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EOCAP-HSI FINAL Briefing RIT Technical Activities

Explore using hyperspectral imagery to assess water quality near shorelines, in agriculture, urban areas, and more. Techniques include modeling and analysis for various water quality factors.

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EOCAP-HSI FINAL Briefing RIT Technical Activities

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  1. EOCAP-HSI FINAL BriefingRIT Technical Activities John Schott, RIT PI schott@cis.rit.edu (716)475-5170 Rolando Raqueno, RIT raqueno@cis.rit.edu(716)475-6907 http://www.cis.rit.edu/~dirs January 16-17, 2001 Hyperspectral Water Quality

  2. Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality Agriculture Urban bacteria CDOM phytoplankton macrophytes particles & algae Bottom Type A Bottom Type B Hyperspectral Water Quality

  3. Modeling Strategy • Solar Spectrum Model (MODTRAN) • Atmospheric Model (MODTRAN) • Air-Water Interface (DIRSIG/Hydrolight) • In-Water Model (HYDROMOD= • Hydrolight/OOPS + MODTRAN) • Bottom Features(HYDROMOD/DIRSIG) Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality MODTRAN ALGE Model Agriculture Urban bacteria phytoplankton CDOM macrophytes HydroLight particles & algae Bottom Type A Bottom Type B Hyperspectral Water Quality

  4. Real Image Simulated Image Long Term Approach: Integrated hybrid physical models validated and fine tuned by real imagery ALGE: Hydrodynamic Modtran Hydrolight DIRSIG difference RMS

  5. Hyperspectral Imagery Hyperspectral Water Quality

  6. Overview: Big Picture [ ] Concentrations Model Inherent Optical Properties Reflectance, r(l) Model Atmosphere Radiance, L Digital Counts Hyperspectral Water Quality

  7. Signal Sources Atmosphere to Sensor 80% 10% 10% Air/Water Transition Water/Air Transition In Water

  8. Remote Sensing Water Quality Tool: HydroMod Hyperspectral Water Quality

  9. absorption IOPs Water Absorption Total suspended material DOC Chlor a Wavelength Hyperspectral Water Quality

  10. Normalized Scattering Distribution of theFournier-Forand Phase Function with Parameters (nu,n)

  11. Example LUT Entries [C]=13 [SM]=0 [CDOM]=0 [C]=0 [SM]=0 [CDOM]=0 [C]=0 [SM]=0 [CDOM]=50 Hyperspectral Water Quality

  12. Look Up Table LUT j [ C ] k [ CDOM ] i [ SM ] Each entry in the LUT [i.e. LUT (i,j,k)] corresponds to a particular output of the Hydrolight code in the form of a spectral vector. These may be in terms of Lλ(h), -Rλ0 or +Rλ0. Hyperspectral Water Quality

  13. Simple Fitting ST truth data λ TRUE min [(ST - SP)2 ] FALSE Final [CHL] [CDOM] [TSS] SQ Error [CHL] [TSS] [CDOM] LUT j [ CDOM ] [ C ] k i [ SM ] Sp predicted [CHL] [CDOM] [TSS]

  14. Squared Error Interpolated LUT values observation Squared Error = Σ (RLUT - Robs)2 Iterate using a downhill simplex (Amoeba) algorithm to minimize squared error term. Hyperspectral Water Quality

  15. C C C C Trilinear Interpolation SMi,Cj+1,CDOMk SMi+1,Cj+1,CDOMk SMl,Cm,CDOMk SMi,Cm,CDOMk SMi+1,Cm,CDOMk Smi+1,Cj,CDOMk SMi,Cj,CDOMk SMl,Cm,CDOMn CDOM SMi,Cj+1,CDOMk+1 SMi+1,Cj+1,CDOMk+1 SMl,Cm,CDOMk+1 SMi,Cm,CDOMk+1 SMi+1,Cm,CDOMk+1 SMi,Cj,CDOMk+1 Smi+1,Cj,CDOMk+1 SM

  16. Sample Comparison of Spectral Curve Fit CHL=6.3, TSS=2.0, CDOM=4.8 CHL=0.0006, TSS=3.09, CDOM=5.7 ASD Spectra Hyperspectral Water Quality

  17. Calibrating AVIRIS Images Figure 1: AVIRIS and Ground Truth Estimates for HYDROMOD Based ELM Low Signal Pixel High Signal Pixel Hyperspectral Water Quality

  18. Assume cloud R » 0.9 Estimate • water constituents in clear water • (use ground truth if available) • to predict R using HydroMod for • the specific conditions under study • Perform Linear transform of • Radiance to reflectance, L=mR+b • NB accounts not only for atmos- • phere, but for any first order • model-atmosphere-sensor mismatch ELMIncluding Model correction Hyperspectral Water Quality

  19. Lake Ontario 0.06 Reflectance 0.04 Long Pond 0.02 0.06 400 500 600 700 Reflectance Wavelength 0.04 0.02 400 500 600 700 Wavelength Cranberry Pond 0.06 Braddock Bay Reflectance 0.06 0.04 Reflectance 0.04 0.02 400 500 600 700 0.02 Wavelength 400 500 600 700 Wavelength After ELM Calibration AMOEBA FIT AMOEBA FIT AMOEBA FIT AMOEBA FIT Hyperspectral Water Quality

  20. Long Pond ELM Control Point Reflectance Simulated by HydroMod using Lab Measured Concentrations CHL = 62.96 microgram/L TSS = 22.44 milligram/L CDOM = 6.12 scalar Hyperspectral Water Quality

  21. Assume cloud R » 0.9 Estimate • water constituents in clear water • (use ground truth if available) • to predict R using HydroMod for • the specific conditions under study • Perform Linear transform of • Radiance to reflectance, L=mR+b • NB accounts not only for atmos- • phere, but for any first order • model-atmosphere-sensor mismatch ELMIncluding Model correction Hyperspectral Water Quality

  22. Atmospheric Compensation Improvement with Addition of Ground Truth Data Point Hyperspectral Water Quality

  23. Weighted Fitting ST truth data Final [CHL] [CDOM] [TSS] Weighting function MIN [(ST - SP)2 ] SQ Error FALSE TRUE LUT j [ CDOM ] [ C ] k i [ SM ] Sp predicted [CHL] [CDOM] [TSS]

  24. Braddock Bay Cranberry Pond Long Pond Buck Pond Round Pond Russell Station AVIRIS (Color Infrared) May 20, 1999 Northwest Ponds of Rochester EmbaymentLake Ontario Lake Ontario Bathymetry (feet) Hyperspectral Water Quality

  25. to quantify multiple water quality parameters (chlorophyll, suspended solids, & yellowing organics). Hyperspectral data: solar glint AVIRIS Flightlines May 20, 1999 11:45 AM Hyperspectral Water Quality Digital Imaging and Remote Sensing Laboratory

  26. May 20, 1999 AVIRIS-MISI Flight AVIRIS Study Area Hyperspectral Water Quality

  27. Phenomenology/Ground Truth in-water optical properties MISI underflight image of Ginna Power Plant spectral measurements field support • Reference: • Schott, Barsi, de Alwis, Raqueno. “Application of LANDSAT 7 to Great Lakes Water Resource Assessment,” presented at the International Association for Great Lakes Research 43rd Conference on Great Lakes and St. Lawrence River Research, Cornwall, Ontario, May, 2000. • Schott, Gallagher, Nordgren, Sanders, Barsi. “Radiometric calibration procedures and performance for the Modular Imaging Spectrometer Instrument (MISI).” Proceedings of the Earth Intl. Airborne Remote Sensing Conference, ERIM, 1999. • Schott, Nordgren, Miller, Barsi. “Improved mapping of thermal bar phenomena using remote sensing,” presented at the International Association for Great Lakes Research (IAGLR) Annual Conference, McMaster University, Hamilton, Ontario, May 1998. Digital Imaging and Remote Sensing Laboratory

  28. Aviris GT Hyperspectral Water Quality

  29. CHL Ground Truth Comparison RMS = 11.6 mg/m3 18% of [CHL] range Hyperspectral Water Quality

  30. TSS Ground Truth Comparison Glint Area RMS = 4.0 g/m3 17.8% of [TSS] range Hyperspectral Water Quality

  31. CDOM Ground Truth Comparison Glint Area RMS = 2.2 [scalar] 17.2% of [CDOM] range Hyperspectral Water Quality

  32. Evidence of solar glint slicks AVIRIS Rochester Embayment May 20, 1999 Hyperspectral Water Quality

  33. Scalar Concentration of CDOM CDOM(350 nm)=5.0 CDOM(350 nm)=1.0 CDOM(350 nm)=0.2 Hyperspectral Water Quality

  34. CHL Model Prediction Meansvs. Ground Truth Hyperspectral Water Quality

  35. CDOM Model Prediction Meansvs. Ground Truth Hyperspectral Water Quality

  36. TSS Model Prediction Means vs. Ground Truth Hyperspectral Water Quality

  37. Lake Bottom at Different Spatial Resolutions AVIRIS: 20 meter pixels Rochester Embayment May 20, 1999

  38. Lake Bottom at Different Spatial Resolutions Region: Lake Ontario North of Irondequoit Bay AVIRIS with 20m pixels MISI with 9ft pixels Hyperspectral Water Quality

  39. Hyperspectral Imaging for Bottom Type Classification and Water Depth Determination M.S. Thesis Defense Nikole Wilson 10 Aug 2000

  40. Depth Varies Linearly Case 1 constant bottom Philpot’s synthetic data a|| has a parallel relationship with direction of changing depth Depth varies linearly X at 650 nm X at 550 nm Hyperspectral Water Quality

  41. Case 2 : Varied depth, bottom type Data form separate but parallel clusters in linearized space Clusters separated in linearized space by a distance relating to differences in bottom reflectances X at 650 nm X at 550 nm Hyperspectral Water Quality

  42. Data CollectionGinna Bottoms Redrock with algae Gray rock 1 Red rock Light gray rock Yellow rock Gray rock 2 Hyperspectral Water Quality

  43. Ontario Beach Qualitative Results Depth Bottom 1 2 Rock 2 1.6 Sand 3 2.4 Rock 4 2.2 Sand Depth 4 3 2 1 Picking up different bottom type

  44. Lake Bottom at Different Spatial Resolutions Lake Ontario at Cranberry Pond Lake Ontario at Russell Station solar glint MISI with 2ft pixels MISI with 4ft pixels

  45. Lake Ontario Bathymetry

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