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WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI
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WE1.L09 - GLOBAL BIOMASS ESTIMATES FROM DESDYNI

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  • NOTE: For this exercise, woody savannas were considered deciduous
  • Transcript

    • 1. Global Biomass Estimates from NASA’s DESDynI Mission <br />Ralph Dubayah<br />University of Maryland<br />Sassan Saatchi<br />NASA/JPL<br />Bruce Cook<br />NASA/GSFC<br />1<br />
    • 2. 2<br />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?<br />
    • 3. Outline<br />DESDynI Biomass Goals and Requirements<br />Science and Measurement Objectives<br />Science Rationale <br />Measurement Approach<br />MultibeamLidar<br />Synthetic Aperture Radar<br />Fusion Approaches<br />Radar/lidar<br />Ecosystem modeling<br />Current Biomass Science Activities<br />Summary<br />3<br />
    • 4. Science Objective 1: Biomass<br />Characterize global distribution of aboveground vegetation biomass<br />Desired Final Data Products <br />Global biomass at 250 m with accuracy of 10 MgC/ha (or 20%, not to exceed 50 Mg/ha) at 3 years.<br />Forest canopy height and profiles, spatial and vertical structure, biomass from SAR<br />Measurement Objectives<br />Multi-beam lidar, polarimetric L-band SAR<br />Instruments<br />
    • 5. Global Biomass and Carbon<br />Accurate estimate of forest biomass critical<br />Role of forests in global carbon cycle and relation to atmospheric CO2 requires knowledge of stocks, disturbance and recovery<br />Potential pool when burned or cleared<br />DESDynI biomass reduces<br />uncertainty in these terms<br />
    • 6. Why These Requirements?<br />Biomass Accuracy<br />Need to reduce uncertainty in terrestrial carbon components to level of oceans/atmosphere<br />Use of data for climate treaties requires high accuracy<br />Biomass Resolution<br />Match scale of environmental gradients<br />Match scale of disturbances<br />Significant improvement over existing and planned methods of global biomass estimation<br />6<br />
    • 7. DESDynI Measurement Approach<br />Combine beauty of vertical structure measurements of lidar with powerful coverage potential of radar<br />7<br />Radar Beam<br />Laser Beam<br />Lidar Waveform<br />Signal Start<br />HV<br />Tree Height<br />HHVV<br /> Backscatter<br />Ground Peak<br />HH VV HV<br />
    • 8. Lidar Measurement<br />Radar Measurement<br />Radar Mapping<br />DESDynI Ecosystems Structure Measurement Concept<br />Lidar<br />Backscatter<br />Vertical Structure<br />Medium to high biomass<br />Lidar Sampling<br /> GLOBAL BIOMASS, FLUXES & BIODIVERSITY<br />MODELS/ <br />FUSION<br />Radar<br />Backscatter<br />Volume Structure<br />Disturbance<br />Low to medium biomass<br /> Vegetated Ecosystems<br />
    • 9. Forest Structure from Lidar<br />DIRECT<br /><ul><li>Tree height
    • 10. Waveform metrics
    • 11. Canopy cover profile
    • 12. Vertical/spatial variation metrics (e.g. entropy)</li></ul>MODELED<br /><ul><li>Crown volume
    • 13. Vertical foliage profile
    • 14. Tree density
    • 15. Basal area
    • 16. LAI</li></ul>BIOMASS<br />
    • 17. Lidar-Based Approaches<br />Lidar heights and metrics combined with field data to predict biomass<br />Footprint-level biomass estimates<br />Use existing relationships because of geolocation errors<br />Grid cell based biomass estimates<br />Average biomass or average lidar metrics <br />10<br />Lidar<br />Metrics<br />EAGB = f(lidar metrics)<br />Field Biomass<br />Statistical Models<br />Machine Learning Models<br />Ecosystem Models<br />
    • 18. m1<br />m1<br />m2<br />Stratification by landcover type (e.g. from Landsat or radar) greatly facilitates areal mean estimation<br />1 km<br />m4<br />m3<br />Biomass Stocks from Lidar<br /><ul><li>Assumption that sufficient beams, suitable orbit, minimal radar fusion (e.g. co-kriging) achieves 250 m resolution</li></li></ul><li>Estimating Biomass Errors From Lidar<br />DESDynI produces EOM track spacing of 472 m at equator across track<br />30 m posting along track<br />Assuming 3 yr mission<br />Grid average height error function of:<br />Number of measurements<br />Sensor error<br />Variance of height<br />Requires about 50 observations per cell<br />12<br />
    • 19. Lidar Observations By Biome<br />Lidar by itself does not meet resolution requirements for all biomes. Fusion with radar, passive optical required<br />* SE = § Assuming 1 m tree height = 15 Mg ha-1<br />
    • 20. L-band Measurement of Structure<br />14<br />PolarimetricImage of La Selva<br />LHH, LHV, LVV<br />Image Segmentation <br />
    • 21. L-band Sensitivity to Biomass<br />15<br />Low or no sensitivity above 100 Mg/ha<br /> Fusion with lidar samples may extend this range<br />Highest biomass areas estimated using dense grid of lidar samples<br />alone at coarser resolutions<br />Many different studies achieved 10-20% accuracy for<br />biomass below < 100 Mg/ha<br />
    • 22. Total Biomass Estimation Errors<br />LiDAR:<br /><ul><li>Sampling error dominates model error particularly in areas of low biomass.</li></ul>Radar:<br /><ul><li>Model uncertainties dominate due to absence of sampling error.</li></ul>Fusion:<br /><ul><li>Algorithms used to either improve model error (lidar radar) or improve sampling error (radar lidar)</li></li></ul><li>Requirement for Polarization<br />
    • 23. Approaches to Fusion For DESDynI<br />Geostatistical/Covariance Methods<br />Use radar to extend estimates of sparser lidar samples<br />e.g. kriging, spatially-explicit regressions<br />Statistical Methods<br />Bayesian and other techniques to predict forest structure<br />Use lidar estimates of structure as independent and validation data <br />Machine-Learning Methods<br />Non-parametric techniques that discover complex relationships between radar, other environmental data and structure<br />Use lidar estimates of structure as training and validation data<br />Physically-based (radiative transfer) methods<br />Radar backscatter models incorporate canopy structure from lidar<br />e.g. derive foliage area volume density from lidar profiles<br />18<br />
    • 24. Example: ALOS/PALSAR & ICESat/GLAS<br />1. RADAR mosaic<br />2. LIDAR track<br />3. Segmentation (could include historic multi-source data)<br />Generate LIDAR Segment Means<br />5. Map Segments<br />6. Predict Unmapped Segments from Models:<br /> - statistical &<br /> - location-based<br />7. Repeat with every new acquisitions<br />J. Kellndorfer WHRC<br />
    • 25. L-band Radar & LVIS data<br />Inventory Plot Data<br />Random Sampling<br />25 m Resolution<br />0.25 ha plots<br />
    • 26. Distribution of Aboveground Forest Biomass in Borneo<br />AGLB Mg/ha <br />Bare<br />Savanna<br />0-25<br />3<br />4<br />5<br />6<br />7<br />8<br />9<br />10<br />11<br />12<br />13<br />Validation:<br />69% Overall Accuracy (95% CI)<br />over all biomass classes <br />25-50<br />50-75<br />0-150<br />75-100<br />100-150<br />150-200<br />200-250<br />250-300<br />300-350<br />350-400<br />> 400<br />Maximum entropy approach with ICEAST and ALOS<br />
    • 27. Biomass From Ecosystem Modeling<br /><ul><li>Ecosystem models provide physically-based alternative to statistical methods for biomass
    • 28. Consistent framework
    • 29. Allows for prognostic and diagnostic estimates
    • 30. DESDynI structure estimates used to initialize models</li></ul>Ecosystem Model<br />Biomass Maps<br />Old Growth Forest<br />DESDynI<br />Structure<br />Data<br />1° x 1°<br />ED Model Carbon Flux<br />Initialized Using<br />ICESAT Heights<br />Older Secondary Forest<br />Height<br />Young Secondary Forest<br />Pasture<br />More Biomass<br />
    • 31. Current Biomass Science Activities<br />Algorithm Development<br />Lidar/SAR vegetation structure<br />Impacts of noise, topography, geolocation<br />LIDAR/SAR Fusion<br />Airborne LIDAR and SAR, ICESAT<br />Sampling Strategies<br />Field Studies<br />Ongoing data collection and analysis at legacy West Coast, East Coast, Boreal, Tropical sites<br />Ecosystem Modeling Studies<br />Modeling requirements for biomass, flux & habitat<br />Global modeling frameworks<br />23<br />
    • 32. Field Activities<br />24<br />ICESAT LIDAR<br />ALOS SAR<br />LVIS<br />Large Footprint<br />LIDAR<br />UAVSAR<br />Small Footprint<br />LIDAR<br />Field<br />Measurements<br />Ground<br />LIDAR<br />
    • 33. ECHIDNA Ground LIDAR<br />25<br />Alan Strahler – Boston University<br />
    • 34. Cal/Val: Hubbard Brook, NH<br />26<br />LVIS Waveform Lidar<br />Discrete Return Lidar<br />UAVSAR<br />
    • 35. Summary<br />27<br />DESDynI revolutionary mission for biomass<br />Provide vertical and spatial structure at fine scales globally<br />Address critical environmental issues on the effects of changing climate and land use on biomass<br />Plenty of work remains before 2017 launch<br />JGR and RSE special issues<br />desdyni.jpl.nasa.gov<br />
    • 36. Co-Authors<br />28<br />

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