DESDynI

Global Biomass Estimates

from NASA’s DESDynI

Mission

Ralph DubayahUniversity of Maryland

Sassan SaatchiNASA/JPL

Bruce CookNASA/GSFC

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DESDynI2

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?

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Outline

DESDynI Biomass Goals and Requirements Science and Measurement Objectives Science Rationale

Measurement Approach Multibeam Lidar Synthetic Aperture Radar

Fusion Approaches Radar/lidar Ecosystem modeling

Current Biomass Science ActivitiesSummary

DESDynI

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.

Measurement Objectives

Forest canopy height and profiles, spatial and vertical structure, biomass from SAR

Instruments Multi-beam lidar, polarimetric L-band SAR

COVERAGE RESOLUTION ACCURACY

Global < 250 m20%

(< 50 Mg/ha)

DESDynI

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 reducesuncertainty in these terms

DESDynI

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

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DESDynI

DESDynI Measurement Approach

Combine beauty of vertical structure measurements of lidar with powerful coverage potential of radar

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Tree Height

Ground Peak

Signal Start

Laser Beam

Lidar Waveform

HH VV HV

HV

HHVV

Bac

ksca

tter

Radar Beam

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Lidar

Backscatter

Vertical Structure

Medium to high biomass

DESDynI Ecosystems Structure Measurement Concept

Lid

ar M

eas

ure

men

tR

ada

r M

easu

rem

ent

Lidar Sampling

Radar Mapping

GLOBAL BIOMASS, FLUXES & BIODIVERSITY

Vegetated Ecosystems

Radar

Backscatter

Volume Structure

Disturbance

Low to medium biomass

MODELS/

FUSION

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Forest Structure from Lidar

DIRECT

• Tree height

• Waveform metrics

• Canopy cover profile

• Vertical/spatial variation metrics (e.g. entropy)

BIOMASS

MODELED

• Crown volume

• Vertical foliage profile

• Tree density

• Basal area

• LAI

DESDynI

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

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LidarMetrics

Field Biomass

EAGB = f(lidar metrics)

Statistical ModelsMachine Learning Models

Ecosystem Models

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m1

m1 m2

m4m3

Biomass Stocks from Lidar

Assumption that sufficient beams, suitable orbit, minimal radar fusion (e.g. co-kriging) achieves 250 m resolution

1 km

Stratification by landcover type (e.g. from Landsat or radar) greatly facilitates areal mean estimation

DESDynI

Estimating Biomass Errors From Lidar

DESDynI produces EOM track spacing of 472 m at equator across track 30 m posting along track Assuming 3 yr mission

Grid average height error function of: Number of measurements Sensor error Variance of height Requires about 50

observations per cell

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Box size (km)

Centered on hole

Centered on cross

1 132 148

0.75 100 50

0.5 22 33

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IGBP Biome

Distribution(MOD12Q1)

Total area (106

km2)

Leaf-on shots km-2

after 3 years

Height error (m)

for 1 km grid cell*

Biomass error§

(Mg ha-1)

Resolution (m)required for

n=49

Evergreen needleleaf forest

4.8 141 0.59 9 590

Evergreen broadleaf forest

13.2 74 0.81 12 810

Deciduous needleleaf forest

2.7 32 1.23 18 1230

Deciduous broadleaf forest

2.1 41 1.10 16 1100

Mixed forest 5.9 37 1.15 17 1150

Woody savanna 13.4 39 1.12 17 1120

GLOBALAVERAGE 42.3 61 0.89 13 890

* SE = § Assuming 1 m tree height = 15 Mg ha-1

€

σn

Lidar Observations By Biome

Lidar by itself does not meet resolution requirements for all biomes. Fusion with radar, passive optical required

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L-band Measurement of Structure

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LHH, LHV, LVV

Polarimetric Image of La Selva

Image Segmentation

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L-band Sensitivity to Biomass

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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

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Total Biomass Estimation Errors

LiDAR:• Sampling error dominates model

error particularly in areas of low biomass.

Radar:• Model uncertainties dominate due to

absence of sampling error.

Fusion:• Algorithms used to either improve

model error (lidar radar) or improve sampling error (radar lidar)

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Requirement for Polarization

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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

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DESDynI

1. RADAR mosaic

2. LIDAR track

3. Segmentation (could include historic multi-source data)

4. Generate LIDAR Segment Means

5. Map Segments

Example: ALOS/PALSAR & ICESat/GLAS

6. Predict Unmapped Segments from Models:

- statistical & - location-based

7. Repeat with every new acquisitions

J. Kellndorfer WHRC

L-band Radar & LVIS data

25 m Resolution 0.25 ha plots

Random Sampling Inventory Plot Data

0-15075-100

100-150

150-200

200-250

250-300

300-350

AGLB Mg/ha

25-50

50-75

0-25

Bare

350-400

Savanna

Distribution of Aboveground Forest Biomass in Borneo

> 400

3456789

10111213

Validation:69% Overall Accuracy (95% CI)

over all biomass classes

Maximum entropy approach with ICEAST and ALOS

DESDynI

Biomass From Ecosystem Modeling

Ecosystem models provide physically-based alternative to statistical methods for biomass Consistent framework Allows for prognostic and diagnostic estimates

DESDynI structure estimates used to initialize models

Young Secondary Forest

Older Secondary Forest

Pasture

More Biomass

Hei

ght

Old Growth ForestDESDynI Structure

Data

Ecosystem Model Biomass Maps

1° x 1°ED Model Carbon Flux

Initialized UsingICESAT Heights

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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

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Field Activities

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LVISLarge Footprint

LIDAR UAVSAR

ICESAT LIDARALOS SAR

Small FootprintLIDAR

FieldMeasurements

GroundLIDAR

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ECHIDNA Ground LIDAR

25Alan Strahler – Boston University

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Cal/Val: Hubbard Brook, NH

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LVIS Waveform Lidar Discrete Return Lidar

UAVSAR

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Summary

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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 issuesdesdyni.jpl.nasa.gov

DESDynI28

Kathleen Bergen Forrest Hall George Hurtt

University of Michigan UMBC/NASA University of New Hampshire

Richard Houghton Josef Kellndorfer Michael Lefsky

Woods Hole Research Institute

Woods Hole Research Institute

Colorado State University

Hank ShugartUniversity of Virginia

Marc SimardNASA/JPL

Jon RansonNASA/GSFC

Bryan BlairNASA/GSFC

Co-Authors