Preparing for Operational Mapping of Harmful Algal Blooms and Related Water

Quality Parameters with HyspIRI

Richard Beck1, Hongxing Liu1, Haibin Su2, Qiusheng Wu1, Bo Yang1, Thomas Bridgeman6

1University of Cincinnati, 2Texas A&M Kingsville, 3University of Toledo

Great Lakes & Great Lakes Basins

Spatial Extent of Great Lakes Basins

Map of the Great Lakes with Lat/Lon and basin outline

the largest system of fresh surface water on earth

Lake Erie

Geographic Characteristics the shallowest and smallest by volume

Lake Erie shoreline image.

Lake Erie

Invaluable Freshwater Natural Resources the warmest and most biologically productive of the

Great Lakes

walleye fishery is the best in the world.

drinking, recreation, industry, and waste disposal

Yellow perch

White Perch

Walleye Fishing

Walleye Fish

Walleye Fish

Walleye Fish

Lake Erie

Environmental problems Harmful algal blooms: worst in the past 30 years

Harmful algae bloom, Ohio.Lake Erie. September 2009.

Harmful algae bloom, Ohio.Lake Erie. August 2010.

Project Background

US Army funded project PI: Richard Beck

2008, 2009, 2010, 2011

Comprehensive Geospatial Data Acquisitions Airborne hyperspectral Imaging

Aerostat-based continuous sensing

Shipborne sensing & water sampling

In situ field sampling & observations

Airborne Hyperspectral Imaging

AISA Eagle hyperspectral imager

Airborne Hyperspectral Imaging

AISA Eagle hyperspectral imager Pushbroom spectrometer

Integrated DGPS/INS navigation

built by Specim Ltd., Oulu Finland

Spectral range: 430-970 nm

126 user-defined bands, about 5 nm bandwidth

Flight height: 5,000 feet AGL

1 m spatial resolution

1024 m ground swath

AISA Optical Design

Preliminary Research Results

Hyperspectral image processing Advantages over traditional multi-spectral imagery

100s of Bands

Band2

.52-.60

Band3

.63-.69

Band4

.79-.90

Band5

1.55-1.75

Band7

2.08-2.35

Band1

.45-.52

Band6

10.4-12.4

Visible SWIR LWIR

1000s of BandsUltraspectral

Broadband

Hyperspectral

Multispectral

Airborne Hyperspectral Imaging

Hyperspectral Imaging Lake Erie, particularly Maumee Bay

Two years, multiple, repeat observations

Large quantity of hyperspectral images

Primary Imaging Area

Aerostat-based Continuous Sensing

Aerostat observations Continuous, real-time

transmission

Spectrometer

Video images

Aerostat-based Continuous Sensing

Aerostat observations Continuous, real-time

transmission

Nikon Camera

Video Camera

Spectrometer

New Hardware and Software

for Sensor Nodes

Video RecordingGeoreferenced visualizationof aerostat imagery in Google Earth via cellular data and land networks

Georeferenced visualizationof aerostat imagery in Google Earth via cellular data and land networks

Shipborne Water Sensing & Sampling

Shipborne sensing Two boats, one barge

Sonar imaging

Depth sounding

Water temperature

Medium-sized boat

Small boat

Barge

Shipborne Water Sensing & Sampling

Shipborne sensing Two boats, one barge

Sonar imaging

Depth sounding

Water temperature

Sonar ImagerSmall boatMedium-sized boat

Thermal sensor

Depth sensor

Sonar ImageDepthTemperature

Shipborne Water Sensing & Sampling

Onboard sensing Spectroradiometer

Chl-a concentration

Turbidity

CDOM

Camera image

Medium-sized boat

Barge

Preliminary Research Results

Hyperspectral image processing Caligeo processing system

radiometric and geometric corrections on AISA imagesa separate module under the ENVI-IDL software

Visual quality checking Radiometric correction

Georeferencing & Orthorectification

Atmospheric correction

Spectral Angle Map of MicrocystisHarmful Algal Bloom on western Lake Erie.

Shipborne Water Sensing & Sampling

Onboard sensing Spectroradiometer

Chl-a concentration

Turbidity

CDOM

Camera image

Shipborne Water Sensing & Sampling

Onboard water truth sampling Lab attributes

Chl-a concentration Turbidity CDOM Other chemical parameters

Thomas BridgemanUniversity of Toledo

USGS, NOAA, OSIP, UACE Data

USGS Stream gage & water quality data Watershed analysis

USGS, NOAA, OSIP, UACE Data

NOAA RECON Stations Real-time data

Preliminary Research Results

Water quality mapping & modeling Radiance components over Water Body

Total radiance (Lt): Lp = atmospheric path radiance, Ls = free-surface layer reflectance, Lv = subsurface volumetric scattering, Lb = bottom reflectance

Water Quality Inversion Models Empirical semi-analytical, and analytical

Preliminary Research Results

Water quality mapping & modeling Empirical models

ChlaShafique et al. 2004

16.0 micrograms/Lvs.7.6 and 2.3 Micrograms/Las sampled Chla

Frohn andAutrey, 20095.57 micrograms/Lvs.7.6 and 2.3 Micrograms/Las sampled

Preliminary Research Results

Water quality mapping & modeling Empirical models

New EPA Turbidity algorithmBy Frohn and Autrey (2009)(green refl + red refl)/blue refl

TurbidityFrohn and Autrey, 2009

150 NTUs from algorithmvs.44.1 and 47.9 NTUas sampled

NTUs  of 72 sampledNear by witha max value of439.2

Conclusions

1. The Chlorophyll a algorithm developed for  use on the Ohio River for the EPA by Frohn and Autrey (2009) appears to work well without modification for either channel width or pixel size on Lake Erie.

We will test it further this summer.

2. The Turbidity algorithm developed for use on the Ohio River for the EPA by Frohn and Autrey (2009) is within a factor of three on Lake Erie.

The difference could be due to sediment size or type or lack of a bottom signature during the thick algal blooms among other factors.

The hyperspectral imager allows us to follow the theoretical basis for thisalgorithm more closely.  We will move the choice of bands somewhat to betterexploit the spectral differences of turbidity and Chl a this summer.

3. We are pleasantly surprised by the performance of these unmodified algorithms.Both show promise for use on Lake Erie for water quality monitoring.

Preliminary Research Results

1. Mapping Microcystis Harmful Algal Blooms with VNIR Hyperspectral imagery is easy.

2. High spatial resolution hyperspectral data was useful for prototyping HAB mapping (you can see the boats) but is too fine for routine, wide area application.

3. Very low spatial resolution (1 km) spatial resolution data is too coarse for near shore applications

4. Routine operational 60 meter HyspIRI data would be ideal for near shore HAB mapping and other water quality studies on the Great Lakes.