Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016–2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area

Potential risks assessment of heavy metal(loid)s contaminated vegetables in Pakistan: a review

Heavy metal(loid)s (HM) contamination and associated potential health risks are an issue of global concern. The pathways through which these HM affect human body include food crops, vegetables, animal products, and processed food items. This study aim to assess HM concentrations in vegetables studied across Pakistan. The concentration trends of HM followed a decreasing order: Punjab > Khyber Pakhtunkhwa > Sindh > Baluchistan > Gilgit Baltistan. The daily intake of metal(loid)s through consumption of HM was calculated and found the highest for Fe and lowest for Cd and Se. The maximum hazard quotient value was observed for As and Cd in the Punjab and Khyber Pakhtunkhwa provinces and surpassed the threshold limit (<1). Results showed that children are at higher risks to both noncarcinogenic and carcinogenic risks. Higher HM concentrations and associate health risks in Punjab province could be attributed to industrial discharge and use of sewage-contaminated water for irrigation of vegetables

Geology as a proxy for Vs30-based seismic site characterization, a case study of northern Pakistan

Earthquakes, with their unpredictable and devastating nature, have resulted in large damages worldwide. Seismic site characterization maps (SSCMs) are frequently and effectively used to demarcate the locations that are prone to amplified seismic response. The time-averaged shear wave velocity of top 30 m of earth surface (Vs30) is effectively used as a parameter to evaluate seismic amplification. Northern Pakistan is one of the most seismically active region in the world with 2005 Kashmir earthquake as the most devastating natural disaster. However, for most of the country, the seismic site characterization maps are not available. Geological units and topographic slope are used as proxies for Vs30-based SSCMs around the world and in northern Pakistan. However, the studies in northern Pakistan are lacking field-based validation of the estimated Vs30 and hence the proxy-based SSCMs might be unrealistic. The aim of this study is to correlate instrument-based Vs30 measurements with geological units and remote sensing-derived topographic slope to develop a more realistic SSCM for the study area, located in the seismically active northern Pakistan. Geology of the study area has significant impact on the estimated Vs30 and hence is used as a proxy for SSCM. The developed SSCM shall assist in developing earthquake mitigation strategies in the region

Comparison of landslide susceptibility models and their robustness analysis: a case study from the NW Himalayas, Pakistan

Machine learning methods are considered as most effective approaches to accomplish landslide susceptibility analysis around the globe. Landslide susceptibility maps (LSMs) have been frequently executed by statistical models in NW Himalaya. However, the comparison and applications of the statistical models with modern machine learning techniques has not been fully explored in this region. Hence, this study aims to compare the predicted performance of statistical and popular machine learning models to explore robust landslide prediction model in the landslide-prone area of NW Himalaya and investigate the compensations and limitations of these models to grasp a more precise and consistent result. This study presented machine learning approaches based on the artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR) and the statistical methods based on the frequency ratio (FR), information value (InfoV) and weight of evidence (WoE). For this purpose, first an inventory map of 1507 landslides was prepared and randomly divided into training (70%) and testing (30%) dataset. Furthermore, 12 landslide conditioning factors (LCFs) were extracted from geospatial dataset to prepare thematic layers in ArcGIS. Thereafter, factor analysis was performed to eliminate colinear and least important variables which can mislead the results. The results showed that all selected LCFs are noncolinear and have significant contribution on landslides initiation, however, lithology, slope angle, annual rainfall and landuse were most influential factors. For modeling purpose, landslide inventory was correlated against all LCFs and trained into six models to produce respective LSMs. Finally, the performance of produced LSM models was validated and compared through area under receiver operating characteristic curve (AUROC), Accuracy, Recall, F1-score and Cohen’s Kappa coefficients to assess the robustness of employed models. The results exhibit that the performance scores of machine learning models were considerably superior than statistical models. While, the AUROC values based on validation dataset indicate that LR (0.89) has better prediction ability followed by SVM (0.86), ANN (0.84), FR (0.83), InfoV (0.82) and WoE (0.81) in this study. Therefore, it is reasoned out that the machine learning methods are more reliable in generating adequate LSMs. However, the LR is recommended as most efficient model for predicting landslide susceptible zones in study region and thus can be considered as robust model for landslide susceptibility assessment in similar geo-environmental regimes