits some uncertainties to be investigated in the future.
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