Monitoring above-ground forest biomass: A comparison of cost and accuracy between LiDAR assisted multisource programme and field-based forest resource assessment in Nepal
Analyzing forest monitoring costs and accuracy of forest carbon stock estimates are important criteria in the framework of Reducing Emission from Deforestation and Forest Degradation (REDD), because Monitoring, Reporting and Verification (MRV) system has been seen as an investment that aims to generate financial benefits to forest owners. Thus, comparisons of cost efficiency and accuracy were carried out between the LiDAR (Light Detection and Ranging) Assisted Multisource Programme (LAMP) and the field-based multisource Forest Resource Assessment (FRA) applied in the 23500 km2 Terai Arc Landscape (TAL) of Nepal in 2011 to estimate Above Ground Biomass (AGB). The model-based LAMP was applied by integrating 5% LiDAR sampling, wall to wall RapidEye satellite image and field sample plot inventory. The design-based FRA was carried out to generate comprehensive forest resource information. Administrative and initial variable costs of both approaches were calculated separately, and converted to unit costs for comparison. To compare the subsequent forest monitoring costs, cumulative costs were derived on the basis of the calculated present variable items and expenditures. The accuracies were calculated by using mean error of mean biomass estimates (tons/ha) at different spatial scales ranging from 1 to 350,000 ha forests. Design-based FRA was found to be cost-efficient (USD 0.22/ha) as compared to the LAMP approach (USD 0.28/ha) for baseline data collection, whereas administrative cost of multisource FRA (USD 0.26/ha) was significantly higher. Although a huge amount of data were generated through multisource FRA in each cycle, the LAMP approach appears to be cost-efficient to estimate AGB in subsequent forest inventory. The mean errors in the LAMP-derived mean biomass estimate were significantly smaller at all spatial resolutions than the FRA-plot-derived mean biomass estimate. The study concludes that spatial accuracy of LAMP is good enough to estimate biomass stock of Community Forests (CFs) where average size of CF is 150 ha in the study area.
Banko Janakari, Vol. 23, No. 1, Page 12-22
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