**5. Conclusion**

Remote sensing has significantly advanced spatial analyses on terrestrial vegetation for various fields of science. However, mapping of benthic vegetation or submerged aquatic vegetation (SAV), using remotely sensed data is complicated due to several factors including atmospheric interferences, variability in water depth and bottom albedo, and water column attenuation by scattering and absorption. Hence, correction for the atmospheric and the overlying water column effects is necessary to retrieve any quantitative information for SAV from satellite and airborne images, especially when using hyperspectral data. Significant misclassification of the SAV often occurs due to the lack of information on *in situ* water depths and water column optical properties. Most of the currently available radiative transfer models only work well when applied to mapping of benthic features in relatively clear aquatic environments, but they do not correct for strong water absorption of the near infrared energy. The fluctuating water depths, high amounts of suspended particles and colored dissolved organic matter in shallow littoral zones make it even more challenging to map benthic vegetation using remotely sensed data. A new waterdepth correction algorithm was developed conceptually, and calibrated and validated using experimental and field data. The effects of the overlying water column on upwelling hyperspectral signals were modeled by empirically separating the energy absorbed and scattered by the water using data collected through a series of controlled experiments. The empirically driven algorithm significantly restored the vegetation signals, especially in the NIR region. Due to the restored NIR reflectance, which serves as the primary cue for discriminating SAV from other substrates, use of the water corrected airborne data increased the NDVI values for the SAV pixels and also improved the seagrass classification accuracy. Our continuing efforts to incorporate turbidity and CDOM into the algorithm, in developing a graphical user interface, and in implementing the algorithm into a module that can be called from commercially available image processing software promise a userfriendly application and wide use of the algorithm in the near future.

### **6. Acknowledgment**

The work was supported by grants from the National Geospatial-Intelligence Agency (Grant No. HM1582-07-1-2005 and HM1582-08-1-0049) and National Oceanic and Atmospheric Administration-Environmental Cooperative Sciences Center (Grant No.NA17AE1626).

#### **7. References**

304 Remote Sensing – Applications

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The water correction algorithm improved the classification accuracy results in an image subset from an overall accuracy of 28% to approximately 36%. Identification of the species *Halodule wrightii* improved from 33% user's accuracy to almost 78%, and *Thalassia testudimun* from 0% users accuarcy to almost 17%. Although these numbers appear to be somewhat low, several factors must be considered: this analysis only used a subset of the imagery, which allowed the area analysed to have less variation, but there was also less training and

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**4. Current efforts in developing water correction module and graphical user** 

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**13** 

*Brazil* 

**Remote Sensing and Environmental** 

*¹Federal University of Pará, Amazon Advance Studies (NAEA) ²Federal University of Pará, Center of Environment (NUMA)* 

Milena Andrade¹ and Claudio Szlafsztein²

**Sensitivity for Oil Spill in the Amazon, Brazil** 

The use of remote sensing has become a fundamental tool for the identification and analysis of different types of risks in coastal zones. The numerous and, in some cases, recent incidents of oil spills have encouraged companies and government agencies to improve methods, both anticipatory and corrective, to minimize damages. The term 'risk' denotes the possibility that adverse effects may occur as a result of natural events or human activities (Kates et al., 1985). Risk is defined as an association between the hazard´s characteristics (e.g. frequency, magnitude and location) and the vulnerability of affected human populations, environment and infrastructure (Wisner et al., 2004). Risk can be classified by their origin, such as natural, social, or technological (Renn, 2008). Oil spills are an example of the last category, and the coastal areas are one of the most impacted. Environmental sensitivity to oil impacts can be defined through the coastal Environmental Sensitivity Index (ESI), which considers: (i) the geomorphologic aspects such as type and slope of coastline and the degree of exposure to the energy of waves and tides; (ii) oil sensitive biological resources; and (iii) the socio-economic activities that can be affected by oil spills (Gundlach

In Brazil, environmental sensitivity mapping has been carried out under the law 9966/2000, which gave the Ministry of the Environment (Climate Change and Environmental Quality Secretary) responsibility to identify, locate and define the boundaries of ecologically sensitive areas with respect to the spill of oil and other dangerous substances in waters within national jurisdiction. This way, based on PETROBRAS (2002) and NOAA (2002), the specifications and technical standards for preparing environmental sensitivity maps for oil spills in coastal and marine zones was elaborated upon (MMA, 2002). Such environmental sensitivity maps provide information in an easy format being useful to determine priorities to impact protection and mitigation. Identification and mapping is developed at three levels: (i) Strategic (1:500,000 for the entire area of a hydrographical basin); (ii) Tactical (1:150,000 for the entire coastline mapped); and (iii) Operational (up to 1:50,000 for a highrisk/sensitivity areas). Each of these mapping scales uses specific tools for remote sensing

The Amazonian coastal zone extends along ~2250 km, not including the several inlets, islands and small estuaries, which punctuate the coastline (Souza Filho et al., 2005a). This

**1. Introduction** 

and GIS tools.

& Hayes, 1978; Dutrieux et al., 2000).

*International Conference on Remote Sensing for Marine and Coastal Environments,* Vol. I, pp. 657-666, ISSN 1066-3711, Orlando, Florida, USA, March 17-19, 1997

