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Masouleh is one of the ancient cities located in a high mountainous area in Gilan province of northern Iran. The region is threatened by a hazardous landslide, which was last activated in 1998, causing 32 dead and 45 injured. Significant temporal decorrelation caused by dense vegetation coverage within the landslide area makes the use of Synthetic Aperture Radar Interferometry (InSAR) for monitoring landslide movement very challenging. In this paper, we investigate the capability of three InSAR time-series techniques for evaluating creep motion on Masouleh landslide. The techniques are Persistent Scatterer Interferometry (PSI), Small BAseline Subset (SBAS) and SqueeSAR. The analysis is done using a dataset of 33 TerraSAR-X images in SpotLight (SL) mode covering a period of 15 months between June 2015 and September 2016. Results show the distinguished capability of SqueeSAR method in comparison to 2 other techniques for assessing landslide movement. The final number of scatterers in the landslide body detected by PSI and SBAS are about 70 and 120 respectively while this increases to about 345 in SqueeSAR. The coherence of interferograms improved by about 37% for SqueeSAR as compared to SBAS. The same rate of displacement was observed in those regions where all the methods were able to detect scatterers. Maximum rates of displacement detected by SqueeSAR technique in the northern edge, older and younger part of the landslide body are about -39, -65 and -22 mm/y, respectively. [ABSTRACT FROM AUTHOR]

Shabkola is a village located in Mazandaran province of northern Iran that suffers from the mass movement happening in the upstream. Deforestation and changes to land use are the main reasons for the soil instability in this region, which together with steep slope, relatively high precipitation rate and natural erosion has led to such a condition. The area of mass movement is approximately 90 hectares which is a big threat for people living in the region. In this study, we have utilized two different geodetic techniques including InSAR time-series analysis and GPS measurements to assess slope stability in Shabkola. The SAR dataset includes 19 ALOS/PALSAR images spanning from July 2007 to February 2011 while GPS observations are collected in 5 campaigns from September 2011 to May 2014. Displacement as much as approximately 11.7 m in slope direction was detected by GPS observations for the 2011-2014 time period. Most of the slope geometry is in north-south direction, for which the sensitivity of InSAR for displacement detection is low. However, ALOS PALSAR data analysis revealed a previously unknown landslide, covered by dense vegetation in the northern part of main Shabkola landslide, showing line-of-sight velocity of approximately 2cm/year in the time period 2007-2011. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the effect of various Global Digital Elevation Models (GDEMs) in producing high-resolution topography model using TanDEM-X (TDX) Coregistered Single Look Slant Range Complex (CoSSC) images. We selected an image acquired on Jun 12th, 2012 over Doroud region in Lorestan, west of Iran and used 4 external digital elevation models in our processing including DLR/ASI X-SAR DEM (SRTM-X, 30m resolution), ASTER GDEM Version 2 (ASTER-GDEMV2, 30m resolution), NASA SRTM Version 4 (SRTM-V4, 90m resolution), and a local photogrammetry-based DEM prepared by National Cartographic Center (NCC DEM, 10m resolution) of Iran. InSAR procedure for DEM generation was repeated four times with each of the four external height references. The quality of each external DEM was initially assessed using ICESat filtered points. Then, the quality of, each TDX-based DEM was assessed using the more precise external DEM selected in the previous step. Results showed that both local (NCC) DEM and SRTM X-band performed the best (RMSE< 9m) for TDX-DEM generation. In contrast, ASTER GDEM v2 and SRTM C-band v4 showed poorer quality. [ABSTRACT FROM AUTHOR]

Remote sensing plays a key role in monitoring and assessing environmental changes. Because of its special imaging characteristics such as high-resolution, capabilities to obtain data in all weather conditions and sensitivity to geometrical and dielectric properties of the features, Synthetic Aperture Radar (SAR) technology has become a powerful technique to detect small scale changes related to earth surface.SAR images contain the information of both phase and intensity in different modes like single, dual and full polarimetric states which are important in order to extract information about various targets. In this study we investigate phenological changes in an agricultural region using high-resolution X-band SAR data. The case study is located in Doroud region of Lorestan province, west of Iran. The purpose is to investigate the ability of copolar and interferometric coherence extracted from TanDEM-X dual polarimetry (HH/VV) in bistatic StripMap mode for tracking the phenological changes of crops during growing season. The data include 11 images acquired between 12.06.2012 and 02.11.2012 and 6 images acquired between 30.05.2013 and 04.08.2013 in the CoSSC format. Results show that copolar coherence is almost able to follow phenological changes but interferometric coherence has a near constant behaviour with fluctuations mainly related to baseline variations. [ABSTRACT FROM AUTHOR]

Due to its special imaging characteristics, Synthetic Aperture Radar (SAR) has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM) classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient. [ABSTRACT FROM AUTHOR]