Restrictions on human and industrial activities due to the coronavirus (COVID-19) pandemic have resulted in an unprecedented reduction in energy consumption and air pollution around the world. Quantifying these changes in environmental conditions due to government-enforced containment measures provides a unique opportunity to understand the patterns, origins and impacts of air pollutants. During the lockdown in Pakistan, a significant reduction in energy demands and a decline of ∼1786 GWh (gigawatt hours) in electricity generation is reported. We used satellite observational data for nitrogen dioxide (NO₂), carbon monoxide (CO), sulphur dioxide (SO₂), aerosol optical depth (AOD) and land surface temperature (LST) to explore the associated environmental impacts of shifts in energy demands and emissions across Pakistan. During the strict lockdown period (March 23 to April 15, 2020), we observed a reduction in NO₂ emissions by 40% from coal-based power plants followed by 30% in major urban areas compared to the same period in 2019. Also, around 25% decrease in AOD (at 550 nm) thickness in industrial and energy sectors was observed although no major decrease was evident in urban areas. Most of the industrial regions resumed emissions during the 3rd quarter of April 2020 while the urban regions maintained reduced emissions for a longer period. Nonetheless, a gradual increase has been observed since April 16 due to relaxations in lockdown implementations. Restrictions on transportation in the cities resulted in an evident drop in the surface urban heat island (SUHI) effect, particularly in megacities. The changes reported as well as the analytical framework provides a baseline benchmark to assess the sectoral pollution contributions to air quality, especially in the scarcity of ground-based monitoring systems across the country.

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO₂ and breathing out O₂—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.

This study investigated the performances of different techniques, including random forest (RF), support vector machine (SVM), maximum entropy (maxENT), gradient-boosting machine (GBM), and logistic regression (LR), for landslide susceptibility mapping (LSM) in the rugged terrain of northern Pakistan. Initially, a landslide inventory of 200 samples was produced along with an additional 200 samples indicating nonlandslide areas and divided into training (70%) and validation (30%) groups using a stratified loop-based random sampling approach. Then, a geospatial database of 12 possible landslide influencing factors (LIFs) was generated, including elevation, slope, aspect, topographic wetness index (TWI), topographic position index (TPI), distance to drainage, distance to fault, distance to road, normalized difference vegetation index (NDVI), rainfall, land cover/land use (LCLU), and a geological map of the study area. None of the LIFs were redundant for the modeling, as indicated by the multicollinearity test (tolerance > 0.1) and information gain ratio (IGR > 0). We extended the evaluation measures of each algorithm from area-under-the-curve (AUC) analysis to the calculation of performance overall (POA) with the help of precision, recall, F1 score, accuracy (ACC), and Matthew’s correlation coefficient (MCC). The results showed that the SVM was the most promising model (AUC = 0.969, POA = 2669) for the LSM, followed by RF (AUC = 0.967, POA = 2656), GBM (AUC = 0.967, POA = 2623), maxENT (AUC = 0.872, POA = 1761), and LR (AUC = 0.836, POA = 1299). It is important to note that the SVM, RF, and GBM were the top performers, with almost similar accuracy. Thus, each of these could be equally effective for LSM and can be used for risk reduction and mitigation measures in the rugged terrain of Pakistan and other regions with similar topography.

Land subsidence, as one of the engineering geological problems in the world, is generally caused by compression of unconsolidated strata due to natural or anthropogenic activities. We employed interferometric point target analysis (IPTA) as a multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique on ascending and descending Sentinel-1A the terrain observation with progressive scans SAR (TOPSAR) images acquired between January 2015 and December 2018 to analyze the spatio-temporal distribution and cause of subsidence in Abbottabad City of Pakistan. The line of sight (LOS) average deformation velocities along ascending and descending orbits were decomposed into vertical velocity fields and compared with geological data, ground water pumping schemes, and precipitation data. The decomposed and averaged vertical velocity results showed significant subsidence in most of the urban areas in the city. The most severe subsidence was observed close to old Karakorum highway, where the subsidence rate varied up to −6.5 cm/year. The subsidence bowl profiles along W–E and S–N transects showed a relationship with the locations of some water pumping stations. The monitored LOS time series histories along an ascending orbit showed a close correlation with the rainfall during the investigation period. Comparative analysis of this uneven prominent subsidence with geological and precipitation data reflected that the subsidence in the Abbottabad city was mainly related to anthropogenic activities, overexploitation of water, and consolidation of soil layer. The study represents the first ever evidence of land subsidence and its causes in the region that will support the local government as well as decision and policy makers for better planning to overcome problems of overflowing drains, sewage system, littered roads/streets, and sinking land in the city.

Background: Forests are fundamental in maintaining water supplies, providing economic goods, mitigating climate change, and maintaining biodiversity, thus providing many of the world’s poorest with income, food and medicine. Too often, forested lands are treated as “wastelands” or “free” and are easily cleared for agricultural and infrastructure expansion. Methods: In this paper, the sustainability of two forest ecosystems (state and community/private owned) was evaluated using SPOT-5 satellite images of 2005 and 2011. This study was conducted in a sub-watershed area covering 468 km², of which 201 km² is managed by the state and 267 km² by community/private ownership in the Murree Galliat region of Punjab Province of Pakistan. A participatory approach was adopted for the delineation and demarcation of forest boundaries. The Geographic Object-Based Image Analysis (GEOBIA) technique was used for identification and mapping of ten Land Cover (LC) features. Results: The results show that between the years 2005 to 2011, a total of 55 km² (24 km² in state-owned forest and 31 km² in community/private forest) was converted from forest to non-forest. The conclusion is that conservation is more effective in state-owned forests than in the community/private forests. Conclusions: These findings may help to mobilize community awareness and identify effective initiatives for improved management of community/private forest land for other regions of Pakistan.

Mapping mangrove (littoral and swamps) ecosystems is challenging due to the qualitative and quantitative nature of the surrounding water and mudflats. However, accurate assessment of mangroves is required to determine carbon credits. This research study explores five pan-sharpening algorithms with the aim of determining the best algorithm to map mangrove ecosystems from very high resolution satellite images. In this research, a multidimensional evaluation was employed to pinpoint the best algorithm from among five advanced algorithms, i.e. Ehler’s transformation (ET), modified intensity hue saturation (MIHS), wavelet transformation (WT), optimized high pass filter addition (OHPFA), and subtractive resolution merge (SRM). These approaches involve the calculation of spectral root mean square error (RMSE), Sobel filter RMSE and correlation coefficient (r). OHPFA and SRM provided good results during this assessment. Object-based image analysis was incorporated to further assess the best technique between these two approaches for assessing mangrove tree canopy by calculating under and over segmentation. The SRM algorithm provides the best results with a kappa coefficient (κ) of 0.875 and an accuracy of 92.3% when compared with ground data. This research is very useful in various applications such as calculation of crown projection area using high resolution satellite images for estimation of blue carbon in mangrove trees.