Search

The present study intends to understand and disclose the role of information infrastructure in climate change adaptation and its underlying barriers in the communities of socio-ecological system (SES) in the Central Himalaya. The study makes use of primary data in its research methodology which comprises the use of questionnaires, oral interviews, and review of relevant literature. The data were collected using a questionnaire-based survey from 85 households in 11 villages. The study results show that road accessibility and irrigation facilities remarkably influence the educational, professional, and economic conditions of the communities. The research reveals how the flow of information through television, newspapers, village elders, and mobile phones influences perceptions of climate change in a different order. Information infrastructure availability is higher with the communities having access to the road network and irrigation facilities. The study concludes that in SES a good information infrastructure is highly relevant for reducing the current and future vulnerability of SESs to climate change. The study also provides recommendations for the dissemination of information on climate adaptation that suit the needs and demands of the Himalayan SESs and thus could help to close existing information gaps and barriers.

This study is motivated by the recent observed realization of Pseudo Chiral Magnetic Effect (PCME) into the continuum limit of the low-energy tight binding description of graphene. We here proposed the quantum description of PCME by investigating the Landau level structure of the system. The different physical quantities like current, chiral currents, number densities and chiral condensate are discussed in this context with this Landau level description of the system. Further, the effect of different physical conditions to the system is studied by analyzing the behavior of Landau levels under these conditions. This provides a microscopic understanding of the phenomenon. It has been shown that largest contribution to the PCME currents and number densities comes from the spin polarized lowest Landau level which corresponds to the system characteristics at low-temperature and high magnetic field. However, the contribution to the PCME current from the higher Landau levels is just opposite to that from the lowest Landau level. We have further established general expressions for all these physical quantities regarding to any higher-order Landau level.

The present study evaluates and compares performance of three supervised classification algorithms namely Maximum Likelihood (MXL), Artificial Neural Network (ANN) and Support Vector Machine (SVM), using very high resolution WorldView-2 satellite imagery for vegetation type/land cover (VT/LC) mapping in Keoladeo National Park (KNP), India. We mapped 16 (8 gregarious VT and 8 LC) classes, and used Bootstrap (with 100 iterations) method for accuracy assessment. All three algorithms produced high overall accuracy (OA) (67–85%) and kappa (K) (65–83) values. Visual comparison of the predictions revealed that SVM (OA = 85.12% (K = 83.9) with 3.85% width of confidence interval) performed the best followed by ANN (69.72% (67.32) with 4.43%) and MXL (67.37% (65.22) with 4.33%). This research provides insight for selection of classification algorithm for detailed VT/LC mapping of wetland associated systems using very high resolution satellite data. The findings of this research are useful for environmental management, restoration and conservation planning of KNP, India. The database will be of high value for future development and sustainability issues in the park.

The fragile mountains of Himalaya are one of the most complex and heterogeneous mountain socio-ecological system (SES) due to its high diversity and intricate linkages of biophysical, sociocultural, and political aspects. An absence of substantial place-based knowledge and failed developmental initiatives due to absence of holistic approach in Himalaya has motivated to conduct a systematic review. In this study, we systematically review peer-reviewed literature focused on SESs (n = 53) in the Himalayan countries (Bhutan, China, India, Nepal, and Pakistan). The review summarizes the existing knowledge of Himalayan SES scholarship. It documents the trends and distribution of Himalayan SESs and its scholarship. We examined the year-wise, spatial (geographical), and methodological trends in SES discourse. The review appraises the conceptualization of the term SES. Further, major drivers impacting the Himalayan SESs are identified and discussed. Our findings highlight that 87% of literature has attempted country-specific study, with Nepal and India in the lead. Such a spatial disparity compounded by the complexity arising from a dearth of inconsistent conceptualization, cross-scale interactions and geospatial exploration of Himalayan SESs mask the deeper realities of unique Himalayan SESs. Thus, we advocate for a comprehensive definition and characteristic attributes of SES while considering cross-scale interactions and inherent mountain specificities. The decisions that direct sustainable development of Himalaya are made locally, and these decisions are influenced by social and ecological values. Targeting locally perceived characteristics attributes of SES in decision-making can ensure sustainable future of Himalaya.

Recorded seasonal variation and uneven distribution of rainfall is one of the major issues to the agrarian society and the domestic water users today. For strategic planning of water use, scientific estimation of water balance in different spatio-temporal scale is necessary. For this, the present paper intends to investigate monthly and annual water balance state of Mayurakshi River basin (including 45 sub-basins) using precipitation (P), runoff (Q), evapotranspiration (ET) and recharge/soil moisture. P, Q, ET, respectively, vary from 1625.97 mm to 2037.93 mm, 182.19 mm to 1503.15 mm and 1050 mm to 1500 mm for the entire basin. The monthly pattern shows significant variation attributed to strong seasonality of P and temperature (T). Monsoon months recorded heavy maximum P, Q and actual ET and on the other hand, all these are very poor during pre-monsoon season. Groundwater recharge annually differs spatially, but annual average recharge is 307mmwith main contribution in monsoon months. Water deficit condition prevails in pre-monsoon season; strong groundwater lowering and drying of top soil are some explicit evidences of the same. This seasonal water scarcity withstands against multi-cropping strategies of the agrarian communities. To cope with this emerging situation, pressure on groundwater is mounting but it is the fact that this resource is also highly limited and precious. Quantification of monthly water balance and water deficit will be inputs to the strategy development for sustainable water resource management.

Urban landscapes are now the most inhabited places by the majority of global population. The rapid and large-scale land use land cover (LULC) changes induced by urbanization are declining the ecosystem services (ESs) provisioning capacity of natural and semi-natural ecosystems of urban landscapes. The main aim of the study was to assess the ecological changes caused by LULC change in the urbanized capital of the Central India, Bhopal. This study relied on satellite images (from 1988 to 2018) for generation of spatio-temporal LULC database and direct benefit transfer ecosystem service valuation scheme for quantifying the ecosystem service value (ESV). The results showed decline in total ESs by US $ 2.00 million. Though water body covers least area out of 5 LULC types of the city, it remains the top contributor to total ESV in each reference year. The ecosystem service function analysis aided in identifying, regulating and provisioning services as the key contributors. In addition, assessment of ESs under two alternate future urban growth scenarios highlights the importance of conservation of natural ecosystems within the urban area. The results of this study can provide insights to the urban planners in effective decision making about how the ecosystems and their ecosystem service functions can be sustainably managed and strategically developed for the overall well-being of the growing urban population.

The land-use and land-cover (LULC) mapping is crucial for the management and planning of a nation. The land use and land cover are the two separate terminologies often used interchangeably. We mapped LULC for the nations of South Asian Association for Regional Cooperation represented by Afghanistan, Bangladesh, Bhutan, India, Nepal, the Maldives, Pakistan and Sri Lanka covering an area of ca. 5,100,000 km2. Mapping LULC is a cumbersome process for a larger area; nevertheless, in recent decades the satellite-based Earth observation has provided a great impetus and various approaches have been adopted to map LULC. In this study, we used the multinomial logit model (Mnlogit) to classify Moderate Resolution Imaging Spectroradiometer (MODIS)-derived time-series data for mapping LULC. The MODIS-archived images for the years 2004–2007 and 2016 were used to map LULC for corresponding years. For the same years, maps were available from IGBP, FAO and NR Census, India, for comparison; hence, these years were selected. The classification of images for each of the years was found to be more consistent and accurate in comparison with the existing LULC maps of IGBP and FAO, and hence a robust approach. The method can be adopted to map global resources. We achieved overall accuracy ranging from 76 to 82% with kappa statistic (K-hat) ranging from 0.64 to 0.73, which is better than previous works. The study demonstrates an effective way to utilize remote sensing data for mapping LULC for a larger area with reasonable accuracy.

Assessment of six heavy metals (Zn, Cu, Mn, Pb, Cd and As) in water, sediment and macrovegetation of Lake Nainital was made from September 2016 to February 2017 to evaluate their status in the lake. Three sites, viz. S1 (aeration center), S2 (platform near the Naina Devi) and S3 (boat stand in Tallital), were selected, and fortnightly, sampling was made. The study shows that the water of the lake is contaminated with Pb and Mn poses threat to human as the lake water is used for domestic water supply.

Road networks are permanent manmade features altering the landscape structure and subsequently influence regional landscape ecology. Taking south-western foothills of Central Himalaya, as a case, t...

Indices are designed to differentiate land use and land cover classes to avoid misinterpretation of landscape features. The resemblances of spectral reflectance of mines with urban built-up and barren land cause difficulties in identification of objects. Open pit mines of Rampura-Agucha for Zn and Pb were selected for this study. The freely available data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was selected from the year of 2001 and 2003. It is observed that b1-b5/b1+b5 equation of ASTER imagery significantly differentiate Zn-Pb mine from urban settlement and other features. The reflected range (µm) for b1 and b5 is 0.52-0.60 (Visible and Near-Infrared) and 2.145-2.185 (Shortwave Infrared) respectively. The pixel values indicate higher reflectance of open pit suggesting feasibility of equation for differentiating it from barren and built-up area. The mine is rich in sphalerite followed by galena, pyrite and pyrrhotite in different proportions of abundance. Spectral reflectance depends on type of minerals hence need further studies to develop the index according to specific minerals and mines. In the mining regions, the role of temperature, moisture content, vegetation covers and high concentration of pollutants in variation of spectral reflectance are highly important. The developed index would be beneficial for tracing the extent of overburden dumps, tailings and mines at faster rate.

The western Himalaya is one of the most climate-sensitive and ecologically vulnerable ecosystems of the world. In the recent past, the region has undergone rapid alterations owing to climate change and paced urbanization. These alterations have significantly impacted Terrestrial Net Primary Productivity (TNPP) of the region. The present study takes the emerging urbanizing centers: Pithoragarh (Uttarakhand) and Dharamsala (Himachal Pradesh), situated in Indian western Himalaya to estimate TNPP dynamics of various land use classes. The study demonstrates usage of Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) for predicting a high spatio-temporal Normalised Difference Vegetation Index (NDVI) imagery obtained by fusing spatial details of Landsat NDVI and temporal details of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The predicted NDVI showed a good agreement with actual Landsat NDVI (R2=0.64 and 0.89; RMSE: 0.09 and 0.04; p < 0.01 for Dharamsala and Pithoragarh, respectively), therefore was reliable for TNPP estimations. This was assimilated in Carnegie Ames Stanford Approach (CASA) model for TNPP estimation for the years 2001 to 2019. The preliminary results show a net loss in TNPP in both of the urbanizing centers. During the study period 2001-2019, TNPP fluctuated annually and showed a decreasing trend of 1475.77 g C m-2 year-1 and 790.84 g C m-2 year-1 in Dharamsala and Pithoragarh, respectively. Among the forest vegetation classes, Oak the most dominant forest class experienced the highest decline in TNPP accounting for 67.55% and 34.04% of net TNPP loss in Dharamsala and Pithoragarh, respectively. The urban expansion contributed to 14.77% (Dharamsala) and 9.77% (Pithoragarh) decline of net TNPP loss. The results provide a better understanding of spatio-temporal dynamics of TNPP consequent to climatic variability and urbanization and provide a theoretical reference for future urban planning.

Himalayan region is a critical part of the globe. In recent years, vegetation cover in this region is undergoing considerable changes attributed ongoing to climatic and anthropogenic factors. The present study aims to capture the interannual vegetation changes over 19 years and explore how topographic and climatic variables contribute to the observed changes. Satellite-derived Normalized Difference Vegetation Index (NDVI) dataset (2001–2019) was used to examine the spatio-temporal patterns of vegetation in Uttarakhand state in the Indian western Himalayas. Further analysis explored variation across elevation, temperature, precipitation, and vegetation types. Most parts of the Uttarakhand region experienced increasing NDVI trends, particularly in the Needleleaved Evergreen and Broadleaved Deciduous forest types; however, negative trends were observed in shrublands.

The Himalayan glacierized region has experienced a substantial rise in number and area of glacial lakes in the past two decades. These glacial lakes directly influence glacier melt, velocity, geometry, and thus overall response of the glacier to climate change. The sudden release of water from these glacial lakes poses a severe threat to downstream communities and infrastructure. Thereby, regular monitoring and modelling of these lakes bear significance in order to understand regional climate change, and mitigating the anticipated impact of glacial lake outburst flood. Here, we proposed an automated scheme for Himalayan glacial lake extent mapping using multisource remote sensing data and a state-of-the-art deep learning technique. A combination of multisource remote sensing data [Synthetic Aperture Radar (SAR) coherence, thermal, visible, near-infrared, shortwave infrared, Advanced Land Observing Satellite (ALOS) DEM, surface slope and Normalised Difference Water Index (NDWI)] is used as input to a fully connected feed-forward Convolutional Neural Network (CNN). The CNN is trained on 660 images (300×300×10) collected from 11 sites spread across Himalaya. The CNN architecture is designed for choosing optimum size, number of hidden layers, convolutional layers, filters, and other hypermeters using hit and trial method. The model performance is evaluated over 3 different sites of Eastern Himalaya, representing heterogenous landscapes. The novelty of the presented automated scheme lies in its spatio-temporal transferability over the large geographical region (~8477, 10336 and 6013 km2). The future work involves Intra-annual lake extent mapping across High-Mountain Asian region in an automated fashion. Keywords: Glacial Lake, convolutional neural network, semantic segmentation, remote sensing, Himalaya, SAR and climate change

Climate change mitigation strategies require long time removal and storage of carbon; thus, enhancing soil carbon stock is an appealing way to increase carbon sink potential and control emissions owing to associated ecosystem benefits. Understanding soil organic carbon (SOC) stock in the semiarid landscape is vital for natural based climate solutions and mechanisms. The carbon stock in soil represents 25% of the potential of natural climate solutions and wetlands have around 72% mitigation potential for soil carbon. Wetlands have a very complex natural system and provide a potential sink of atmospheric carbon. Particularly the role of wetlands in arid and semiarid lands has become vital as they not only provide a water source and livelihood options to the local community but also play an important role in maintaining ecosystem services. However, only limited studies have been conducted to assess the roles and potentials of wetlands in carbon sequestration in a semiarid region. The geospatial technologies provide a cost-effective and more accurate estimation of SOC stock in these ecosystems. The SOC distribution in wetland ecosystems and their carbon sequestration potential studies are crucial to understanding the global carbon budget. The present study area Keoladeo National Park is an ecologically important forested wetland situated in semiarid India with a heterogeneous landscape. Current research work illustrated a hybrid interpolation method for estimating the distribution of soil carbon in different vegetation type/land cover (VT/LC) using point survey data (prepared after laboratory test) with remote sensing. The map prepared has given satisfactory results with more than 80 present accuracy. SOC distribution data were collected from 130 plots from both the surface (0-15 cm) and subsurface soil (15-30 cm) covering all the 15 VT/LC classes. SOC was found to be significantly related to VT/LC type and water availability. The spatial distribution of SOC shows a wide range with an average value of around 1.5%; the seasonal distribution shows an increased amount of carbon in pre monsoon season and a high amount of carbon in the surface soil. The concentration of SOC (around 2.5%) has been observed to be more in wetland and grassland soils in both the seasons that cover about 13% and 27% area of the park, respectively. SOC stock management in this region is vital in observing the local community needs, which is mainly dependent on the park for livestock food. Further geospatial analysis of soil carbon stock potential will add value to the study. Synergising climate change mitigation strategies and community requirements are needed to enhance vulnerable communities' benefits. Keywords: Soil carbon, semiarid region, remote sensing, climate change mitigation.

The Himalayan cryosphere hosts the largest concentration of glaciers, outside the polar regions, thereby provides extensive resource and opportunity for irrigation, hydro-power generation, and eco-tourism to the people of Central and Southeast Asia. Like any other mountain region, Himalayan glaciers are responding to climate change in terms of length, area, volume, equilibrium line altitude, flow velocity and elevation change. However, the rate of change of these glaciers remains controversial which causes gaps in our understanding of current state of Himalayan glaciers. Through this chapter, we provide a comprehensive picture of glaciers in the Western Himalaya (WH) on the basis of published scientific data and to bridge in some of these gaps. Here, a multi-parametric approach is adopted in order to provide a coherent picture of glacier health in WH. Further, a detailed analysis is carried out at basin scale in order to demonstrate the spatial heterogeneity among glaciers across WH. The chapter also highlights factors responsible for spatial heterogeneneity of glaciers across WH. Additionally, increasing risk of Glacial Lake Outburst Flood (GLOF) to the downstream region is discussed in brief.

A comprehensive analysis of climate data (1958-2018) is carried out at the national scale in India to assess spatiotemporal variation in aridity. The aridity is analyzed using UNEP (United Nations Environment Programme) Aridity Index (AI), which is the ratio between Precipitation (P) and Potential Evapotranspiration (PET). Freely available Terra-Climate database, P and PET variables, offered an unprecedented opportunity for monitoring variations in AI and aridity index anomalies (AIA) at interseasonal and inter-decadal basis. The study also assesses longer term patterns of P and AI anomalies with vegetation anomalies. The results indicate that significant clustered areas with maximum dryness are located at west-central part of India, the state of Maharashtra. Overall, there is a gradual increase in the extent of arid zone during 60-year period and spatially maximum extent of percentage change in aridity area is observed. The change patterns of AI in India are largely driven by the changing patterns of precipitation. The maximum impact of decline in precipitation on AIA was observed during Kharif season frequently, for every 4-5 years during 1972-1992. The pattern repeated in the last few recent years (2013- 2018), the decline in precipitation resulted increased aridity. The study also reveals that the availability and usage of irrigation sources have increased from 2014 to 2018. Thus, despite of less precipitation positive vegetation has been resulted in this period. The findings are important to understand the impacts of climate change on land use pattern, and land and water resource management.

Land use land cover characterization and mapping have become a prerequisite in all environmental Planaing. The array of satellites deployed in the space provides multi-temporal images that can be used for the land use land cover classification. But, much often these multi-temporal images have data noise and anomaly owing to the cloud and atmospheric effects. This brings pseudo hikes and lows in data adding classification with possible errors. We present a method for the removal of data anomaly where monthly data of MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (MODIS 13Q1) was used for the classification of images over a large area encompassing the SAARC nations. MODIS multi-temporal data were filtered usinga Savitzky-Golay (S-G) algorithm which provided smoothened data and the seasonality (start, end of the season) were identified. Phenology profile curves were created for the characterization of the agriculture and forestry feature classes. The S-G filtered images and raw MODIS data phenology profile curves were compared for the eleven classes of land cover, viz., ever green needle forest, ever green broad leave, deciduous broad leave, shrub, savannas, grass, agriculture, built-up, water, snow (ice), and barren. Spectral signature separability was also compared using Euclidean spectral distance method. In conclusion, it was observed that multi-spectral S-G filtered data were more useful for the classification of agriculture and forestry classes for a larger coverage.

Threats of changing climatic conditions on Himalayan agriculture communities are well established and observed. predisposed marginalized social and fragile ecological conditions have increased vuln...

Mines show spectral resemblance with other landscape features; hence, their identification with satellite imagery can be difficult. To address this, land surface temperature (LST) derived from thermal infrared images of satellite remote sensing data was used to differentiate mines. Higher surface temperatures were observed for mined land than other classes (built-up and fallow land) in nighttime data. This indicates that the increased surface temperature of the other classes is due to solar heating while geothermal and pyrite oxidation contribute warmth at mined sites. Nighttime LST can be used to locate mines and their spatial extent despite the low spatial resolution of satellite data. It also confirms a mine’s heat island phenomenon due to geothermal energy.

Ecosystem Services (ESs) refer to the direct and indirect contributions of ecosystems to human well-being and subsistence. Ecosystem valuation is an approach to assign monetary values to an ecosystem and its key ecosystem goods and services, generally referred to as Ecosystem Service Value (ESV). We have measured spatiotemporal ESV of 17 key ESs of Sundarbans Biosphere Reserve (SBR) in India using temporal remote sensing (RS) data (for years 1973, 1988, 2003, 2013, and 2018). These mangrove ecosystems are crucial for providing valuable supporting, regulatory, provisioning, and cultural ecosystem services. We have adopted supervised machine learning algorithms for classifying the region into different ecosystem units. Among the used machine learning models, Support Vector Machine (SVM) and Random Forest (RF) algorithms performed the most accurate and produced the best classification estimates with maximum kappa and an overall accuracy value. The maximum ESV (derived from both adjusted and non-adjusted units, million US$ year −1 ) is produced by mangrove forest, followed by the coastal estuary, cropland, inland wetland, mixed vegetation, and finally urban land. Out of all the ESs, the waste treatment (WT) service is the dominant ecosystem service of SBR. Additionally, the mangrove ecosystem was found to be the most sensitive to land use and land cover changes. The synergy and trade-offs between the ESs are closely associated with the spatial extent. Therefore, accurate estimates of ES valuation and mapping can be a robust tool for assessing the effects of poor decision making and overexploitation of natural resources on ESs.

Growth of Indian urban population is bound to have a substantial influence on the economic and environmental stability of India. India has the potential of adding double the current urban populatio...

The paper reviews the status of glacier mapping with special reference to the Indian Himalaya. The review provides information on various satellite remote sensing data interpretation methods used w...

This paper reports changes in different glaciers of Bhaga basin located in western Himalaya, from 1979 to 2017. Glacier boundaries were delineated through semi-automated approach using Landsat sate...

Various scientific researches were conducted to monitor human activities and natural phenomena with the availability of various night time satellite data such as Defense Meteorological Satellite Program (DMPS). Population growth especially in a faster growing economy like China is an important indicator for assessing socio-economic development, urban planning and environmental management. Thus, spatial distribution of population is instrumental in assessing growth and developmental activities in Beijing city of China. The satellite observation data derived from Defense Meteorological Satellite Program (DMSP) was utilized to estimate population density through the measurement of light flux with radiometric recording. The data was calibrated using C0, C1, C2 parameters before processing. Population density of Beijing city was estimated using light volume of this calibrated data. Regression analysis between urban population and light volume revealed high correlation ( r 2 = 0.89 ) . Thus, population density can effectively be estimated using light intensity. The model used for estimating urban population density can effectively be utilized for other major cities of the world.

Global road network has expanded at an unprecedented rate in the last century, and is needed for economic and social growth. However, the road networks through forest landscapes open it up for further exploitation and act as permanent scars on these fragile ecosystems. This has driven greater interest in studying ecological impacts of the road networks on forest cover dynamics. Our study assessed changes in forest canopy density (FCD) using satellite-based indices, and forest fragmentation using Morphological Spatial Pattern Analysis (MSPA) for 2000 and 2016 in the Central Himalaya. The analysis reports reduction in areas with high FCD from 85% of geographical area in 2000 to 76% in 2016. Specific to road sites, maximum change has taken place owing to expansion of national highway (NH) 72 from two-lane in 2000 to four-lane in 2016. MSPA results also support this argument as forest fragmentation has substantially increased along the road transects, especially NHs and metalled roads. The study asserts that metalled forest roads at lower elevations and closer to the forest periphery have greater detrimental effects on surrounding ecosystems than unmetalled forest roads. Reason being continuous disturbance due to heavy traffic load, ease of accessibility to forest and added anthropogenic disturbances near the periphery. As ecological impacts of road network have not been mainstreamed into research and policy making in Himalaya, the findings from this study shall improve knowledge on the current status and potential future pathways of sustainable infrastructure development in Himalaya in particular and mountains around the globe in general.

Assessment of past and future urban growth processes helps the decision makers to evaluate and formulate the policy documents. In an attempt to make such assessments, this study compares th...

Mines result in land use and land cover (LULC) change due to degradation of natural resources and establishment of new infrastructure for ore extraction and beneficiation. The present study was carried out to, with objectives, (1) characterize LULC change (from 1975 to 2017) in Khetri copper mine region, (2) spatial distribution of pollution indices and (3) spectral response of elemental concentration of soil and groundwater using Landstat and ASTER satellite data. The study was designed to fulfil the objectives and for the same NDVI values were calculated for LULC classification and generated maps were analyzed for landscape pattern. Spatial distribution of pollution indices calculated using geochemical data of soil and groundwater was plotted to understand the impact of contamination on landscape pattern. The correlation of spectral response of Landstat bands with heavy metals concentration was plotted to assess their possible use in quantification of heavy metals. Results show constant increase in settlements, mines and open area while vegetation cover has decreased. Landscape and class level metrics (number of patch, patch density, aggregation index and landscape shape index) indicate increase in the fragmentation of landscape in recent years. Shannon’s Evenness Index indicates increase in uniformity in landscape and it is attributed to loss of vegetation and agriculture patches. Pollution indices, Pollution Load Index for soil is high near the overburden materials and Index of Environmental Risk (IER) and Contamination Index for ground water is high near abandoned mines. Spectral bands 5 and 6 (SWIR 1) show significant negative correlation, and 9 (Cirrus) shows significant positive correlation with metal concentration in soil and water suggesting the possible use of remote sensing in assessment of metal concentration at ground level. Thus, it can be concluded that mines significantly influence the landscape pattern and remote sensing could be used for the assessment and predication of heavy metal contamination at broader scale in a cost-effective way.

Assessing biophysical variables are essential for evaluation of carbon dynamics due to anthropogenic activities. Biomass carbon is an important biophysical parameter of forest ecosystems that indicates carbon mitigation and human–forest interactions. Spectral modeling approach was used to assess the relation of Normalized Difference Vegetation Index (NDVI) with biomass carbon, crown density, tree density, slope, altitude, aspect, species, and forest division in temperate conifer region of Himalaya. Field inventory was recorded from 188 biomass plots of 0.1 ha each across the study area. NDVI was observed to have a positive relation with aboveground biomass carbon, crown density, tree density, and altitude. The NDVI and ABC values ranged from (0.11 to 0.43) and (1.54 to 276.82 t ha−1), respectively. Among the aspects, highest and lowest average NDVI was observed for south east (0.289) and north (0.258), respectively. Similarly highest and lowest average aboveground biomass carbon was observed for north east (72.63 t ha−1) and east (44.30 t ha−1), respectively. NDVI expressed a fairly good relation with biophysical parameters including altitude, aspect, crown density, tree density, species, and location (forest division). NDVI using principal tree species composition (forest type) revealed a relation with aboveground biomass carbon for Cedrus deodara (R2 = 0.63), Mixed I (R2 = 0.61), Pinus wallichiana (R2 = 0.57), and Mixed-II (R2 = 0.48). NDVI demonstrates potential to understand biomass carbon variability through establishment of relations with forest biophysical parameters using spectral modeling approach. Varying NDVI can be ascribed to vegetation canopy density, number of stems, species, and altitude. The database and established relations would help indicate biomass carbon dynamics and enable to adopt site-specific management. The study further helps draw inferences on mitigation and adaptation perspectives in view of varying biophysical conditions that occur in a forest.

Ecosystem Services (ESs) are bundles of natural processes and functions that are essential for human well-being, subsistence, and livelihoods. The ‘Green Revolution’ (GR) has substantial impact on the agricultural landscape and ESs in India. However, the effects of GR on ESs have not been adequately documented and analyzed. This leads to the main hypothesis of this work – ‘the incremental trend of ESs in India is mainly prompted by GR led agricultural innovations that took place during 1960 - 1970’. The analysis was carried out through five successive steps. First, the spatiotemporal Ecosystem Service Values (ESVs) in Billion US$ for 1985, 1995, and 2005 were estimated using several value transfer approaches. Second, the sensitivity and elasticity of different ESs to land conversion were carried out using coefficient of sensitivity and coefficient of elasticity. Third, the Geographically Weighted Regression model was performed using five explanatory factors, i.e., total crop area, crop production, crop yield, net irrigated area, and cropping intensity, to explore the cumulative and individual effects of these driving factors on ESVs. Fourth, Multi-Layer Perceptron based Artificial Neural Network was employed to estimate the normalized importance of these explanatory factors. Fifth, simple and multiple linear regression modeling was done to assess the linear associations between the driving factors and the ESs. During the observation periods, cropland, forestland and water bodies contributed to 80%–90% of ESVs, followed by grassland, mangrove, wetland and urban built-up. In all three evaluation years, the highest estimated ESVs among the nine ES categories was provided by water regulation, followed by soil formation and soil-water retention, biodiversity maintenance, waste treatment, climate regulation, and greenhouse gas regulation. Among the five explanatory factors, total crop area, crop production, and net irrigated area showed strong positive associations with ESVs, while cropping intensity exhibited a negative association. Therefore, the study reveals a strong association between GR led agricultural expansion and ESVs in India. This study suggests that there should be an urgent need for formulation of rigorous ecosystem management strategies and policies to preserve ecological integrity and flow of uninterrupted ESs and to sustain human well-being.

Aerosols in the atmosphere cause the continuous deterioration of global air quality day by day. The higher concentration of aerosol loading over Northern India affects the Earth’s radiation budget directly through absorption and scattering of solar radiation and/or indirectly by alteration of microphysical parameters which results in the modulation of the hydrological cycle and global food security. Northern India has several densely populated cities that have recorded a significant amount of anthropogenic aerosols in the atmosphere. This region has dynamic meteorological patterns and experienced severe aerosol episodes including dust storms, increased biomass, and agricultural burning which caused an enhancement in aerosol loading over Northern India. This region is characterized by highly mixed aerosols that can affect the climate on a regional as well as global scale. Therefore, it becomes necessary to measure the physical, optical, and chemical properties of aerosols concerning local/regional meteorology. Several past studies reported that fine aerosol concentrations were found higher over Northern India during post-monsoon and winter seasons which are mainly attributed to high anthropogenic activities during these seasons. Agricultural crop residue/biomass burning predominantly governs aerosol climatology during post-monsoon and winter seasons, while the transported dusts from the Middle East and the Thar Desert are the major causes of aerosol loading during pre-monsoon seasons. This chapter describes the characteristics of atmospheric aerosol, their emission sources along with the significance of spaceborne measurements, and their impact on regional climate over Northern India.

The transport sector emerged as one of the largest contributors to atmospheric warming. In India, the transport sector consumes ~17% of total energy and produces about 60% of total greenhouse gases (GHG) during various activities. Particularly in Delhi, the rising ambient concentrations of particulate matter (PM) lead to thousands of premature deaths and several million asthma attacks every year. The outdoor exposure of high PM2.5 concentration in Delhi causes lung impairment to human beings. Several policy measures have been implemented from time to time by the Indian Government to control vehicular pollution, such as unleaded fuel implement in 1996 (0.15 g/l) and further reduction of lead (0.25%) and sulfur (0.5%) in fuel in 1998 and 1996, respectively. Besides the improvement of fuel quality, scrapping of 8-year-old buses and all other vehicles which were forced to meet Euro-1 norms except passenger cars was implemented in 2000. However, to reduce PM load in Delhi, the Indian Government implemented several policies like the Green Tribunal Act 2015; as per this act, 10-year-old diesel vehicles were banned in Delhi. The deployment of BS-VI vehicle and electric vehicles in the country is also proposed by the government up to 2020. These policies indicate the severity of transport sector emission and its adverse impacts on the environment and health. Hence, the chapter is focused on the roadway transport adverse effects and possible mitigation measures.

The shift towards urban living is changing food demand. Past studies on India show significant urban–rural differences in food consumption. However, a scientific understanding of the underlying relationships between urbanization and food consumption is limited. This study provides the first detailed analysis of how urbanization influences both quantity and diversity of food consumption in India by harnessing the strength of multiple datasets, including consumer expenditure surveys, satellite imagery, and census data. Our statistical analysis shows three main findings. First, in contrast to existing studies, we find that much of the variation in food consumption quantity is due to income and not urbanization. After controlling for income and state-level differences, our results show that average consumption is higher in urban than rural areas for fewer than 10% of all commodities. That is, there is nearly no difference in average consumption between urban and rural residents. Second, we find the influence of urbanization as a population share on food consumption diversity to be statistically insignificant (p-value > 0.1). Instead, the results show that infrastructure, market access, percentage working women in urban areas, and norms and institutions have a statistically significant influence. Third, all covariates of food consumption diversity we tested were found to be associated with urbanization. This suggests that urbanization influences on food consumption are both indirect and multidimensional. These results show that increases in the urban population size alone do not explain changes in food consumption in India. If we are to understand how food consumption may change in the future due to urbanization, the study points to the need for a more complex and multidimensional understanding of the urbanization process that goes beyond demographic shifts.

In India, a reduction in wheat crop yield would lead to a widespread impact on food security. In particular, the most vulnerable people are severely exposed to food insecurity. This study estimates the climate change vulnerability of wheat crops with respect to heterogeneities in time, space, and weighting methods. The study uses the Intergovernmental Panel on Climate Change (IPCC) framework of vulnerability while using composite indices of 27 indicators to explain exposure, sensitivity, and adaptive capacity. We used climate projections under current (1975&ndash, 2005) conditions and two future (2021&ndash, 2050) Representation Concentration Pathways (RCPs), 4.5 and 8.5, to estimate exposure to climatic risks. Consistency across three weighting methods (Analytical Hierarchy Process (AHP), Principal Component Analysis (PCA), and Equal Weights (EWs)) was evaluated. Results of the vulnerability profile suggest high vulnerability of the wheat crop in northern and central India. In particular, the districts Unnao, Sirsa, Hardoi, and Bathinda show high vulnerability and high consistency across current and future climate scenarios. In total, 84% of the districts show more than 75% consistency in the current climate, and 83% and 68% of the districts show more than 75% consistency for RCP 4.5 and RCP 8.5 climate scenario for the three weighting methods, respectively. By using different weighting methods, it was possible to quantify &ldquo, method uncertainty&rdquo, in vulnerability assessment and enhance robustness in identifying most vulnerable regions. Finally, we emphasize the importance of communicating uncertainties, both in data and methods in vulnerability research, to effectively guide adaptation planning. The results of this study would serve as the basis for designing climate impacts adjusted adaptation measures for policy interventions.

Agriculture productivity is often guided by prevailing climate and management interventions. The climatic variations over a region on a temporal scale may have favorable or adverse feedback on productivity. Evaluation of the influences of climate change on agriculture productivity is essential to ensure food security. The South Asian Association for Regional Cooperation (SAARC) is consortium of Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka, which represents a region where agriculture drives the economy and people are primarily dependent on it for their food requirements. Climate change is expected to influence the productivity of this region, which further depends on climate and other driving factors. We assessed the influences of climate change on agriculture productivity (Net Primary Productivity, NPP) using JULES (“Joint UK Land Environment Simulator”), which is a Dynamic Global Vegetation Model (DGVM). JULES was used to process NPP for the future climate change scenario of the year 2050 under low emission represented by RCP 2.6. The climate change projections–based Coordinated Regional Climate Downscaling Experiment (CORDEX) for the South-Asian region was used to simulate productivity using the JULES model. A slight decrease in productivity was witnessed which had spatial variability across the SAARC nations. However, the majority of the agriculture area (87% of the total agriculture land) is expected to witness moderate change where productivity would fall by 1 to 3 Mg-C/ha/year. We present agriculture productivity estimates of SAARC nations under climate change influence, which would assist planners in ensuring the food security of the region through proper management interventions.