title: Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA.
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.
authors.affiliation: Department of Geography and Environmental Science & Policy, University of South Florida, Tampa, FL, USA. cameyer2@mail.usf.edu
authors.forename: Cynthia A
authors.initials: CA
authors.lastname: Meyer
authors.forename: Ruiliang
authors.initials: R
authors.lastname: Pu
mesh_terms: D059001:Aquatic Organisms / Q000145:classification / Q000254:growth & development*; D003247:Conservation of Natural Resources / Q000379:methods*; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods; D005431:Florida; D063245:Plant Development; D010944:Plants / Q000145:classification; D058998:Remote Sensing Technology; D018531:Spacecraft