Better detection and discrimination of seagrasses using fused bathymetric lidar and hyperspectral data

Transcription

1 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 PIANC, Dredging /8/

2 Joint Airborne Lidar Bathymetry Technical Center of Expertise Annual Technical Workshop June 2012, Chicago SOPs OPERATIONS Collection Areas Sensors and systems RESEARCH AND DEVELOPMENT Data Exploitation 11/8/

3 CHARTS 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 nm wavelength 1 m pixel w/ 36 spectral bands Optech SHOALS-3000 Integrated Laser DuncanTech-4000 RGB System camera Itres CASI-1500 Hyperspectral Imager SHOALS-3000 Operator Console CASI-1500 Operator Console Bottom Aircraft Port Applanix DSS megapixel (5436 X 4092) ~ 5 cm / pixel (at 400m) Color (VIS) or Color IR (CIR) Includes POS / AV 11/8/

4 Hardware development Coastal Zone Mapping and Imaging Lidar shorter laser pulse length circular scan faster laser pulse rate shorter system response 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 concept of operations larger field-of-view single-laser solution CZMIL laser sub-system more sensitive receivers segmented detector

5 National Coastal Mapping Progress Number of times surveyed since 2004 One Time Two Times Three Times Four Times Five Times Six Times 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

6 National Coastal Mapping Program 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 Captiva Island, FL, 2010

7 Bathymetry and topography Marquette Harbor, MI

8 Dredging Operations and Environmental Research Work Unit: Use of Airborne Lidar and Hyperspectral Data to Detect and Discriminate SAV Species at Corps Dredging Sites 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 Submersed Eelgrass spectra, Plymouth Harbor, MA

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

10 Measurement Plymouth Harbor Acoustic SAV survey 3 regions, 10 miles transects Buzzard s Bay 3 regions, 12 miles transects Purpose 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) Spectral reflectance (shallow) Water column optical properties Potential accuracy data points Ground Truth Data Summary 7 sites (88 samples) 9 sites (94 samples) 201 samples 121 samples Spectral optimizer calibration, accuracy 7 sites 9 sites Spectral optimizer calibration

11 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

12 Hyperspectral and Bathy Lidar Inputs Reflectance Depth

13 Image Processing Methods: DPS Spectral Optimization: Parameter Input Sand Eelgrass Brown Algae Chl-a concentration 440nm 532 nm

14 Bottom Reflectance Imagery Seagrass

15 Image Processing Methods: Classification Zoom 1: Seagrass ROI Selection Zoom 2: Brown/red algae

16 Results

17 Overall Accuracy Results Plymouth Harbor Decision level Spectral Optimized CASI/LiDAR (corrected bottom reflectance) CASI only, water leaving reflectance Input Data 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/noeelgrass 88% 80% 83% 82% 88% 76% 73% 3-class : macroalgae, eelgrass, unveg 1. Supervised classification using Spectral Angle Mapper 2. Unsupervised classification using Isodata (20 iterations, 10 classes) 3. Charlie Costello, MA Dept of Environmental Protection

18 Summary and Benefits Determine the level of information needed for dredging operations planning Determine appropriate data requirements for specific SAV mapping tasks 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

19 Products: 1) ARC GIS Explorer Online: 2) Ground Truth Technical Report

20 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

21 Questions? Seabrook, NH, federal navigation project and backbay marsh