1. 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

2. Content  Introduction  Study Area  Materials and Methods  Results  Discussion and Conclusions 6.6.2016

3. 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. 

4. Nepal at the forefront: Quantifying forest carbon with LiDAR-assisted method

5. 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)

6. 6.6.2016 Dense forest from lidar Degraded forest/encroachment from lidar Near Khallagath, Kanchanpur district

7. TAL Area 2.3 million hectares Number of blocks 20 + 2 (ICIMOD biomass sites) Total Area Covered 1200 sq. km Biomass Site Legend Blocks for LiDAR Mapping TAL Boundary District Boundary ^ Biomass site Study Area

8. Tiger range countries

9.  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

10. 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. 11 TAL Preliminary Result

12. 12 Ludikhola Watershed, Gorkha Khayerkhola Watershed, Chitwan Preliminary Result

13. 6.6.2016 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

14. 6.6.2016 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

15. Test result: AGB in 1999

16. Test result: AGB in 2010

17. Test result: difference in AGB 1999 - 2010

18.  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

19.  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…

20. 6.6.2016 Thank you! basanta.gautam@arbonaut.com