The Antarctic and Greenland polar ice caps are the largest mass of ice in world. Globally the climate system is considerably affected by these ice sheets. Several natural and anthropogenic activities have affected the balance of mass of ice sheets. Ice sheets mass loss is a consequence of changes of patterns of precipitation, changing wind patterns, increasing global temperature and increased glacial flow. Nearly 75% of the ice mass loss has been observed in these regions since last ten years. A sharp increase in ice mass loss in Antarctic and Greenland regions are detected through 0.3mm increase in sea level per year. In this research paper Satellite remote sensing techniques including Enhanced Thematic Mapper Plus (ETM+) is used to monitor and reveal the patterns of ice melt and glacier flow in these regions.

The coastline of Pakistan comprises of five significant sites comprising of mangroves including Indus Delta which contains extensive mangroves zones and termed as the largest arid mangrove found globally. This study evaluate the current extent of mangroves along the Western edge of Indus Delta and provide the most recent forest cover assessment of mangroves. Moreover, this study identifies the changes occurred in forest cover from the years 2000 to 2020. Landsat 5 Thematic Mapper (TM), 8 Operational Land Imager (OLI) and Landsat 7 ETM data were used for mangroves mapping through supervised classification method. The results displayed that total area of mangrove forest cover was nearly 279.094km², 395.77km², 306.58km² in the years 2000, 2010 and 2020 respectively. This study indicates an increase in area of mangrove cover from 29% to 41% from the year 2000 to 2010. Regeneration of mangrove mostly took place around the southern region of the Indus Delta. The mangrove specie has decreased from 41% to 31% from the year 2010 to 2020. The major causes of this reduction were urban water and industrial pollution, over-fishing in Indus delta, freshwater diversion for agriculture, and overharvesting of mangroves by the local communities, coastal erosion and sedimentation.

The global temperature trends are being changed due to anthropogenic activities. The natural ecosystems and human societies are affected by this rapid climate change. These changes are caused by the increasing concentration of carbon dioxide and other green house gases including methane and oxides of nitrogen and sulphur. These changes can be identified using accurate data related to variations in temperature and precipitation. We used MODIS GLOVIS LST V6 global datasets to compute pixel-based temperature and mapped the trends. The considerable warming trends are exhibited by Arctic regions which are warming twice as compared to other parts of world. The largest increase in precipitation occurs in Northern Europe at the rate of 12.9mm per decade. The concentration of carbon dioxide has been raised up to 4.14 ppm in atmosphere by December 2020. This increased concentration has raised the global temperature up to 1.2°C since pre industrial era. Remotely sensed datasets provided promising results.

Hypsometric Integral (HI) displays the effect of active tectonics and sensitivity on geomorphic structures. In this study we calculated HI values for Hunza valley to investigate neotectonics, development of topographic structures and process of erosion using SRTM DEM 90m. ArcGIS and MATLAB is used to generate HI and hypsometric curve (HC). We generated HI and HC values by using D8 algorithm in MATLAB to extract drainage basins for 5 and 6 Strahler orders. HI and HC values show the stages of erosion for instance high values of HI and convex HC displays young and tectonically active stage. We used different grid sizes in ArcGIS to calculate maximum, mean and minimum elevation utilizing different statistical techniques. We used Local Indicator of Spatial Autocorrelation (LISA) instead of Global Moran Index to determine the extent of distribution of clustered, dispersed and randomized HI values. This technique indicates high positive z score for auto correlated data. Regions with high HI value indicate relative uplift, undissected and young structures while low HI values indicate sediment accumulation and shallow earthquakes.

COVID-19 pandemic has severely affected the food supply throughout the world. Pandemics affect the economy of nations badly but a number of countries were facing food insecurity even before COVID-19 pandemic. In this paper yearly data of food security is collected from the year 2015 to 2020 to inspect the consequences of poverty and COVID-19 pandemic through spatial regression analysis. The analysis shows that the food insecurity index has increased up to 33.5% by the year 2020 due to prevailing COVID- 19 pandemic. The Asian residents which are already living in developing countries have faced higher food insecurity between the years 2015 and 2018. The spatial regression analysis babbled that the discriminations like race, religion and creed doesn’t play any significant role in poverty and food insecurity. The primary factor of food insecurity is poverty. The poverty affected strongly during the years 2015 and 2018, the condition was worsened by the arrival of COVID-19 pandemic in 2020.