Slide 1

Benthic Mapping using Remote Sensing DataJ. Cho

Department of Integrated Environmental ScienceBethune-Cookman UniversityDaytona Beach, FL

1Elements of Remote Sensingdata acquisition and analysissunCamera system2 Electromagnetic (EM) RadiationConsists of An electrical field (E)A magnetic field (M)Both fields travel at the speed of light (c) c = v c: 3 x 108 m/secv: frequency: wavelength

http://www.astronomynotes.com/light/s3.htm3EM Spectrum

http://en.wikipedia.org/wiki/File:EM_spectrum.svg4

(Jensen, 2007)5

http://ian.umces.edu/learn/modulepopup/barrier_islands_and_sea_level_rise/a_closer_look_at_seagrasses7

535 nm760 nmSpectral Reflectance Characteristics of Vegetationand Conventional Vegetation Indices

Image source: http://extnasa.usu.edu/on_target/images_independent/nir_vegetation_graph.gifSpectral reflectance of Submerged Aquatic Vegetation (SAV) at Varying Depths

10 I. Water Correction Algorithm DevelopmentRationale and ObjectiveRemote 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 seagrassesHyperspectral images corrected for conventional atmospheric distortionsPure water effect removalWater color, turbidity effects reductionDepth effect adjustmentImages with enhanced benthic featuresSelection of critical bandsand data Reduction & compressionControlled experimentsAlgorithm developmentWater Correction Algorithm Development for Benthic MappingWaterLrLsLvLbLaLL = Ls + Lv + La + Lr + Lb* 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

53cm

Pulley SystemStringString1.2 m82 cm(Washington et al. 2012)

Reflectance(corrected) = f (ref(measured), depth)

1 x (1-Aw/2) x Rr x (1-Aw/2) + Rw= RmWater Volumetric ReflectanceWater Absorption

(Cho et al. 2010))2020Data Extension into Deeper Depths

Beer-Lambert Law

where Iz, 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)

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

(Gaye et al. 2011)

Graphical User Interface

(Cho et al. 2013)

(Cho et al. 2013)

(Cho et al. 2013)

(Cho et al. 2013)

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

28553 nm (Green Color Energy)

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

(Cho et al.)741 nm (NIR)

(Cho et al.)Applications of the TechnologyJohn 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..Classification Results Depth Extrapolation out to 5 m. 535, 600, 620, 638, 656 nmClassProducers AccuracyUsers AccuracyBare66.7%69.6%Halodule29.0%39.1%Thalassia46.2%40.0%Ruppia0%0%Mixed23.3%22.6%Overall Accuracy38%ClassProducers AccuracyUsers AccuracyBare73%66%Halodule64%64%Thalassia46%55%Ruppia0%0%Mixed47%39%Overall Accuracy62%ClassProducers AccuracyUsers AccuracyBare50%58%Halodule50%6%Thalassia14%33%Ruppia10%4%Mixed27%62%Overall Accuracy27%ClassProducers AccuracyUsers AccuracyBare81%71%Halodule37%43%Thalassia40%33%Ruppia00%Mixed34%44%Overall Accuracy45%Classification Results Depth Extrapolation out to 0.6 m. 554, 695, 723, 742, 809 nmProblemsMulti-spectral data and the current chlorophyll algorithms cannot distinguish seagrass from algal signals.Airborne hyperspectral data are costly and have temporal/spatial limitations. SeagrassVascular plantsGenerally have higher Chl concentrations compared to macroalgae

Seagrass coverage is the prime indicator of the health of the Lagoon (1). 36MacroalgaeNon vascularVarying levels of Chlorophyll and colors

Benthic Remote SensingHyperspectral remote sensing has been suggested to be an effective tool in distinguishing spectral patterns of benthic habitats.(Fyfe 2003; Kutser et al. 2005)38Goal and ObjectivesGoal: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 mappingObjectives:Find spectral characteristics that can distinguish seagrass signals from those of macroalgae.Map seagrass and macroalgae in Indian River Lagoon using satellite data.

The specific objectives of this study were to39HICOThe 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.Study Site (Indian River Lagoon)

MethodsObtaining 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.42

Cho et al. 2014Peak in seagrass is higher because of the (Seagrass has more clorophele stronger absorbtion of red) seagrass is greener; 43

ResultsCho et al. 2014The first derivative showed that the red slope between 679.024 nm (HICO band 49) and 690.48 nm (HICO band 51) were positive and had distinct magnitude for each substrate. For example, seagrass magnitude was higher than both mixed beds and macroalgae. Similarly, the NIR slope between 696.208 nm (HICO band 52) and 742.032 nm (HICO band 60) were negative with the exception of exposed substrate and had distinct magnitude for each substrate types.

To find the region to seee distinct differences among the classes that we used!

44

ResultsCho et al. 2014Graphical analysis to find how well the slope algorithm will help in distinguishing different benthic habitats from each other45Results

Cho et al. 2014Results

Cho et al. 2014Accuracy AssessmentOverall Accuracy (%)KappaSlopeRED

64.21

0.5264

SlopeNIR

63.16

0.5171

Supervised- (SAM)47.50

0.3466

Unsupervised

25.00

0.0722

Cho et al. 2014Accuracy AssessmentClassProducer Accuracy (%)User Accuracy (%)SlopeREDSeagrass5268.42Macroalgae9052.04SlopeNIRSeagrass10042.55Macroalgae7285.71Supervised (SAM)Seagrass2555.56Macroalgae1633.33Cho et al. 2014Producer's accuracy is a reference-based accuracyConsumer's accuracy is a map-based accuracy

http://biodiversityinformatics.amnh.org/index.php?section_id=34&content_id=131

49ConclusionThe 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 and SlopeNIR, with a supervised classification method will lead to higher accuracies in distinguishing key vegitation.Finding only key features is better than having a lot of data. We have to be cautious with usijng this. The slope methods should be used along with the supervised in order to obtain results required.

If accompanied with a supervised classification method, the new slope methods, SlopeRED and SlopeNIR, will significantly improve accuracies in distinguishing seagrass beds from benthic macroalgae.

Based on statistical analysis the both slope algorithms show or give better results than the conventional methods50AcknowledgmentsNational Geospatial-Intelligence Agency (NGA).U.S. Naval Research Laboratory (NRL).The National Aeronautics and Space Administration (NASA).Oregon State University (OSU).Florida Space Grant Consortium.St. Johns River Water Management District (SJRWMD).

Improvement of Water Correction for Seagrass/Macroalgae DiscriminationIRL Initial Water Correction ResultsIRL Initial Water Correction ResultsOriginalWater-CorrectedSlopeREDSlopeNIRSlopeREDSlopeNIR1.658-1.6153.234-2.457IRL Initial Water Correction ResultsIRL Initial Water Correction ResultsOriginalWater-CorrectedSlopeREDSlopeNIRSlopeREDSlopeNIR0.873-0.6771.725-0.842