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Remote Sensing Technique for Montoring Aquatic Vegetation

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dc.contributor.advisor Qu, John J.
dc.contributor.author Blanco, Alfonso Fernandez en_US
dc.creator Blanco, Alfonso Fernandez en_US
dc.date.accessioned 2013-08-09T15:39:28Z
dc.date.available 2013-08-09T15:39:28Z
dc.date.issued 2013 en_US
dc.identifier.uri http://hdl.handle.net/1920/8267
dc.description.abstract Hydrilla is an important submerged aquatic vegetation because it has a large capacity to absorb pollutants and it is an indicator of the eutrophic status of a waterbody. Monitoring and restoration of submerged aquatic vegetation is key for the preservation and restoration of the Chesapeake Bay. Remote sensing techniques have been used for assessing wetlands and non-invasive aquatic species, but there is limited studies of hydrilla monitoring combined with space-borne, airborne and in-situ remote sensing measurements for detecting and mapping hydrilla infestation. The first objective of this research was to establish a database of hydrilla spectral signatures from an experimental tank and from a field setting using a handheld spectrometer. The spectral signatures collected will be used to identify the optimal spectral and spatial characteristics that are required to identify and classify the distribution of hydrilla canopies in water bodies. The second objective is to process and analyze two hyperspectral images from a space-borne (Hyperion) and airborne (AISA) sensors with ENVI for detecting and mapping the infestation of hydrilla vertillicata in a coastal estuary in Chesapeake Bay. The third objective was to validate the satellite and airborne hyperspectral images with the spectral signatures collected with the in-situ field measurements. In addition, the Hyperion and AISA imaging results were compared with ground surveys and aerial photos collected by the Maryland Department of Natural Resources and the Virginia Institute of Marine Sciences for verifying the extent and the location of the hydrilla canopies. The hyperspectral analysis of both sensors provided for a dual results, one is the identification and classification of hydrilla from hyperspectral imaging sensors and secondly the identification of algae blooms in very productive waters. A hydrilla spectral signature database was established and housed in GMU's EastFIRE Lab of Environmental Science and Technology Center (ESTC) which other researches, consultants, and academia can access for future studies. The achievement of these mapping techniques will provide a more cost-effective (eventually), timely, and repeatable method for creating an accurate baseline for detecting and mapping hydrilla. en_US
dc.format.extent 159 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2013 Alfonso Fernandez Blanco en_US
dc.subject Remote sensing en_US
dc.subject Environmental science en_US
dc.subject Water resources management en_US
dc.subject Chesapeake Bay en_US
dc.subject Hydrilla en_US
dc.subject Hyperion en_US
dc.subject Hyperspectral en_US
dc.subject Remote sensing en_US
dc.subject Submerged Aquatic Vegetation en_US
dc.title Remote Sensing Technique for Montoring Aquatic Vegetation en_US
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Earth Systems and Geoinformation Sciences en
thesis.degree.grantor George Mason University en


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