Vulnerability analysis in areas vulnerable to anthropogenic pollution has become a key element of sensible resource management and land use planning. This study is intended to estimate aquifer vulnerability using the DRASTIC model and using the vertical electrical sounding (VES) and electrical conductivity (EC) outcomes. The model allows for the identification of hydrogeological environments within the scope of the research, based on a composite definition of each environment’s main geological, geoelectrical, and hydrogeological factors. The results from the DRASTIC model were divided into four equal intervals, high, medium, low, and very low drastic index values. The SW area and NE area depict drastic index values from medium to very high, making it the most vulnerable zone in the study area, while the NW and SW areas show low to very low drastic index values. In addition, the results from the VES and EC the freshwater aquifer in the NE area and brackish water in the SE area, while the rest of the area falls into the category of brackish water. Overall, it can be concluded that areas having freshwater assemblages are on the verge of becoming contaminated in the future while the rest of the NW and SW areas constitute less vulnerable zones. The validation conducted for DRASTIC and EC shows a nearly positive correlation. Wastewater treatment policies must be developed throughout the studied region to prevent contamination of the remaining groundwater.
In arid and semi-arid regions, it is essential to monitor the spatiotemporal variability and dynamics of vegetation. Among other provinces of Pakistan, Punjab has produced a significant number of crops. Recently, Punjab, Pakistan, has been described as a global hotspot for extremes of climate change. In this study, the soil adjusted vegetation index (SAVI), normalized vegetation difference index (NDVI), and enhanced vegetation index (EVI) were comprehensively evaluated to monitor vegetation change in Punjab, Pakistan. The time-series MODIS (Moderate Resolution Imaging Spectroradiometer) data of different periods were used. The mean annual variability of the above vegetation indices (VIs) from 2000 to 2019 was evaluated and analyzed. For each type of vegetation, two phenological metrics (i.e., for the start of the season and end of the season) were calculated and compared. The spatio-temporal image analysis of the mean annual vegetation indices revealed similar patterns and varying vegetation conditions. In the forests and vegetation areas with sparse vegetation, the EVI showed high uncertainty. The phenological metrics of all vegetation indices were consistent for most types of vegetation. However, the NDVI result had the greatest variance between the start and end of season. The lowest annual VI variability was mainly observed in the southern part of the study area (less than 10% of the study area) based on the statistical analysis of spatial variability. The mean annual spatial variability of NDVI was <20%, SAVI was 30%, and EVI ranged between 10–20%. More than 40% of the variability was observed in the NDVI and SAVI vegetation indices.
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