Title

Alternative spatially enhanced integrative techniques for mapping seagrass in Florida's marine ecosystem.

SelectedWorks Author Profiles:

Barnali Dixon

Document Type

Article

Publication Date

2013

Date Issued

January 2013

Date Available

June 2014

ISSN

0143-1161

Abstract

Seagrass is an important component of coastal marine ecosystems. Seagrass mapping provides a means for assessing seagrass health by monitoring the spatial distribution and density of seagrass habitat in coastal waters. Recent image processing and satellite technologies present the opportunity to leverage quantitative techniques that have the potential to improve upon traditional photo-interpretation techniques in terms of cost, mapping fidelity, and objectivity. Integrated spatial and spectral processing techniques were identified as an alternative method for mapping seagrass extent and density from an IKONOS satellite image of Springs Coast, Florida. These spatially enhanced integrative mapping techniques objectively standardize seagrass-monitoring efforts and enhance mapping capabilities by characterizing spatial seagrass density gradients. A combination of water column correction, pixel classification, and image segmentation techniques provided a seagrass density index map that represented seagrass density and distribution with high spatial detail and overall accuracy (77%) comparable to photo-interpretation techniques. Satellite imagery-based spatially enhanced image processing techniques were found to provide a consistent, quantitative, and cost-effective alternative for seagrass mapping in Springs Coast with the potential to be transferred to other parts of the world. A cost savings analysis concluded that there was a 13% cost saving using satellite photo-interpretation and a 47% cost saving using enhanced satellite classification when compared to aerial photo-interpretation.

Comments

Abstract only. Full-text article is available only through licensed access provided by the publisher. Published in International Journal of Remote Sensing, 34(4), 1248-1264. DOI: 10.1080/01431161.2012.721941 Members of the USF System may access the full-text of the article through the authenticated link provided.

Language

en_US

Publisher

Taylor & Francis

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.