No abstract

Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme-or LAMP-for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution.

In preparation for participation in funding mechanisms established under the United Nations’ framework for reducing emissions from deforestation and forest degradation (REDD+), the Government of Nepal has developed a sub-national reference level (RL) for the 12 districts of Terai Arc Landscape (TAL) in partnership with the WWF-Nepal, WWF-US and Arbonaut Ltd., Finland. The reference level was established using LiDAR–Assisted Multisource Programme (LAMP), an innovative effort that utilizes existing national forest and survey data, field sampling, satellite imagery, and airborne LiDAR data to measure deforestation and forest degradation, regrowth and maintenance of forests, and the resulting emissions and sequestration of CO2 in the project districts for the period 1999–2011. This effort was designed to create a sub-national RL that meets the highest international standards for integrity and transparency and followed closely the guidelines of the Methodological Framework (MF) defined by the Forest Carbon Partnership Facility (FCPF) at the World Bank and Guidelines defined by Intergovernmental Panel on Climate Change (IPCC).The present analysis shows that during the 12-year period between 1999 and 2011 a net total of 52,245,991 tons CO2 (tCO2e) was emitted from the forest sector in the TAL, an average emission of 4,353,833 tons CO2e per year. The results presented here reflect the first iteration of the TAL RL and a major milestone in an on-going process that will further refine and improve the RL in the months ahead based on external review and input and additional field verification and data analysis.Banko Janakari, Vol. 24, No. 1, pp-23-33

Under the United Nations Framework Convention on Climate Change (UNFCCC), many tropical developing countries have agreed to participate in the Reducing Emissions from Deforestation and Forest Degradation as well as conservation and enhancement of carbon stocks and sustainable management of forests (REDD+) programme so as to receive payments for their contribution in reducing emissions from forestry sector. The emission reduction is measured in terms of quantifications of carbon dioxide (CO2) equivalent, upon which payments are made. To quantify emissions in terms of CO2 equivalent, a process called measurement/monitoring, reporting and verification (MRV) has been developed, which forms the backbone of performance-based payment under the REDD+ mechanism. This paper primarily reviews the principles and methods of MRV. By taking the case of the Terai Arc Landscape (TAL) of Nepal, a sub-national level proposed project, the paper demonstrates how an institutional mechanism for MRV can be designed and practiced at national level considering national circumstances and existing institutions. Also, the cost effectiveness and transparency of the MRV process are identified as important elements. DOI: http://dx.doi.org/10.3126/jfl.v11i2.8621   Journal of Forestry and Livelihood Vol.11(2) 2013 46-54

DOI: http://dx.doi.org/10.3126/jfl.v11i2.8623 Journal of Forestry and Livelihood Vol.11(2) 2013 65-68