Benthic Mapping using Remote Sensing Data

1. Benthic Mapping using Remote Sensing Data J. Cho Department of Integrated Environmental Science Bethune-Cookman University Daytona Beach, FL

2. Elements of Remote Sensingdata acquisition and analysis Camera system sun

3. Electromagnetic (EM) Radiation • Consists of • An electrical field (E) • A magnetic field (M) • Both fields travel at the speed of light (c) • c = vλ • c: 3 x 108 m/sec • v: frequency • λ: wavelength http://www.astronomynotes.com/light/s3.htm

4. EM Spectrum http://en.wikipedia.org/wiki/File:EM_spectrum.svg

5. (Jensen, 2007)

7. http://ian.umces.edu/learn/modulepopup/barrier_islands_and_sea_level_rise/a_closer_look_at_seagrasseshttp://ian.umces.edu/learn/modulepopup/barrier_islands_and_sea_level_rise/a_closer_look_at_seagrasses

8. 535 nm 760 nm

9. Spectral Reflectance Characteristics of Vegetationand Conventional Vegetation Indices Image source: http://extnasa.usu.edu/on_target/images_independent/nir_vegetation_graph.gif

10. Spectral reflectance of Submerged Aquatic Vegetation (SAV) at Varying Depths

11. I. Water Correction Algorithm Development

12. Rationale and Objective • Remote detection of benthic features (i.e. seagrass) has been limited because of numerous factors including the influence of the water column • To develop algorithms that reduce water effects to improve remote detection and classification of shallow underwater features and seagrasses

13. Controlled experiments Selection of critical bands and data Reduction & compression Algorithm development Pure water effect removal Hyperspectral images corrected for conventional atmospheric distortions Water color, turbidity effects reduction Depth effect adjustment Images with enhanced benthic features

14. Water Correction Algorithm Development for Benthic Mapping L = Ls +Lv+ La + Lr + Lb L Lr Ls Lv Water La Lb

15. * water absorption can be derived from the following way 1 x (1-Aw/2) x Rr x (1-Aw/2) + Rw= Rm Aw and Rw are the functions of water depth * For any bottom panel, Rr can be directly measured Algorithm to model water effects

16. Pulley System String String 1.2 m 82 cm 53cm (Washington et al. 2012)

20. Reflectance(corrected) = f (ref(measured), depth)1 x (1-Aw/2) x Rr x (1-Aw/2) + Rw= Rm Water Volumetric Reflectance Water Absorption (Cho et al. 2010))

21. Data Extension into Deeper Depths • Beer-Lambert Law where Iz, λ is the light intensity at a given depth z, I0 is the light intensity present before any contact is made with the absorbing medium, and Kd is the downwelling attenuation coefficient. (Washington et al. 2012)

22. SAV (Seagrass) Pixels (24 pixels) (Cho et al. 2011)

23. (Gaye et al. 2011)

24. Graphical User Interface (Cho et al. 2013)

25. (Cho et al. 2013)

26. (Cho et al. 2013)

27. (Cho et al. 2013)

28. Airborne AISA Hyperspectral data Acquisition and Analyses Mission-Aransas NERR, TX: July 2008 Application of the Algorithm on image data (Cho et al.)

29. (Cho et al.) 553 nm (Green Color Energy)

30. (Cho et al.) 694 nm (Red Energy)

31. (Cho et al.) 741 nm (NIR)

32. Applications of the Technology • John Wood, Ph.D. candidate, • Harte Research Institute Fellow in the Coastal and Marine Systems Sciences Program at Texas A&M University-Corpus Christi • The dissertation title: • Geospatial Analysis of Seagrass Remote Sensing Data From Redfish Bay, Texas..

33. Classification ResultsDepthExtrapolation out to 5 m. 535, 600, 620, 638, 656 nm

34. Classification ResultsDepthExtrapolation out to 0.6 m. 554, 695, 723, 742, 809 nm

35. Problems • Multi-spectral data and the current chlorophyll algorithms cannot distinguish seagrass from algal signals. • Airborne hyperspectral data are costly and have temporal/spatial limitations.

36. Seagrass • Vascular plants • Generally have higher Chl concentrations compared to macroalgae

37. Macroalgae • Non vascular • Varying levels of Chlorophyll and colors

38. Benthic Remote Sensing • Hyperspectral remote sensing has been suggested to be an effective tool in distinguishing spectral patterns of benthic habitats.(Fyfe 2003; Kutser et al. 2005)

39. Goal and Objectives Goal: • To develop a novel approach using satellite data that can be efficiently used to distinguish seagrass and macroalgae signals and help facilitate accurate benthic vegetation mapping Objectives: • Find spectral characteristics that can distinguish seagrass signals from those of macroalgae. • Map seagrass and macroalgae in Indian River Lagoon using satellite data.

40. HICO • The Hyperspectral Imager for the Coastal Ocean (HICO) is a hyperspectral sensor onboard the International Space Station (ISS). • HICO has a high signal-to-noise ratio that can facilitate benthic habitat mapping.

41. Study Site (Indian River Lagoon)

42. Methods • Obtaining and pre-processing HICO data over the Indian River Lagoon (March 2013). • Developing spectral models. • Benthic classification using four methods • supervised – Spectral Angle Mapper (SAM), • unsupervised, and • two new models – SlopeRED, and SlopeNIR) • Performance comparison of the four methods using high resolution aerial photos and field survey data.

43. Cho et al. 2014

44. Results Cho et al. 2014

45. Results Cho et al. 2014

46. Results Cho et al. 2014

47. Results Cho et al. 2014

48. Cho et al. 2014 Accuracy Assessment

49. Accuracy Assessment Cho et al. 2014

50. Conclusion • The study demonstrates that the advantage of selecting key narrow bands to accentuate the subtle differences between seagrass and macroalgae, which conventional classification methods do not perform well. • Combining the slope methods, SlopeRED andSlopeNIR, with a supervised classification method will lead to higher accuracies in distinguishing key vegitation.