Record Display for the EPA National Library Catalog

RECORD NUMBER: 463 OF 635

OLS Field Name OLS Field Data
Main Title Remote sensing techniques for montoring aquatic vegetation /
Author Blanco, Alfonso
Publisher George Mason University,
Year Published 2013
OCLC Number 863639022
Subjects Aquatic plants--Monitoring. ; Aquatic plants--Remote sensing. ; Remote sensing.
Internet Access
Description Access URL
http://hdl.handle.net/1920/8267
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
EJBM  QK930.B43 2013 Headquarters Library/Washington,DC 11/25/2013
Collation xiii, 158 leaves : illustrations, maps, charts ; 28 cm.
Notes
Includes curriculum vitae (page 158). Thesis director: John J. Qu. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Earth Systems and Geoinformation Sciences. Includes bibliographical references (pages 145-157).
Contents Notes
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.