August 5 – 7, 2008NASA Habitats Workshop Optical Properties and Quantitative Remote Sensing of...

August 5 – 7, 2008NASA Habitats Workshop

Optical Properties and Quantitative Remote Sensing of Kelp Forest and Seagrass

Habitats

Optical Properties and Quantitative Remote Sensing of Kelp Forest and Seagrass

Habitats Richard C. Zimmerman - Bio-Optics Research Group, Dept. Ocean, Earth and Atmospheric Sciences, Old Dominion University, Norfolk VA

Victoria J. Hill - Bio-Optics Research Group, Dept. Ocean, Earth and Atmospheric Sciences, Old Dominion University, Norfolk VA

W. Paul Bissett - Florida Environmental Research Institute, Tampa FL

Heidi M. Dierssen - Coastal Ocean Laboratory for Optics and Remote Sensing, University of Connecticut, Avery Point CT

Dave Siegel - Institute for Computational Earth System Science, Department of Geography, University of California, Santa Barbara, CA

Richard C. Zimmerman - Bio-Optics Research Group, Dept. Ocean, Earth and Atmospheric Sciences, Old Dominion University, Norfolk VA

Victoria J. Hill - Bio-Optics Research Group, Dept. Ocean, Earth and Atmospheric Sciences, Old Dominion University, Norfolk VA

W. Paul Bissett - Florida Environmental Research Institute, Tampa FL

Heidi M. Dierssen - Coastal Ocean Laboratory for Optics and Remote Sensing, University of Connecticut, Avery Point CT

Dave Siegel - Institute for Computational Earth System Science, Department of Geography, University of California, Santa Barbara, CA


Quantitative Remote Sensing in Optically Deep Habitats is Complicated Enough:

Quantitative Remote Sensing in Optically Deep Habitats is Complicated Enough:

• Water is a dark target• Variable constituent

effects (Chl a, TSM, CDOM) on IOP’s and AOP’s

– composition of matter– particle size

• Reliable retrieval algorithms

– require local calibration, esp. in coastal waters

– remains an active area of inquiry

• Water is a dark target• Variable constituent

effects (Chl a, TSM, CDOM) on IOP’s and AOP’s

– composition of matter– particle size

• Reliable retrieval algorithms

– require local calibration, esp. in coastal waters

– remains an active area of inquiry

Absorption and scattering by water


Quantitative Remote Sensing in Optically Shallow Habitats Also Requires

Quantitative Remote Sensing in Optically Shallow Habitats Also Requires

• Bathymetry and knowledge of water column IOPs and AOPs to retrieve Rb

• Quantitative relationships between Rb and biomass for benthic and floating targets (kelp canopy, SAV, rocky reefs, corals, sand and mud)

• Bathymetry and knowledge of water column IOPs and AOPs to retrieve Rb

• Quantitative relationships between Rb and biomass for benthic and floating targets (kelp canopy, SAV, rocky reefs, corals, sand and mud)Variable depth

Floating surface canopy

Submerged aquatic vegetation

Absorption and scatter ing by water


How can we use imaging spectroscopy to remotely quantify abundance and productivity of giant

kelp forests and seagrass meadows?

How can we use imaging spectroscopy to remotely quantify abundance and productivity of giant

kelp forests and seagrass meadows?

• The need– Better understand and manage the

dynamics of macrophyte “engineers” that define ecosystems

• The challenge– Biomass distributed through 10 – 20 m

of water– Large variations in standing biomass

(intra- and interannual variations)– Water column clarity often low– Distribution is patchy, small and close

to land

• The need– Better understand and manage the

dynamics of macrophyte “engineers” that define ecosystems

• The challenge– Biomass distributed through 10 – 20 m

of water– Large variations in standing biomass

(intra- and interannual variations)– Water column clarity often low– Distribution is patchy, small and close

to land


The OpportunityThe Opportunity

• Floating kelp canopy provides a strong reflecting target similar to terrestrial vegetation

• Seagrass light requirements limit depth distributions to above 10% isolume ( < 2.3) – within the visible range of remote sensing

• Floating kelp canopy provides a strong reflecting target similar to terrestrial vegetation

• Seagrass light requirements limit depth distributions to above 10% isolume ( < 2.3) – within the visible range of remote sensing


The General Approach for SAV:The General Approach for SAV:


We can obtain bathymetry from Rrs

We can obtain bathymetry from Rrs

to 7 m in clear Bahamian water using R555:R670 band ratiosDierssen et al. 2003

and to 2 m in turbid coastal waters based on logarithmic intensity of R810

Bachman et al. 2008, Hill et al. in prep


And in combination with knowledge of water column optical

properties, retrieve Rb

And in combination with knowledge of water column optical

properties, retrieve Rb

from Dierssen et al. 2003 from Hill et al. in prep


Knowledge of Rb provides a way to retrieve LAI……..

Knowledge of Rb provides a way to retrieve LAI……..

from Dierssen et al. 2003


……that can be mapped across the submarine landscape as biomass and productivity

……that can be mapped across the submarine landscape as biomass and productivity

from Dierssen et al. 2003 from Hill et al. in prep


• Spectra of floating kelp canopies obtained from airborne imaging spectrometers are similar to lab measures of individual kelp blade reflectances:

• Spectra of floating kelp canopies obtained from airborne imaging spectrometers are similar to lab measures of individual kelp blade reflectances:

Strong NIR signal is brighter than open water but darker than land

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

400 500 600 700 800 900

Wavelength (nm)R

rs (

sr-1

)

Mature

Immature

Senescent

PHILLS Canopy


Easy to build reliable automated masks for kelp canopy analysis:

RGB image of Campus Point

Easy to build reliable automated masks for kelp canopy analysis:

RGB image of Campus Point


Easy to build reliable automated masks for kelp canopy analysis

Land and Water Masked using NIR Spectral slope


Land and Water Masked using NIR Spectral slope



NDVI Map of Giant Kelp Canopy


NDVI Map of Giant Kelp Canopy


• Converting NDVI into absolute kelp abundance and productivity:

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

y = 0.1105x

R2 = 0.7823

0

0.5

1

1.5

2

2.5

0 5 10 15 20

Diver Measured BAI

Op

tica

lly D

ete

rmin

ed

BA

I

y = 13.303x

R2 = 0.9829

0

0.5

1

1.5

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2.5

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3.5

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0 0.05 0.1 0.15 0.2 0.25 0.3

Frond mass (kg DW)

Fro

nd B

lade

Are

a (m

2 fro

nd-1

)

Standing crop (kg m-2)0.0 0.5 1.0 1.5 2.0

NP

P (

g d

ry m

ass m-2d-1

)

0

10

20

30

40R2 = 0.623P < 0.0001

a.

Macrocystis pyrifera (giant kelp) NPP = 14.7 x Standing Crop


Carmel Bay

Santa Barbara Coastal LTER


NDVI Derived Density and Productivity of Giant Kelp: Carmel Bay November 2004

NDVI Derived Density and Productivity of Giant Kelp: Carmel Bay November 2004

• 15 Km of irregular coastline

• 1.7 Km2 of kelp canopy

• Biomass: 1400 metric tons dry kelp biomass

• NPP: 19 metric tons dry biomass d-1

• 15 Km of irregular coastline

• 1.7 Km2 of kelp canopy

• Biomass: 1400 metric tons dry kelp biomass

• NPP: 19 metric tons dry biomass d-1

Kelp Density(Kg DW m-2)0.54 – 0.610.62 - 0.680.69 – 0.750.75 – 0.820.83 – 0.880.89 – 0.95

Kelp Productivity(g DW m-2 d-1)

8 – 99.1 – 10

10.1 – 1111.1 – 1212.1 – 1313.1 - 14


NDVI Derived Density and Productivity of Giant Kelp: Santa Barbara Coastal LTER

Region March 2006

NDVI Derived Density and Productivity of Giant Kelp: Santa Barbara Coastal LTER

Region March 2006

Kelp Density(Kg DW m-2)0.54 – 0.610.62 - 0.680.69 – 0.750.75 – 0.820.83 – 0.880.89 – 0.95

Kelp Productivity(g DW m-2 d-1)

8 – 99.1 – 10

10.1 – 1111.1 – 1212.1 – 1313.1 - 14

• 35 Km mostly linear coastline

• 1.9 Km2 kelp canopy

• Biomass: 1100 metric tons

• NPP: 17 metric tons per day

• 35 Km mostly linear coastline

• 1.9 Km2 kelp canopy

• Biomass: 1100 metric tons

• NPP: 17 metric tons per day


Carmel Beach November 2004Campus Point, Santa Barbara March 2006

• Temporal and spatial differences may reflect:– ocean forcing– seasonality– biogeographic provinces

• Requires routine observations

• Temporal and spatial differences may reflect:– ocean forcing– seasonality– biogeographic provinces

• Requires routine observations


Challenges to Routine Use of High Resolution Imaging SpectroscopyChallenges to Routine Use of High Resolution Imaging Spectroscopy

• High spatial and spectral resolution currently available only from airborne sensors, not satellites

• Mobilization costs are considerable for airborne missions

• Weather is ALWAYS a factor

• High spatial and spectral resolution currently available only from airborne sensors, not satellites

• Mobilization costs are considerable for airborne missions

• Weather is ALWAYS a factor


Can we employ coarser spectral and spatial resolution instruments

on orbit?

Can we employ coarser spectral and spatial resolution instruments

on orbit?• More reliable temporal coverage• Minimal mobilization costs• NDVI only requires 2 bands (vis & NIR)

– how narrow?

• Some orbiting multi-spectral imagers (e.g. SPOT) provide up to 10 m spatial resolution but optical bands are 70 – 80 nm wide– Is that good enough?

• More reliable temporal coverage• Minimal mobilization costs• NDVI only requires 2 bands (vis & NIR)

– how narrow?

• Some orbiting multi-spectral imagers (e.g. SPOT) provide up to 10 m spatial resolution but optical bands are 70 – 80 nm wide– Is that good enough?


Area and Productivity Estimates Depend on Spatial Resolution

Area and Productivity Estimates Depend on Spatial Resolution

• Bias increases as spatial resolution decreases

• Not a linear function of spatial resolution

• Resolution “classes” result from– inherent scale of kelp

patches– spectral averaging as

pixel size increases

• Bias increases as spatial resolution decreases

• Not a linear function of spatial resolution

• Resolution “classes” result from– inherent scale of kelp

patches– spectral averaging as

pixel size increases

1

1.5

2

2.5

3

0 50 100 150 200 250

Spatial Resolution (m2)

Nor

mal

ized

Bia

s

Area

Biomass


ConclusionsConclusions• Remote sensing reflectance of submerged and floating coastal

macrophytes can be used to estimate spatial and temporal patterns of biomass and system productivity

• Remote sensing may be the only way to provide accurate data at the ecosystem scale needed for research and management

• Quantitative results are biased by instrument resolution

• Airborne instruments can provide the required resolution but routine deployment is challenging

• Orbiting instruments may be useful if we can understand impacts of resolution on biomass and productivity estimates

• Remote sensing reflectance of submerged and floating coastal macrophytes can be used to estimate spatial and temporal patterns of biomass and system productivity

• Remote sensing may be the only way to provide accurate data at the ecosystem scale needed for research and management

• Quantitative results are biased by instrument resolution

• Airborne instruments can provide the required resolution but routine deployment is challenging

• Orbiting instruments may be useful if we can understand impacts of resolution on biomass and productivity estimates

August 5 – 7, 2008NASA Habitats Workshop Optical Properties and Quantitative Remote Sensing of...

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