1. June 2007SBC LTER Annual Meeting Bio-Optical Assessment of Giant kelp Dynamics Richard C. Zimmerman Old Dominion University Norfolk VA W. Paul Bissett Florida Environmental Research Institute Tampa FL Daniel C. Reed University of California Santa Barbara, CA Richard C. Zimmerman Old Dominion University Norfolk VA W. Paul Bissett Florida Environmental Research Institute Tampa FL Daniel C. Reed University of California Santa Barbara, CA

2. June 2007SBC LTER Annual Meeting Biomass explains most variation in primary productivity Macrocystis pyrifera (giant kelp) NPP = 14.7 x Standing Crop Can BIOMASS be determined remotely?

3. June 2007SBC LTER Annual Meeting Can we use imaging spectroscopy to remotely assess giant kelp forest productivity? The challenge –Biomass distributed through 10 – 20 m of water –Large variations in standing biomass (intra- and interannual variations) –Water column clarity often low The opportunity –Floating canopy provides a strong reflecting target The challenge –Biomass distributed through 10 – 20 m of water –Large variations in standing biomass (intra- and interannual variations) –Water column clarity often low The opportunity –Floating canopy provides a strong reflecting target

4. June 2007SBC LTER Annual Meeting PHILLS spectra are similar to lab measures: NDVI can provide optical estimates of kelp abundance

5. June 2007SBC LTER Annual Meeting 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

6. June 2007SBC LTER Annual Meeting NDVI Derived Density and Productivity of Giant Kelp Kelp Density (Kg DW m -2 ) 0.54 – 0.61 0.62 - 0.68 0.69 – 0.75 0.75 – 0.82 0.83 – 0.88 0.89 – 0.95 Kelp Productivity (g DW m -2 d -1 ) 8 – 9 9.1 – 10 10.1 – 11 11.1 – 12 12.1 – 13 13.1 - 14

7. June 2007SBC LTER Annual Meeting M M M M I I I I M M M I I S S S S S S S S S Kelp reflectance spectra show age-dependent differences

8. June 2007SBC LTER Annual Meeting M M M M I I I I M M M I I S S S S S S S S S Age Class-dependent spectral slopes…..

9. June 2007SBC LTER Annual Meeting M M M M I I I I M M M I I S S S S S S S S S …may permit estimation of mean age class (i.e. condition) of the kelp canopy

10. June 2007SBC LTER Annual Meeting Conclusions Biomass is a strong predictor of submerged macrophyte productivity Biomass of coastal macrophytes quantified from R rs can be used to estimate system productivity –Submerged seagrass meadows –Giant kelp forests Analysis of spectral slopes from hyperspectral imagery may provide additional information on age and condition of populations that produce floating canopies Biomass is a strong predictor of submerged macrophyte productivity Biomass of coastal macrophytes quantified from R rs can be used to estimate system productivity –Submerged seagrass meadows –Giant kelp forests Analysis of spectral slopes from hyperspectral imagery may provide additional information on age and condition of populations that produce floating canopies