Proposal Title

Accuracy Assessment and Development of Digital Elevation Models (DEMs) in Salt and Brackish Marshes of Coastal Georgia

Primary Faculty Mentor’s Name

Christine Hladik

Proposal Track

Student

Session Format

Poster

Abstract

By: Maggie Aurelio

Abstract

Tidal marshes are considered an important resource to coastal Georgia. Having accurate knowledge of elevation is important, especially for predicting sea level rise impacts; however, digital elevation models for tidal marshes overestimate elevations due to the presence of tall, dense vegetation. The first objective of this study is to assess the accuracy of digital elevation models (DEMs) derived from light detection and ranging (LIDAR) data for both salt and brackish marshes of coastal Georgia and quantify error metrics. The mean error and root mean squared error (RMSE) will be calculated by comparing DEM elevations to real time kinematic (RTK) data at selected ground control points. It is expected that mean errors for the original DEM will range from 0.05 to 1.0 m, with greater errors in the brackish marshes due to taller vegetation in comparison to salt marshes. The second objective is to compare multiple methods of DEM generation to determine which method produces the most accurate representation of elevation in the tidal marshes. Two general DEM generation methods will be evaluated. The first is to produce a corrected DEM by subtracting the mean error for each habitat (salt, brackish) from the original DEM elevation using ArcGIS Spatial Analyst. The second technique is to experiment with DEM gridding and interpolation methods including: inverse distance weighting, minimum bin method, and geostatistical kriging. These methods would allow for the creations of new LIDAR-derived DEMs using Quick Terrain Modeler and Surfer programs. The accuracy of all DEMs will be compared using the mean and RMSE metrics. It is expected that the corrected DEM and the DEM generated using the minimum elevation at each location will have the least errors and thus provide a more accurate representation. It is unknown how much errors will be reduced using the DEM interpolation methods, as no one has examined this for both salt and brackish marshes to our knowledge.

Keywords

Tidal marshes, Salt marshes, Brackish marshes, Digital Elevation Models (DEMs)

Award Consideration

1

Location

Concourse/Atrium

Presentation Year

2014

Start Date

11-15-2014 9:40 AM

End Date

11-15-2014 10:55 AM

Publication Type and Release Option

Presentation (Open Access)

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Nov 15th, 9:40 AM Nov 15th, 10:55 AM

Accuracy Assessment and Development of Digital Elevation Models (DEMs) in Salt and Brackish Marshes of Coastal Georgia

Concourse/Atrium

By: Maggie Aurelio

Abstract

Tidal marshes are considered an important resource to coastal Georgia. Having accurate knowledge of elevation is important, especially for predicting sea level rise impacts; however, digital elevation models for tidal marshes overestimate elevations due to the presence of tall, dense vegetation. The first objective of this study is to assess the accuracy of digital elevation models (DEMs) derived from light detection and ranging (LIDAR) data for both salt and brackish marshes of coastal Georgia and quantify error metrics. The mean error and root mean squared error (RMSE) will be calculated by comparing DEM elevations to real time kinematic (RTK) data at selected ground control points. It is expected that mean errors for the original DEM will range from 0.05 to 1.0 m, with greater errors in the brackish marshes due to taller vegetation in comparison to salt marshes. The second objective is to compare multiple methods of DEM generation to determine which method produces the most accurate representation of elevation in the tidal marshes. Two general DEM generation methods will be evaluated. The first is to produce a corrected DEM by subtracting the mean error for each habitat (salt, brackish) from the original DEM elevation using ArcGIS Spatial Analyst. The second technique is to experiment with DEM gridding and interpolation methods including: inverse distance weighting, minimum bin method, and geostatistical kriging. These methods would allow for the creations of new LIDAR-derived DEMs using Quick Terrain Modeler and Surfer programs. The accuracy of all DEMs will be compared using the mean and RMSE metrics. It is expected that the corrected DEM and the DEM generated using the minimum elevation at each location will have the least errors and thus provide a more accurate representation. It is unknown how much errors will be reduced using the DEM interpolation methods, as no one has examined this for both salt and brackish marshes to our knowledge.