Social-ecological and institutional factors affecting forest and landscape restoration in the Chittagong Hill Tracts of Bangladesh

Bangladesh government has recently pledged to restore 0.75 million ha of degraded forestland as part of its commitment to the Bonn Challenge, however little is known about the potential challenges and opportunities involved in achieving that goal. Using secondary literature complemented by expert consultation and a field survey, we examined the outcomes and limitations of previous restoration programmes and identified key social, ecological and institutional aspects crucial for a successful forest restoration programme in the Chittagong Hill Tracts (CHT) of Bangladesh. The CHT region accounts for over a third of state-owned forests, and it supports a large part of the country's forest-dwelling ethnic populations, although most of the forestland is severely degraded. Our analysis revealed that past programmes had utilised participatory tree planting, horticulture and rubber-based agroforestry to restore degraded forestland and improve community livelihood in the CHT. However, past restoration programmes merely emphasised improving tree cover without considering the ecological functionality, biodiversity and carbon co-benefits of restored forests. The duration of these pro-grammes was also relatively short, and there was no clear plan for engaging local communities in the restoration activities beyond the programme period. Among other things, the local ethnic community's land rights issue remained unresolved and the participant's land ownership influenced their willingness to participate effectively in any restoration programme. Households with secured land rights had a more positive attitude towards participating in forestland restoration than those with unsecured land rights. Suitable acts and policies that would allow people to legally continue to use tree-based land in the regions (i.e. forest and land tenure rights) are also lacking. Future forest and landscape restoration (FLR) programmes may thus need to focus on improving the biodiversity and ecological functionality of those restored forests, resolving local people's forest and land tenure rights and involving them in site-specific restoration interventions. The engagement of local and regional-level multi-stakeholders in such an FLR programme is also essential for realising the restoration's multiple social and ecological benefits

Estimating fresh grass/herb biomass from HYMAP data using the rededge position

Remote sensing of grass/herb quantity is essential for rangeland management of livestock and wildlife. Spectral indices such as NDVI, determined from red and near infrared bands are affected by variable soil and atmospheric conditions and saturate in dense vegetation. Alternatively, the wavelength of maximum slope in the red-NIR transition, termed the red edge position (REP) has potential to mitigate these effects. But the utility of the REP using air-and spaceborne imagery is determined by the availability of narrow bands in the region of the red edge and the simplicity of the extraction method. Very recently, we proposed a simple technique for extracting the REP called the linear extrapolation method [Cho and Skidmore, Remote Sens. Environ., 101(2006)118.]. The purpose of this study was to evaluate the potential of the linear extrapolation method for estimating fresh grass/herb biomass and compare its performance with the four-point linear interpolation and three-point Lagrangian interpolation methods. The REPs were derived from atmospherically corrected HYMAP images collected over Majella National Park, Italy in July 2004. The predictive capabilities of various REP linear regression models were evaluated using leave-one-out cross validation and test set validation methods. For both validation methods, the linear extrapolation REP models produced higher correlations with grass/herb biomass and lower prediction errors compared with the linear interpolation and Lagrangian REP models. This study demonstrates the potential of REPs extracted by the linear extrapolation method using HYMAP data for estimating fresh grass/herb biomass