Volume 25, Issue 2 (2021)                   MJSP 2021, 25(2): 183-205 | Back to browse issues page

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Yamani M, Heydarian L, goorabi A, Maghsoudi M. Modeling Domain Instabilities Using Time Series Analysis Radar images with SBAS technique. MJSP 2021; 25 (2) :183-205
URL: http://hsmsp.modares.ac.ir/article-21-40275-en.html
1- Faculty of Geography, University of Tehran , myamani@ut.ac.ir
2- Faculty of Geography, University of Tehran
Abstract:   (2349 Views)
Nowadays, given the rapid growth of population, development of infrastructure is inevitable and the pressure of human needs on the soil and exploitation of areas around cities in rural areas are increasing. Access to surface water, fertile soil, groundwater, access to transit roads, etc. have made establishing of new cities compulsory despite the environmental hazards in those areas.
Land deformation as an environmental hazard may be related to tectonic activities such as earthquakes, faults, volcanoes, landslides and anthropogenic processes such as groundwater exploitation, which threaten urban areas. Land surface subsidence is recognized as a potential problem in many areas. This phenomenon is most often caused by human activities, mainly from the removal of subsurface water. Also, Iran with rough and mostly mountainous topography, have a high potential for landslides and instability of slopes.
 Pardis new city in the east part of Tehran is one of the areas most prone to Domain Instabilities. The location of the city and its expansion toward the steep slopes have made it susceptible to all kinds of natural hazards, so the main purpose of the study is investigate the potential of landslide and subsidence in Pardis.
 
 
Material and Methods
This research consists of two stages: first, ground surface deformation was estimated using radar interferometry technique. Then, landslide susceptible zoning was carried out using Fuzzy and AHP methods.
We applied SBAS algorithm to the 27 SAR images of the Sentinel-1 satellite, in ascending orbit for the time period of 2016.01.06.-2018.12.21. The first step of the SBAS procedure involves the selection of the SAR data pairs to generate the interferograms; the selected images are characterized by a small temporal and spatial separation (baseline) between the orbits in order to limit the noise effect usually referred to as decorrelation phenomena. The second step of the procedure involves the retrieval of the original (unwrapped) phase signals from the modulo-2 π restricted (wrapped) phases directly computed from the interferograms.
In the next stage, landslide susceptibility zones have been evaluated using both fuzzy logic and analytical hierarchy process (AHP) models, as a weighting technique to explore landslide susceptibility mapping. In the modelling process, eight causative variables including aspect, slope degree, altitude, distance from the road, distance from the fault, distance from the river, lithology and land use were identified for landslide susceptibility mapping.
 In fuzzy logic the degree of membership of variables may be any real number from 0 (non-membership) to 1 (full membership) which reflects a degree of membership (Zadeh, 1965). By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. After Fuzzification of all layers, since the causative factors are not the same value, the AHP method to determine the weights was performed. The AHP methodology consists of pairwise comparison of all possible pairs of factors. The relative rating for the dominance between each pair of factors was guided by expert knowledge. After obtaining weight of each factors, these weights are multiplied in the map calculated by fuzzy membership.
 
                                                                                                                  
Results and  Discussion
We used 27 c-band sentinel-1 images for the 2016-2018 period and the Small BAseline Subset (SBAS) approach to investigate land deformation in Pardis. Result of the deformation map of Pardis show that the northern part is uplifted with an annual rate of 25 mm/yr. The uplift of the northern part can be attributed to tectonic factors and the southern part of the basin subsided with an annual rate of -35 mm/yr. Thereafter landslide susceptibility areas have been evaluated. Geomorphological variables (slope, aspect, elevation, river), geology variables (lithology, fault) and anthropogenic variables (land use, roads) have been used for generation of the landslide susceptible map. The results of the landslide susceptible map indicate that the northern part of the Pardis basin have a high potential for landslides. Landslide susceptible map is classified into five classes: very high, high, medium, low and very low.
 Medium to very high susceptible class covered 40% of the study area which overlay on uplifting areas resulting from radar technique.
 
Conclusion
SBAS time series method has been used to detect ground surface deformation and vertical movements. This method is based on an appropriate combination of multi look DInSAR Interferograms. Deformation map indicate that northern part of the basin, uplifted and southern part subsided. The cities of Pardis, Roodehen and Boomehen in the southern part, subsided a mean rate of respectively -35, -31 and -29 mm/year. The northern part uplifted with a mean rate of 25 mm/year which can be attributed to tectonic activity. Then, the landslide susceptibility map was created using both Fuzzy and AHP methods. The result show that more than 40% of the basin is exposed to landslides. The results of both methods SBAS time series analysis, landslide susceptibility mapping, demonstrated domain instabilities in northern part of the basin. As a result, identifying instable areas seems necessary for the urban development of the Pardis. 
Key words: Pardis city, SBAS time series analysis, landslide, subsidence
Full-Text [PDF 1438 kb]   (808 Downloads)    
Article Type: Original Research | Subject: techniques if spatial / locational data processing in environmental planning
Received: 2020/01/29 | Accepted: 2020/09/30 | Published: 2021/07/1

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