Modeling directional reflectance spectra of coastal marsh vegetation
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Modeling directional reflectance spectra of coastal marsh vegetation for remote sensing applications. Proposal Defense Kevin Ross Turpie Dept of Geography University of Maryland 6 December 2006. Dr. Michael Kearney Dr. Stephen Prince Dr. Michelle Hofton Dr. Robert Hudson

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Modeling directional reflectance spectra of coastal marsh vegetation

for remote sensing applications

Proposal Defense

Kevin Ross Turpie

Dept of Geography

University of Maryland

6 December 2006

Dr. Michael Kearney

Dr. Stephen Prince

Dr. Michelle Hofton

Dr. Robert Hudson

Dr. David Tilley

Brackish and Salt Marshes vegetation


  • Important habitat for coastal flora and fauna.

  • High primary productivity (0.5 to 6.2 kg C m-2 yr-1) (Day et al. 1989).

  • Strong nutrient sink - reduces eutrophication.

  • Sediment sink - reduces silting.

  • Wave energy sink - protect coasts.

  • Key pathway of detritus and CDOM to coastal systems.

  • (Bouchard et al. 2003, Mendonca et al. 2004)

  • Salinity and hydrology produce characteristic zonation.

  • Inundated graminaceous and herbaceous monospecific canopies.

Brackish and Salt Marshes vegetation


  • Sea level rise - drown marsh, increase erosion, change hydrology and salinity gradient.

  • Disturbances

    • Storms - effect hydrology, nutrient flux, erosion.

    • Construction - effect nutrient flux and hydrology.

    • Fire - can damage rhizomes, effects not fully understood.

  • Invasive Species - can affect trophic, edaphic, hydological characteristics of the marsh.

Statement of Problem vegetation

  • BRDF of vegetation canopies changes with wavelength, thus spectral features change with viewing and solar angles.

  • This affects remote sensing techniques that depend on the marsh spectral characteristics (e.g., classification).

  • This can be compensated for with a canopy RT model, but water produces BRDF affects in inundated canopies that is not handed by current models.

Example: vegetation

BRDF Effects

from Water

Data provided by Schill (Schill 2004).

from Vanderbilt et al. 2002,

with permission

Spartina patens vegetation

Schoenoplectus americanus






Reflectance (%)

Reflectance (%)
























Wavelength (nm)

Wavelength (nm)

Example: Spectral Effects of Water

Data digitzed from Stutzer 2004


  • Objective: Develop an RT model for the marsh canopy.

  • Development - Build RT model on existing work.

  • Validation - Validate the model against field data.

  • Model Inversion - Test model inversion against field data.

  • Closure Experiment - Test agreement of model, ground truth, and RS data.

  • Geometric Optimization - Optimize viewing and solar angles for best vegetation spectral signature.

Role of a Marsh Canopy RT Model vegetation



  • Canopy Structure

  • Geochem Cycling

  • Coastal Ecology




  • Albedo

  • Light Field / FPAR



  • Productivity

  • Energy Budget

  • Climatology

  • Image Comparison

  • Classification

  • Vegetation Indices

  • Apps for RS

Adjust for BRDF

  • Invasive Species

  • Dieback

  • Regn Monitoring

Applications for RS

  • Geochem Cycling

  • Coastal Ecology

  • Standing biomass

  • Litter Decomposition

  • Productivity

  • Sub-lethal Stress




Adjust for


  • Local Monitoring

  • Vic Cal

  • Validation

  • Tie to RS Imaging

Marsh Canopy Radiative Transfer Model vegetation

Model Selection Criteria

  • Accounts for majority of variation in BRDF.

  • Few input parameters.

  • Invertible.

  • Accessible code.

  • Flexibility of design and implementation.


  • Must add aquatic background (Water RT).

  • May need to generalize for RS applications.

  • Any parameterization done against training data.

Marsh Canopy Radiative Transfer Model vegetation




Air-Water Interface




Marsh Canopy Radiative Transfer Model vegetation

Sample of Existing Models



Raytran Govaerts & Verstraete 1998,

SPRINT Goel & Thompson

Radiosity / Rectangular Cell

Kimes et al. 1984,

DART Gastellu-Etchegorry et al. 1996

Röhrig et al. 2000


Turbid Medium

Kubelka Monk 1931

Duntley 1942

Allen Gayle & Richardson 1971


SAIL Verhoef 1984

DISORD Myneni et al. 1988

Geometric Optical

CR Kuusk 1995, 1996

MCRM Kuusk 1995, 1-D (Markov Chain)

IAPI Iaquinta et al. 1997

NADIM Gobron et al. 1997

2-Layer Kuusk 2001

Ni et al. 1999

FLAIR White et al. 2001


Ross 1981

Strahler & Jupp 1991

Ross-thick Roujean et al. 1992

RPV Rahman et al. 1993

Walthall 1995

Ross-thin Wanner et al. 1995

Li-sparse Wanner et al. 1996

Bicheron & Leroy 2000

MRPV Martonchik et al. 2002

SGM Chopping et al. 2003


SAIL-H Kuusk,

GeoSAIL Huemmrich 2001

García-Haro & Sommer 2002

Field Data vegetation

  • Phase I - Locate sites and plan itinerary.

  • Phase II - Measurements:

  • Multiple monospecific canopy types.

  • BRF on SPP from 60º to 60º.

  • Plant R and T.

  • LAI, LAD, Height.

  • Soil and water samples - optics.

  • Tide, weather, salinity.

  • Digitial photos.

Validation and Closure vegetation

  • Validation

    • Constrained optimization to fit model to validation dataset from field data.

    • Test hypothesis that model fitted input and results are within confidence intervals for observations.

  • Inversion - Invert model for comparison with field data (no constraints).

  • RS Closure - Compare with data at remote sensing scales. Key candidates:

    • AISA - airborne hyperspectral, with roll maneuver

    • CHRIS / Proba - ESA sat with5 angles, hyperspectral

Geometry Optimization vegetation

  • Create “pure” vegetation spectral signature.

    • leaf reflectance and transmittance.

    • modeled BRDF for dense canopy and “black background.”

  • Create synthetic dataset from field data for canopy and background conditions.

  • Autocorrelate veg signature across entire modeled BRDF.

  • Use results to identify regions of viewing and solar angles ideal for identification (e.g., spectral angle type classification).

  • See if inverse of the model improves in these regions.

Research Timeline vegetation

Disseminating Results vegetation

Primary Publications Proposed

Disseminating Results vegetation

General Structure of Proposed Dissertation

Work so far… vegetation

  • Literature search.

  • Contacted and/or met with many researchers.

  • Consulted and observed researchers doing field work.

  • Spoke with government and other groups on marsh issues.

  • Acquired marsh BRDF data and leaf optics and analyzed.

  • Acquired several models and did initial experiments.

  • Explored several coastal marshes by boat, SUV, and foot.

  • Wrote proposal for special use permit of CBMNWRC.

  • On science team for CBMNWRC; expected to file regular reports.

  • Wrote proposal for CHRIS/Proba data.

  • SpecTIR Corp has agreed to shoulder IR&D costs for AISA flight.

  • Got approval for radiometry instruments from GSFC.

  • Have access to lab equipment for water analysis at GSFC.

  • Got back-up and lab equipment at USDA.

  • Borrowing a ASD handheld spectrometer.

  • Attended data user workshop for MISR.

Work so far… vegetation

Analysis of Schill BRDF data

for Spartina alterniflora

data from Schill, TNC

data from Ramsey and Rangoonwala, USGS

Viewing vegetation

Angle (º)

Work so far…


Compiled and ran models







data from Ramsey and Rangoonwala, USGS

Work so far… vegetation

Adviser, discussions on research topic.

Marsh field trip, April 2006

Discussions on marsh research, 2005

SAIL model, BRDF info

Contacts on marsh research

Talks & meeting on marsh field work

Discussion, supported marsh RT model

Field work experience and discussion

Discussion on modeling canopy RT

USDA contact, met and discussed work

USDA contact, met and discussed work

Info and advise on marsh work

Discussed modeling canopies

Discussed modeling canopies

Hyperion project scientist

Experience with CDOM measurements

Discussed flyover of LIDAR

Discussed flyover of LIDAR

Discussed radiometry and BRDF

Discussed goniometers, toured facility

Dr. Michael Kearney

Dr. David Tilley

Dr. Andy Rogers

Dr. Fred Huemmrich, UMBC

Dr. Victor Klemas, U of Del

Dr. Richard Field, U of Del

Dr. John Jensen, U of SC

Dr. Betsy Middleton, NASA

Dr. John Norman, U of Wisc

Dr. Martha Anderson, USDA

Dr. Charlie Walthall

Dr. Andrew Baldwin, UMCP

Dr. Narandra Goel

Dr. Wenhan Qin

Dr. Steven Unger, NASA

Dr. Antonio Mannino, NASA

Dr. Wayne Wright, NASA

Dr. Amar Nayegandhi, USGS

Dr. James Irons, NASA

Dr. James Butler, NASA

Work so far… vegetation

Dr. Vern Vanderbilt, NASA

Dr. Lawrence Corps, USDA

Roger Stone, US FWS

Dr. Dixie Birch, US FWS

Dr. Glenn Carowan, US FWS

Dr. Craig Daughtry, USDA

Dr. Nancy Adamson, U of Md CE

Leslie Hunter-Caro, Environmental Concern

Penny Grealy, Environmental Concern

Dr. David Nemerson, National Aquarium

Dr. LeeAnne Chandler, DNR

Dr. Roman Jensien, MCBP

Jay Charland, Assateague Coast Keeper

Dr. Darlene Wells, DNR/MGS

Dr. Fred Irani, DNR

Dr. Court Stevenson, UMCES

Dr. Steven Schill

Dr. Amina Rangoonwala, USGS

Dr. Elijah Ramsey III, USGS

Dr. Oliver Weatherbee, SpecTIR Corp.v

Lengthy discussion on RT and RS.

Radiometric field work

CBMNWRC info, tour of Blackwater

CBMNWRC info, handled proposal

CBMNWRC info, proposal info

Radiometric field work

Contacts on growing S. alterniflora

Info on growing S. alterniflora

Info on growing S. alterniflora

Info on growing S. alterniflora

Contacts on critical areas

Tour of two coastal bay marshes

Tour of one coastal bay marsh

Info on critical areas

Info on RS work, contacts on field work

Discussion on research topic

S. alterniflora BRDF Data

Optic data and papers

Optic data and papers

SpecTIR contact, info on AISA

Work so far… vegetation

Discussed marsh spectra and instruments

Discussed marsh classification & collab

Contacts for instrument and data

MISR proj scientist, PARABOLA III

Advice and help with GSFC rad inst

Info on marsh field work

Discussion on marsh BRDF and RS

Discussed measuring BRDF

Discussed measuring BRDF and DART

Discussed instruments for BRDF

Lengthy discussion on BRDF and RS

Provided paper on sun glint work

Lengthy discussion on BRDF field inst

Info on flight cost, photos, etc.

Info on KNMEC and Bishop’s Head

Info on KNMEC

Info on DNR mapping resources

Info on ASD resources

Contact for Hyperion data

Tour and overview of GSFC goniometer

Dr. Francisco Artigas, MERI

Dr. Martha Gilmore, Wesleyan U

Dr. William Lawrence, BSU

Dr. David Diner, NASA

Milton Hom, NASA

Amy Jacobs, Delaware DNR

Dr. Ann Nolin, OSU

Dr. Don Deering, NASA

Dr. Dan Kimes, NASA

Dr. V. Martins

Dr. Ray Hunt, USDA

Dr. Susan Ustin, UCD/CSTARS

Dr. Mark Chopping, Montclair State U

Kent Lawrenson, DCAir Photos

Cassy Gurbisz, CBF

Matt Mullin, CBF

Kevin Boone, DNR

David Hatchell, ASD Inc.

Lawrence Ong, SSAI

Georgi Georgiev, SSAI

BACKUP SLIDES vegetation

Flow of Planned Research Tasks and Products vegetation

Task Flow Diagram

Generate Synthetic Data

Research Products

Characterization of canopy reflectance for marsh canopies

Develop Model

Collect Field Data

Characterization of canopy structure parameters

Directional reflectance model for marshes

Validate Model

Model inversion using ground truth



Strategy for measuring canopy reflectance

Validated of model for remote sensing

Obtain and Process RS Data

Closure Experiment

Validation of optimized geometry

Inversion of model using remote sensing data

Study Sites vegetation




Fishing Bay

Maple Dam


Bishops Head


Hoopers Strait

Schneider et al. 2004 vegetation

Kuusk 2004 2006/12/05

Schill 2000

Walter-Shea, Mesarch 1998 2006/12/05

Kuusk 2004 2006/12/05

Terminology of Radiometry vegetation

Radiant Flux

(Energy / time)

(Joules / s or Watts)

(Watts m-2 sr-1)


(Watts m-2)



Terminology of Radiometry

Specular reflectance

Terminology of Radiometry vegetation

Bidirectional Reflectance Distribution Function (BRDF)


Bidirectional Reflectance Factor (BRF)

Terminology of Radiometry vegetation


  • Inherent optical property: is independent of source or receiver.

  • Instantaneous quantity: can only be approximated in the real world; usually by measuring BRF.

  • Can vary with the wavelength of light, so spectra can change with geometry.

  • BRDF is dependent on:

    • underlying structure of the reflecting medium,

    • optical properties of the medium’s constituents,

    • optical properties of the medium’s background.

Sky vegetation









Marsh Canopy Radiative Transfer Model

BRDF Spectral Effects in Grass vegetation


Forward Scattering

Sun in front of viewer

Sun behind viewer

Photos from Sandmeier et al. 1999