Basanta Shrestha

Development of 2010 national land cover database for the Nepal

Land cover and its change analysis across the Hindu Kush Himalayan (HKH) region is realized as an urgent need to support diverse issues of environmental conservation. This study presents the first and most complete national land cover database of Nepal prepared using public domain Landsat TM data of 2010 and replicable methodology. The study estimated that 39.1% of Nepal is covered by forests and 29.83% by agriculture. Patch and edge forests constituting 23.4% of national forest cover revealed proximate biotic interferences over the forests. Core forests constituted 79.3% of forests of Protected areas where as 63% of area was under core forests in the outside protected area. Physiographic regions wise forest fragmentation analysis revealed specific conservation requirements for productive hill and mid mountain regions. Comparative analysis with Landsat TM based global land cover product showed difference of the order of 30-60% among different land cover classes stressing the need for significant improvements for national level adoption. The online web based land cover validation tool is developed for continual improvement of land cover product. The potential use of the data set for national and regional level sustainable land use planning strategies and meeting several global commitments also highlighted.

The glaciers of the Hindu Kush Himalayas: current status and observed changes from the 1980s to 2010

The fate of the Hindu Kush Himalayan glaciers has been a topic of heated debate due to their rapid melting and retreat. The underlying reason for the debate is the lack of systematic large-scale observations of the extent of glaciers in the region owing to the high altitude, remoteness of the terrain, and extreme climatic conditions. Here we present a remote sensing–based comprehensive assessment of the current status and observed changes in the glacier extent of the Hindu Kush Himalayas. It reveals highly heterogeneous, yet undeniable impacts of climate change.

Understanding Land Cover Change Using a Harmonized Classification System in the Himalaya A Case Study From Sagarmatha National Park, Nepal

Abstract Land cover assessment and monitoring of land cover dynamics are important to understand social and ecological processes in mountain protected areas. However, variations in the use of legends and classification systems sometimes pose challenges. The landscape of Sagarmatha National Park and Buffer Zone (SNPBZ) has seen many changes in the past few decades. Mapping of land cover in SNPBZ was carried out to fill gaps in basic databases for the area. A review of past land cover initiatives and existing data revealed differences in methodologies and definitions that made them incompatible for cross-region applications. For the present study, a legend was developed using the standard Land Cover Classification System (LCCS) methodology developed by the Food and Agriculture Organization and the United Nations Environment Programme, a comprehensive and standardized a priori classification system designed for mapping exercises independent of scales or means. The changes in land cover were analyzed using Landsat Thematic Mapper, Landsat Enhanced Thematic Mapper Plus, and Advanced Spaceborne Thermal Emission and Reflection Radiometer images from 1992 to 2006. Land cover maps were generated using object-based image analysis supplemented by ancillary information. Extensive fieldwork was carried out for ground truthing and validation. The use of LCCS was instrumental in bringing general understanding of the classification systems and helping to gain greater clarity and accuracy in the results. About 70% of the SNPBZ area is covered by snow and ice, glaciers, bare rocks, and bare soil. Altitude and its influence on climatic conditions have dominated the distribution pattern of vegetation in SNPBZ. The analysis showed that forest is being converted into shrub at elevations between 3000 and 4000 m, while shrub is decreasing between 4000 and 5000 m. A major decrease in snow cover is seen above 5000 m. Harmonization of the classification system helped to gain more reliable information on changes, as comparisons were made between the classes with consistent definitions.

Determination of snow cover from MODIS data for the Tibetan Plateau region

This paper addresses a snow-mapping algorithm for the Tibetan Plateau region using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Accounting for the effects of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in the rugged Tibetan Plateau region, the surface reflectance is retrieved from the TOA reflectance after atmospheric and topographic corrections. To reduce the effect of the misclassification of snow and cloud cover, a normalized difference cloud index (NDCI) model is proposed to discriminate snow/cloud pixels, separate from the MODIS cloud mask product MOD35. The MODIS land surface temperature (LST) product MOD11_L2 is also used to ensure better accuracy of the snow cover classification. Comparisons of the resulting snow cover with those estimated from high spatial-resolution Landsat ETM+ data and obtained from MODIS snow cover product MOD10_L2 for the Mount Everest region for different seasons in 2002, show that the MODIS snow cover product MOD10_L2 overestimates the snow cover with relative error ranging from 20.1% to 55.7%, whereas the proposed algorithm estimates the snow cover more accurately with relative error varying from 0.3% to 9.8%. Comparisons of the snow cover estimated with the proposed algorithm and those obtained from MOD10_L2 product with in situ measurements over the Hindu Kush-Himalayan (HKH) region for December 2003 and January 2004 (the snowy seasons) indicate that the proposed algorithm can map the snow cover more accurately with greater than 90% agreement

Improving Communication for Management of Social-ecological Systems in High Mountain Areas: Development of Methodologies and Tools—The HKKH Partnership Project

Abstract The planning and sustainable management of complex social-ecological systems (SESs) in high mountain areas such as the Hindu Kush–Karakoram–Himalaya (HKKH) region requires an approach that takes account of both environmental issues and local population needs. The HKKH Partnership Project developed methodologies and tools for systemic planning and management of social-ecological systems at local, regional, and national levels in the HKKH region, with a special focus on 3 protected areas in Nepal, Pakistan, and China. The adopted approach brought together researchers, policy-makers, and managers; it bridged the gap between research and management priorities and enabled communication to address the needs of communities while promoting conservation. Lessons learned are described and conclusions made on appropriate methods for the management of SESs in other regions.

An Integrated Decision Support Toolbox (DST) for the Management of Mountain Protected Areas

Abstract New tools and methodologies are required in systemic planning and management of mountain protected areas. Among others we propose here a decision support toolbox (DST) conceived as an integrated collection of both soft and hard system methodologies, consisting of participatory and computer-based modules to provide a set of integrated, self-contained tools and approaches to support decision-making processes in the management of mountain protected areas. The Sagarmatha National Park and Buffer Zone (SNPBZ) in Nepal was taken as a pilot case. A number of participatory exercises such as participatory 3-dimensional modeling, scenario planning, and qualitative modeling were carried out to understand social-ecological processes and generate a systemic view over space and time. The qualitative models were then converted into computer-based system dynamics models. The design and development of DST software were carried out with an incremental and modular approach. This process involved stakeholder analysis and decision-making processes through a series of consultations. The software was developed with the main modules including scenario analysis, spatial analysis, and knowledge base. The scenario analysis module runs system dynamics models built in Simile software and provides functions to link them with spatial data for model inputs and outputs. The spatial analysis module provides the basic geographic information system functions to explore, edit, analyze, and visualize spatial information. The knowledge base module was developed as a metadata management system for different categories of information such as spatial data, bibliography, research data, and models. The development of DST software, especially system dynamics modeling and its linkage with spatial components, provided an important methodological approach for spatial and temporal integration. Furthermore, training and interactions with park managers and concerned stakeholders showed that DST is a useful platform for integrating data and information and better understanding ecosystem behavior as a basis for management decisions.

The Integrated Web Portal (IWP)

Abstract The Hindu Kush–Karakoram–Himalayan (HKKH) Partnership Project involves implementation of various applied research and development activities that require integration of diverse data and information as well as interpretation of various forms of information and knowledge, such as system dynamics models, simulation results, historical databases, and technical reports. In order to bring these together and promote interdisciplinary collaboration, communication, and dissemination of information among concerned stakeholders and general public users, an Integrated Web Portal (IWP) with a customized Content Management System (CMS) was developed as part of the HKKH project. Several features that were incorporated as modules are discussed here.

HIMALA: climate impacts on glaciers, snow, and hydrology in the Himalayan region

Abstract Glaciers are the largest reservoir of freshwater on Earth, supporting one third of the worlds population. The Himalaya possess one of the largest resources of snow and ice, which act as a freshwater reservoir for more than 1.3 billion people. This article describes a new project called HIMALA, which focuses on utilizing satellite-based products for better understanding of hydrological processes of the river basins of the region. With support from the US Agency for International Development (USAID), the International Centre for Integrated Mountain Development (ICIMOD), together with its partners and member countries, has been working on the application of satellite-based rainfall estimates for flood prediction. The US National Aeronautics and Space Administration (NASA) partners are working with ICIMOD to incorporate snowmelt and glacier melt into a widely used hydrological model. Thus, through improved modeling of the contribution of snow and ice meltwater to river flow in the region, the HIMALA project will improve the ability of ICIMOD and its partners to understand the impact of weather and climate on floods, droughts, and other water- and climate-induced natural hazards in the Himalayan region in Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan.

Improvement of MODIS snow cover algorithm for the Hindu Kush-Himalayan region

This work aimed to refine the Moderate Resolution Imaging Spectroradiometer (MODIS) based snow cover algorithm for the Hindu Kush-Himalayan (HKH) region. Taking into account the effect of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in heavily rugged Tibet plateau region, the surface reflectances were retrieved from the TOA reflectances after atmospheric and topographic corrections. To reduce the effects of the snow/cloud confusion, a normalized difference cloud index (NDCI) model was proposed to discriminate snow/cloud pixels, apart from use of the MODIS cloud mask product MOD35. Furthermore, MODIS land surface temperature (LST) product MOD11_L2 have been used to ensure better accuracy of the snow cover pixels. Comparisons of the resultant MODIS snow cover with those obtained respectively from high resolution Landsat ETM+ data and the MODIS snow cover product MOD10_L2 for the Mount Everest region at different seasons, showed overestimation of the MOD10_L2 snow cover with the differences of 50%, whereas the improved algorithm can estimate the snow cover for HKH region more precisely with absolute accuracy of 90%.