Kamna Sachdeva

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.

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.

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.

An indicator-based approach to assess village-level social and biophysical vulnerability of agriculture communities in Uttarakhand, India

With the growing recognition to myriad forms of current and future threats in the mountain agriculture systems, there is a pressing need to holistically understand the vulnerability of mountain agriculture communities. The study aims to assess the biophysical and social vulnerability of agriculture communities using an indicator-based approach for the state of Uttarakhand, India. A total of 14 indicators were used to capture biophysical vulnerability and 22 for social vulnerability profiles of 15285 villages. Vulnerability analysis was done at village level with weights assigned to each indicator using Analytical Hierarchical Process (AHP). The results of the study highlight the presence of very high biophysical vulnerability (0.82 ± 0.10) and high social vulnerability (0.65 ± 0.15) within the state. Based on the results, it was found that incidences of high biophysical vulnerability coincide with presence of intensified agriculture land and absence of dense forest. Higher social vulnerability scores were found in villages with an absence of local institutions (like Self Helping Groups (SHGs)), negligible infrastructure facilities and higher occupational dependence on agriculture. A contrast was observed in the vulnerability scores of villages present in the three different altitudinal zones in the study area, indicating respective vulnerability generating conditions existing in these three zones. Biophysical vulnerability was recorded to be highest in the villages falling in the lower zone and lowest in the upper zone villages; whereas, social vulnerability was found to be highest in the middle zone villages and lowest in lower zone villages. Our study aids policy makers in identifying areas for intervention to expedite agriculture adaptation planning in the state. Additionally, the adaptation programmes in the region need to be more context-specific to accommodate the differential altitudinal vulnerability profiles.