Showing 3 results for Radar
Dr Fariba Esfandyari, Mr Ehsan Ghale, Ms Maryam Mohamadzadeh Shishegaran,
Volume 0, Issue 0 (3-1921)
Abstract
One of the dangers that has occurred in many areas in recent years is the dangers of subsidence. Iran's geographical location has put many of its regions at risk. High precision radar interferometry technique is one of the most suitable methods for detecting and measuring subsidence. In this study, in order to identify and measure subsidence in Ardabil plain, the Sentinel 1 radar image interference technique of 2015 and 2020 has been used. In order to verify, the data of piezometric wells and land use maps in the area were used. According to the results, the maximum subsidence rate in 5 years in the region is estimated at 17 cm. The results also showed that the highest subsidence rates in the period 2015 to 2020 are in the next categories of rangeland uses with a value of 17 cm, soil value of 14 cm and rainfed agricultural and residential areas with a value of 13 and 12 cm. respectively, 12 cm subsidence for residential use can be due to demolition and construction of large buildings. Also, the relationship between subsidence and changes in groundwater level showed that in a period of 5 years, the groundwater level has decreased by 4 meters. This drop in groundwater level has led to land subsidence in the study area.
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.
Mr Shokrollah Kiani, Mr Ahmad Mazidi, Mr Seyed Zein Al-Abedin Hosseini,
Volume 24, Issue 74 (12-2024)
Abstract
Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of 1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.
