Estuaries are environmentally, ecologically and environmentally important places as they act as a meeting place for land, freshwater and marine ecosystems. They are also called nurseries of the sea as they often provide nesting and feeding habitats for many aquatic plants and animals. These estuaries also withstand the worst of some natural disasters, especially hurricanes. The estuaries as well as the harbored ecosystems undergo significant changes in terms of water quality, vegetation cover etc. and these components are interrelated. When hurricane makes landfall it is necessary to assess the damages as quickly as possible as restoration and recovery processes are time-sensitive. However, assessment of physical damages through inspection and survey and assessment of chemical and nutrient component changes by laboratory testing are time-consuming processes. This is where remote sensing comes into play. With the help of remote sensing images and regression analysis, it is possible to reconstruct water quality maps of the estuary affected. The damage sustained by the vegetation cover of the adjacent coastal watershed can be assessed using Normalized Difference Vegetation Index (NDVI) The water quality maps together with NDVI maps help observe a dynamic sea-land interaction due to hurricane landfall. The observation of hurricane impacts on a coastal watershed can be further enhanced by use of tasseled cap transformation (TCT). TCT plots provide information on a host of land cover conditions with respect to soil moisture, canopy and vegetation cover. The before and after TCT plots help assess the damage sustained in a hurricane event and also see the progress of recovery. Finally, the use of synthetic images obtained by use of data fusion will help close the gap of low temporal resolution of Landsat satellite and this will create a more robust monitoring system.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-6688 |
| Date | 01 January 2017 |
| Creators | Mostafiz, Chandan |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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