Land Cover and InSAR

InSAR WorkshopOctober 21, 2004, Oxnard, CA

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Land-Cover and Land-Cover Change: What is it?

Land Cover: Most of the Earth’s Land-Mass is Covered by Vegetation – Forest and Shrublands (Temperate, Tropical,

Woodlands, Semi-desert) – Herbaceous (Grassland, Agriculture, Tundra)– Human Dominated (Urban, Peri-Urban)– Wetland-Coastal

Land-Cover Change: Drivers and Consequences– Anthropogenic: Land-Cover Conversion,

Urbanization – Natural Hazards: Fire, Wind, Earthquakes,

Flooding, Volcanoes, Landslides, Desertification, Insects/Pests

– Global climate change

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Land Cover Considered as Ecosystems is Multi-Dimensional

Ecosystem

“A topographic unit,a volume of land andair plus organic contentthat extend areally overa particular part of theEarth’s surface for acertain time.”

(Rowe, 1961; Bailey, 1996)

Macroclimate

Biota

Landform

Soils

Groundwater

Bedrock

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X-band RCS

Slicer and GeoSAR Tree Heights

Use of Interferometry for Estimationg Vegetation Height

When the signal return comes from multiple heights, a unique signature is observed by the interferometer

When the signal return comes from multiple heights, a unique signature is observed by the interferometer

h H cos sin 1 4B

alti

tude

, H

baseline, B

path length difference,

terrain height, h

terrain height

hv

vol vol ei vol

(z)e ikzzdz(z)dz

vol eikzhv / 2sinc kzhv / 2

hv 24

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1 vol • SLICER tree height (blue line)• GeoSAR X- minus P-band height (red line)• GeoSAR X-band interferometric estimate of tree height (green circles)

Comparison between LIDAR and Radar Height Estimates

GeoSAR Swath: 10km

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Multi-baseline Interferometry Provides Vertical Structure of Vegetation

Reigber, A., Moreira, A., “First Demonstration of Airborne SAR Tomography Using Multibaseline L-Band Data,” IEEE Trans. Geosci. Rem. Sens., 38(5), 2000.

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Carbon Cycle and Ecosystems

T

T

2002 2010 2012 2014 20152004

Reduced uncertainties in fluxes and coastal C dynamics

Funded

Unfunded

Profiles of Ocean Particles

New Ocean Carbon / Coastal Event Observations

N. America’s carbon budget quantified

Global Atmospheric CO2 (OCO)

2006 2008

Process controls identified; errors in sink reduced

NA Carbon NA Carbon Global C Cycle

T = Technology development

Regional carbon sources/sinks quantified for planet

IPCC IPCC

Effects of tropical deforestation quantified; uncertaintiesin tropical carbon source reduced

= Field Campaign

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Vegetation 3-D Structure, Biomass, & DisturbanceT Terrestrial carbon stocks &

species habitat characterized

Models w/improved ecosystem functions

High-Resolution Atmospheric CO2

TCarbon export to deep ocean

Sub-regional sources/sinks

Integrated global analyses

CH4 sources characterized and quantified

Report

P

Vegetation (AVHRR, MODIS)

Ocean Color (SeaWiFS, MODIS)

Land Cover (Landsat) Land Cover (LDCM) Land Cover (LDCM II)

Vegetation, Fire (AVHRR, MODIS) Ocean Color/Vegetation (VIIRS/NPP) Ocean/Land (VIIRS/NPOESS)

Models & Computing Capacity

Case Studies

Process UnderstandingImprovements:

Human-Ecosystems-Climate Interactions (Coupling, Model-Data Fusion, Assimilation)

Physiology & Functional Groups

Partnership

Southern Ocean Carbon Program

N. American Carbon Program

Land Use Change in Amazonia

Global CH4; Wetlands, Flooding & Permafrost

Global C Cycle

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2002: Global productivity and land cover resolution coarse; Large uncertainties in biomass, fluxes, disturbance, and coastal events

Systematic Observations

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MULTI-DIMENSIONAL FORESTED ECOSYSTEM STRUCTURE: REQUIREMENTS FOR REMOTE SENSING OBSERVATIONS

Final Report of the NASA Workshop, June 26-28, 2003, Annapolis Maryland

Kathleen Bergen, Robert Knox, Sassan Saatchi, Editors

Workshop Organizing Committee

Co-chairsRobert Knox, NASA Goddard Space Flight Center

Kathleen Bergen, University of MichiganDiane Wickland, NASA Headquarters

CommitteeCraig Dobson, NASA Headquarters/University of MichiganBill Emanuel, NASA Headquarters/University of Virginia

Carolyn Hunsaker, USDA Forest ServiceSassan Saatchi, NASA Jet Propulsion Laboratory

Hank Shugart, University of Virginia

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Land-Cover Grand Challenges for InSAR (Breakout 1 Results)

1. 3D Vegetation structure (for habitat, biomass, fire behavior, classification, economic valuation, windfall, and more)

2. Change detection over time.

a) Detection of landcover disturbance/change, natural hazard assessment & monitoring

b) 3D vertical profile change: height (first order), profile change (higher order)

3. Conversion of vegetation height and profile into biomass/carbon (global carbon cycle)

4. Below-canopy topography and mapping of topographic change

5. Characterization of ecophysiology (net primary productivity, moisture conditions of soil and vegetation, vegetation stress/disease)

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Existing Sensors/Data

Airborne– AIRSAR– GeoSAR

Shuttle-borne– SIR-C– SRTM

• c-band • x-band

Space-borne– Envisat– Radarsat

Utility– Answer specific but limited science

questions; Confirm desired InSAR parameters

– C-band has some utility to vegetation science

Limitations– Airborne and Shuttle:

• limited spatio-temporal coverage• Data may have limited or difficult

access– Spaceborne:

• repeat-pass C-band has limitations in vegetation capabilities due to temporal decorrelation

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Near-Term Sensors/Data

ALOS-PALSAR– L-band pol

UAV SAR– airborne– L-band pol– interferometric

Utility– PALSAR good experimental platform– good parameters– could contribute to change detection– UAV L-band SAR will do repeat pass

and can be used to study temporal decorrelation and vegetation structure

Limitations– ALOS-PALSAR has long repeat causing

large temporal decorrelation– UAV somewhat limited coverage/access

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Potential L-HH InSAR Mission

L-band InSAR has strong capabilities in the area of land-cover and land-cover change

Zero Baseline L-HH InSAR– Can be used for temporal decorrelation– Yet to be developed empirical models may be related to vegetation

characteristics

Non-Zero Baselines L-HH InSAR (km scale equatorial separation), – Provides topographic map (useful for both vegetation structure

and permanent scatterer deformation measurement)– Correlation signature related to vegetation structure– 1 to 4 (optimal) occurrences per year useful– Repeat period that minimizes temporal decorrelation is desirable

(useful for both vegetation and deformation)

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Augmentation of L-HH InSAR Mission(in ascending cost order)

Bandwidth (from 15 Mhz to 80Mhz)– Better spatial resolution (current 100 m is useful, 15-30 also

would be good)Polarization - polarimetric capability– Pol InSAR - improved vertical structure accuracy & land-

cover type discriminationDual frequency– Add X-band to the L-band– Provides two height estimates that can be used to expand

observationSingle pass formation flying– Two identical L-HH sensors (solves the temporal

decorrelation and choice of baseline/s issues)– Possible to implement multi-baseline interferometry for 3-D

structure mapping

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Long-Term InSAR Mission Strategies “Wish-list”

Vegetation 4D Structure Observatory – with parameters and spatial and temporal resolutions

ideal for vegetation structure and biomass– fusion of

• InSAR (wide-swath 4D structure) – multifrequency– polarimetric– multibaseline

• Lidar (small-swath, sampling, profiles)• Hyperspectral (canopy chemistry)

Improved Data AccessImproved education and training

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Land-Cover Group Conclusions

Land-Cover & Vegetation InSAR needs are converging with Solid Earth Science

Strong interest in: 3D Vegetation Structure, Disturbance/Natural Hazards, Biomass/Carbon, Topography, Ecophysiology/moisture stress

L-HH InSAR orbiting sensor would be significant step forward in InSAR capabilities for land-cover and vegetation structure; enthusiastic participants!– Additional considerations

• primary: encourage flexibility in incorporating non-zero baseline opportunities

• secondary: have identified list of potential enhancements

Long-term Mission includes fusion of– InSAR - height, biomass, structure over swaths– Lidar - high resolution profiles– Hyperspectral - canopy chemistry

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Annapolis Vegetation Structure Workshop

50% Ecological Science Community– academic, agency, and other scientists funded by NASA,

NSF, USDA USFS, Conservation & Science Non-profit

50% Technological Science Community– NASA HQ and Science Centers, academic– Canadian and European Scientists & Science Centers

Results Indicated Very Strong interest in:– Biomass/Carbon, Ingesting 3-D data into Ecological Models,

Biodiversity and Habitat Management, Disturbance– Vegetation Height & Vegetation Profiles, Biomass at several

scales– Imaging SAR, InSAR and fusing of SAR-lidar-hyperspectral