Tahereh Karimi, Amir Karam, Parviz Zeaiean Firuzabadi, Seyyed Mohammad Tavakkoli Sabour,
Volume 0, Issue 0 (3-1921)
Abstract
Abstract
The catchment area of Alamut River in Qazvin province is witnessing numerous landslide hazards and landslides every year, which cause significant economic and sometimes life-threatening losses. Diagnosing the unstable areas of slopes through soil texture characteristics is a difficult task due to the difficulties of obtaining soil samples in mountainous areas. For this reason, in the present study, by using Sentinel A1 radar data, by determining the percentage of clay and sand in the soil, the soil texture map at the depths of 10, 60, 100 and 200 cm with two random forest (RF) and support vector machine (SVM) algorithms was produced in the eastern Alamut region, which was verified with soil profile samples. The results indicated that the Kappa index was more accurate in the RF model at three depths of 10, 60 and 100 cm. Then, by extracting the soil moisture map from Sentinel 2 data, at the same time as examining the internal friction angle of the types of soils in the region, comparing the slope and profile of the slopes and the shape of the convex (divergent) and concave (convergent) slopes, the unstable areas of slope movements, RF and SVM models were specified and validated with GPS data, field visits and Google Earth. Research findings show that the instability map resulting from the RF model has a higher accuracy (AUC=0.93) than the instability map resulting from the SVM model (AUC=0.90) and there is more instability in areas with medium to high slope and with soil texture of sandy clay loam and sandy loam. . This method has many advantages in preparing the soil texture map, determining the unstable areas of the slopes against mass movements and landslides, determining the vulnerable areas in mountainous areas and reducing financial and human losses.
Tahereh Karimi, Amir Karam, Parviz Zeaieanfirouzabadi, Seyyed Mohammad Tavakkoli Sabour,
Volume 25, Issue 79 (12-2025)
Abstract
Slope hazards and landslides annually inflict substantial damage in the mountainous regions of Iran, particularly within the eastern Alamut area of Qazvin province. Recent advancements in radar technology have facilitated the detection of ground surface movements, including slow slope motions and active landslides. The present study employs Sentinel 1A satellite descending data from 2018 to 2020, utilizing the Small Baseline Subset (SBaS-InSAR) methodology alongside digital elevation model (DEM) difference techniques. This approach aims to extract slope movements and Earth surface displacements, serving the critical objective of identifying new and active landslides while updating the landslide map to enhance landslide risk prediction. The results indicate that the SBaS model, which was corroborated with GPS data, field investigations, and Google Earth imagery, demonstrated a commendable level of accuracy (AUC = 0.78). The average annual movement over the study period was estimated to range from -48.6 to 40.2 mm, leading to the identification of fourteen landslide zones in the region, several of which continue to exhibit activity. Specifically, the landslide that transpired in Khobkuh on April 3, 2020, was assessed using the DEM difference model, which estimated surface changes between -1.62 and 2.75 meters. Conversely, the differential interferometry model calculated the displacement rate in this area to be between -25 and 70 mm. These methodologies offer significant advantages for estimating Earth surface displacement, subsidence, and landslides, facilitating the identification of vulnerable areas in mountainous regions and contributing to the mitigation of financial and human losses.
