A Systematic Approach toward Detection of Seagrass Patches from Hyperspectral Imagery

Abstract

Changes in the coverage of seagrass populations are considered to be a key indicator of the health and biodiversity of coastal ecosystems. The overall extent of seagrass meadows is declining worldwide, primarily due to human-induced disturbances. In Tampa Bay, Florida, a nearly 35% loss of seagrass coverage occurred from the 1950s to the 2000s. This decline was primarily due to the effects of human population growth. To examine closely the continuing declining trend of this major indicator of the health of coastal ecosystems, a systematic approach for extracting seagrass patches using EO-1 Hyperion hyperspectral imagery has been developed. In our previous work, a method based on Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) was developed and successfully applied to military object recognition using hyperspectral and multispectral imagery. It showed great potential in target detection of hyperspectral imagery. In this work, it is extend and applied in seagrass extraction.

This study includes (a) dimensionality reduction of the hyperspectral data, (b) seagrass extraction using LEGION and four other methods, and (c) analysis and evaluation of the results in an experiment involving two test sites at Tampa Bay, Florida. The results demonstrated that the methodology has the potential to provide timely seagrass coverage information for coastal zone management at greatly increased efficiency.

Keywords:

• Seagrass
• feature extraction
• EO-1 Hyperion hyperspectral imagery

Acknowledgements

The authors thank Prof. DeLiang Wang for providing the LEGION method for this research. We gratefully acknowledge the collaboration of researchers in the office of the Tampa Bay Estuary Program (TBEP). We appreciate the time and constructive comments from the reviewers. Research funding for this study was partially provided by the National Geospatial-Intelligence Agency and the NOAA Sea Grant Program.