“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

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

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

The Need

The Location Rachel Carson Reserve

Beaufort, NC

Cape Lookout

Methods Remote Sensing

• Aerial digital orthophotography – 4-band CIR (color infrared)

True color composite image 4-band CIR composite image

Methods Remote Sensing •LiDAR – Light Detection And Ranging

Source: www.dot.state.oh.us Source: www.franepal.org

Methods Remote Sensing

• SAR - Synthetic Aperture Radar

Source: JAXA

Methods

Remote Sensing

• SAR - Synthetic Aperture Radar

Multi-date ALOS PALSAR, polarization HH and HV Multi-date ALOS PALSAR, fine beam std. ~6m

Source: www.cis.rit.edu/gallery/index.php/

Methods

Remote Sensing

•LiDAR – Light Detection And Ranging

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

HOW - Methods

Classification

•OBIA – object based image analysis

A subset of the 31,934 total polygons

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)

RESULTS

RESULTS Accuracy Assessment – error matrix

RESULTS SLAMM Model

Current Condition

Habitat Classifications

GrassyDune/ScrubShrub/Forest

Supratidal Wetlands

Intertidal Wetlands

Sandy Beach/Dune

Intertidal Flats

Water

RESULTS SLAMM Model

2050 (1 meter rise by 2100)

RESULTS SLAMM Model

2100 (1 meter rise)

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.

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

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

QUESTIONS?