On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.

On 2 May 2014, a large-scale landslide in Abe Barek, Badakhshan, Afghanistan, produced extensive damage to the buildings and killed hundreds of people. Evaluations of the extent and the volume of the displaced materials are vital for post-disaster management activities. In this study, we present the applicability of a nonlinear geometric correction technique for decreasing the undesired registration errors between pre- and post-event digital elevation models (DEMs) generated from high-resolution stereo pair satellite imagery, identifying landslide affected areas, and quantifying the landslide volume from DEMs of difference (DoD) analysis. The nonlinear mapping method consists of shifting vector generation in subareas of the DEMs, consensus operations, and interpolation of the shifting vectors. The quality assessment confirmed that the method outperformed the simple DoD technique by eliminating a large-scale of geometric errors in an unaffected area. We estimated the volume of the landslide as 1.05 × 10⁶ m³ from the DoD corrected by the nonlinear method, and discussed the relationship between the area and volume compared to those of the previous studies.

Evaluations of the extent and the volume of the displaced materials are vital for post-disaster management activities. In this study, we present a nonlinear geometric correction technique for identifying landslide affected areas and estimating the volume by using pre-and post-event digital elevation models (DEMs) generated from high-resolution stereo pair satellite imagery of the Abe Barek landslide, which was a devastating landslide that occurred On May 2, 2014, in the northern part of Afghanistan. The proposed method consists of shifting vector generation in subareas of the DEMs, consensus operations, and interpolation of the shifting vectors. The quality assessment confirmed that the proposed method outperformed simple DEMs of difference technique by eliminating a large scale of geometric errors in unaffected areas. We estimated the landslide volume as 1.06 x 10 ⁶ m ³ from the pre-and post-event DEMs corrected by the proposed method. Finally, the building damage assessment on the landslide-affected area has been carried out in terms of the deposition depths obtained in this analysis.