**7. Case study**

340 Remote Sensing – Applications

The result of this operation generates a new band—the image with water column correction for a band pair (depth-invariant index). Since the values of this band are whole numbers with decimals and can be negative, in order to visualize them they need to be converted into an 8-bit format, that is, gray values between 0 and 255. To this end, minimum and maximum values for the resulting image must be found and linearly distributed between the values 1 and 255 (0 is not included because it is assigned to the masked surface area). The depthinvariant index is essential when the objective of the study is to extract spectral data for

The classification of a satellite image consists of assigning a group of pixels to specific thematic classes based on their spectral properties. The spatial classification of underwater coastal ecosystems is one of the most complex processes in thematic cartography using satellite images. As previously mentioned, this can be attributed primarily to the influence of the atmosphere and the ocean water column, through which electromagnetic radiation passes. In addition, it is worth mentioning that these ecosystems undergo constant variation, especially after significant events such as strong hurricanes. Nevertheless, different authors (Mumby et al., 1997; Andréfouët & Payri 2000; Mumby and Edwards 2002; Andréfouët et al., 2003; Pahlevan et al., 2006; Call et al., 2003, etc.) have been using remote sensing to develop different classification methods for these

The maximum likelihood classifier is the most common method, and has been used by authors such as Mumby et al. (1997), Andréfouët et al. (2000), Mumby and Edwards (2002), Andréfouët et al. (2003), Pahlevan et al. (2006), and Benfield et al. (2007). Its primary advantage is that it offers a greater margin for accounting for the variations in classes through the use of statistical analysis of data, such as the mean, variance and covariance. The results of the method can be improved with the incorporation of additional spatial information during the post-classification process, since this helps to spectrally separate the

Another method also used by Mumby et al. (1997) is agglomerative hierarchical classification with group-average sorting. An alternative proposal is object-oriented classification, which consists of two steps, segmentation and classification. Segmentation creates image-objects and is used to build blocks for further classifications based on fuzzy logic. Another method that has been used is ISODATA (iterative self-organizing data analysis), which uses a combination of Euclidian squared distance and the reclassification of the centroid (Call et al., 2003). In this study, ISODATA was used to perform the

ISODATA is an unsupervised classification method as well as a way to group pixels, and uses the minimum spectral distance formula. It begins with groups that have arbitrary means and each time the pixels in each of the iterations are regrouped and the means of the

The algorithm for obtaining the classification is based on the following parameters:

submerged aquatic environments.

**6. Review of classification methodologies** 

ecosystems and, in particular, for coral reefs.

**6.1 ISODATA (Iterative Self Organizing Data Analysis)** 

groups change. The new means are then used for the next iterations.

classes that had been mixed.

classification.

The Chinchorro Biosphere Reserve (Fig. 4) is located in the open Caribbean Sea, 30.8 km east of the coastal city of Mahahual, which is the closest continental point. The coral reef of Chinchorro Bank, Mexico, is part of the great reef belt in the western Atlantic, the second largest in the world, and is the biggest oceanic reef in Mexico. With a reef lagoon area of 864 km2, it is considered a pseudo-atoll or reef platform (Camarena, 2003). Chinchorro Bank is a reef complex that contains an extensive coral formation with a vast wealth and diversity of species and high ecological, social and cultural value. It inherently provides certain services, including the protection of the coast from battering by storms and hurricanes. The area has been exploited by fishing and tourist-related scuba diving over the past decades. The Chinchorro Bank supports pristine reefs, coral patches, extensive areas of seagrass, microalgae beds and sand beds. The reserve's ecosystems are marked by mangroves and reef zones. The composition of the taxocenosis of coral is known to contain hexacorals,

Fig. 4. Study Area: Chinchorro Bank, Mexico.

Satellite Remote Sensing of Coral Reef Habitats Mapping in

Chinchorro Bank, where the depths of the zone can be seen.

explained further below.

Shallow Waters at Banco Chinchorro Reefs, México: A Classification Approach 343

It is also very important to note that bathymetry is one of the most relevant factors in the dynamic ecology of coral reefs. Numerous reef studies show that coral species diversity tends to increase as a function of depth, reaching its maximum between 20–30 m and diminishing with greater depth (Huston, 1985). This depth effect results in a marked zonation of the reef community (Aguilar-Perera and Aguilar-Dávila, 1993). While the upper depth limits of corals are controlled by various physical and biological factors, their maximum depth depends largely upon light availability (García-Ureña, 2004). The bathymetric soundings for Chinchorro Bank used by this study were done in 2008 by the Mexican Navy (SEMAR, 2008). The depth of the interior of the bank varies. The northern portion is shallower, between 1 and 2 m, the depth of the central portion ranges between 3 and 7 m, and the southern portion is deepest, varying between 8 and 15 m (SEMAR, 2008). There are 4 emerged zones within the bank, known as keys, which have high ecological value because of their diverse species of flora and aquatic and land fauna (Camarena, 2003). Figure 5b shows bathymetry data for the

In situ sampling data were provided by SEMAR. Data from Carricart-Ganivet et al. (2002) were also used. Based on these data, 4 of the most representative classes were determined: 1) coral mass, 2) coral patches, 3) seagrass and algae and 4) sand. The ocean and keys, or emerged areas, are not part of the classification criteria, though they are also represented. Unfortunately, the databases for the in situ sampling have disadvantages—such as mixing classes in the same point and lack of definition of the benthic bottom, among others—that prevent their being used for validation purposes. Only data for sand provided by SEMAR do not present these disadvantages and could be used for water column correction, as

Fig. 5. Information resources. a) Landsat 7-ETM+ image and b) depth of the Chinchorro Bank

octocorals and hydrozoas and a reported 95 different species (Camarena, 2003). The diversity of the fauna in the Chinchorro Bank is very high and includes several phyla, families, genres and species, with at least 145 macro invertebrate and 211 vertebrate species, in addition to the corals (Bezaury et al., 1997).

The biogeographic region of Chinchorro Bank is delimited on the north by the Caribbean Province which extends along Central and South America. This province begins in Cabo Rojo, in southern Tampico, and extends into eastern Venezuela and the northern Orinoco delta. The land biota is greatly similar to that of the continent and is therefore considered to be part of the Yucateca Province. It is located in the Mexican Caribbean, across from the southeastern coast of the state of Quintana Roo, between the 18º47'-18º23' N and 87º14'- 87º27' W parallels. It is 30.8 km from the continent and separated from it by a wide canal 1000 m deep. The shape of Chinchorro Bank is elliptic, with a reef lagoon that includes a sandy bank 46 m long (north-south) and 18 km wide (east-west) at its broadest part. The total area is 144360 ha. The periphery of the bank is bordered by active coral growth on the eastern (windward) margin, which forms a coral reef, or breaker, while along the western margin (leeward) the breaker disappears and the coral growth is semicontinuous and diffuse (Camarena, 2003). There are four emerged zones within the bank—known as "Cayo Norte" (two islands), "Cayo Centro" and "Cayo Lobos"—whose ecological value is very high because of their diverse species of land and water flora and fauna (Camarena, 2003).

#### **8. Information resources**

The geospatial database used in this study includes a Landsat 7-ETM+ image (Table 1), bathymetric information and in situ data for sand (Figure 5). The digital data were projected to UTM (Universal Transverse Mercator) zone 16 north with WGS-84 datum. ERDAS, GEOMATIC 10.2 and ArcMap 9.3 were used to process the data.

The importance of choosing the type of image with which to work is well-known, particularly because the users will need to make sure to use images that are suitable to the purpose of the study. The nature of a platform-sensor system determines the characteristics of the image's data (Green, 2000). The Landsat 7-ETM+ (Table 1) image obtained had no cloud cover. It is worth noting that this type of images provides adequate coverage of the area for regional and temporal monitoring studies.


Table 1. Characteristics of the Landsat 7-ETM+ image used

octocorals and hydrozoas and a reported 95 different species (Camarena, 2003). The diversity of the fauna in the Chinchorro Bank is very high and includes several phyla, families, genres and species, with at least 145 macro invertebrate and 211 vertebrate species,

The biogeographic region of Chinchorro Bank is delimited on the north by the Caribbean Province which extends along Central and South America. This province begins in Cabo Rojo, in southern Tampico, and extends into eastern Venezuela and the northern Orinoco delta. The land biota is greatly similar to that of the continent and is therefore considered to be part of the Yucateca Province. It is located in the Mexican Caribbean, across from the southeastern coast of the state of Quintana Roo, between the 18º47'-18º23' N and 87º14'- 87º27' W parallels. It is 30.8 km from the continent and separated from it by a wide canal 1000 m deep. The shape of Chinchorro Bank is elliptic, with a reef lagoon that includes a sandy bank 46 m long (north-south) and 18 km wide (east-west) at its broadest part. The total area is 144360 ha. The periphery of the bank is bordered by active coral growth on the eastern (windward) margin, which forms a coral reef, or breaker, while along the western margin (leeward) the breaker disappears and the coral growth is semicontinuous and diffuse (Camarena, 2003). There are four emerged zones within the bank—known as "Cayo Norte" (two islands), "Cayo Centro" and "Cayo Lobos"—whose ecological value is very high because of their diverse species of land and water flora and fauna (Camarena,

The geospatial database used in this study includes a Landsat 7-ETM+ image (Table 1), bathymetric information and in situ data for sand (Figure 5). The digital data were projected to UTM (Universal Transverse Mercator) zone 16 north with WGS-84 datum. ERDAS,

The importance of choosing the type of image with which to work is well-known, particularly because the users will need to make sure to use images that are suitable to the purpose of the study. The nature of a platform-sensor system determines the characteristics of the image's data (Green, 2000). The Landsat 7-ETM+ (Table 1) image obtained had no cloud cover. It is worth noting that this type of images provides adequate coverage of the

> Date 2000-03-29 Scan time 16:03:05 Path/Row 18/47

Spatial resolution (m) 30 Spectral bands used 3 Spectral range (μm) 0.5-0.69 Azimuth 116.29 Solar angle 59.43

GEOMATIC 10.2 and ArcMap 9.3 were used to process the data.

area for regional and temporal monitoring studies.

Table 1. Characteristics of the Landsat 7-ETM+ image used

in addition to the corals (Bezaury et al., 1997).

2003).

**8. Information resources** 

It is also very important to note that bathymetry is one of the most relevant factors in the dynamic ecology of coral reefs. Numerous reef studies show that coral species diversity tends to increase as a function of depth, reaching its maximum between 20–30 m and diminishing with greater depth (Huston, 1985). This depth effect results in a marked zonation of the reef community (Aguilar-Perera and Aguilar-Dávila, 1993). While the upper depth limits of corals are controlled by various physical and biological factors, their maximum depth depends largely upon light availability (García-Ureña, 2004). The bathymetric soundings for Chinchorro Bank used by this study were done in 2008 by the Mexican Navy (SEMAR, 2008). The depth of the interior of the bank varies. The northern portion is shallower, between 1 and 2 m, the depth of the central portion ranges between 3 and 7 m, and the southern portion is deepest, varying between 8 and 15 m (SEMAR, 2008). There are 4 emerged zones within the bank, known as keys, which have high ecological value because of their diverse species of flora and aquatic and land fauna (Camarena, 2003). Figure 5b shows bathymetry data for the Chinchorro Bank, where the depths of the zone can be seen.

In situ sampling data were provided by SEMAR. Data from Carricart-Ganivet et al. (2002) were also used. Based on these data, 4 of the most representative classes were determined: 1) coral mass, 2) coral patches, 3) seagrass and algae and 4) sand. The ocean and keys, or emerged areas, are not part of the classification criteria, though they are also represented. Unfortunately, the databases for the in situ sampling have disadvantages—such as mixing classes in the same point and lack of definition of the benthic bottom, among others—that prevent their being used for validation purposes. Only data for sand provided by SEMAR do not present these disadvantages and could be used for water column correction, as explained further below.

Fig. 5. Information resources. a) Landsat 7-ETM+ image and b) depth of the Chinchorro Bank

Satellite Remote Sensing of Coral Reef Habitats Mapping in

Shallow Waters at Banco Chinchorro Reefs, México: A Classification Approach 345

Fig. 6. Steps for water column correction: (a) spectral radiance of bands 1 and 2

depths.

Covariance (

Table 3. Calculation of ratio of attenuation coefficients

ki/kj. The pixels are thus classified for different types of bottoms.

(atmospherically corrected), (b) exponential decay of the radiance for bands 1 and 2 using natural logarithms and (c) biplot of bands 1 and 2 for a single bottom (sand) at different

Ratio 1/2 Ratio 1/3 Ratio 2/3

*ij* ) 0.3200 0.1178 0.2327

aij -0.0593 -0.0031 0.0184 ki/kj 0.94 0.99 1.00

Figure 6c shows the biplot of the logarithmically transformed bands 1 and 2, representing the attenuation coefficient (ki/kj) for bands 1 and 2. It is important to mention that if different types of bottoms are represented in a biplot, they would theoretically represent a line with a similar behavior, varying in position only due to differences in spectral reflectance. The gradient of the line would be identical since ki/kj does not depend on the type of bottom. The intersection of the line with the y-axis represents the depth-invariant index, since each type of bottom has a unique y-intersect regardless of depth. Each pixel is assigned an index depending on the type of bottom, which is obtained using the natural logarithm transformation for each band and the connection of the coordinate to the origin of the y-axis through gradient line
