Forests

Forests cover around a quarter of the Hindu Kush Himalayas. They’re an integral part of the transboundary landscape, connecting numerous ecosystems and conserving biodiversity, sustaining livelihoods, providing timber and other resources and guarding against natural disasters such as landslides, rock falls and avalanches. 

The health and vitality of many forest ecosystems are already affected by climatic as well as land use changes. While the latter may outweigh the former at this point, climate change adds a challenging dimension to future forest management, as greenhouse gas and carbon sequestration are among the key functions that fast-degrading mountain forests must perform. 

In the future, forest management will have to be sensitive to biodiversity and climate needs without short-changing the local communities that look to the forests for immediate goods and benefits. It’s more important than ever to understand the power relations among the various actors involved in forest management, the often unequal distribution of costs and benefits of forest exploitation, and the latest developments in science, economics and sustainable forest management. 

The paramount role of forests in adapting to and mitigating the impact of climate change has become a global concern. Yet forest ecosystems continue to degrade and fragment. ICIMOD is seeking the knowledge and management tools to reverse that trend and preserve the forests of the Hindu Kush Himalayas for the benefit of the region’s people and the future of the world.

Mega event

Transforming Mountain Forestry 

Bridging transboundary challenges with 21st century paradigms for the welfare of mountain people,forests and environment in the Hindu Kush Himalayas

Dehradun, India

18 - 22 January 2015

Relevant Publications

Go to HimalDoc

Datasets

Digital line dataset of Location of Reserve Forest area of Divang Valley, India. This dataset is created using Base map published Survey of India.


View Metadata

Hazard, vulnerability and risk due to flood are calculated at Village Development Committee (VDC) level of Nepal. Hazard index has been calculated as an arithmetic sum of flood frequency, height above nearest drainage (HAND), river density and forest density. Vulnerability index is the function of population as an Exposure, Sensitivity (sex ratio, dependency ratio and child women ratio) and Resilience (road density, population in abroad, literacy, transport facility, etc). Then risk is the sum of hazard and vulnerability index.


View Metadata

Leaf Area Index (LAI) generated based on Landsat-8 the OLI cloud free images. To generate tree canopy height map, a density scatter graph between the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud, and Land Elevation Satellite (ICESat) estimated maximum height and Landsat LAI nearest to the center coordinates of the GLAS shots show a moderate but significant exponential correlation (31.211*LAI0.4593, R2= 0.33, RMSE=13.25 m).


View Metadata

From the field well distributed circular (750m2 and 500m2), 1124 field plots (0.001% representation of forest cover) measured which were used for estimation AGB (ton/ha) using Sharma et al. (1990) proposed equations for all tree species of Nepal. A satisfactory linear relationship (AGB = 8.7018*Hmax-101.24, R2=0.67, RMSE=7.2 ton/ha) achieved between maximum canopy height (Hmax) and AGB (ton/ha).


View Metadata

At the watershed level, land cover data was prepared using object image classification technique. GeoEye-1 images captured 15 December, 2012 was used.


View Metadata

At the watershed level, land cover data was prepared using object image classification technique. GeoEye-1 images captured 2nd November, 2009 was used.


View Metadata

At the watershed level, crown projection area (CPA) vs. basal area (BA) model was developed and validated. At the watershed level, for CPA delineation, region growing technique was adopted. GeoEye-1 images captured on 15 December, 2012 was used.


View Metadata

At the watershed level, crown projection area (CPA) vs. basal area (BA) model was developed and validated. At the watershed level, for CPA delineation, region growing technique was adopted. GeoEye-1 images captured on 2nd November, 2009 was used.


View Metadata

In the watershed level, existing lease hold forest boundary delineated to know the exact area under the lease hold forest. GeoEye-2009 image used for this pupose in the Kayerkhola watershed


View Metadata

The Above Ground Biomass(AGB) data obtained from the model was converted into carbon stock by applying a conversion factor of 0.47, as suggested by IPCC.


View Metadata



Science applications

Videos and Stories