1. Global Biomass Estimates from NASA’s DESDynI Mission Ralph Dubayah University of Maryland Sassan Saatchi NASA/JPL Bruce Cook NASA/GSFC 1

2. 2 How are the Earth's carbon cycle and ecosystems changing, and what are the consequences for the Earth's carbon budget, ecosystem sustainability, and biodiversity?

3. Outline DESDynI Biomass Goals and Requirements Science and Measurement Objectives Science Rationale Measurement Approach MultibeamLidar Synthetic Aperture Radar Fusion Approaches Radar/lidar Ecosystem modeling Current Biomass Science Activities Summary 3

4. Science Objective 1: Biomass Characterize global distribution of aboveground vegetation biomass Desired Final Data Products Global biomass at 250 m with accuracy of 10 MgC/ha (or 20%, not to exceed 50 Mg/ha) at 3 years. Forest canopy height and profiles, spatial and vertical structure, biomass from SAR Measurement Objectives Multi-beam lidar, polarimetric L-band SAR Instruments

5. Global Biomass and Carbon Accurate estimate of forest biomass critical Role of forests in global carbon cycle and relation to atmospheric CO2 requires knowledge of stocks, disturbance and recovery Potential pool when burned or cleared DESDynI biomass reduces uncertainty in these terms

6. Why These Requirements? Biomass Accuracy Need to reduce uncertainty in terrestrial carbon components to level of oceans/atmosphere Use of data for climate treaties requires high accuracy Biomass Resolution Match scale of environmental gradients Match scale of disturbances Significant improvement over existing and planned methods of global biomass estimation 6

7. DESDynI Measurement Approach Combine beauty of vertical structure measurements of lidar with powerful coverage potential of radar 7 Radar Beam Laser Beam Lidar Waveform Signal Start HV Tree Height HHVV Backscatter Ground Peak HH VV HV

8. Lidar Measurement Radar Measurement Radar Mapping DESDynI Ecosystems Structure Measurement Concept Lidar Backscatter Vertical Structure Medium to high biomass Lidar Sampling GLOBAL BIOMASS, FLUXES & BIODIVERSITY MODELS/ FUSION Radar Backscatter Volume Structure Disturbance Low to medium biomass Vegetated Ecosystems

10. Waveform metrics

11. Canopy cover profile

13. Vertical foliage profile

14. Tree density

15. Basal area

16. LAIBIOMASS

17. Lidar-Based Approaches Lidar heights and metrics combined with field data to predict biomass Footprint-level biomass estimates Use existing relationships because of geolocation errors Grid cell based biomass estimates Average biomass or average lidar metrics 10 Lidar Metrics EAGB = f(lidar metrics) Field Biomass Statistical Models Machine Learning Models Ecosystem Models

19. Lidar Observations By Biome Lidar by itself does not meet resolution requirements for all biomes. Fusion with radar, passive optical required * SE = § Assuming 1 m tree height = 15 Mg ha-1

20. L-band Measurement of Structure 14 PolarimetricImage of La Selva LHH, LHV, LVV Image Segmentation

21. L-band Sensitivity to Biomass 15 Low or no sensitivity above 100 Mg/ha Fusion with lidar samples may extend this range Highest biomass areas estimated using dense grid of lidar samples alone at coarser resolutions Many different studies achieved 10-20% accuracy for biomass below < 100 Mg/ha

23. Approaches to Fusion For DESDynI Geostatistical/Covariance Methods Use radar to extend estimates of sparser lidar samples e.g. kriging, spatially-explicit regressions Statistical Methods Bayesian and other techniques to predict forest structure Use lidar estimates of structure as independent and validation data Machine-Learning Methods Non-parametric techniques that discover complex relationships between radar, other environmental data and structure Use lidar estimates of structure as training and validation data Physically-based (radiative transfer) methods Radar backscatter models incorporate canopy structure from lidar e.g. derive foliage area volume density from lidar profiles 18

24. Example: ALOS/PALSAR & ICESat/GLAS 1. RADAR mosaic 2. LIDAR track 3. Segmentation (could include historic multi-source data) Generate LIDAR Segment Means 5. Map Segments 6. Predict Unmapped Segments from Models: - statistical & - location-based 7. Repeat with every new acquisitions J. Kellndorfer WHRC

25. L-band Radar & LVIS data Inventory Plot Data Random Sampling 25 m Resolution 0.25 ha plots

26. Distribution of Aboveground Forest Biomass in Borneo AGLB Mg/ha Bare Savanna 0-25 3 4 5 6 7 8 9 10 11 12 13 Validation: 69% Overall Accuracy (95% CI) over all biomass classes 25-50 50-75 0-150 75-100 100-150 150-200 200-250 250-300 300-350 350-400 > 400 Maximum entropy approach with ICEAST and ALOS

28. Consistent framework

29. Allows for prognostic and diagnostic estimates

30. DESDynI structure estimates used to initialize modelsEcosystem Model Biomass Maps Old Growth Forest DESDynI Structure Data 1° x 1° ED Model Carbon Flux Initialized Using ICESAT Heights Older Secondary Forest Height Young Secondary Forest Pasture More Biomass

31. Current Biomass Science Activities Algorithm Development Lidar/SAR vegetation structure Impacts of noise, topography, geolocation LIDAR/SAR Fusion Airborne LIDAR and SAR, ICESAT Sampling Strategies Field Studies Ongoing data collection and analysis at legacy West Coast, East Coast, Boreal, Tropical sites Ecosystem Modeling Studies Modeling requirements for biomass, flux & habitat Global modeling frameworks 23

32. Field Activities 24 ICESAT LIDAR ALOS SAR LVIS Large Footprint LIDAR UAVSAR Small Footprint LIDAR Field Measurements Ground LIDAR

33. ECHIDNA Ground LIDAR 25 Alan Strahler – Boston University

34. Cal/Val: Hubbard Brook, NH 26 LVIS Waveform Lidar Discrete Return Lidar UAVSAR

35. Summary 27 DESDynI revolutionary mission for biomass Provide vertical and spatial structure at fine scales globally Address critical environmental issues on the effects of changing climate and land use on biomass Plenty of work remains before 2017 launch JGR and RSE special issues desdyni.jpl.nasa.gov

36. Co-Authors 28