Anusheema Chakraborty

Development of Decadal (1985–1995–2005) Land Use and Land Cover Database for India

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

Vulnerability of agro-ecological zones in India under the earth system climate model scenarios

India being a developing economy dependent on climate-sensitive sector like agriculture is highly vulnerable to impacts of global climate change. Vulnerability to climate change, however, differs spatially within the country owing to regional differences in exposure, sensitivity, and adaptive capacity. The study uses the Hadley Centre Global Environment Model version 2-Earth System (HadGEM-ES) climate projections to assess the dynamics in vulnerability across four climate change exposure scenarios developed using Representative Concentration Pathways (RCPs). The analysis was carried out at subnational (district) level; the results were interpreted and reported for their corresponding agro-ecological zones. Vulnerability of each district was quantified using indicators capturing climatic variability, ecological and demographic sensitivity, and socio-economic capacity. Our analysis further assigns probabilities to vulnerability classes of all the 579 districts falling under different agro-ecological zones. The results of the vulnerability profile show that Western plains, Northern plains, and central highlands of the arid and semi-arid agro-ecological zones are the most vulnerable regions in the current scenario (1950–2000). In the future scenario (2050), it extends along districts falling within Deccan plateau and Central (Malwa) highlands, lying in the arid and semi-arid zones, along with regions vulnerable in the current scenario, recording the highest vulnerability score across all exposure scenarios. These regions exhibit highest degree of variation in climatic parameters, ecological fragility, socio-economic marginality, and limited accessibility to resources, generating conditions of high vulnerability. The study emphasizes on the priority to take up adaptive management actions in the identified vulnerable districts to not only reduce risks of climate change, but also enhance their inherent capacity to withstand any future changes in climate. It provides a systematic approach to explicitly identify vulnerable regions, where regional planners and policy makers can build on existing adaptation decision-making by utilizing an interdisciplinary approach in the context of global change scenario.

Mapping disaster vulnerability in India using analytical hierarchy process

Disasters are the coincidences between hazardous events, elements at risk, and conditions of vulnerability. Vulnerability integrates social and environmental systems to reduce the intensity and frequency of these risks. By categorizing regions according to their level of vulnerability, one can examine and assess the possible impacts of developmental and environmental degradation processes. This study is an attempt to map the sub-national areas (districts) in India that are vulnerable to natural and climate-induced disasters. The assessment is considered under the framework of Intergovernmental Panel on Climate Change definition of vulnerability. Using analytical hierarchy process as a multi-criteria decision-mapping method, vulnerability is measured in terms of exposure, sensitivity, and adaptive capacity. Based on this mapping assessment, districts in the state of Arunachal Pradesh, Assam, Bihar, Himachal Pradesh, Jharkhand, Manipur, Meghalaya, Mizoram, Uttar Pradesh, Uttarakhand, and West Bengal are the most vulnerable regions; while districts in the state of Punjab, Haryana, Gujarat, Tamil Nadu, Maharashtra, Goa, Andhra Pradesh, Kerala, and Karnataka are among the least vulnerable regions. The results of this study can serve as the basis for targeting prioritization efforts, emergency response measures, and policy interventions at district level for mitigating disaster vulnerability in the country.

The Climate Change Conundrum and the Himalayan Forests: The Way Forward into the Future

The earth’s climate has always been changing, but with current rate of change, forests might not be able to adapt to rapid changes in climate variables, along with increased risk of extreme climate events. Climate change has significantly altered structure, composition and distribution patterns of forests across the globe. The Himalayan forests are sensitive to climate change impacts, but the extent and magnitude of potential response is still not well-understood. Climate change, however, cannot be considered as the only driving force responsible for changes in the type, distribution and coverage of vegetation, as other anthropogenic disturbances equally play a crucial role in accelerating the alterations in this region. With the impeding uncertainties involving climate change and increased dependence of communities on forests and forest-based resources, adaptation must be considered along with mitigation in the foreseeable future. The limited understanding of ecosystem dynamics in the Himalayan region poses a grand challenge for many research programmes in ecology and conservation. The scientific literature shows efforts to model species distribution based on current climate conditions and forecast species distribution based on future climate scenarios. These studies indicate the possibility of extinction and migration of many species; nonetheless, they fail to integrate human influence to changing environmental conditions. Addressing potential impacts of climate change is both urgent and difficult. The hypothesis foretelling the future requires much improvement in its forecasting skills in order to accurately predict the eventual fate for sustainable forest management.

Multi-criteria approach to geographically visualize the quality of life in India

ABSTRACT Quality of Life (QoL) is the description and evaluation of coincidences among social, economic and ecological conditions in a particular community, locality, region or a country. By categorising regions according to their QoL, one can examine and assess not only the possible impacts of development programmes, but also the pressure from environmental degradation processes. This study maps QoL at the sub-national level (districts) in India, conceptualized under three pillars of sustainable development. The assessment uses 10 sub-indices constructed using 54 indicators (49 from Census of India database and 5 remote sensing inputs). Recognising that not every indicator is of equal importance, analytical hierarchy process (AHP) was used to assign weights to the indicators and sub-indices. Furthermore, geostatistical Moran’s I clustering was done to assign priority to QoL classes. Distribution of high QoL shows correspondence with the network of national highways throughout the country. Significant dependence of QoL was observed with urban population (r2 ~ 0.75–0.95), rural population (r2 ~ 0.75–0.98) and Human Development Index (HDI) (r2 > 0.7) for different states. The geostatistical analysis identifies clusters of districts which can significantly improve the living conditions with priority actions, and where interventions and long-term planning would be required. The results of this study can serve as the basis for targeting prioritization efforts, and policy interventions at district level for improving QoL and, perhaps, achieving Sustainable Development Goals (SDGs) as well.

Capturing forest dependency in the central Himalayan region: Variations between Oak ( Quercus spp.) and Pine ( Pinus spp.) dominated forest landscapes

Our study explores the nexus between forests and local communities through participatory assessments and household surveys in the central Himalayan region. Forest dependency was compared among villages surrounded by oak-dominated forests (n = 8) and pine-dominated forests (n = 9). Both quantitative and qualitative analyses indicate variations in the degree of dependency based on proximity to nearest forest type. Households near oak-dominated forests were more dependent on forests (83.8%) compared to households near pine-dominated forests (69.1%). Forest dependency is mainly subsistence-oriented for meeting basic household requirements. Livestock population, cultivated land per household, and non-usage of alternative fuels are the major explanatory drivers of forest dependency. Our findings can help decision and policy makers to establish nested governance mechanisms encouraging prioritized site-specific conservation options among forest-adjacent households. Additionally, income diversification with respect to alternate livelihood sources, institutional reforms, and infrastructure facilities can reduce forest dependency, thereby, allowing sustainable forest management.

Building a Resilient Community Against Forest Fire Disasters in the Northeast India

Forest fire distribution, severity and consequences are closely linked to modern human activities. Fire events are major cause of change in forest structure and function, challenging the supply of ecosystem goods and services.

Agriculture in the western Himalayas – an asset turning into a liability

ABSTRACT Agriculture in the Himalayas has witnessed dynamic shifts and acute crises over the last decade. This viewpoint identifies and discusses the factors leading to de-agrarianisation and land abandonment in the western Himalayas. Data on farming preferences and bottlenecks for agricultural production were also gathered through a short survey with farmers. Escalating male out-migration, land fragmentation and heightened exposure to uncertain environmental conditions have transformed agriculture into a liability for the farmers. The viewpoint suggests that effective implementation of land consolidation reforms in the region would enhance agricultural production, curtail mass migration, and potentially insulate farmers from the ongoing agrarian crisis.

A Reflection on Image Classifications for Forest Ecology Management: Towards Landscape Mapping and Monitoring

Abstract Different remote sensing techniques act as an alternative to traditional fieldwork; thereby, providing an efficient and time-saving approach to extract land use land cover (LULC) information, especially in isolated and inaccessible regions. They serve as a vital source for generating information for land resource managers and forest ecosystem conservationists. However, the major issues that determine the accuracy of such spatially explicit information are the types of data used and the choice of an appropriate classification algorithm. Theres a pre-emptive need to improve algorithm-benchmarking with consensus on both LULC definitions and reference maps for their effectiveness aimed at conservation and management efforts. In this chapter, we elaborate on the classification methods available for information extraction from satellite data. As an effort of this chapter, we explore the accuracy of different classification methods in a complex heterogeneous terrain of the central Himalayan region in Asia. We explore both parametric and non-parametric classifiers to produce maps with major forest types in the study region. We recommend more directed research efforts required to reduce uncertainties of mapping methodologies, while quantifying factors affecting forest classification, especially with the integration of ancillary information and multi-source data to improve classification accuracy in a remote sensing context.