**Section 3**

**Oceans and Cryosphere** 

294 Remote Sensing – Applications

Takamura, T., Nakajima, T., Overview of SKYNET and its activities. Optica Pura y Aplicada

Tang, J., Xue, Y., Yu, T., Guan,Y. Aerosol optical thickness determination by exploiting the

Todate, Y., Minomura, M., Kuze, H., Takeuchi, N., On the retrieval of aerosol information on

synergy of TERRA and AQUA MODIS. Remote Sensing of Environment Vol. 94,

the land surface from Landsat-5/TM data, in Proceedings of the 36th Annual Meeting of the Remote Sensing Society of Japan, (2004), pp.57 - 58. (*in Japanese*) Yoshii, Y., Kuze, H., Takeuchi, N., Long-path measurement of atmospheric NO2 with an

obstruction flashlight and a charge-coupled-device spectrometer. Appl. Opt., Vol.

Vol. 37, (2004), pp. 3303–3308, ISSN 0030-3917

(2005), pp. 327–334, ISSN 0034-4257

42, (2003), pp. 4362-4368, ISSN 0003-6935

**12** 

**Remote Sensing of** 

*Bethune-Cookman University,* 

*Daytona Beach, FL* 

*Corpus Christi, TX* 

*USA* 

**Submerged Aquatic Vegetation** 

*2Department of Geosciences, Mississippi State University 3Northern Gulf Institute and Geosystems Research Institute,* 

*4Harte Research Institute for Gulf of Mexico Studies,* 

*1Department of Integrated Environmental Science,* 

*Mississippi State University, MS State, MS* 

*Texas A&M University-Corpus Christi,* 

Hyun Jung Cho1, Deepak Mishra2,3 and John Wood4

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.,

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 various plant characteristics, including vegetation type (Geerken et al., 2005), vegetation cover (du Plessis, 1999), vegetation water content (Jackson et al., 2004), biomass and productivity (Fang et al., 2001), chlorophyll level (Wu et al., 2008), PAR absorbed by crop canopy (Goward & Huemmrich, 1992), and flooded biomass (Beget et al., 2007) at a broad span of scales from

individual leaf areas to global vegetation dynamics.

**1. Introduction** 

2007).
