**Satellite Remote Sensing of Coral Reef Habitats Mapping in Shallow Waters at Banco Chinchorro Reefs, México: A Classification Approach**

Ameris Ixchel Contreras-Silva1, Alejandra A. López-Caloca1, F. Omar Tapia-Silva1,2 and Sergio Cerdeira-Estrada3 *1Centro de Investigación en Geografía y Geomática "Jorge L. Tamayo" A.C., CentroGeo 2Universidad Autónoma Metropolitana, Unidad Iztapalapa 3Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO Mexico* 

#### **1. Introduction**

330 Remote Sensing – Applications

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sensitivity in Cardos Island State Park and surroundings areas, São Paulo, Brazil.

Interest in protecting nature has arisen in contemporary society as awareness has developed of the serious environmental crisis confronting us. One of the ecosystems most impacted is the coral reefs, which while offering a great wealth of habitats, diversity of species and limitless environmental services, have also been terribly damaged by anthropogenic causes. One example of this is the oil spill from petroleum platforms (in the recent case of the Gulf of Mexico). The effects of global warming—such as the increase in the incidence and intensity of hurricanes and drastic changes in ocean temperature—have caused dramatic damage, such as the bleaching and decrease of coral colonies. In light of this devastating situation, scientific studies are needed of coral reef communities and the negative effects they are undergoing.

The case study presented in this work takes place in the Chinchorro Bank coral reefs in Mexico. These are part of the great reef belt of the western Atlantic, with a biological richness that inherently provides environmental, economic and cultural services at the local scale as well as worldwide. Nevertheless, these services have been weakened for decades due to overexploitation, inducing imbalances and problems in the zone. Over recent decades, numerous biological communities that house constellations of species—whose natural evolutionary process dates back million of years (Primack et al., 1998)—have been alarmingly degraded. If this trend continues, the entire evolution that is sustained by the life of these communities will disappear in a relatively short period of time.

This study clearly demonstrates the application of state-of-art Remote Sensing (RS) in coral ecosystems. It includes an analysis based on the application of Iterative Self Organizing Data Analysis (ISODATA) as a classifier for generating classes of benthic ecosystems present in a coral reef system, using satellite images (Landsat 7-ETM+).

Satellite Remote Sensing of Coral Reef Habitats Mapping in

ecosystem and predict possible future changes.

**3. Spectral reflectance of coral** 

well as to design viable alternatives for their conservation.

(Brock et al., 2006).

Shallow Waters at Banco Chinchorro Reefs, México: A Classification Approach 333

urban stain, vegetation coverage, the structure of the hydrographic basins, etc. Intrinsic conditions of coral reefs can be described, which are largely defined by the inflows and outflow and their transport of sediments and export of dissolved organic matter. This enables us to understand the patterns involved in coral whitening, among other events

The coral reefs—located in relatively clear water—allow us to use passive optic sensors (Benfield et al., 2007). The more common satellite sensors that have been used to study this are SPOT, Landsat TM and ETM+ (Andréfouët & Riegl 2004; Benfield et al., 2007; Mumby 2006; Mumby et al., 2004; Mumby and Harborne 1998). Studies previously conducted (Green, 2000; Mumby et al., 1999) have observed that Landsat and SPOT images are suitable for mapping corals, sands, and seagrass, depending on their resolution. Nevertheless, it is important to note that various types of habitats can be represented in one Landsat image pixel (or others with less spatial resolution), which may limit classification abilities (Benfield et al., 2007). Previous studies conducted (Green, 2000; Mumby et al., 1999) have observed that according to the resolution of Landsat images, they are suitable for mapping sea corals, sands and seagrass. Based on this assumption, the data obtained from Landsat and SPOT are adequate for simple complexity mapping (3-6 classes, such as seagrass, sand, dead corals and some species of corals) but for more complex targets (7-13 classes) they are limited by their spatial and spectral resolution. (Mumby, 1997; Andréfouët et al., 2003; Capolsini et al., 2003). To a lesser extent, SeaWiFS (seaviewing wide field of view sensors) have also been used, as well as IKONOS with higher spatial resolution, LIDAR and SONAR, among others (Andréfouët & Riegl 2004; Andréfouët et al., 2003; Brock et al., 2006; Elvidge et al., 2004; Liceaga-Correa & Euan-Avila, 2002; Hsu et al., 2008; Lesser and Mobley, 2007). It is important to note that analytical methods as well as spatial modeling, statistics and empirical methods at different scales and for different applications have been used in direct relation to ecological processes of reefs (Andréfouët & Riegl 2004). The use of airborne remote sensors, such as CASI (Compact Airborne Spectrographic Imager) with a high spectral or hyperspectral resolution, has gradually been increasing in this type of studies, to the extent that the specialists mention that mapping reefs using air or satellite sensors have proven to be more effective than fieldwork (Mumby, 1999). Nevertheless, field measurements cannot be discarded, since they provide us with the basis for corroborating the information obtained from satellite images. In addition, images from satellite sensors provide the opportunity to conduct multi-temporal monitoring (Helge et al., 2005) in order to identify the status of an

According to the above, it can be stated that studies applying RS in coastal ecosystems and, specifically, in coral reef ecosystems provide information and knowledge that can successfully be applied to define management strategies for these important ecosystems, as

To make observations, we move vertically and gradually from the coral surface to the water surface, measuring the changes in the quantity of light in the water column that falls directly on the coral. The quantity of light present obviously affects the amount that is reflected by

the coral, and is therefore a crucially important parameter for mapping it.
