**Remote Sensing of Submerged Aquatic Vegetation**

Hyun Jung Cho1, Deepak Mishra2,3 and John Wood4 *1Department of Integrated Environmental Science, Bethune-Cookman University, Daytona Beach, FL 2Department of Geosciences, Mississippi State University 3Northern Gulf Institute and Geosystems Research Institute, Mississippi State University, MS State, MS 4Harte 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 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.

Remote Sensing of Submerged Aquatic Vegetation 299

Remote sensing of benthic habitats is complicated because of several factors including (1) atmospheric interferences, (2) variability in water depth, (3) water column attenuation, and (4) variability in bottom albedo or bottom reflectance. In the case of aquatic remote sensing, the total signal received at satellite altitude is dominated by radiance contributed by atmospheric scattering, and only 8-10% of the signal corresponds to the water reflectance and reflectance from benthic features (Kirk, 1994, Mishra et al., 2005). Therefore, it is advisable to correct for atmospheric effects to retrieve any quantitative information for surface waters or benthic habitats from satellite images. Therefore, the lack of a rigorous absolute atmospheric correction procedure can introduce significant errors to a satellite derived benthic habitat map. There is also a tendency among benthic mapping researchers to use a relative atmospheric correction procedure such as a deep-water pixel correction, especially when local aerosol data and

Knowledge of the optical properties of the water column can help eliminate changes in reflectance attributable to variable depth and water column attenuation effects, which often lead to misclassification of the benthos (Mishra et al., 2005). Mishra et al (2005) proved that to derive accurate bottom albedo or bottom reflectance using a radiative transfer model, water depth and water column optical properties (absorption and backscattering) should be known for the study area. Knowledge of optical bottom albedo for shallow waters is necessary to model the underwater and above-water light field, to enhance underwater object detection or imaging, and ultimately to determine the distribution of benthic habitats (Gordon & Brown, 1974). Mishra et al (2005, 2006, 2007) also point out that the signals measured by a sensor from above the water surface of a shallow marine environment are highly affected by phytoplankton abundance (chlorophyll absorption), water column interactions (absorption by water and scattering by suspended sediments), and radiance reflected from the bottom. For the bottom contribution to be retrieved by a sensor the water column contributions have to be removed and the optical properties have to be known or at least be derivable. However, it is very challenging to measure these optical properties accurately because of logistical issues and

**2.3 Dilemmas in remote sensing of shallow aquatic system and SAV** 

validation data are lacking. This often causes mediocre classification results.

instrumentation errors, which also leads to an inaccurate benthic mapping project.

confusion in satellite imagery.

Variability in bottom types and hence albedo gives rise to a mixed spectral response that often reduces the classification accuracy. Specific problems such as complex benthic combinations (e.g., sandy areas with variable amounts of algal cover; variation in color, texture, size), and error in depth estimation can also have a considerable impact on the classification results. Mishra et al (2005) proposed several solutions to increase the number of elements separable by a classification scheme and the classification accuracies including an extensive field campaign acquiring substantial samples to enable statistical evaluation for each class and deriving detailed ecological and biological information for each *in situ* data point. Close-range hyperspectral studies that may aid in discriminating between different types of benthic features can be used to develop baseline spectra to help minimize spectral

Shallow littoral areas (generally the areas between the shoreline to a water depth 2 m) are one of the most productive habitats, yet the most sensitive landscape to human-induced environmental alteration and global climate changes. Modeling of optical water properties for the littoral zone is more complicated due to rapidly changing water depth and/or substrate and higher amounts of Colored Dissolved Organic Matter (CDOM) and/or
