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This study employed remote sensing (optical and synthetic aperture radar) and data analysis techniques to quantify vertical ground displacements and assess their contribution to coastline erosion. To provide evidence from Pakistan, we selected the coast of Karachi—a mega-city located along the dynamic coastline of the Indus River Delta—which has been experiencing severe coastal erosion during the last few decades. Observations from the C-band Envisat/ASAR and Sentinel-1A sensors over the 2004–2010 and 2014–2016 periods, respectively, enabled us to study vertical ground displacements in the study area, providing a long-term assessment during 2004–2016. Results suggest that some areas along the Karachi coastline are subsiding at comparable rates to or even much higher than the relative sea-level rise (SLR, ~1.9 mm/yr), which may amplify the rates of relative SLR in coming years, along with accelerating coastal erosion. Various parts of the study area along the coast are unstable and undergoing displacement. Landsat images from 1989 to 2018 (10-year temporal resolution) were further used to examine the state of coastline erosion using three statistical approaches (i.e., End Point Rate (EPR), Linear Regression Rate (LRR), and Least Median of Squares (LMS)). While the erosion underlaid the majority of the eastern sections of the study area, the ground displacements were spatially heterogeneous across the study area and along the coastline. Erosion rates of ~2.4 m/yr spatially corresponded with ground displacement rates of up to ~−1.4 cm/yr, but not all the coastline segments with high annual mean erosion rates were associated with local mean subsidence. The causes of ground displacements and coastline erosion were analyzed, and results were interpreted by integrating spatial ancillary information. Results indicate that rapid urbanization, construction on reclaimed land, coastline erosion favoring seawater intrusion, failed drainage/sewerage networks, and soil liquefaction are contributing to the site-specific variations in the land displacement in Karachi. [ABSTRACT FROM AUTHOR]

Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models' sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area. [ABSTRACT FROM AUTHOR]

From a geological standpoint, northern Pakistan is one of the most active and unstable areas in the world. As a consequence, many massive landslides have occurred in the area in historical times that have destroyed infrastructure, blocked the Hunza River, and damaged the Karakoram Highway repeatedly. However, despite the high frequency of large magnitude landslide events, and the consequent damages, the entire area is largely understudied, mainly due to the difficult logistics and the large distances involved. This work is aimed at applying the potential use of Interferometric Synthetic Aperture Radar (InSAR) for landslide identification and investigation for the Hunza-Nagar Region. Sentinel-1 images covering a period of more than two years (February 2017–August 2019) were used and processed by adopting the small baseline subset (SBAS) method. The obtained deformation rate measured along the line of sight (VLOS) varies from −114 to 20 mm/year. The downslope velocity deformation rates (Vslope) range from 0 to −300 mm/year. The Vslope stability threshold for our study area was calculated to be −14 mm/year from the Vslope standard deviation. Four active landslides with Vslope exceeding 14 mm/year were recognizable and have been confirmed by field inspection. The identified landslides listed from the most active to least active are the Humarri, Mayoon, Khai, and Ghulmet landslides, respectively. VLOS exceeding 114 mm/year was observed in the Humarri landslide, which posed a threat of damming a lake on the Hispar River and was also a risk to the Humarri Village located below the landslide. The maximum mean deformation detected in the Ghulmet, and Mayoon landslide was in the order of 30 mm/year and 20 mm/year, respectively. More importantly, it was found that in places, the slope deformation time series showed a patchy correlation with precipitation and seismic events in the area. This may indicate a complex, and possibly uncoupled, relationship between the two controlling agents promoting the deformation. However, the collective impact of the two factors is evident in the form of a continuously descending deformation curve and clearly indicates the ground distortion. The results indicate a potentially critical situation related to the high deformation rates measured at the Humarri landslide. On this specific slope, conditions leading to a possible catastrophic failure cannot be ruled out and should be a priority for the application of mitigation measures. [ABSTRACT FROM AUTHOR]