1. Use of Lidar for estimating Reference Emission Level in Nepal S.K. Gautam DFRS, Nepal

2.  Nepal’s ER-Program covers 12 jurisdictional Terai districts out of 75 districts of the country;  Total area under the ER-Program is 2.3 million ha (about 15% of the country);  About 52% of the ER-Program area (1.18 million ha) is under different types of forest cover.  The area is linked with eleven trans-boundary protected areas across Nepal and India and is home to flagship species like tigers, rhinos, Asiatic wild elephants, and many other endangered species.  Total population of the ER-Program area is 7.35 million and constitute about 27% of total population (2011 population census) 2 Introduction: Study Area

3.  Samples (5%) of LiDAR data to calibrate satellite models;  Reference field sample plots to calibrate/validate LiDAR models;  Landsat satellite imagery for wall-to-wall biomass map. 3 Introduction: LAMP

4.  Stratification from a Landsat-based forest classification map.  Weight calculated for every block as a product of the importance of the forest types and the inverse of the forest types area.  The forest classification was used as a priori information to calculate weighting function for random block and systematic plot design.  5 km x 10 km systematic grid over the study area where ew is the expert weight and A is the area LiDAR block design

5. Forest typeArea, km 2 Expert weightArea-normalized weight Hill-sal 3625 100 541 Sal 3458 200 1135 Mixed 1299 200 3020 Riverine 180 100 10880 Grass 873 50 1124 Degraded forest 1098 50 893 Chir pine4421004436 Shadow5981003283 Non-forest804300 Forest type map with forest type weights. The larger weights are with brighter tones in gray-scale. Black = zero weight (non-forest). LiDAR block design

6. The basic math is: Activity Data (ha change/year) × Emissions Factors (tCO 2 /ha) = tCO 2 /year Activity data is be based on satellite information (past) or assumptions (future) Emissions factors are based on field measurements and allometric equations Basics of a REDD+ RL

7. Defines forest/non-forest for 1999 inception date of RL with 1998 Topographic basemaps Utilizes satellite analysis for 1999, 2001, 2006, 2009 and 2011 to delineate structural classes of intact, degraded and deforested Bases classification on fractional image indexes (i.e., % vegetation) and temporal analysis drawing on work by leader in the field Carlos Souza of Brazil Develops land cover change matrix by tracking changes between the different structural classes between 4 time- periods Activity Data

8. DFRS, FRA, Arbonaut and WWF collaborate in collection of LiDAR data covering 5% of TAL program area in 2011 Field plots collected in 2011 (738 calibration plots) and 2013 (46 validation plots) Uses allometric equations of Sharma and Pukkala (1990) to estimate biomass for ground plots (same equations used by FRA) Emissions Factors

9. Model to correlate LiDAR-based above-ground biomass estimates for each forest condition (intact, deforested, degraded and regeneration) and forest type (Sal, Sal mixed, Other mixed and Riverine) IPCC default values used to calculate mean carbon density for regeneration and below-ground carbon based on biomass estimates Emissions Factors

10. Method: LAMP Workflow

11. CO 2 Emissions (tCO 2 e) PeriodAbove- ground Below- ground Total 1999-200213,136,4302,627,28615,763,716 2002-20061,736,537347,3072,083,845 2006-20099,644,6981,928,94011,573,637 2009-201119,020,6613,804,13222,824,793 Total 12-yr43,538,3258,707,66552,245,991 Average annual 3,628,193725,6394,353,833 Average annual net CO 2 emissions (tCO2e) in TAL between 1999 and 2011. Results: Historical CO2 emissions

12. Results: Historical Carbon Stock Loss

13. a) Comparison to independent field plots b) Leave-one-out validation Accuracy assessment LiDAR model LAMP model (Landsat) Field-measured biomass (t/ha) Estimated biomass (t/ha) R 2 = 0.9 R 2 = 0.92 R 2 = 0.5 R 2 = 0.52

14. Emissions reduction targets Intervention Cumulative emissions reduction from BAU (millions of tons CO 2 e) 5 years (2015 -2020) 10 years (2015 - 2025) 15 years (2015 - 2030) Sustainable management of forests 9.929.249.0 Installed biogas plants0.93.46.5 Improved cook stoves0.31.12.0 Land use planning2.88.313.9 Private forestry/tree nurseries 0.10.71.4 Total14.042.772.8

15. Endorsed by the FCPF

16. Research & Development: AGB in 2010

17. Research & Development: AGB in 1999

18. Research and Development: Difference in AGB between 1999-2011

19.  The cost of this project is USD 0.28/ha  Our experience shows that 1-2% LiDAR coverage is sufficient for this integrated approach  But LiDAR is needed only once  Subsequent monitoring is based on new satellite images to which the LAMP models are applied Costs and Future Monitoring

20. Basic image processing steps in ImgTools

21. Decision Tree and Definition of Forest for Terai Arc Landscape

22.  Four major forest types: 1) Sal forest, 2) Sal dominated mixed forest, 3) other than Sal dominated forest (i.e. “other forest”) and 4) Riverine.  The four forest types were overlaid on the forest structural map (Joshi et al. 2003) to generate forest types and conditions maps for each time period.  The study assumed forest types do not change from one type to another type (i.e., from Sal forest to mixed forest or riverine forest or vice versa) in 10-20 years; Forest types and conditions map

26. Thank You