Remote Sensing for Coastal Applications: Implications for ...

Remote Sensing for Coastal Applications: Implications for Ecosystem Services Molly Reif USAE Research & Development Center Environmental Laboratory Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), Kiln, MS

ACES December 2010

US Army Corps of Engineers

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Joint Airborne Lidar Bathymetry Technical Center of eXpertise JALBTCX Operations USACE

Technology Evolution Navy

Coastal Measurements & Data Usage

Sensors & Systems

NOAA

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USACE National Coastal Mapping Program (NCMP)

• Funded by HQ • Initiated in FY2004 • Collect lidar elevation and imagery to support regional sediment management/navigation • Focus on sandy shorelines • 5-year national cycle

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CHARTS

System III Specifications 3,000 Hz Pulse Rate (hydro) 20,000 Hz Pulse Rate (topo) RGB Digital camera (~20 cm pixel) CASI-1500 Hyperspectral Imager 1500 cross-track pixels 380 – 1050 nm wavelength 1 m pixel w/ 36 spectral bands

Optech SHOALS Integrated Laser DuncanTech-4000 RGB camera Itres CASI-1500 System Hyperspectral Imager

SHOALS-3000 Operator Console

CASI-1500 Operator Console

Bottom Aircraft Port

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NCMP – Collection Scheme Bathymetric and Topographic lidar Topo 1 m postings, 500 m onshore, 200% Bathy 5 m postings, 1000 m offshore Geographic coordinates NAVD88 vertical datum

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USACE NCMP Surveys and Products

Available data products: 2004 Post-hurricane 2004 2005 Post-hurricane 2005 2006-2008 Post-hurricane 2008 2009-2010

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ASCII xyz RGB mosaics Zero contour 1-m bathy/topo DEMs LAS format topo 1-m bathy/topo bare earth DEMs Hyperspectral mosaics Laser reflectance Basic landcover classification

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USACE NCMP Products

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EL and JALBTCX  EL has teamed up with JALBTCX to assist with the development and expansion of environmental data products  GOAL: identify/expand environmental data products, utilizing (1) imagery resources of JALBTCX and (2) environmental expertise in EL to address environmental/geospatial needs of the coastal districts.

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EL Research Focus Areas Wetlands

Ecosystem Restoration

Invasive Species

Endangered Species Navigation Dredging

Environmental Characterization Natural Resource Management BUILDING STRONG®

Environmental Systems Branch

MISSION: Identification, mapping, and modeling of environmental conditions in support of diverse military and civil requirements. Development of environmental sensing, characterization, and monitoring capabilities necessary to quantify environmental site conditions. Model development for the prediction and visualization of dynamic environmental characteristics for civil and military applications.

Upwelling to Downwelling Ratio Interpolation

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SM2 SM3 BAYN01 BAY01 FREDA01 FREDA02 FREDA03 FREDA04 FREDA05 FREDA06 FREDA07

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Geospatial Data Analysis Facility FOCUS: Application of Geospatial Technologies Toward Civil and Military Environmental Applications

• Custom Geospatial Model Development

• ESRI Model Builder Expertise • Image Processing and Analysis • Unclassified to Top-Secret • Multispectral and Hyperspectral Platforms • Active and Passive Systems

• Custom Database Development • SDSFIE Experts • Serving Geospatial Data Via Web Interface • Improvised Explosive Device Defeat Organization (SIPRNET) • Development of Innovative Applications of Combined Lidar and Hyperspectral Imagery

• CHARTS system

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Wetland Environmental Technologies Research Facility (WETRF) FOCUS: Application of geospatial technologies to address issues of specific concern to coastal Louisiana and northern Gulf of Mexico wetlands

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Scope ► ►

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Impact ►

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Wetland science data integration Geospatial analyses Informs coastal restoration and protection planning and management

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Expert technical staff stationed at LSU campus Relationship with USGS NWRC and LA OCPR Interdisciplinary team

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Objective • Target features spectrally with hyperspectral and structurally with lidar through image fusion

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Considerations for Ecosystem Services • Remote sensing can provide baseline data for assessing trends and conditions in ecosystem services • Way to link ecosystem type/condition with service • Spatially explicit data have proven useful for estimating values and associated changes in services • Direct input into ecological models; proxy for complex ecosystem functions • Image fusion provides way to analyze two aspects of ecosystem components at once: structural (canopy characteristics, diversity, volume) + thematic (type, condition)

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Landuse/Landcover and Species Composition • Landuse/landcover, Species Composition/Distribution, Habitat/ Biodiversity Mapping and Species Richness • • • •

basic groups or categories of landcover specific plant species or species assemblages/groups critical habitats using standard classification systems link landcover with value

Portsmouth Harbor, New Hampshire. (a) True color image extracted from hyperspectral mosaic. (b) Basic image classification.

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Landuse/Landcover and Species Composition Coastal Habitats - Spain

Barrier Island Habitats – Horn Island

Hyperspectral

Lidar

Combined

Chust, G. et al 2008. Coastal and estuarine habitat mapping, using lidar height and intensity and multi-spectral imagery. Estuarine, Coastal and Shelf Science. 78:633-643.

Reflectance (%)

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Lucas and Carter. 2009. Decadal Changes in Habitat-Type Coverage on Horn Island, Mississippi, USA. Journal of Coastal Research, In Press.

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Change Detection •

Habitat degradation, fragmentation, and loss – value change estimation 2005

2006

2007

Lidar

Imagery

Land Cover

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Invasive Species Detection • Mapping Invasive and Indicator Species • •

Spectrally and structurally target species of interest Emphasize changes in composition, structure and function in ecosystems caused by invasives

Common Reed

Gilmore, M.S. et al. 2008. Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh. Remote Sensing of Environment. 112:4048-4060.

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Wetlands and Beach Characterization • Mapping Wetland Habitats and Beach Morphology • • •

Spectrally and structurally target wetland species (distribution and condition) Emphasize species pattern characterization and zonation related to elevation gradients Characterize erosion/sedimentation and beach types for monitoring

Sadro, S. et al. 2007. Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations. Remote Sensing of Environment. 110:226-239.

Deronde, B. et al. 2006. Use of airborne hyperspectral data and laserscan data to study beach morphodynamics along the Belgian coast. Journal of Coastal Research. 22(5):1108-1117.

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Forest Characterization and Metrics • Forest/Woodland Structural Characterization and Metrics • • •

Structure: canopy height, density, crown size/shape Metrics: above-ground biomass, basal area, dbh, volume, quadratic mean stem diameter, and leaf area index Forest-related applications: fire fuel studies, etc.

Above Ground Biomass

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Ecosystem Characterization • Mapping ecosystem characteristics •

Identify unique characteristics in landscape parameters/attributes

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Model habitat for invasive/threatened/endangered species and restoration/conservation

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Model habitat changes to examine trade-offs

Potential habitat for invasive species (Lepidium)

Ustin, S.L. and Hestir, E.L. Hyperspectral & Lidar Sensing of the Delta. (presentation). Center for Spatial Technologies & Remote Sensing (CSTARS). UC Davis.

Current Distribution Potential Distribution

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Discrimination of Submerged Aquatic Vegetation Species 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 ASD measurements of SAVs in Plymouth Harbor and Buttermilk Bay, MA., 13 July 2010 measured with artificial illumination (ASD ProLamp) 0.9

Reflectance

0.75 0.6 0.45

Zostera (Plymouth) Rockweed Green Filamentous algae Red epiphytic macroalgae Leafy macroalgae Ulva Zostera w/ heavy epi. "pink leaf", Buttermilk Codium Grosalus "Portforia", branching macro. Zostera w/ heavy epi(2)

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Spectral seafloor reflectance

Lidar seafloor reflectance

Seafloor classification Map

Phinn, S. et al 2008. Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in STRONG Moreton Bay BUILDING (Australia). Remote Sensing of ® Environment. 112:3413-3425.

Heavy-metal Stamp Sands Migration in Lake Superior Purpose: Classify submersed lake bottom surfaces using hyperspectral and lidar bottom reflectance. Map stamp sands distribution and estimate movement and loss of stamp sands to lake.

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Limitations/Challenges • System trade-offs (spatial vs. spectral resolution) • Lack of temporal frequency for change detection • Unknown accuracy and validation • Ground truth needed for processing and interpretation • Lack of data continuity makes data comparison difficult • Weather and other factors affect aerial survey capabilities and image quality

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Questions? Molly Reif [email protected]

JALBTCX www.jalbtcx.org

EL http://el.erdc.usace.army.mil/

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