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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.

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Presentation on theme: "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."— Presentation transcript:

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: 1.Tropical biomass mapping in Kalimantan by integrating ALOS PALSAR and LIDAR data 2.Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Source: US Forest Service. V1, May 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 and speckle  Using RADAR in combination with LIDAR can help 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, and mask preparation  Classification  17 strata ● Unsupervised segmentation, postprocessing, validation, and 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, 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 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

8 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 8 2. Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Naesset et al. (2011),“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

9 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 9 Use of LIDAR and InSAR as auxiliary data to estimate forest biomass in a boreal forest area Methodology  Stratification of forest land into four strata, through interpretation of aerial photographs (photogrammetry)  Collecting field data: ● For sample survey plots and large field plots ● For measurements of tree diameter (d bh ) and 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

10 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 10 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 two 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

11 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 11 Estimation of aboveground biomass  Estimation of aboveground 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

12 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 12 Comparison of model-assisted estimation of biomass and observed biomass Source: Naesset et al. 2011, fig. 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)

13 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 13 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

14 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 14 Recommended modules as follow-up  Modules 3.1 to 3.3 to proceed with REDD+ assessment and reporting

15 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 15 References  Brown, S., 1997. Estimating Biomass and Biomass Change of Tropical Forests: a Primer (FAO Forestry Paper-134), FAO, United Nations, Rome.  Di Gregorio, A., and Louisa J.M. Jansen. 2000. Land Cover Classification System (LCCS): Classification Concepts and User Manual. Rome: Food and Agricultural Organization. http://www.fao.org/docrep/003/x0596e/X0596e00.htm#P-1_0.  IPCC (Intergovernmental Panel on Climate Change). 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. (Often IPCC GPG.) Geneva, Switzerland: IPCC. http://www.ipcc-nggip.iges.or.jp/public/gp/english/.  Kenzo, T., R. Furutani, D. Hattori, J. J. Kendawang, S. Tanaka, K. Sakurai, and I. Ninomiya. 2009. “Allometric Equations for Accurate Estimation of Aboveground Biomass in Logged-over Tropical Rainforests in Sarawak, Malaysia.” Journal of Forest Research 14 (6): 365–372. doi:10.1007/s10310- 009-0149-1  Ketterings Q. M., R. Coe, M. van Noordwijk. 2001. “Reducing Uncertainty in the Use of Allometric Biomass Equations for Predicting Aboveground Tree Biomass in Mixed Secondary Forests.” Forest Ecology and Management 146: 199–209.

16 Module 2.8 Overview and status of evolving technologies REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 16  Næsset, E., Gobakken, T., Solberg, S., Gregoire, T.G., Nelson, R., Ståhl, G., Weydahl, D., 2011. “Model- assisted Regional Forest Biomass Estimation Using LiDAR and InSAR as Auxiliary Data: A Case Study from a Boreal Forest Area.” Remote Sensing of Environment 115 (12): 3599-3614.  Quinones, M., C. Van der Laan, D. Hoekman, and V. Schut., 2014. Integration of Alos PalSAR and LIDAR IceSAT data in a multistep approach for wide area biomass mapping. Presentation Living Planet, Edinburg, September 2013.

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