Polarimetric synthetic‐aperture radar (SAR) remote sensing has been widely used for structural and biophysical parameters retrieval of forest vegetation. It has been found that the combination of polarimetric properties and interferometric characteristics of SAR remote sensing provides the capacity to retrieve forest height. The prime objective of this research was to investigate the potential of Polarimetric Synthetic Aperture Radar Tomography (PolTomSAR) for the forested and river region of Mondah National Park, Gabon. SAR tomography is an improved method for acquiring the height of geographical features. UAVSAR L‐band fully polarimetric multibaseline data have been used in this research (1.275 GHz). SAR data and ground data over the area have been collected in the year 2016. With the superresolution‐based Capon algorithm, multiple scatterers located at a different vertical position in the same azimuth range cell has been resolved and reconstructed. This work provides a framework for Capon‐based tomographic processing of multibaseline UAVSAR data for vertical profile retrieval of forest vegetation. The height profile of the forest patch having sparse, as well as dense vegetation, were retrieved. The vertical profile for a single azimuthal bin was obtained in range direction. The tomographic profile obtained was cross‐checked with the field‐measured forest height for the 16 locations in Mondah National Park, Gabon. To check the accuracy of the applied method, the statistical method of R² and root‐mean‐square error (RMSE) is employed. The obtained RMSE of the result is 4.21 m and R² is 0.92. The obtained results were concluded to find the potential of the Capon algorithm for the tomographic reconstruction of UAVSAR data.

DInSAR is a renowned method for estimating land subsidence based on the principles of interferometric synthetic aperture radar using different series of the temporal dataset. The present study has been performed using GMTSAR software with Sentinel 1 SAR data of C band for the duration of 2017–2019 (January to April) and focused particularly over the area of Jagadhri city which is situated 100 km away from Chandigarh, which has been identified under the potential threat of land subsidence. The DInSAR method has been applied in this study that came up with an outcome of three interferograms and yearly displacement that broadcast an update on the diagnosis of subsidence activity in the area. A total of six Single Look Complex (SLC) datasets were selectively chosen with a minimum temporal and spatial baseline so that the problem of decorrelation would be minimal. Goldstein filtering has been applied to the deburst interferograms which reduced the noise and, in turn, improved the quality of output. The city is located on the western bank of river Yamuna and about 55 km on the east of Ambala. Due to the presence of unconsolidated sediments in the aquifer system and over-exploitation of groundwater to meet the domestic needs has led to surface deformation in and around the city area. The outcome of this study identifies the area of depression quite distinctly while the accuracy has been assessed by ground survey. The rate of subsidence estimated approximately 4.98 cm/year which can prove to be disastrous over the course of time.

In this research paper, focus is given on exploring the potential of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique for the measurement of landslide, which is the extension of existing DInSAR technique. In PSInSAR technique, the movement is measured by finding the phase shift in the scatterers present in the study area through the course of time. The backscattering of such a scatterer does not change during the study. By using this technique, 32 datasets acquired over a period of time i.e. from 2009 to 2011 over the area of Troms County of Lyngen Fjord, Norway are analysed. The dataset utilised are acquired with TerraSAR-X and TanDEM-X pair, in Stripmap mode of acquisition. Coregistration of dataset with subpixel accuracy is done with master images is done to align all the dataset correctly. APS estimation is done in order to remove the phase decorrelation caused by the atmosphere, movement, etc. using algorithms for phase unwrapping which allowed the processing of sparse data and the effect of atmosphere is reduced by doing analysis on temporal basis of the phase shift in interferograms of successive datasets. By this study it has been tried to show the estimation of shift can be done by the temporal analysis of the data acquired by TerraSAR-X. The velocity output is displayed in a map reflecting the velocity of movement. Apart from this, the data properties such as baseline distribution both temporal and spatial are displayed in a chart. Other outputs obtained are the atmospheric Phase Screen, sparse point distribution, reflectivity map of the study area etc. are displayed using a map of terrain. The output velocity obtained of the terrain movement is found to be in the range of −40 mm/yr to −70 mm/yr.