Seagrass Resource Assessment Using Remote Sensing Methods in St. Joseph Sound and Clearwater Harbor, Florida, USA
Landsat TM, maximum likelihood, Mahalanobis distance, Gulf of Mexico, submerged aquatic, vegetation
Digital Object Identifier (DOI)
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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Citation / Publisher Attribution
Environmental Monitoring and Assessment, v. 184, issue 2, p. 1131-1143
Scholar Commons Citation
Meyer, Cynthia A. and Pu, Ruiliang, "Seagrass Resource Assessment Using Remote Sensing Methods in St. Joseph Sound and Clearwater Harbor, Florida, USA" (2012). School of Geosciences Faculty and Staff Publications. 358.