August 5 – 7, 2008NASA Habitats Workshop 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

August 5 – 7, 2008NASA Habitats Workshop 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

August 5 – 7, 2008NASA Habitats Workshop Quantitative Remote Sensing in Optically Shallow Habitats Also Requires Bathymetry and knowledge of water column IOPs and AOPs to retrieve R b Quantitative relationships between R b 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 R b Quantitative relationships between R b and biomass for benthic and floating targets (kelp canopy, SAV, rocky reefs, corals, sand and mud)

August 5 – 7, 2008NASA Habitats Workshop 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

August 5 – 7, 2008NASA Habitats Workshop The 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

August 5 – 7, 2008NASA Habitats Workshop The General Approach for SAV:

August 5 – 7, 2008NASA Habitats Workshop We can obtain bathymetry from R rs to 7 m in clear Bahamian water using R 555 :R 670 band ratios Dierssen et al and to 2 m in turbid coastal waters based on logarithmic intensity of R 810 Bachman et al. 2008, Hill et al. in prep

August 5 – 7, 2008NASA Habitats Workshop And in combination with knowledge of water column optical properties, retrieve R b from Dierssen et al from Hill et al. in prep

August 5 – 7, 2008NASA Habitats Workshop Knowledge of R b provides a way to retrieve LAI…….. from Dierssen et al. 2003

August 5 – 7, 2008NASA Habitats Workshop ……that can be mapped across the submarine landscape as biomass and productivity from Dierssen et al from Hill et al. in prep

August 5 – 7, 2008NASA Habitats Workshop 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

August 5 – 7, 2008NASA Habitats Workshop Easy to build reliable automated masks for kelp canopy analysis: RGB image of Campus Point

August 5 – 7, 2008NASA Habitats Workshop Easy to build reliable automated masks for kelp canopy analysis Land and Water Masked using NIR Spectral slope

August 5 – 7, 2008NASA Habitats Workshop Easy to build reliable automated masks for kelp canopy analysis NDVI Map of Giant Kelp Canopy

August 5 – 7, 2008NASA Habitats Workshop 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 Macrocystis pyrifera (giant kelp) NPP = 14.7 x Standing Crop

August 5 – 7, 2008NASA Habitats Workshop Carmel Bay Santa Barbara Coastal LTER

August 5 – 7, 2008NASA Habitats Workshop NDVI Derived Density and Productivity of Giant Kelp: Carmel Bay November Km of irregular coastline 1.7 Km 2 of kelp canopy Biomass: 1400 metric tons dry kelp biomass NPP: 19 metric tons dry biomass d Km of irregular coastline 1.7 Km 2 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 – – – – – 0.95 Kelp Productivity (g DW m -2 d -1 ) 8 – – – – –

August 5 – 7, 2008NASA Habitats Workshop NDVI Derived Density and Productivity of Giant Kelp: Santa Barbara Coastal LTER Region March 2006 Kelp Density (Kg DW m -2 ) 0.54 – – – – – 0.95 Kelp Productivity (g DW m -2 d -1 ) 8 – – – – – Km mostly linear coastline 1.9 Km 2 kelp canopy Biomass: 1100 metric tons NPP: 17 metric tons per day 35 Km mostly linear coastline 1.9 Km 2 kelp canopy Biomass: 1100 metric tons NPP: 17 metric tons per day

August 5 – 7, 2008NASA Habitats Workshop Carmel Beach November 2004 Campus 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

August 5 – 7, 2008NASA Habitats Workshop Challenges 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

August 5 – 7, 2008NASA Habitats Workshop 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?

August 5 – 7, 2008NASA Habitats Workshop 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

August 5 – 7, 2008NASA Habitats Workshop Conclusions 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