Spectral Requirements for Resolving Shallow Water Information Products W. Paul Bissett and David D. R. Kohler

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 2 Coastal Ocean Imaging Spectroscopy Phytoplankton CDOM-Rich Water Suspended Sediments Benthic Plants 1/Kd Optically-ShallowOptically-Deep Micro-bubbles Whitecaps Shallow Ocean Floor

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 3 Spectral Resolution Depth = 6.5 m IOP = Case 1 Bottom = soft coral Depth = 13 m IOP = Pure H 2 0 Bottom = sponge

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 4

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 5 Why (Hyper) Spectral? Discriminate analysis suggests independent vectors of information in hyperspectral data stream. But which bands are a priori the rights one to measure? Additional bands give greater degrees of freedom with which to attempt more complicated inversion algorithms to retrieve depth-dependent IOPs, which include products such as bathymetry, in situ IOPs, bottom classification.

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 6 Joint HSI/LIDAR Data Set

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION x 10 6 Total Points 39% of the LUT results are +/- 10% 73% of the LUT results are +/- 25% A normal distribution is plotted for reference. The LUT results have less error than would be expected for a normally distributed population.

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 8

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 9 NOAA/Florida Marine Research Institute Benthic Assessment of the Florida Keys

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 10 Comparison of LUT and NOAA Bottom Types 16 m resolution Unknown pixels removed from both data sets. NOAA 1992 FERI LUT 2002 Sand Seagrass Coral Hardbottom

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 11 Error Matrix of LUT/NOAA Comparison NOAA Bottom TypeSandCoralSeagrassHardbottomLUT Total LUT % Error Producer’s Accuracy LUT Sand %48.38% Coral %0.98% Seagrass %85.25% Hardbottom %13.88% Total Instances NOAA Total NOAA Error39.27%99.80%29.38%63.01% User’s Accuracy 60.73%0.20%70.62%36.99% Total Classification Accuracy 61.52%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 12 Evidence of Algal Overgrowth in the Florida Keys Typical algal overgrowth on coral rubble in Florida. Photo courtesy of U.S. Department of the Interior, U.S. Geological Survey, Center for Coastal Geology Caulerpa brachypus is a nonnative macroalgae that has invaded Florida's coral reefs. Photo courtesy of Harbor Branch Oceanographic Institution, Inc.

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 13 Simulating Impacts of Sensor Design Created 216K different hyperspectral Rrs(λ) spectra with various water depths, IOPs, and bottom reflectance spectra. These spectra were used to create reduced resolution spectra of 5, 15, 25, and 35 nm continuous spectra to test impact of sensor spectral resolution and radiometric discretization.

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 14 Spectral Resolution in Shallow Water (OC4) 9,754 Hydrolight runs with various depths (0.1 – 20 meters), IOPs (25 with chl = 0.0. to 0.2), bottom reflectances (60 different spectra). Max[Rrs(443), Rrs(490), Rrs(510)] /Rrs(555) = 1.0 ± OC4 Chlorophyll a = 2.32 mg/m 3

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 15 Radiometric Discretization Depth = 25 m IOP = Case 1 / chl of 2 mg/m3 Bottom = clean sand Dynamic Range was set so that the sensor would not saturate over dry sand. However, the Rrs does not include atmosphere. Inclusion of the atmosphere would dramatically worsen this discretization error, since most of the signal is from the atmosphere, and the bit resolution of the dark signals would thereby be lessened.

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 16 Requirements Analysis 216k simulated shallow water reflectance spectra Resampled each to represent different combinations of sensor bit levels (8, 10, 12, and 14) and band widths (5, 15, 25, and 30) Found the absolute spectral difference from the original and resampled spectra to determine the amount of information lost due to the configuration –1) Σ {absolute[ Rrs(λ) – Rrs_resample(λ)]} –2) Average (absolute{ [ Rrs(λ) – Rrs_resample(λ)] / Rrs(λ) } )

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 17 Histogram of Average Relative Error (Focus on Discretization)

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 18 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per Bit Level that fell beyond 50% relative error: 14 bit – 0.00% 12 bit – 0.02% 10 bit – 10.92% 8 bit – 41.92%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 19 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per Bit Level that fell beyond 50% relative error: 14 bit – 0.01% 12 bit – 0.02% 10 bit – 6.51% 8 bit – 34.97%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 20 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per Bit Level that fell beyond 50% relative error: 14 bit – 0.45% 12 bit – 0.56% 10 bit – 7.82% 8 bit – 36.55%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 21 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per Bit Level that fell beyond 50% relative error: 14 bit – 0.96% 12 bit – 1.89% 10 bit – 8.23% 8 bit – 35.28%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 22 Histogram of Average Relative Error (Focus on Band Width)

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 23 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per width that fell beyond 50% relative error: 35 nm – 0.96% 25 nm– 0.45% 15 nm– 0.01% 5 nm– 0.00%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 24 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per width that fell beyond 50% relative error: 35 nm – 1.89% 25 nm– 0.56% 15 nm– 0.02% 5 nm– 0.02%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 25 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per width that fell beyond 50% relative error: 35 nm – 8.23% 25 nm– 7.82% 15 nm– 6.51% 5 nm– 10.92%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 26 Contours of 3-D Avg Rel Err Surface (Viewed from Top) Percentage of counts per width that fell beyond 50% relative error: 35 nm – 35.28% 25 nm– 36.55% 15 nm– 34.97% 5 nm– 41.92%

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 27 HES-CW Channel Specifications

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 28 Summary Imaging spectroscopy can address the requirement to simultaneous solve for bathymetry, IOPs, and bottom reflectance in the shallow water environment. This will allow for the production of robust image products, including water clarity, extreme phytoplankton concentrations (red and brown tides), resuspended sediments, river discharges. This analysis suggests there may be little difference between 12 and 14 bits discretization, and 5 and 10 nm band width. –However, this analysis does not include TOA reflectance, which will reduce the bit resolution of the Rrs. –It also does not include noise, which will impact the simulated results.

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 29 Summary The real issue are how the impacts of the requirements affect image products. These products require new algorithms, which to date have not been completely developed and tested (cart and horse problem). –One of the issues not addressed in this study is that average percentage errors are not the same as reduce spectral information. The requirements for resolution, calibration, and SNR should be driven by product generation. Real high spatial and spectral data sets exist, with future collections planned, that may be used to develop these products.

Copyright © 2004 Florida Environmental Research Institute Unpublished work - all rights reserved NOAA COAST Workshop September 29,, 2004 UNCLASSIFED PROPRIETARY INFORMATION 30 Total Kelp : 3.93 sq km Total Kelp : 0.55 sq km Total Kelp : 5.73 sq km Total Kelp : 0.76 sq km Total Kelp : 2.99 sq km Total Kelp : 1.11 sq km Monterey Bay Morro Bay San Luis Bay Big Sur CICORE Central California 2002