1 / 28

Hyper s p e c t r a l

Using Imagery to Detect Submerged Aquatic Vegetation in the St. Johns River. Hyper s p e c t r a l. Courtney Hart. D. Dobberfuhl, J. Jordan. St. Johns River. 31,494 km 2 watershed blackwater system total drop of 8.5m over 490 km. Lower Basin of the SJR. Wide (~3-5 km)

hansel
Download Presentation

Hyper s p e c t r a l

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using Imagery to Detect Submerged Aquatic Vegetation in the St. Johns River Hyperspectral Courtney Hart D. Dobberfuhl, J. Jordan

  2. St. Johns River • 31,494 km2 watershed • blackwater system • total drop of 8.5m over 490 km

  3. Lower Basin of the SJR • Wide (~3-5 km) • Average depth = 3.35 m • At sea level for last 170 km

  4. Submerged Aquatic Vegetation Good indicator of ecosystem health Provides feeding and nursery grounds for many animals Adds dissolved oxygen to the river Vegetated areas support almost 3 times the invertebrates as bare areas SAV improves the habitat quality for invertebrates in both the sediment and water column

  5. Water Column Characteristics St. Johns River (It is a manatee. Really)

  6. Monitoring Methods Aerial photography - time intensive - underestimates grass beds - difficulty distinguishing among emergents, algae, and SAV - can cover a large area Groundtruthing Transects - high detail - high accuracy - labor and time intensive

  7. Hyperspectral Imagery • Mounted sensor that collects image data in hundreds of • adjacent bands • Very high spectral resolution • Includes more of the electromagnetic spectrum • Potential to distinguish algae, emergent vegetation, and SAV • species Cool colors = low reflectance Warm colors = high reflectance

  8. Bands 2003 1 2 3 4 5 6 7 89 10 12 13 14 15 16 17 18 11 Wavelength (nanometers) 19 Band

  9. Bands 2006 Wavelength (nanometers) Band 1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 11

  10. Spectral Signature Curves http://www.geog.ucsb.edu/~rivers/sa/bananal-web/specsigtypical.html

  11. Bandwidths 2003 2006 * fewer bands, narrower bandwidths

  12. Imagery Received 2003 • CASI data (compact airborne spectrographic imager) • 67 images captured Sept 13-18, 2003 • 2m spatial resolution • Swath width 1024 m • Post Processing - Radiometric correction - Across track illumination correction - Geocorrected to 2004 DOQs (1 m resolution) • Data in Standard Radiance Units (SRU)

  13. Imagery Received 2006 • CASI data (compact airborne spectrographic imager) • 87 images captured April 2006 • 1.2m spatial resolution • Swath width 615 m • Post Processing - Radiometric correction - Across track illumination correction - Atmospherically corrected using ENVI’s FLAASH processing - Geocorrected to 2004 DOQs (1 m resolution) - QA QC • Data in Standard Radiance Units (SRU)

  14. Pilot Study 2003 Looking for presence/absence of SAV Used parent swath (29-2) and 4 secondary swaths Developing methodology to apply to remaining 62 images

  15. Pilot Study 2006 Looking for presence/absence of SAV Used corresponding images from 2003 study Used 4 additional images for spatial variability Developing spectral library to apply to remaining 78 images

  16. Ground Truthing 2003 2006 • Transect sites for classification • Transect sites for QA • 107 sites identified and GPSed • 48 used for classification/59 for QA • Transect sites for QA

  17. Transects 2003 & 2006

  18. Field Verification 2006 • Homogeneous areas are identified and GPSed in the field

  19. Spectral Angle Mapper • SAM is a presence/absence classification tool using spectral matching • Physically-based spectral classification that uses an n-dimensional angle to match pixels to reference spectra • Insensitive to illumination and albedo effects • Smaller angles represent closer matches to reference spectrum • May leave unclassed pixels

  20. Spectral Signatures – Image 56b λBand Blue 1, 2, 3 Green 5 – 11 Red 12-17 NIR 18-19 2003

  21. Spectral Signatures - Image 25 λBand Blue 1, 2, 3, 4 Green 5, 6, 7, 8 Red 9, 10, 11 NIR 12-17 2006

  22. Spectral Library

  23. Classification • Giving each pixel a class designation based on its value across all bands • Supervised vs. unsupervised

  24. Classification – Image 66 Transect GT074 bedlength = 62 m Image SAV bedlength = 64.5 m (overclassed by ~ 4%)

  25. Classification – Images 18 & 19 Transect GT091 bedlength = 118 m Image SAV bedlength image 18 = 33.6 m (underclassed by ~ 72 %) Image SAV bedlength image 19 = 71 m (underclassed by ~ 39%)

  26. Things to Do • Edit spectral library and reclassify images • Perform accuracy assessment on classified images using groundtruthing transects and field verified data

  27. Acknowledgements Dr. Dean Dobberfuhl St. Johns Water Management District Dr. Jonathan Jordan Florida Dept. of Environmental Protection Pink Floyd kind contribution to the title page graphic

  28. Image Processing 2003 • Normalization • Radiance equalization stretch to reduce swath-to-swath variation • Secondary swaths are stretched to range of parent swath and shifted higher or lower according to lowest DN value • Calibration • Linear calibration equation applied to images based on bright and dark targets positioned in parent swath • Signatures from targets were measured at the time of the 2003 data collection • All swaths, including the parent, are calibrated

More Related