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Forests offer an important basis for creating and safeguarding more climate-resilient communities over Hindu Kush Himalayan region
. The forest ecosystem vulnerability assessment to climate change and developing knowledge base to identify and support relevant adaptation strategies is realized as an urgent need. The multi scale adaptation strategies portray increasing complexity with the increasing levels in terms of data requirements, vulnerability understanding and decision making to choose a particular adaptation strategy. We present here how such complexities could be addressed and adaptation decisions could be either directly supported by open source remote sensing based forestry products or geospatial analysis and modelled products. The forest vulnerability assessment under climate change scenario coupled with increasing forest social dependence was studied using IPCC Landscape scale Vulnerability framework in Chitwan-Annapurna Landscape (CHAL) situated in Nepal. Around twenty layers of geospatial information on climate, forest biophysical and forest social dependence data was used to assess forest vulnerability and associated adaptation needs using self-learning decision tree based approaches. The increase in forest fires, evapotranspiration and reduction in productivity over changing climate scenario was observed. The adaptation measures on enhancing productivity, improving resilience, reducing or avoiding pressure with spatial specificity are identified to support suitable decision making. The study provides spatial analytical framework to evaluate multitude of parameters to understand vulnerabilities and assess scope for alternative adaptation strategies with spatial explicitness
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Deforestation is a major environmental challenge in the mountain areas of Pakistan
. The study assessed trends in the forest cover in Chitral tehsil over the last two decades using supervised land cover classification of Landsat TM satellite images from 1992, 2000, and 2009, with a maximum likelihood algorithm. In 2009, the forest cover was 10.3% of the land area of Chitral (60,000 ha). The deforestation rate increased from 0.14% per annum in 1992–2000 to 0.54% per annum in 2000–2009, with 3,759 ha forest lost over the 17 years. The spatial drivers of deforestation were investigated using a cellular automaton modelling technique to project future forest conditions. Accessibility (elevation, slope), population density, distance to settlements, and distance to administrative boundary were strongly associated with neighbourhood deforestation. A model projection showed a further loss of 23% of existing forest in Chitral tehsil by 2030, and degradation of 8%, if deforestation continues at the present rate. Arandu Union Council, with 2212 households, will lose 85% of its forest. Local communities have limited income resources and high poverty and are heavily dependent on non-timber forest products for their livelihoods. Continued deforestation will further worsen their livelihood conditions, thus improved conservation efforts are essential. Look Inside 5 Shares Other actions Export citation Register for Journal Updates About This Journal Reprints and Permissions Add to Papers Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn Related Conten
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Water vulnerability is one of the major challenges facing people in the Himalayan river basins and is expected to increase with climate and other change
. In order to develop appropriate and effective adaptation strategies, it is necessary to understand the level and spatial distribution of water vulnerability and the underlying factors contributing to it, both biophysical and socioeconomic. The development and application of a water vulnerability assessment model at district level, and its use in adaptation planning, is described using the transboundary Koshi River basin as an example. The whole basin showed a relatively high degree of water vulnerability, with mountain districts the most vulnerable followed by the mid-hills and the plains. The mountain and mid-hill areas were more vulnerable in terms of resource stress and ecological security, whereas the plains areas were more vulnerable in terms of development pressure; all parts of the basin were vulnerable in terms of management capacity. Significant correlation among the four components indicated that improvements in resource availability, ecological security, and management capacity would reduce development pressure and overall vulnerability. Adaptation plans need to be based on district-specific vulnerability characteristics; some suggestions and recommendations for adaptation plans are made
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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
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Land cover (LC) is one of the most important and easily detectable indicators of change in ecosystem services and livelihood support systems
. This paper describes the decadal dynamics in LC changes at national and sub-national level in Bhutan derived by applying object-based image analysis (OBIA) techniques to 1990, 2000, and 2010 Landsat (30 m spatial resolution) data. Ten LC classes were defined in order to give a harmonized legend land cover classification system (LCCS). An accuracy of 83% was achieved for LC-2010 as determined from spot analysis using very high resolution satellite data from Google Earth Pro and limited field verification. At the national level, overall forest increased from 25,558 to 26,732 km2 between 1990 and 2010, equivalent to an average annual growth rate of 59 km2/year (0.22%). There was an overall reduction in grassland, shrubland, and barren area, but the observations were highly dependent on time of acquisition of the satellite data and climatic conditions. The greatest change from non-forest to forest (277 km2) was in Bumthang district, followed by Wangdue Phodrang and Trashigang, with the least (1 km2) in Tsirang. Forest and scrub forest covers close to 75% of the land area of Bhutan, and just over half of the total area (51%) has some form of conservation status. This study indicates that numerous applications and analyses can be carried out to support improved land cover and land use (LCLU) management. It will be possible to replicate this study in the future as comparable new satellite data is scheduled to become available
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Forest fires pose a threat more serious than illegal felling in developing countries and are a cause of major concern for environmental security
. Fires in tropical forests, though not devastating on a large scale as compared to large and infrequent fires in boreal or Mediterranean systems, still cause loss to biodiversity and economic and monetary value. In India, human-induced forest fires increasingly affect legally protected nature conservation areas. An array of satellite sensors that are now available can be deployed to monitor such events on a global and local scale. The present study uses night-time Advanced Along Track Scanning Radiometer (A)ATSR satellite data from the last nine years to identify high fire-prone zones, fire affected areas in protected zones and the distribution of these incidents in relation to bio-geographic zones. Central India, with its vegetation type that is just right for fire ignition and spread, was observed to be the most severely affected area with maximum fire incidences. The bio-geographic zone comprising this area–such as the Deccan peninsula, which includes provinces like Central Highlands, Eastern Highlands, Central Plateau and Chhota Nagpur–was observed to be the most affected, accounting for approximately 36% of the total fire occurrences during the period 1997–2005. In protected areas, 778 fire incidents were observed within the last eight years. Comparison of (A)ATSR fire locations with MODIS active fire data for the Western Ghats (mainly of tropical evergreen forests and savannahs) and the Eastern Ghats (tropical deciduous) showed a spatial agreement of 72% with a minimum distance between the two products of 100 m. This study focuses on regions in India that are vulnerable to forest fires during specific time-frames and appraises the situation with an aim to minimize such incidents, if not completely stop the fire spread and its consequent destruction and loss. Our main objective is to understand seasonal and spatial variation in fire pattern and to identify zones of frequent burning
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A majority of the research on forest fragmentation is primarily focused on animal groups rather than on tree communities because of the complex structural and functional behavior of the latter
. In this study, we show that forest fragmentation provokes surprisingly rapid and profound alterations in tropical tree community. We examine forest fragments in the tropical region using high-resolution satellite imagery taken between 1973 and 2004 in the Southern Western Ghats (India) in relation to landscape patterns and phytosociological datasets. We have distinguished fragmentation in six categories-interior, perforated, edge, transitional, patch, and undetermined-around each forested pixel. Furthermore, we have characterized each of the fragment class in the evergreen and semi-evergreen forest in terms of its species composition and richness, its species similarity and abundance, and its regeneration status. Different landscape metrics have been used to infer patterns of land-use changes. Contiguous patches of > 1,000 ha covered 90% of evergreen forest in 1973 with less porosity and minimal plantation and anthropogenic pressures; whereas in 2004, the area had 67% forest coverage and a high level of porosity, possibly due to Ochlandra spread and increased plantations which resulted in the loss of such contiguous patches. Results highlight the importance of landscape metrics in monitoring land-cover change over time. Our main conclusion was to develop an approach, which combines information regarding land cover, degree of fragmentation, and phytosociological inputs, to conserve and prioritize tropical ecosystems
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This study used time series remote sensing data from 1973, 1990 and 2004 to assess spatial forest cover change patterns in the Kalakad-Mundanthurai Tiger Reserve (KMTR), South Western Ghats (India)
. Analysis of forest cover changes and its causes are the most challenging areas of landscape ecology, especially due to the absence of temporal ground data and comparable space platform based data. Comparing remotely sensed data from three different sources with sensors having different spatial and spectral resolution presented a technical challenge. Quantitative change analysis over a long period provided a valuable insight into forest cover dynamics in this area. Time-series maps were combined within a geographical information system (GIS) with biotic and abiotic factors for modelling its future change. The land-cover change has been modelled using GEOMOD and predicted for year 2020 using the current disturbance scenario. Comparison of the forest change maps over the 31-year period shows that evergreen forest being degraded (16%) primarily in the form of selective logging and clear felling to raise plantations of coffee, tea and cardamom. The natural disturbances such as forest fire, wildlife grazing, invasions after clearance and soil erosion induced by anthropogenic pressure over the decades are the reasons of forest cover change in KMTR. The study demonstrates the role of remote sensing and GIS in monitoring of large-coverage of forest area continuously for a given region over time more precisely and in cost-effective manner which will be ideal for conservation planning and prioritization
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Trans-scale information on biodiversity is fast becoming the critical for policy decision and action
. Remote sensing systems addressing the structure and biophysical processes have the ability to achieve a seamless scalable information scheme. Necessity of down-scaling the coarse scale database to implementation scale is quite high under current circumstances, as practical schemes/ measures to reverse the erosion of biodiversity are needed. The nesting of information required should address the landscape heterogeneity and stakeholder coflicts arising out of socio-economic dimensions. Species level distributions can be predicted based on genetic algorithm-oriented fundamental niche mapping, enabling prioritization of areas for monitoring and conservation. Geoinformatics rendering of diversity on the principles of landscape ecology, integrated with spatialized anthropogenic demand patterns can be a potential interface to resolve conflicts in stake management
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