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Better detection and discrimination of seagrasses using fused bathymetric lidar and

hyperspectral data Bruce Sabol and Molly Reif US Army Engineer Research and Development Center, Environmental Laboratory Bruce.M.Sabol@usace.army.mil

PIANC, Dredging 2012

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Joint Airborne Lidar Bathymetry Technical Center of Expertise

OPERATIONS

RESEARCH AND DEVELOPMENT

Annual Technical Workshop June 2012, Chicago

Sensors and

systems

SOPs Collection Areas

Data Exploitation

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CASI-1500 Operator Console

SHOALS-3000 Operator Console

Bottom Aircraft Port

Optech SHOALS-3000 Integrated Laser

System DuncanTech-4000 RGB

camera Itres CASI-1500 Hyperspectral

Imager

System Specifications 3,000 Hz Pulse Rate (hydro) 20,000 Hz Pulse Rate (topo)

1 Hz Digital camera (~35 cm pixel) CASI-1500 Hyperspectral Imager

1500 cross-track pixels 380 – 1050 nm wavelength

1 m pixel w/ 36 spectral bands

CHARTS

Applanix DSS 322 •22.2 megapixel (5436 X 4092) •~ 5 cm / pixel (at 400m) •Color (VIS) or Color IR (CIR) •Includes POS / AV

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Coastal Zone Mapping and Imaging Lidar

CZMIL concept of operations

Hardware development

faster area coverage operation in more turbid and deeper waters improved performance in breaking waves improved navigation hazard detection improved accuracy for depth measurement, water

column properties, and bottom characterization higher-density topographic and shallow

bathymetric measurements

CZMIL laser sub-system

faster laser pulse rate

single-laser solution

larger field-of-view

more sensitive receivers

segmented detector

circular scan

shorter system response

shorter laser pulse length

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Products • ASCII XYZ • Aerial photos • Zero contour • Aerial photo mosaics • 1-meter bathy/topo DEM • LAS format topo • 1-meter bathy/topo bare earth DEM • Hyperspectral image mosaics • Laser reflectance images • Basic landcover classification • Volume change

National Coastal Mapping Progress

Number of times surveyed since 2004

One Time Two Times Three Times Four Times Five Times Six Times

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National Coastal Mapping Program

Captiva Island, FL, 2010

• Develop regional, repetitive, high-resolution, high-accuracy elevation and imagery data

• Build an understanding of how the coastal zone is changing

• Facilitate management of sediment and projects at a regional, or watershed scale

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Bathymetry and topography

Marquette Harbor, MI

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Purpose: evaluate and demonstrate the use of fused airborne hyperspectral and bathymetric lidar data to detect and discriminate species of estuarine SAV and macroalgae in two representative small-craft dredged harbors; compare with other established airborne imagery analysis techniques

Support For: • Planning dredging operations • Mitigating ecological damage • Monitoring SAV

Dredging Operations and Environmental Research Work Unit:

Use of Airborne Lidar and Hyperspectral Data to Detect and Discriminate SAV Species at Corps Dredging Sites

Submersed Eelgrass spectra, Plymouth Harbor, MA

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Study Sites: September 15-16, 2010 Survey Plymouth Harbor, MA Buttermilk Bay, MA

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http://www.coastalamericafoundation.org/

Measurement Plymouth Harbor

Buzzard’s Bay Purpose

Acoustic SAV survey 3 regions, 10 miles transects

3 regions, 12 miles transects

SAV mapping for point selection

Drop camera 60 sites 60 sites Accuracy

Diver observation 22 sites 22 sites

Diver sampling 9 sites 9 sites Species, biomass, accuracy

Submersed spectral reflectance (diver w/ video)

7 sites (88 samples)

9 sites (94 samples)

Spectral optimizer calibration, accuracy

Spectral reflectance (shallow)

201 samples 121 samples

Water column optical properties

7 sites 9 sites Spectral optimizer calibration

Potential accuracy data points

299 221

Ground Truth Data Summary

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Image Processing Methods: Overall Approach • Coastal Zone Mapping and Imaging Lidar (CZMIL) Data

Processing System (DPS)

– DPS with Spectral Optimization to characterize seafloor and water column

* Spectral curve fitting approach using radiative transfer theory to invert the hyperspectral image with lidar depth as a constraint for modeling water column constituents and estimating bottom reflectance

– Classification of seafloor reflectance to solve for species

Water leaving reflectance

Water column attenuation

CDOM absorption

Chl concentration

Active seafloor reflectance

Spectral seafloor reflectance

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Hyperspectral and Bathy Lidar Inputs

Reflectance Depth

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Image Processing Methods: DPS

• Chl-a concentration

• CDOM @ 440nm

• Backscattering @ 532 nm

Spectral Optimization: Parameter Input

Sand Eelgrass Brown Algae

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Bottom Reflectance Imagery

Seagrass

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Image Processing Methods: Classification

Zoom 1: Seagrass

Zoom 2: Brown/red algae

ROI Selection

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Results

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Decision level

Input Data Spectral Optimized CASI/LiDAR (corrected bottom reflectance)

CASI only, water leaving reflectance

3-color Geo-Eye I (spectral degraded CASI)

1:12,000 RGB aerial photography from Duncan Tech digital camera

Super.1 Unsuper.2 Super. Unsuper. Super. Unsuper. Manual photointerp.3

Veg/no-Veg 90% 87% 85% 78% Eelgrass/no-eelgrass

88% 80% 83% 82%

3-class : macro-algae, eelgrass, unveg

88% 76% 73%

1. Supervised classification using Spectral Angle Mapper 2. Unsupervised classification using Isodata (20 iterations, 10 classes) 3. Charlie Costello, MA Dept of Environmental Protection

Overall Accuracy Results – Plymouth Harbor

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Summary and Benefits

• Benefits:

• More accurately identify SAV presence and type to reduce SAV impacts and potential dredge restrictions

• Model results with SAV type may provide a better understanding of impacts and potential exposure resulting from navigation and dredging

• Detailed SAV in dredge planning for identification of source/disposal sites and alternative scenario comparison

• Determine the level of information needed for dredging operations

planning

• Determine appropriate data requirements for specific SAV mapping tasks

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Products: 1) ARC GIS Explorer Online: www.coastalamericafoundation.org/savdoer.html

2) Ground Truth Technical Report

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Acknowledgements: the team

• NAE: Bill Hubbard, Ben Loyd, Angela Repella, Phoebe Chu

• EPA: Phil Colarusso and dive team • MA DEP: Charlie Costello • U. of Connecticut: Dr. Heidi Dierssen and grad

students • Optech International: Jen Aitken • ERDC: Bruce Sabol, Molly Reif, Candice Piercy,

Jessie Jarvis • JALBTCX: Chris Macon • Coastal Diving Service

BUILDING STRONG® Sea

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Molly.K.Reif@usace.army.mil www.jalbtcx.org