Tahereh Karimi, Amir Karam, Parviz Zairean Firuzabadi, Seyyed Mohammad Tavakkoli Sabour,
Volume 0, Issue 0 (3-1921)
Abstract
Abstract
Every year slope hazards and landslides cause significant damage in the mountainous areas of Iran, including the eastern Alamut region in Qazvin province. Recently, radar data has been widely used to detect ground surface movements, slope slow motions, and active landslides. In the present study, using the Sentinel 1A satellite descending data in the period from 2018 to 2020, with the Small Baseline Subset (SBaS-InSAR) and also with the digital elevation model (DEM) difference methods, slope motions and Earth surface displacements have been extracted to provide the important goal of detecting new and active landslides and updating the landslide map to predict landslide risk. Results show that in the SBaS model, which was validated with GPS data, field visits and Google Earth images, accuracy was relatively good (AUC = 0.78), and the average annual movement during this period was estimated at -48.6 to 40.2 mm and fourteen landslide zones in the region, are identified among which some of the previous landslides are still active. To detect the landslide that occurred in Khobkuh on April 3rd, 2020, DEM difference model estimated the surface changes between -1.62 to 2.75 meters and differential interferometry model estimated the displacement rate in this area from -25 to 70 mm. These methods have many advantages for estimating the Earth surface displacement, subsidence and landslides, determining vulnerable areas in mountainous areas and reducing financial and human losses.
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.
