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REMOTE SENSING – APPLICATIONS_2

Nowadays it is hard to find areas of human activity and development that have not profited from or contributed to remote sensing. Natural, physical and social activities find in remote sensing a common ground for interaction and development. This book intends to show the reader how remote sensing impacts other areas of science, technology, and human activity, by displaying a selected number of high quality contributions dealing with different remote sensing applications. | Section 3 Oceans and Cryosphere 12 Remote Sensing of Submerged Aquatic Vegetation Hyun Jung Cho1 Deepak Mishra2 3 and John Wood4 department of Integrated Environmental Science Bethune-Cookman University Daytona Beach FL department of Geosciences Mississippi State University 3Northern Gulf Institute and Geosystems Research Institute Mississippi State University MS State MS Harte Research Institute for Gulf of Mexico Studies Texas A M University-Corpus Christi Corpus Christi TX USA 1. Introduction Remote sensing has significantly advanced spatial analyses of terrestrial vegetation for various fields of science. The plant pigments chlorophyll a and b strongly absorb the energy in the blue centered at 450 nm and the red centered at 670 nm regions of the electromagnetic spectrum to utilize the light energy for photosynthesis. In addition the internal spongy mesophyll structures of the healthy leaves highly reflect the energy in the near-infrared NIR 700- 1300 regions Jensen 2000 Lillesand et al. 2008 . The distinctive spectral characteristics of the green plants low reflectance in the visible light and high reflectance in NIR have have been used for mapping monitoring and resource management of plants and also have been used to develop spectral indices such as Simple Vegetation Index SVI NIR reflectance - red reflectance and Normalized Difference Vegetation Index NDVI NIR reflectance - red reflectance NIR reflectance red reflectance Giri et al. 2007 . The simplicity and flexibility of vegetation indices allow comparison of data obtained under varying light conditions Walters et al. 2008 . NDVI was first suggested by Ruose et al. 1973 and is one of the earliest and most popular vegetation index used to date. It is usually applied in an attempt to decrease the atmospheric and surface Bidirectional Reflectance Distribution Function BRDF effects by normalizing the difference between the red and NIR reflectance by total radiation. Index values have been associated with .