1. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.8 Overview and status of evolving technologies Module developers: Brice Mora, Wageningen University Erika Romijn, Wageningen University Country examples:  Tropical biomass mapping in Kalimantan by integrating ALOS PALSAR and LIDAR data  Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Source: US Forest Service V1, February 2015 Creative Commons License

2. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 2 1. Tropical biomass mapping in Kalimantan by integrating ALOS PALSAR and LIDAR data  Study from Quinones et al. (2014) on estimating tropical forest biomass in Kalimantan using a combination of RADAR and LIDAR  Advantage of RADAR: works under cloudy conditions  Limitations of RADAR: saturation effects & speckle  Using RADAR in combination with LIDAR to overcome the limitations

3. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 3 Classification of forest structural types using RADAR data  Image processing chain ● Data import and metadata extraction, radiometric calibration, coarse geocoding, fine geocoding, and geometric and radiometric terrain correction  Preprocessing ● Strip selection, radiometric correction, ortho- rectification, slope correction, mask preparation  Classification  17 strata ● Unsupervised segmentation, post-processing, validation, LCCS labelling

4. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 4 Vegetation structural type Map Kalimantan

5. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 5 Generation of vegetation height map through fusion of LIDAR and RADAR data  Extraction of vegetation height from LIDAR data for 100.000 points: histogram with distribution of heights for each vegetation structure type (stratum)  Matching of LIDAR height histograms with ALOS PALSAR HV histograms for each vegetation structure type  height map for whole Kalimantan LIDAR height histograms for each stratum RADAR HV histograms

6. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 6 Tropical biomass mapping  Use of 3 different equations to calculate biomass based on the height map: - Bio1 = Height^1.68 - Bio2 = 0.06328*(Height^2.4814) - Bio3 = 9.875+0.04552*(Height^2.5734)  Map Validation with biomass estimates from field data RMSEKetterings et al., 2001 Kenzo et al., 2009 Brown, 1997 Bio110.4710.6910.37 Bio210.9710.2712.37 Bio310.5110.2811.27  Use of 3 different equations to calculate biomass based on field data: - Ketterings et al. (2001) BIO = 0.066*D^2.59 - Kenzo et al. (2009) BIO = 0.0829*D^2.43 - Brown (1997) BIO = 0.118*D^2.53

7. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 7 2. Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Study from Naesset et al., 2011 (RSE): “Model-assisted regional forest biomass estimation using LIDAR and InSAR as auxiliary data: A case study from a boreal forest area”  Enhancing biomass estimation with input from forest structure parameters which were measured with LIDAR and InSAR techniques

8. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Methodology  Stratification of forest land into 4 strata, through interpretation of aerial photographs (photogrammetry)  Collecting field data ● For sample survey plots & large field plots ● Measurements of: tree diameter (d bh ), tree height ● Computed from field measurements: Lorey’s mean height h L, basal area (G), number of trees per hectare (N)  Acquiring LIDAR and InSAR data

9. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Acquiring LIDAR and InSAR data  Acquiring LIDAR data for each grid cell of the study area ● Canopy height distributions, including order statistics: height deciles and maximum height value ● Canopy density distributions  Acquiring SRTM InSAR (X-band) data to produce a digital surface model (DSM) and digital Height Error Model (HEM) and 2 datasets of pixel-level canopy heights: ● Subtracting the LIDAR terrain model from InSAR DSM ● Subtracting the terrain model generated from official topographic map from the InSAR DSM

10. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 10 Estimation of Above Ground Biomass  Estimation of Above Ground Biomass (AGB) from field data: ● Using d bh and tree height as independent variables to estimate the mean biomass per hectare for each stratum, which is called “observed biomass”  Model-assisted and model-based regression to estimate AGB, using LIDAR and InSAR as auxiliary data ● using variables from canopy height distributions obtained with LIDAR for 4 forest strata ● using the 2 InSAR height variables for 4 forest strata  Difference between observed biomass and model-assisted estimation of biomass using LIDAR and InSAR data was calculated

11. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 11 Comparison of model-assisted estimation of biomass and observed biomass Source: Naesset et al., 2011. Figure 2. LIDAR estimates InSAR Topo estimates InSAR LIDAR estimates LIDARInSAR TOPO InSAR LIDAR Using unadjusted synthetic estimator) RMSE: 17.3 MD: -4.6 RMSE: 53.2 MD: -20.6 RMSE: 44.1 MD: -21.0 Using adjusted synthetic estimator RMSE:17.7 MD: -4.1 RMSE: 52.7 MD: -19.6 RMSE: 42.6 MD: -18.4 Predicted biomass (Mg/ha) Observed biomass (Mg/ha)

12. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 12 Conclusions – Use for tropical biomass estimation  LIDAR ● Promising for tropical biomass estimation ● High accuracy and high precision of estimates ● However, monitoring costs are high  InSAR ● Moderate accuracy and precision ● RADAR: ability to see through clouds ● Frequent updates at low costs ● Useful when accurate terrain model is used, however these are not widely available in the tropics

13. Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 13 Recommended modules as follow up  Modules 3. to proceed with REDD+ assessment and reporting