marsh habitat mapping and potential responses to sea-level rise rachel carson … · 2014. 5....
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“The edge of the sea is a strange and beautiful place” ̶ Rachel Carson (1907-1964)
MARSH HABITAT MAPPING and POTENTIAL RESPONSES TO
SEA-LEVEL RISE
RACHEL CARSON RESERVE
Margaret Garner, Doctoral Student, Coastal Resources Management, East Carolina University
Dr. Thomas R. Allen, Dept. of Geography, Planning and Environment, East Carolina University
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The Project
• NC Coastal Reserve – NC Sea Grant Coastal Research Fellowship Program
• Needs of NCCR management
• Accurate species (vegetation) inventory
and maps for monitoring
• Vulnerability to Sea Level Rise
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The Project
Objectives
• To improve mapping capabilities using a combination of remote sensing technologies
• To examine the interrelationships between marsh plant communities, elevation/topography and tidal range
• To evaluate the vulnerability of the marshes in the Reserve based on these interrelationship
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The Need
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The Location Rachel Carson Reserve
Beaufort, NC
Cape Lookout
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Methods Remote Sensing
• Aerial digital orthophotography – 4-band CIR (color infrared)
True color composite image 4-band CIR composite image
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Methods Remote Sensing •LiDAR – Light Detection And Ranging
Source: www.dot.state.oh.us Source: www.franepal.org
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Methods Remote Sensing
• SAR - Synthetic Aperture Radar
Source: JAXA
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Methods
Remote Sensing
• SAR - Synthetic Aperture Radar
Multi-date ALOS PALSAR, polarization HH and HV Multi-date ALOS PALSAR, fine beam std. ~6m
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Source: www.cis.rit.edu/gallery/index.php/
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Methods
Remote Sensing
•LiDAR – Light Detection And Ranging
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Methods
• Data Pre-processing
• Re-projected into Universal Transverse Mercator (UTM) coordinate system using WGS84 model
• Images Orthorectified - accurately registered to ground
coordinates, removing distortions that occur during image
capture
• SAR images filtered to remove “speckle”
• Analysis and Mapping
• Erdas Imagine
• ArcGIS 10.1
• Error matrix
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HOW - Methods
Classification
•OBIA – object based image analysis
A subset of the 31,934 total polygons
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Methods Sea Level Rise Modeling
• SLAMM – Sea Level Affecting Marshes Model
• Simulates the effects of long-term sea level rise
• Primary processes - inundation, saturation, erosion, and accretion
• Model input
• Classification data (Aerial Photos, SAR and LiDAR)
• DEM (LiDAR)
• Local data; e.g., accretion and erosion rates (obtained from literature)
• Sea level rise scenarios – 0.5 m (2050) and 1.0 (2100)
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RESULTS
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RESULTS Accuracy Assessment – error matrix
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RESULTS SLAMM Model
Current Condition
Habitat Classifications
GrassyDune/ScrubShrub/Forest
Supratidal Wetlands
Intertidal Wetlands
Sandy Beach/Dune
Intertidal Flats
Water
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RESULTS SLAMM Model
2050 (1 meter rise by 2100)
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RESULTS SLAMM Model
2100 (1 meter rise)
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CONCLUSIONS
• The use of integrated remote sensing data and
OBIA classification methods can provide a high
degree of accuracy for resource mapping and
monitoring in support of interdisciplinary research
and resource management.
• This study demonstrates the potential for
significant marsh loss from sea level rise.
• The maps provided will aid the Reserve managers
in adaptive planning and other management
strategies.
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LIMITATIONS
•Rachel Carson Reserve atypical marsh
•Accretion and erosion rates vary from site
to site, affecting accuracy of models
•Accurate classification of low marsh
difficult, even with high vertical accuracy
elevation data
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References Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry
and Remote Sensing 65, 2-16. doi:10.1016/j.isprsjprs.2009.06.004
DeHouck, A., LaFon, V., Baghdadi, N., and Marieu, V. (2012). Use of optical and radar data in synergy for mapping intertidal flats and coastal salt-marshes (Arcachon Lagoon, France). Geoscience and Remote Sensing Symposium (IGARSS), 2011, IEEE International [Online] Retrieved May 1, 2011 from http://www.halcyon.com/pub/journals/21ps03-vidmar
Hladik, C., and Alber, M. (2012). Accuracy assessment and correction of a LIDAR-derived salt marsh digital elevation model. Remote Sensing of Environment, 121, 224–235. doi:10.1016/j.rse.2012.01.018
Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerfve, B., and Cahoon, D. R. (2002). Responses of coastal wetlands to rising sea level. Ecology, 83, 2869–2877.
Poulter, B., and Halpin, P.N. (2008). Raster modelling of coastal flooding from sea‐level rise. International Journal of Geographical Information Science, 22(2), 167-182. doi:10.1080/13658810701371858
Wang, Y., and Allen, T. R. (2008). Estuarine shoreline change detection using Japanese ALOS PALSAR HH and JERS-1 L-HH SAR data in the Albemarle-Pamlico Sounds, North Carolina, USA. International Journal of Remote Sensing, 29(15), 4429-4442.
Weih, R. C., Jr., and Riggan, N. D., Jr. (n. d.). Object-based classification vs. pixel-based classification: Comparitive [sic] importance of multi-resolution imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-4/C7. Retrieved April 23, 2013 from http://www.isprs.org/proceedings/xxxviii/4-c7/pdf/Weih_81.pdf
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QUESTIONS?