On 31st December 2019, a novel virus was reported from Wuhan City of Hubei Province of China, and later it was recognized as SARS-COV-2 (COVID-19). As the virus is highly human to human contagious, it has spread worldwide within a very short time. Since 24th March 2020, after the first reported case in North East India, the total confirmed cases reached up to 4,633 on 11th June 2020. In this work, an attempt has been made to delineate risk zones of COVID-19 in North East India using the Analytic Hierarchy Process (AHP) and overlay analysis in Geographical Information System (GIS). The evaluation is based on 14 criteria that were classified into promoting and controlling factors. The promoting factors include population size, population density, urban population, elderly population, population below the national poverty line, and percentage of marginal workers. In contrast, the controlling factors include available doctors, other health workers, public health facilities, available beds, governance index (composite and health), and testing laboratories. The results were classified into very high, high, moderate, low, and very low risk zones. Most densely populated states with massive pressure on health facilities are likely to have a higher risk of COVID-19. Assam, Tripura, Meghalaya, and Nagaland show a high COVID-19 risk, which constitutes almost 76.93% of the North East India population, covering 48.80% of surface area. The states under a moderate risk zone include 6.92% of the population over 8.52% of the area. Lastly, 16.15% of the people living over 42.69% of the total area belong to the states with a lower risk zone.

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Water is crucial to human survival. Studies on surface water are well documented but precise knowledge of groundwater resources is difficult. Thus, accurate knowledge of groundwater resources could meet the necessities of water at present and in the long run. The application of the Analytic Hierarchy Process (AHP) and Geographical Information System (GIS) together with multi-criteria parameters has emerged as an efficient technique for delineation of groundwater potential in recent decades. However, no efforts to delineate the groundwater potential have been attempted in the study area to date. Hence, in this study, the groundwater potential of Papumpare district of Arunachal Pradesh was delineated by combining AHP, GIS, and ten thematic layers (geomorphology, geology, slope, lineament density, drainage density, rainfall, distance from the major river, topographic wetness index, soil texture, and land use/land cover). The results show about 64% of the area under poor groundwater potential.Moderate and good groundwater potential is found in 31% and 5% of the area, respectively. Map-removal and single-parameter sensitivity analyses revealed that the groundwater potential map is most sensitive to the annual average rainfall with a mean variation index of 1.05% and a weight of 19.07%. The flood/alluvial plains, Siwalik formations with sediments, and level to gentle slopes receiving high rainfall show good potential, and the dissected hills/valleys, metamorphic rock assemblages, steep slopes with low rainfall reveals poor groundwater potential. The overall accuracy of 81.25% with a Kappa coefficient of 0.72 explains good agreement between the reference data and the map. The estimated area under good groundwater potential appears too little concerning the increasing population and urbanization. Therefore, the state government in general and the water resources and planning department in particular need to formulate suitable strategies to combat the water scarcity scenario waiting ahead.The study suggests raising the use of surface water from nearby rivers to lessen the pressure on groundwater resources.

Sonitpur and Udalguri district of Assam possess rich tropical forests with equally important faunal species. The Nameri National Park, Sonai-Rupai Wildlife Sanctuary, and other Reserved Forests are areas of attraction for tourists and wildlife lovers. However, these protected areas are reportedly facing the problem of encroachment and large-scale deforestation. Therefore, this study attempts to estimate the forest cover change in the area through integrating the remotely sensed data of 1990, 2000, 2010, and 2020 with the Geographic Information System. The Maximum Likelihood algorithm-based supervised classification shows acceptable agreement between the classified image and the ground truth data with an overall accuracy of about 96% and a Kappa coefficient of 0.95. The results reveal a forest cover loss of 7.47% from 1990 to 2000 and 7.11% from 2000 to 2010. However, there was a slight gain of 2.34% in forest cover from 2010 to 2020. The net change of forest to non-forest was 195.17 km2 in the last forty years. The forest transition map shows a declining trend of forest remained forest till 2010 and a slight increase after that. There was a considerable decline in the forest to non-forest (11.94% to 3.50%) from 2000–2010 to 2010–2020. Further, a perceptible gain was also observed in the non-forest to the forest during the last four decades. The overlay analysis of forest cover maps show an area of 460.76 km2 (28.89%) as forest (unchanged), 764.21 km2 (47.91%) as non-forest (unchanged), 282.67 km2 (17.72%) as deforestation and 87.50 km2 (5.48%) as afforestation. The study found hotspots of deforestation in the closest areas of National Park, Wildlife Sanctuary, and Reserved Forests due to encroachments for human habitation, agriculture, and timber/fuelwood extractions. Therefore, the study suggests an early declaration of these protected areas as Eco-Sensitive Zone to control the increasing trends of deforestation.

Water is crucial to human survival. Studies on surface water are well documented but precise knowledge of groundwater resources is difficult. Thus, accurate knowledge of groundwater resources could meet the necessities of water at present and in the long run. The application of the Analytic Hierarchy Process (AHP) and Geographical Information System (GIS) together with multi-criteria parameters has emerged as an efficient technique for delineation of groundwater potential in recent decades. However, no efforts to delineate the groundwater potential have been attempted in the study area to date. Hence, in this study, the groundwater potential of Papumpare district of Arunachal Pradesh was delineated by combining AHP, GIS, and ten thematic layers (geomorphology, geology, slope, lineament density, drainage density, rainfall, distance from the major river, topographic wetness index, soil texture, and land use/land cover). The results show about 64% of the area under poor groundwater potential. Moderate and good groundwater potential is found in 31% and 5% of the area, respectively. Map-removal and single-parameter sensitivity analyses revealed that the groundwater potential map is most sensitive to the annual average rainfall with a mean variation index of 1.05% and a weight of 19.07%. The flood/alluvial plains, Siwalik formations with sediments, and level to gentle slopes receiving high rainfall show good potential, and the dissected hills/valleys, metamorphic rock assemblages, steep slopes with low rainfall reveals poor groundwater potential. The overall accuracy of 81.25% with a Kappa coefficient of 0.72 explains good agreement between the reference data and the map. The estimated area under good groundwater potential appears too little concerning the increasing population and urbanization. Therefore, the state government in general and the water resources and planning department in particular need to formulate suitable strategies to combat the water scarcity scenario waiting ahead. The study suggests raising the use of surface water from nearby rivers to lessen the pressure on groundwater resources.