Landslides pose significant threats to life and property, particularly in mountainous regions. To address this, this study develops a landslide susceptibility model integrating Earth Observation (EO) data, historical data, and Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) ground movement results. The model categorizes areas into four susceptibility classes (from Class 1 to Class 4) using a multi-class classification approach. Results indicate that the Xtreme Gradient Boosting (XGB) model effectively predicts landslide susceptibility with area under the curve (AUC) values ranging from 0.93 to 0.97, with high accuracy of 0.89 and a balanced performance across different susceptibility classes. The integration of MT-InSAR data enhances the model’s ability to capture dynamic ground movement and improves landslide mapping. The landslide susceptibility map generated by the XGB model indicates high susceptibility along the Pacific coast. The optimal model was validated against 272 historical landslide occurrences, with predictions distributed as follows: 68 occurrences (25%) in Class 1, 142 occurrences (52%) in Class 2, 58 occurrences (21.5%) in Class 3, and 4 occurrences (1.5%) in Class 4. This study highlights the importance of considering temporal changes in environmental conditions such as precipitation, distance to streams, and changes in vegetation for accurate landslide susceptibility assessment.

Glaciers are melting at an alarming rate due to global warming. Two major glaciers of India viz. Gangotri and the Siachen are chosen for the velocity mapping. The line-of-sight (LOS) velocity fields are derived using X-band TerraSAR-X and C-band Sentinel-1A datasets. An intensity-based offset tracking method is used to generate LOS velocities of the glaciers. The single look complex (SLC) images of the TerraSAR-X are converted into intensity before applying the offset tracking method, whereas the ground range detected (GRD) products from Sentinel-1A are directly used to estimate the glacier velocities. The Siachen glacier velocity is mapped using three X-band images from 2011 to 2017 and a C-band image between 2017 and 2018. The X-band images in the case of Siachen are available with the long-time interval between the master and slave images. The velocity of the glacier is observed to be around 30–40 cm day⁻¹ from X-band and around 45–50 cm day⁻¹ from C-band. Three X-band images in the year 2012 and a C-band image in the year 2018 are used for the Gangotri glacier velocity estimation. These images are very closely separated in time, and the velocity of the glacier is found to be 15–20 cm day⁻¹. A dataset with a temporal gap of approximately three years is also used for the Gangotri glacier velocity estimation and observed a large difference in velocity (∼10 cm day⁻¹) from that of shorter interval data. Therefore, for a slow-moving glacier like Gangotri, a dataset with a high temporal gap may not give a reliable result. It is also observed that X-band TerraSAR-X results are more accurate than the C-band Sentinel-1A results. The penetration depth of X-band is less compared to C-band, which might result in accurate estimation of glacier surface flow. According to the results, the velocity of the Siachen glacier is increasing at a very high rate.

The 2017 M_w 7.3 Iran-Iraq earthquake occurred in the seismically less active northern Zagros Mountains. In this study, DInSAR technique is used to derive the surface displacements of the earthquake using the Sentinel-1 and ALOS-2 images. One preseismic, three coseismic and a postseismic interferogram are generated. The coseismic interferograms did not show any signs of a surface rupture and indicate a maximum of 80 cm uplift and 50 cm subsidence. The Zenith Total Delay maps derived from the GACOS are used to correct tropospheric delays in the interferograms. After correction, an error of 0-2 cm is observed in the LOS measurements. The preseismic displacement reveals an indication of ground motion before the earthquake occurrence. The postseismic phase indicates afterslip after the earthquake. Localised displacements corresponding to landslides are observed on the mountainous slopes. The 3D displacements reveal approximately 40 cm of surface motion suggesting westward movement and reverse faulting.

The coseismic surface displacement of the 2019 M _w 5.4 Mirpur earthquake is derived using Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. Two Sentinel-1 radar images before and after the earthquake acquired in interferometric wide swath mode are used for displacement map generation. Two definite lobes of deformation corresponding to subsidence and uplift are observed from the displacement map. The results indicate an uplift of 9.5 cm and subsidence of -6.2 cm in the earthquake epicentral region. Using a forward elastic dislocation model the causative source parameters of the earthquake are randomly searched using an iterative approach, which minimizes the error between the InSAR data and the modeled results. The inversion results indicate a rectangular fault of length ~10 km and width ~5 km is responsible for the earthquake. Other source parameters such as strike, dip, depth, and the slip of the earthquake are also calculated during the coseismic inversion.