Table of ContentsEOCAP-HSI FINAL Briefing RIT Technical ActivitiesOutline Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality Long Term Approach: Integrated hybrid physical models validated and fine tuned by real imagery Laurentian Great Lakes Thermal Bar Thermal Bar Process Ontario Mid-lake Temperature Sections LANDSAT Band6 May 11, 1992 Landsat June 23, 1996 Hyperspectral Imagery Objective and Product Detectable Constituents Modular Imaging Spectrometer Instrument Commercial / Public Interest: Commercial / Public Interest: Monroe County Commercial / Public Interest: Great Lakes Protection Fund Commercial / Public Interest: Finger Lakes - Lake Ontario Watershed Protection Alliance Commercial / Public Interest: Commercial / Public Interest: US Department of Agriculture Commercial / Public Interest: Overview: Big Picture Signal Sources Remote Sensing Water Quality Tool: HydroMod absorption IOPs Normalized Scattering Distribution of the Fournier-Forand Phase Function with Parameters (nu,n) Example LUT Entries Look Up Table Simple Fitting Squared Error Trilinear Interpolation Sample Comparison of Spectral Curve Fit Calibrating AVIRIS Images ELM Including Model correction After ELM Calibration Long Pond ELM Control Point ELM Including Model correction Atmospheric Compensation Improvement with Addition of Ground Truth Data Point Piecewise Fitting Spectral Weighting by Component Type Weighted Fitting Northwest Ponds of Rochester Embayment Lake Ontario Hyperspectral data: May 20, 1999 AVIRIS-MISI Flight Phenomenology/Ground Truth Aviris GT CHL Ground Truth Comparison TSS Ground Truth Comparison CDOM Ground Truth Comparison Evidence of solar glint Scalar Concentration of CDOM CHL Model Prediction Means vs. Ground Truth CDOM Model Prediction Means vs. Ground Truth TSS Model Prediction Means vs. Ground Truth Lake Bottom at Different Spatial Resolutions Lake Bottom at Different Spatial Resolutions Hyperspectral Imaging for Bottom Type Classification and Water Depth Determination Depth Varies Linearly Case 2 : Varied depth, bottom type Data Collection Ginna Bottoms Ontario Beach Qualitative Results Lake Bottom at Different Spatial Resolutions Lake Ontario Bathymetry Hyperspectral Requirements Hyperspectral Requirements Hyperspectral Requirements Hyperspectral Requirements Hyperspectral Technology Gaps Hyperspectral Technology Gaps Hyperspectral Technology Gaps Status Payoff Value |
Author: student
Email: raqueno@cis.rit.edu Home Page: www.cis.rit.edu/~dirs |