Analyses of the feasibility of participatory REDD+ MRV approaches to Lidar assisted carbon inventories in Nepal

Abstract: Forests are estimated to sequester and emit respectively 15% and 20% of the CO2 emissions. REDD+ aims at establishing a financial framework to compensate developing countries for reducing Green House Gasses emissions due to decreased deforestation and land degradation. An accurate Monitoring, Reporting and Verification (MRV) of the forest carbon pools is needed. The adoption of State-Of-The-Art remote sensing technologies, such as Lidar in combination with participatory approaches can potentially produce an accurate assessment of the forest resources, ensuring the sustainability of the process. The study aims at defining the feasibility of Lidar assisted Above Ground Biomass (AGB) assessment with a participatory approach. The study compares AGB regression models built with wall-to-wall, low density (0.8 points m-2) laser scanning data and two field datasets collected by professionals and Community Forest User Groups (CFUGs) teams. The models were built using ArboLiDAR©, a tool-box developed in ESRI environment by Arbonaut Oy, that uses a Sparse Bayesian approach to define a set of weights for each independent variable based on the variance of the field measured AGB and the Lidar metrics. Finally the models were validated with Leave-One-Out Cross Validation (LOOCV). The adjusted R2, relative RMSE and BIAS as well as the analyses of the residuals were used to compare the models. In addition the study also analyzed the reliability of the models across different forest structures. The professional model described a greater part of the variability of the AGB (adj.R2=0.75) compared the CFUG model (adj. R2=0.55), moreover the first was slightly more accurate (professional: rel. RMSE= 45.6 %; CFUG: rel. RMSE= 47.2 %). Although both of the models proved to have the mean of the error term not equal to zero and did not follow a normal distribution, the CUFG model showed heteroschedastic residuals. The accuracy improved when applying the models to forests characterized by a more uniform height distribution (rel. RMSE= 32.1 – 45.2 %), whereas it drastically decreased for sparse forests (rel. RMSE= 91.4 -130.5 %). The study concludes that with the limitation of having different sampling designs and measuring techniques the CFUGs models were slightly worst than the professional ones. However, it is likely that with a more accurate retrieval of the GPS plot center and increase of plot size the results can be as good as the ones obtained with professionally collected data.