260 likes | 264 Views
Use of Lidar for estimating Reference Emission Level in Nepal. S.K. Gautam DFRS, Nepal. Introduction: Study Area. Nepal’s ER-Program covers 12 jurisdictional Terai districts out of 75 districts of the country;
E N D
Use of Lidar for estimating Reference Emission Level in Nepal S.K. GautamDFRS, Nepal
Introduction: Study Area • 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 islinked 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)
Introduction: LAMP • Samples (5%) of LiDAR data to • calibrate satellite models; • Reference field sample plots to calibrate/validate LiDAR models; • Landsat satellite imagery forwall-to-wall biomass map.
LiDAR block design • Stratificationfrom 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 Forest type map with forest type weights. The larger weights are with brighter tones in gray-scale. Black = zero weight (non-forest).
Basics of a REDD+ RL • The basic math is: • Activity Data (ha change/year) × Emissions Factors (tCO2/ha) = tCO2/year • Activity data is be based on satellite information (past) or assumptions (future) • Emissions factors are based on field measurements and allometric equations
Activity Data • 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
Emissions Factors • 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 • 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
Results: Historical CO2 emissions Average annual net CO2 emissions (tCO2e) in TAL between 1999 and 2011.
Accuracy assessment • a) Comparison to independent field plots • b) Leave-one-out validation LAMP model (Landsat) LiDAR model Estimated biomass (t/ha) R2 = 0.52 R2 = 0.92 Field-measured biomass (t/ha)
Research and Development: Difference in AGB between 1999-2011
Costs and Future Monitoring • 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
Decision Tree and Definition of Forest for Terai Arc Landscape
Forest types and conditions map • 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;