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Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA

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Abstract

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery provided a suitable option to detect and assess damage of the submerged aquatic vegetation (SAV). This study examined Landsat TM imagery classification techniques to create two-class (SAV presence/absence) and three-class (SAV estimated coverage) SAV maps of the seagrass resource. The Mahalanobis Distance method achieved the highest overall accuracy (86%) and validation accuracy (68%) for delineating the seagrass resource (two-class SAV map). The Maximum Likelihood method achieved the highest overall accuracy (74%) and validation accuracy (70%) for delineating the seagrass resource three-class SAV map. The Landsat 5 TM imagery classification provided a seagrass resource map product with similar accuracy to the aerial photointerpretation maps (validation accuracy 71%). The results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource.

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Authors and Affiliations

  1. Department of Geography and Environmental Science & Policy, University of South Florida, Tampa, FL, USA

    Cynthia A. Meyer & Ruiliang Pu

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  1. Cynthia A. Meyer
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  2. Ruiliang Pu
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Correspondence to Cynthia A. Meyer.

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Meyer, C.A., Pu, R. Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA. Environ Monit Assess 184, 1131–1143 (2012). https://doi.org/10.1007/s10661-011-2028-4

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  • DOI: https://doi.org/10.1007/s10661-011-2028-4

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