RemoteRemote SensingSensing AssessmentAssessment ofof KelpKelp ForestForest andand SeagrassSeagrass MeadowMeadow ProductivityProductivity

RichardRichard C.C. ZimmermanZimmerman OldOld DominionDominion UniversityUniversity NorfolkNorfolk VAVA 2352923529 HeidiHeidi M.M. DierssenDierssen UniversityUniversity ofof ConnecticutConnecticut Groton,Groton, CTCT 0634006340 W.W. PaulPaul BissettBissett FloridaFlorida EnvironmentalEnvironmental ResearchResearch InstituteInstitute TampaTampa FLFL 3361133611

UNOLS Airborne Ocean Science 25 May 2006 Conference AcknowledgementsAcknowledgements •• DanDan Reed,Reed, UCSBUCSB SantaSanta BarbaraBarbara CoastalCoastal LTERLTER –– KelpKelp productivityproductivity datadata •• MikeMike Donnellan,Donnellan, MLMLMLML –– KelpKelp canopycanopy timetime seriesseries

•• FinancialFinancial SupportSupport

UNOLS Airborne Ocean Science 25 May 2006 Conference RemoteRemote SensingSensing ApplicationsApplications inin CoastalCoastal andand OpticallyOptically ShallowShallow EnvironmentsEnvironments

•• CoastalCoastal bathymetrybathymetry •• CoastalCoastal waterwater qualityquality •• ShorelineShoreline dynamicsdynamics –– chemicalchemical pollutionpollution –– erosionerosion && depositiondeposition –– eutrophicationeutrophication –– sedimentsediment loadingloading •• NaturalNatural resourcesresources •• FeatureFeature DetectionDetection –– CoralCoral reefsreefs –– rockrock outcropsoutcrops –– KelpKelp forestsforests andand rockyrocky habitatshabitats –– debrisdebris fieldsfields –– SeagrassSeagrass meadowsmeadows •• SurveillanceSurveillance –– objectobject identificationidentification && trackingtracking

UNOLS Airborne Ocean Science 25 May 2006 Conference RemoteRemote sensingsensing isis usefuluseful forfor distributiondistribution mapsmaps

CanCan itit bebe usedused toto assessassess benthicbenthic ecosystemecosystem processes?processes? PrimaryPrimary productionproduction ratesrates && biogeochemicalbiogeochemical fluxesfluxes provideprovide keykey linkslinks toto understanding:understanding: ––physicalphysical (bottom(bottom up)up) forcingforcing ••temperature,temperature, nutrients,nutrients, waterwater motionmotion –– biologicalbiological (top-down)(top-down) controlcontrol •• directdirect impactsimpacts ofof grazersgrazers •• indirectindirect effectseffects ofof predatorspredators

UNOLS Airborne Ocean Science 25 May 2006 Conference BiomassBiomass explainsexplains mostmost variationvariation inin primaryprimary productivityproductivity

14 Thalassia testudinum (turtlegrass) Macrocystis pyrifera (giant kelp) NPP = 50.5 x Standing Crop 2 40 NPP = 14.7 x Standing Crop 12 R = 0.7195 )

) 2 -1 a. R = 0.623 -1 P<0.001 d -2 d 10 P < 0.0001 -2 30 8

6 20 4 NPP (gDW m (gDW NPP 2 10

0 0.00 0.05 0.10 0.15 0.20 mass m NPP (g dry 0 Standing Crop (Kg m-2) 0.0 0.5 1.0 1.5 2.0 Standing crop (kg m-2)

Can BIOMASS be determined remotely?

UNOLS Airborne Ocean Science 25 May 2006 Conference ContinuousContinuous spectralspectral informationinformation permitspermits retrievalretrieval ofof bathymetrybathymetry andand turtlegrassturtlegrass densitydensity fromfrom thethe samesame datadata setset Dierssen, H., R. Zimmerman, R. Leathers, T. Downes and C. Davis. 2003. Limnol. Oceangr., 48:444-555

2 RRrs(555) rs (555) z =++0.17 0.89 0.23 Bottom Reflectance Leaf Area Index RRrs(670) rs (670)

EzuLu() exp(− K ) RRbrs∝  Lud()zK exp(− ) Bathymetry 0

3 Water Column 6 Optical Properties LAI=−3.8Log R (530) 1.63 [ b ]

UNOLS Airborne Ocean Science 25 May 2006 Conference SeaWiFS/MODIS

ScalingScaling upup toto RegionalRegional && Florida GlobalGlobal DistributionsDistributions Cuba Landsat

Lee Stocking •• Ratio-basedRatio-based algorithmsalgorithms areare Island transferabletransferable toto multi-multi-

spectralspectral platformsplatforms PHILLS (SeaWiFS,(SeaWiFS, MODIS)MODIS) forfor globalglobal coveragecoverage

Seagrasses

Spatial Scale (m)

UNOLS Airborne Ocean Science 25 May 2006 Conference ScalingScaling UpUp thethe HyperspectralHyperspectral Algorithm:Algorithm: WavelengthsWavelengths forfor retrievalretrieval ofof bathymetrybathymetry andand LAILAI areare compatiblecompatible withwith SeaWiFSSeaWiFS andand MODIS,MODIS, notnot LandsatLandsat

SeaWiFS MODIS Landsat PHILLS algorithm

UNOLS Airborne Ocean Science 25 May 2006 Conference Rrs: SeaWiFS vs. PHILLS

UNOLS Airborne Ocean Science 25 May 2006 Conference BahamasBahamas BanksBanks

LAI NPP (m2 m-2) (gC m-2 y-1) Land Land 0 0-100 Land 0.38 100-200 0-2 m 0.75 200-300 2-4 m 1.1 300-500 4-6 m 1.9 500-700 6-8 m >2.6 >700 > 8 m 0 100 Km

2 •• 130,000130,000 kmkm22 totaltotal areaarea •• 78,00078,000 kmkm2 seagrassseagrass •• AverageAverage depthdepth == 33 mm •• MeanMean LAILAI == 1.41.4 MaxMax LAXLAX >> 44 •• MeanMean AnnualAnnual NPPNPP == 2.82.8 xx 10101313 gCgC y y-1-1 •• 0.06%0.06% ofof totaltotal oceanocean NPPNPP

UNOLS Airborne Ocean Science 25 May 2006 Conference -- POPO44 sedimentationsedimentation raterate predictspredicts aboveabove groundground productivityproductivity ofof turtlegrassturtlegrass onon thethe GreatGreat BahamasBahamas BankBank

5

y 4.5 4 3.5 ) -1 3 d -2 2.5 V max = 4.5 ± 1 2 K m = 141 ± 90 2 (g C m R = 0.88 1.5 1 Above Ground Productivit Ground Above 0.5 0 0 500 1000 1500 2000 2500 - -2 -1 PO4 Sedimentation Rate (µmol m d )

UNOLS Airborne Ocean Science 25 May 2006 Conference MapsMaps ofof biomassbiomass distributiondistribution && productivityproductivity cancan bebe invertedinverted toto predictpredict biogeochemicalbiogeochemical dynamicsdynamics

LAI Sedimentation NPP (m2 m-2) (gC(µmol m-2 PO4y-1) m-2 d-1) Land Land 0 0-100< 37 0.38 100-20037 0.75 200-300102 1.1 300-500168 1.9 500-700465 >2.6 >700>610 0 100 Km

UNOLS Airborne Ocean Science 25 May 2006 Conference CanCan wewe useuse imagingimaging spectroscopyspectroscopy toto remotelyremotely assessassess giantgiant kelpkelp forestforest productivity?productivity? •• TheThe challengechallenge –– BiomassBiomass distributeddistributed throughthrough 1010 –– 20 20 mm ofof waterwater –– LargeLarge variationsvariations inin standingstanding biomassbiomass (intra-(intra- and and interannualinterannual variations)variations) –– WaterWater columncolumn clarityclarity oftenoften lowlow •• TheThe opportunityopportunity –– FloatingFloating canopycanopy providesprovides aa strongstrong reflectingreflecting targettarget

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UNOLS Airborne Ocean Science 25 May 2006 Conference Apr 1987

UNOLS Airborne Ocean Science 25 May 2006 Conference May 1987

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UNOLS Airborne Ocean Science 25 May 2006 Conference Jul 1987

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UNOLS Airborne Ocean Science 25 May 2006 Conference Sep 1987

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UNOLS Airborne Ocean Science 25 May 2006 Conference MayJun 19881988

UNOLS Airborne Ocean Science 25 May 2006 Conference Jul 1988

UNOLS Airborne Ocean Science 25 May 2006 Conference Aug 1988

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UNOLS Airborne Ocean Science 25 May 2006 Conference Jun 1989

UNOLS Airborne Ocean Science 25 May 2006 Conference •• PHILLSPHILLS spectraspectra areare similarsimilar toto lablab measures:measures:

0.1 Mature 0.09 Immature 0.08 Senescent 0.07 PHILLS Canopy )

-1 0.06 0.05 (sr rs

R 0.04 0.03 0.02 0.01 0 400 500 600 700 800 900 Wavelength (nm)

NDVI can provide optical estimates of kelp abundance

UNOLS Airborne Ocean Science 25 May 2006 Conference • Converting NDVI into absolute kelp abundance and productivity:

4 2.5

3.5 ) y = 13.303x -1 2 R2 = 0.9829 3 frond 2 2.5 1.5 2

1 1.5

1

Optically Determined BAI Determined Optically 0.5 y = 0.1105x

2 Frond Blade Area (m R = 0.7823 0.5

0 0 0 5 10 15 20 0 0.05 0.1 0.15 0.2 0.25 0.3 Diver Measured BAI Frond mass (kg DW)

• Optical BAI = NDVI/0.71 • True BAI = Optical BAI * 9.04 • Biomass = True BAI/13.3 • Productivity = Biomass * 14.7

UNOLS Airborne Ocean Science 25 May 2006 Conference NDVINDVI DerivedDerived DensityDensity andand ProductivityProductivity ofof GiantGiant KelpKelp

Kelp Density Kelp Productivity (Kg DW m-2) (g DW m-2 d-1) 0.54 – 0.61 8 – 9 0.62 - 0.68 9.1 – 10 0.69 – 0.75 10.1 – 11 0.75 – 0.82 11.1 – 12 0.83 – 0.88 12.1 – 13 0.89 – 0.95 13.1 - 14

UNOLS Airborne Ocean Science 25 May 2006 Conference M Kelp reflectance spectra M M M show age-dependent I I I S I S differences I I S S S S M 0.250 M S Immature Senescent S M 0.200 Mature

S 0.150

0.100 Kelp Reflectance (rel) 0.050

0.000 400 500 600 700 800 900 1000 1100 1200 1300 1400 Wavelength (nm)

UNOLS Airborne Ocean Science 25 May 2006 Conference M Age Class-dependent

M spectral slopes….. M M I I I S S I I I S S 0.18 S S M 0.17 M Senescent slope = 3x10-5 S 2 R = 0.93 S M 0.16

Immature slope = -9x10-5 S 0.15 R2 = 0.99

0.14 Reflectance (rel)

0.13 -5 Mature slope = -5x10 R2 = 0.96 0.12 750 800 850 900 Wavelength (nm)

UNOLS Airborne Ocean Science 25 May 2006 Conference M …may permit estimation of mean age M M M I I I S class (i.e. condition) of I S I I S S the kelp canopy S M S 4.E-05 M 4 8 2 S 2.E-05 Age Class = 2.78 + 1.04x10 ψ -1x10 ψ S M 0.E+00 ψ

S -2.E-05

-4.E-05

Spectral Slope, -6.E-05

-8.E-05

-1.E-04 Immature Mature Senescent Kelp Age Class

UNOLS Airborne Ocean Science 25 May 2006 Conference 0.25 Kelp Canopy 0.2

) 0.15 Resuspended -1 Sediment (sr rs

R 0.1 Green water 0.05

0 400 450 500 550 600 650 700 750 800 850 Wavelength (nm)

UNOLS Airborne Ocean Science 25 May 2006 Conference ConclusionsConclusions

•• BiomassBiomass isis aa strongstrong predictorpredictor ofof submergedsubmerged macrophytemacrophyte productivity productivity

•• BiomassBiomass ofof coastalcoastal macrophytesmacrophytes quantifiedquantified fromfrom RRrsrs cancan bebe usedused toto estimateestimate systemsystem productivityproductivity –– SubmergedSubmerged seagrassseagrass meadowsmeadows –– GiantGiant kelpkelp forestsforests

•• AnalysisAnalysis ofof spectralspectral slopesslopes fromfrom hyperspectralhyperspectral imageryimagery maymay provideprovide additionaladditional informationinformation onon ageage andand conditioncondition ofof populationspopulations thatthat produceproduce floatingfloating canopiescanopies

UNOLS Airborne Ocean Science 25 May 2006 Conference