Estimation of Forest Carbon Using LiDAR Assisted Multi-source Programme in Nepal B. Gautam 1, J. Peuhkurinen 1, T. Kauranne 1, K. Gunia 1, K. Tegel 1, P. Latva-Käyrä 1, P. Rana 1, A. Eivazi 1, A.Kolesnikov 1, J. Hämäläinen 1, S.M. Shrestha 2, S. Gautam 2, M. Hawkes 3, U. Nocker 3, A. Joshi 3, T. Suihkonen 3, P. Kandel 4, S. Lohani 5, G. Powell 5, E. Dinerstein 5, D. Hall 5, J. Niles 5, A. Joshi 6, S. Nepal 7, U. Manandhar 7, Y. Kandel 7, C. Joshi 8
Content Introduction Study Area Materials and Methods Results Discussion and Conclusions
Introduction Global deforestation and forest degradation account for about 18% of annual greenhouse gas emissions; REDD+ could be one of the cheapest options to mitigate global climate change; Accurate and reliable estimation of forest carbon stocks is crutial for REDD+ reference level and MRV; More forest means more carbon, biodiversity, healthy ecosystem.
Nepal at the forefront: Quantifying forest carbon with LiDAR-assisted method
3 rd return from ground 1 st return from tree top 2 nd return from branches 1 st (and only) return from ground LiDAR (Light Detection and Ranging)
Dense forest from lidar Degraded forest/encroachment from lidar Near Khallagath, Kanchanpur district
TAL Area 2.3 million hectares Number of blocks (ICIMOD biomass sites) Total Area Covered 1200 sq. km Biomass Site Legend Blocks for LiDAR Mapping TAL Boundary District Boundary ^ Biomass site Study Area
Tiger range countries
Samples (5%) of LiDAR data to calibrate satellite models; Reference field sample plots to calibrate LiDAR models; Medium (e.g.Landsat) to high resolution (e.g.Rapideye) satellite imagery for wall-to-wall biomass map. 9 Materials
Field plots-based model LiDAR-based model with the field data Model for the LiDAR blocks Generation of surrogate plots Field plots on LiDAR area 5 % of total project area for LiDAR Satellite data-based models calibrated with LiDAR estimates Estimate all variable of interest for a grid using LiDAR treated satellite models Results Landsat-based forest type map for stratification Methods: Work Flow
11 TAL Preliminary Result
12 Ludikhola Watershed, Gorkha Khayerkhola Watershed, Chitwan Preliminary Result
Lidar-model estimates validated against the new field plots. Estimates were calculated using the old lidar model based on small field plots (12.6m radius). New field plots served just for validation. Validation plot size = 2,826m 2 46 validation plots, 30m radius: AGB RMSE 30.8 t/ha RMSE% 17.1% * Bias 2.4 t/ha Bias%1.3% * *relative to reference data mean Result: Validation of LiDAR model
LAMP-model estimates validated against the LiDAR derived estimates at 1 ha. Estimates were calculated using the k-fold cross-validation. LAMP Model AGB RMSE 73.8 t/ha RMSE% 42.1% * Bias 0 Bias%0 Result: LAMP model
Test result: AGB in 1999
Test result: AGB in 2010
Test result: difference in AGB
The LAMP makes best use of field plots from sample locations, LiDAR data from sample areas, and freely available medium-resolution satellite imagery for the entire area of interest. Overall AGB for all of TAL will be very accurate and meet Tier 3 requirements in REDD+ MRV; LAMP makes it possible to directly compute AGB of given areas in the past through a process of satellite image normalization; The future monitoring of REDD+ target biomass and carbon captured in the related carbon pools can be estimated using the LAMP models; 18 Discussion and Conclusions
TAL can be benchmark for Nepal’s REDD+ initiative (subnational RL); Integrates community monitoring with LiDAR estimates (e.g. ICIMOD biomass sites); Forest carbon in tiger habitats (linking wildlife conservation and REDD+ = wildlife premium market); High quality of result is guaranteed by the mathematical consistency that is required of the estimates. This will convince donors of the reproducibility and dependability of the MRV process; Biomass estimation and calculation of REDD+ credits is conducted locally and the results can be viewed over the Internet by the relevant stakeholders. 19 Discussion and Conclusions…
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