Coralline reefs classification in Banco Chinchorro

Coralline reefs classification in Banco Chinchorro, Mexico Ameris I. Contreras-Silvaa & Alejandra A. López-Calocaa* a Centro de Investigación en Geografía y Geomatica “Ing. Jorge L. Tamayo” CentroGeo Contoy 137 Esq. Chemax, Col. Lomas de Padierna. Del. Tlalpan CP 14240 Mexico City (55) 2615 2224 ABSTRACT The coralline reefs in Banco Chinchorro, Mexico, are part of the great reef belt of the western Atlantic. This reef complex is formed by an extensive coralline structure with great biological richness and diversity of species. These colonies are considered highly valuable ecologically, economically, socially and culturally, and they also inherently provide biological services. Fishing and scuba diving have been the main economic activities in this area for decades. However, in recent years, there has been a bleaching process and a decrease of the coral colonies in Quintana Roo, Mexico. This drop is caused mainly by the production activities performed in the oil platforms and the presence of hurricanes among other climatic events. The deterioration of the reef system can be analyzed synoptically using remote sensing. Thanks to this type of analysis, it is possible to have updated information of the reef conditions. In this paper, satellite imagery in Landsat TM and SPOT 5 is applied in the coralline reefs classification in the 19802006 time period. Thus, an integral analysis of the optical components of the water surrounding the coralline reefs, such as on phytoplankton, sediments, yellow substance and even on the same water adjacent to the coral colonies, is performed. The use of a texture algorithm (Markov Random Field) was a key tool for their identification. This algorithm, does not limit itself to image segmentation, but also works on edge detection. In future work the multitemporal analysis of the results will determine the deterioration degree of these habitats and the conservation status of the coralline areas. Keywords: Coral reef, remote sensing, classification, Banco Chinchorro/Mexico.

1. INTRODUCTION The interest in protecting nature has risen in contemporary research as conscience has been acquired of the serious environmental crises we are facing. During the last few decades numerous biological colonies sheltering constellations of species, whose evolution has taken millions of years, have been devastated by human contact (Primack et al., 1998). If this trend continues, thousands of colonies, species and ecological varieties will become extinct in a relatively short lapse of time. The ocean is a vast and complicated system to which a multidisciplinary focus should be given, considering its origin and history, its processes, mechanisms, circulation, composition and life forms, as well as the consequences of its utilization, knowledge and exploitation. Since past decades the loss of biological biodiversity has been increasing in tropical oceanic zones due to human exploitation of the natural resources under non-sustainable patterns which have contributed to the degradation and destruction of natural habitats both on land and on water. Regardless of all the growing ecological conscience campaigns and the declaration of international years of the ocean or of coral reefs, the anthropogenic impacts continue to generate potential threats without precedents to many marine species and even more to the biological interactions of the ocean. The coral reefs shelter the highest concentration of marine biodiversity, working as a kind of oasis within the immense ocean. It is one of the most complex and diverse ecosystems in the entire world, constituting the home for almost a million of different marine species (Monismith, 2007). They have a high aesthetic, recreational, and resource value which has prompted close scientific scrutiny, including long-term monitoring, while facing increasing threats at a local and global scale (Carpenter et al., 2008). In recent years bleaching and reduction of the coral colonies have been observed throughout the entire world. This is mainly due to the production activities in oil platforms, the impact from a

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Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, edited by Christopher M. U. Neale, Antonino Maltese, Proc. of SPIE Vol. 7472, 74720O · © 2009 SPIE · CCC code: 0277-786X/09/$18 · doi: 10.1117/12.830549

Proc. of SPIE Vol. 7472 74720O-1

natural events such as hurricanes and severe climatic changes (increase in temperature and acidification of the ocean, severity of storms and sea-level rise) (Sheppard et al., 2005). Ocean acidification is reducing ocean carbonate ion concentrations and the ability of corals to build skeletons (Carpenter et al., 2008). Local threats include human disturbances such as increased coastal development, sedimentation resulting from poor land-use and watershed management, sewage discharges, nutrient loading and eutrophication from agrochemicals, coral mining, and overfishing. Local anthropogenic impacts reduce the resilience of corals to withstand global threats, resulting in a global deterioration of reef structure and ability of these ecosystems to sustain their characteristic complex ecological interactions (Carpenter et al., 2008). In the year 1998, just in the Caribbean Sea, 12 per cent of the reefs were destroyed and 67 per cent are under imminent risk (Lobe, 2004). From this stems the interest for the study of coralline reefs, since they are ecosystems which offer a wide range of habitats, each one with an intrinsic complexity of species and characteristics. The coastal ecosystems in Mexico and particularly the ones in the Mexican Caribbean are part of a general concern due to their relationship with great developments regarding ecotourism and artisan fishing which generate important job and economical resources. The coralline reefs occupy approximately 700 km of the almost 900 of littoral in Quintana Roo, extending from the north of the Yucatan peninsula towards the south, in front of the coastlines of Belize, Guatemala and Honduras. It is possible to identify and analyze the deterioration of the reef system in a synoptic way through the use of remote sensors (Mumby et al., 2001; Mumby and Edwards, 2002). This type of analysis gives the possibility of having updated information on the condition of the reefs. For this reason we present the coralline reefs classification through the 19702003 period for Landsat TM and 2006 for SPOT 5. This paper presents a contextual classifier based on Markov Random Fields theory. The use of texture algorithms (Markov Random Fields) is a key tool for its identification. The MRF has the advantage of segmenting an image into specific regions, each one having uniform and homogenous properties, whose values are significantly different from those in the neighboring regions. This type of segmentation is helpful in cases where vegetation is present (Lopez-Caloca et al., 2008) as in the coralline reefs in Banco Chinchorro. In terms of result of reflection of the light that penetrates into the water; a clear water column rapidly attenuates wavelengths; therefore, reflected blue band are used most often as they experience less attenuation in water (Gonzales, 2003, Green et al., 1996).

2. STUDY AREA 2.1 Banco Chinchorro The coralline reefs of Banco Chinchorro, Mexico, are part of the great reef belt of the western Atlantic, the second largest in the world. It is also the biggest oceanic reef in Mexico, considered as a pseudo atoll of the Caribbean or as a reef in platform (Camarena, 2003). Chinchorro Bank is a reef complex which presents an extensive coralline formation of vast richness and diversity of species with a high ecological, social and cultural value which inherently provides certain services, among which is highlighted the protection for the coast against storms and hurricanes embattlement. The area has been exploited through fishing and touristic scuba diving for the past decades. The Chinchorro Bank emerges to open sea with its 864 km of area in the reef lagoon, supporting pristine reefs, coralline patches, extensive areas of seagrasses, microalgae beds and sand beds. The ecosystems present at the Reserve are represented by mangroves and reef zones. The known composition of the coralline taxocenosis is represented by hexacorals, octocorals and hydrozoas with 95 reported species (Camarena, 2003). The diversity of the fauna in the Chinchorro Bank is very high and it includes several phyla, families, genres and species, with at least 145 species of macro invertebrates and 211 of vertebrates, additional to the corals (Bezaury et al., 1997). 2.2 Location The seagrasses are the most conspicuous phanerogams (Camarena, 2003). As it is known, seagrass works as breeding, shelter and raising grounds for species of ecological and economical importance, since species of great commercial importance are present there such as the pink snail and the thorny lobster. Banco Chinchorro possesses as well mangle islands which are distributed bordering the Keys or towards its central portion (Camarena, 2003). These include the four species of mangrove (Rhizophora mangle, Laguncularia racemosa, Avicennia germinans and Conocarpus erectus), with great colonies of nesting grounds for marine birds such as the stork. In total, there is a registry for 96 species of migratory and local birds (Bezaury et al., 1997). The small lakes in the Cayos (Keys) give shelter to endangered species such as the American crocodile. The Caretta caretta, Eretmochelys imbricata turtles and the white turtle Chelonia mydas, also use these keys as part of their reproductive cycle as egging grounds, making these areas indispensable for


protection.(Aguilar-Perera y Aguilar-Dávila, 1993). The composition of the algae taxocenosis is represented by chlorophytes, cyanophytes, feophytes and rodophytes, which along with the seagrass and the coralline reef structures contribute to the increase of the habitat mosaics available for the existence and distribution of the benthonic and nektonic fauna diversity (Bezaury et al., 1997). There are no permanent human settlements in the Reserve except for a deployment of the Mexican Army and pile-dwellings used by fishermen as base of operations during fishing seasons. The closest settlements are Mahahual, with a population of 800 and Xcalak with 500 inhabitants (Camarena, 2003). Chinchorro Bank is delimited in its bio-geographic region in the northern portion of the Caribbean region which extends along Central and South America. This region begins from Cabo Rojo, south of Tampico in Mexico, to the East of Venezuela and the Northern part of the Orinoco delta. The terrestrial biota has strong affinities with the continent for which it is considered within the Yucatan province (Camarena, 2003). It is located in the Mexican Caribbean in front of the southeast coast of the state of Quintana Roo, between the parallels 18º47’-18º23’ N; 87º14’-87º27’ W, separated 30.8 km from the continent by a wide canal of 1,000 m of depth. The shape of Chinchorro Bank is elliptical and it presents a reef lagoon which includes a sand bank of 46 km in length (north to south) and 18 km of width (east to west) in its widest part. The total area is of 144,360 ha. The periphery of the bank is bordered by an active coralline growth over the eastern margin (windward) which forms a barrier reef or breaker zone, while along the western side (leeward) the breaker zone disappears and the coralline growth is semi-continuous and diffuse (Camarena, 2003). There are four emerged zones within the bank, known as “Cayo Norte (two islands), Cayo Centro and Cayo Lobos”, which have a very high ecological value, since they present diverse species of land and water flora and fauna (Camarena, 2003). A description of the classes in Banco Chinchorro was made by Aguilar-Perera and Aguilar-Dávila (1993). It includes a sand bank of 46 km in length (north to south) and 15 km in width (east to west). The periphery of the bank is bordered by a more active coralline growth over the oriental margin (windward) which forms a relief barrier. This zone presents sandy bottoms covered by dense marine grass zones to the north and center, as well as coralline patches zones to the south. 2.3 Bathymetry Bathymetry is one of the most relevant aspects 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 y Aguilar-Dávila, 1993).The upper depth limits of corals are controlled by various physical and biological factors; whereas, their maximum depth depends largely upon light availability (García-Ureña, 2004). The bathymetric soundings for Banco Chinchorro were done in 2008 by Secretaría de Marina in Mexico. The depth in the interior of the bank varies, in the northern part (1-2m), in the middle part (3-7m) and the deepest part is found to the south (8-15m) (SEMAR, 2008). In the same way, bathymetry was used, resulting from the Sonar instrument (Sound, Navigation and Ranging) (SEMAR, 2008), considering the depths of the study area for the processing of classes selection. Figure 1 shows the Banco Chinchorro’s bathymetry data, where the depths of the zone can be appreciated.

3. REMOTE SENSING IN CORALLINE REEF STUDIES Remote sensing techniques offer an option to map marine habitats, determining not only the locality and quantity of different benthonic habitats (Kirk, 1994) but also how these habitats are distributed and which is the level of connectivity among them (Rivera et al., 2004). However, Richardson and LeDrew (2006) note that there is a lack of research in the field of remote sensing in the coastal zone in comparison with the one done in open sea. According to these authors this lack is due to the fact that the coastal zone presents a biological and an optical complexity which depends on the spatial and temporal dynamics observed in the marine environment. It is worth noting that the ocean presents optical properties. These are divided in inherent and apparent properties upon which depends the absorption or the reflection of the target which can be detected through remote sensing. These inherent optical properties (Aguirre-Gómez, 2002) are: the absorption coefficient, the scattering coefficient and volume scattering function. Such magnitudes depend solely on the substances which make the marine environment and not on the geometric structure of the luminous field. The diffuse attenuation and reflectance coefficients, among other less important, are the apparent optical properties which depend on the radiation field properties (Preisendorfer, 1986).


Fig.1 Banco Chinchorro map that showing depth (Source: SEMAR, 2008). In order to reach an approximation which could help us to preserve the coral reefs we should understand the physical, chemical, biological and geological dynamics happening within this complex ecosystem (Brock et al., 2006). Regarding these ecosystems, in the last decade it has been confirmed that remote sensing is an excellent study and analysis method which helps to study in a holistic way this complex ecosystem (Kirk, 1994). It is true that the increasing concern regarding preserving these natural reservoirs is growing at a global level and that remote sensing has been validated as a tool to which several applications have been found (Andréfouët et al., 2003): the benthic status of the reef colonies, the areas covered by the coral, the extension of the bleaching, the determination of its geomorphologic structures, as well as mapping the habitat and determining bathymetry and water circulation (Andréfouët and Riegl, 2004). The possibilities of remote sensing range from the direct or indirect detection of the ecosystem’s physical properties as in the case of superficial ocean’s temperature (understood as an important physiological factor which determines the organism’s health), to the study and quantification of the aquatic ecosystems considering the level of biotic and abiotic functionality (Richardson and LeDrew, 2006). Andréfouët and Riegl (2004) mention that the application of remote sensing on coralline reefs has moved from being a tool without an application in this field, to a tool per se indispensable specially when a spatial and temporal context is required. In the same way, they give four reasons explaining this change: 1. The proliferation of new sensors for the acquisition of direct and indirect data for the monitoring of the reefs, 2. The proliferation and improvements on analytical, statistical and empiric approaches, 3. The recognition of a global climatic change due to human lethal anthropogenic impacts for the coralline reefs and 4. A better integration of the technology in the conceptual design of research done in coralline reefs. As mentioned, remote sensing takes different roles in the investigation of corals. However, the goal is to detail an adequate description for each ecosystem and, in general, to provide a data base which will specify the status of the benthic colonies (Brock, et al., 2006). Remote sensing allows us to investigate in a direct way the surroundings of the reef (an aspect which helps determining its ecological dynamic). Some examples are: temperature, height of waves, sea level, shallow zones, as well as quantity of chlorophyll and concentration of dissolved organic matter. In the atmosphere’s case, it is possible to greatly determine the cloud coverage, the quantity of seasonal rain, the presence of pollutants and incidental solar energy (Andréfouët et al., 2003). All of these factors present a direct or indirect influence on the coral reefs, determining its health status (Andréfouët and Riegl, 2004). It is also possible to determine the anthropogenic impacts, in the case of the reef being near a touristic or holiday retreat, through the growth of the urban sprawl, the plant coverage, the structure of the hydrographic basins, etc. In this same pattern, it is also possible to


describe the intrinsic conditions of the reefs, which are greatly defined by the reef system’s incoming and outgoing currents, implying the carrying of sediments and the exportation of dissolved organic matter. This helps understanding, among other things, the patterns which take place when a coral bleaching is detected (Brock et al., 2006). 3.1 Classification Methodologies Coral reefs generally develop in clear waters, which make their study easier and their analysis using passive, active and hyperspectral sensors (Mumby and Green, 2000). This affinity is due to the fact that light is an important regulating factor within its physiology, productivity and ecology. The biological properties of coral reefs generally are conditioning them a live in areas of clear water which transmits sufficiently the solar irradiation to allow algae photosynthesis. The spatial representation of the submerged coastal ecosystems is one of the most complex processes of thematic cartography through satellite images, due to the influence of the atmosphere and the sea water column through which the electromagnetic radiation passes (Bello-Pineda et al., 2004). In addition, it is important to note that these ecosystems suffer from a constant variation, especially after transcendental events such as strong hurricanes. However, several authors (Mumby et al., 1997, Andréfouët et al., 2000, Mumby and Edwards 2002, Andréfouët et al., 2003, Pahlevan et al., 2006, Call et al., 2003, etc.) have been developing and using various methods for the classification of such ecosystems and particularly of coral reefs, through the use of remote sensing. Regarding classifiers which have been used to map coral reefs, the maximum likelihood classifier is the most commonly 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, Benfield et al., 2007, among others. This classifier offers a higher margin for the accounting of class variations through the use of a data statistic analysis of such information like media, variance and covariance. However, improving the results could be possible through the incorporation of additional spatial information during the classification process, since this helps to spectrally separate the classes which have been confused. The method used by Mumby el al., 1997 is the agglomerative hierarchical classification with group-average sorting, which is one of the most commonly used algorithms for this type of studies. An alternative proposal is the object-oriented classification (Benfield et al., 2007), made up of two steps: segmentation and classification. The segmentation stage creates the image objects, which are then used as the building blocks for further classification, based on fuzzy logic. Another method that has been used is the Iterative Self-organizing Data Analysis (ISODATA), which uses a combination of Euclidian distance by squares and a re-classification of the centroid (Call et al., 2003). In this article we propose the use of the new segmentation process for coralline reefs. Markov Random Fields (MRFs) (Deng and Clausi, 2005; Descombes et al., 1996) are a probabilistic model which has been applied to problems of image restoration, reconstruction of surfaces, analysis and synthesis of textures, edge detection and segmentation. (Chelapa and Jain, 1993). MRFs have the capability of modeling spatial interactions between neighboring pixels, unlike other punctual segmentation models which are independently made from the context where the pixel is located, which translates into a low quality segmentation.

4. METHODOLOGY 4.1 Image acquisition and pre-processing For Mumby and others (1997), the most widely used satellites of remote sensing for the purpose of mapping coral-reef systems, are Landsat MSS, Landsat TM, SPOT XS and SPOT Pan. Green and others (2000) presented a comparative study of the obtained precision by different sensors. They concluded that the Landsat TM images are the most exact at delimiting benthonic ecosystem, with a precision of 60%, being additionally the most cost-effective, space-borne sensor, since it provides a global coverage of the coral reefs (Mumby and Edwards, 2002). Th area of analysis presents high cloudiness throughout the year. In order to demonstrate the segmentation algorithm, the cloudless images coming from the Landsat TM, Landsat ETM+ were chosen as well as an image coming from SPOT 5. In Table 1 the dates of acquisition and some spatial characteristics of the used images are described. Table 1. Specifications of the used images Landsat 5 TM 1986-03-31 Date of acquisition 80m Spatial resolution B1(Blue): 0.45Spectral Bands used for 0.52 classification (μm)

Landsat ETM+ 2000-01-25 30m B1(Blue): 0.450.52


Landsat ETM+ 2003-03-06 30m B1(Blue): 0.450.52

SPOT 5 2006-11-08 10m B1(Green):0.500.59

With the objective of attenuating the effects of absorption and light dispersion through the atmosphere; a radiometric calibration was realized as a preprocessing for all the images. These images were processed to reflectance values, including the atmospheric correction. They were geometrically adjusted to the zone’s bathymetry, obtained from sonar data (Sound, Navigation and Ranging) by the SEMAR (2008) in Mexico. The geometric correction was made with geodesic Datum WGS84 and the used cartographic projection was UTM. 4.2 Markov Random Fields (MRFs) There have been reports on several models of segmentation based on MRFs (Deng and Clausi, 2005). Our scheme is based on Descombes et al., (1996). An MRF is a discrete stochastic process whose global properties are controlled by means of local properties. They are defined by local conditional probabilities. In order to analyze spatial or contextual dependencies, MRFs are formulated within the Bayesian working field. The optimal solution for a problem is defined as the estimator of the maximum a posteriori probability (MAP). The problem may be considered as a labeled one using restrictions, where the labeling is the group of assigned labels to the image’s pixels. In this case, the optimal solution is defined by the labeling, through MAP and is calculated by minimizing the a posteriori energy. The MAP is defined by using the Bayes law, based on an a priori model and a probability model (Potts model). The MRF process may be summarized in 3 steps (Chelapa and Jain, 1993): a) Initial segmentation: Pose the problem as labeled, assign to the image’s pixels, being this labeling configuration represented as a solution, b) Parameter estimation: Present a label based on Bayesian’s law, the optimal solution being defined as a MAP labeling configuration; use Gibbs distribution to characterize the distribution a priori in the labeling configuration; and define Gibbs Energy Function, c) Energy function minimization: use an algorithm to minimize the Gibbs Energy distribution Function, this possibly being the Simulated Annealing algorithm, therefore obtaining the MAP labeling configuration. For image segmentation purposes, the characteristics of the image should be modeled in such a way that they can be divided into n different regions. Their estimation can be performed by learning from small areas or by analyzing the histogram when the different classes can be easily separated. A piecewise linear cost function is defined for each class, Figure 2. The different associated parameters reflect the histogram for each class. To define each cost function, we have to estimate the mean u of the class 1, and two deviation parameters, sl and dl. If the distance between a given data pixel and the mean u1 is lower than sl, this pixel belongs to class 1 with high probability. In contrast, if the distance between the data pixel and the mean of the class 1 is greater than dr, there is a low probability that this pixel belongs to class 1.

Fig.2. Cost function for a typical class showing the parameters of the data driven term (Modified: Descombes et al., 1996)

5. RESULTS AND DISCUSSION In order to illustrate the performance of the proposed MRFs model, the parameters for the segmentation were needed to define the number of classes; in the specific case of our images in Banco Chinchorro, three areas were determined: coral/patch reef, others (sand/algae/seagrass) and ocean. In figure 3 a view of the result of an unsupervised classification against the proposed MRFs is shown. It is possible to notice that the edges of the regions are not adequately defined and


there are isolated pixels in the unsupervised segmentation, which does not occur with the MRFs. One of the main problems in the segmentation of coralline reef images like the ones in Banco Chinchorro is the dynamic of the water column which interferes with the segmentation. When applying MRFs iterative algorithm it is possible to incorporate information regarding texture and therefore the segmentation results can be improved as compared against an isodata classification. Based on the results from the segmentation with MRFs the following barrier reef classes were assigned: Cayos (Keys), Patch Reefs and others (sandy bottoms, seagrass, and algae areas). The results of the segmented images are shown on Figure 4. Also the bathymetric map was compared to the results obtained with the segmentation. The results show that the reef patches zones are located at depths of between 3.5 and 11m. These zones are seen clearly defined in the satellite images. The spectral response of the reef zones is different from the sandy bottom zones which are located at depths of between 0.46 and 3.5, which allows for a separation of classes. The results of the segmentation and classification in Banco Chinchorro were evaluated according to visual analysis, depth of this area and considering punctual data of the types of bottom (SEMAR, 2008). We made a global classification for Banco Chinchorro, considering the depth and texture criteria. However, we consider it is possible to define more patterns that will help as training to the algorithm. The obtained results from this preliminary classification are not enough yet for change detection but the properties and functions of this classification algorithm permit to clearly highlight the reef zones.

Fig. 3. Comparison of Isodata and MRF. a) Zoom Landsat ETM+ 2000 RGB (Red, Green, Blue) b) MRF segmentation, c) Unsupervised segmentation (Isodata)


Fig. 4. Classification of the study area: a) Landsat TM 1986, b) Landsat ETM+ 2000, c) Landsat ETM+ 2003 and d) SPOT-5 2006


6. CONCLUSIONS AND FUTURE WORK In this article we have presented the use of MRFs in the segmentation of coralline reefs. Upon the segmentation results obtained, we can observe that by incorporating MRFs, the quality of the segmentation is improved. Furthermore, we observe that the quality of the segmentation, using MRFs models, after being compared against the results from Isodata, show homogeneous classes and a better edge definition. The ecosystem of Banco Chinchorro has an ecological value in diverse studies. However, it is important to characterize the reef area in order to continue with studies that help understanding its dynamics. Our future work will be to improve the information follow-up and extraction processes through the use of satellite images and field data. The satellite images and some additional data as bathymetry offer the possibility to obtain a good overview of the area of interest. However there is a need for multiple sampling points to perform an in situ spectral separation of the biological communities and have better control points. This can provide greater precision for determining the classes and to develop a structural analysis of the areas of reef.

ACKNOWLEDGEMENTS The authors would like to thank for the information provided regarding bathymetry to the Dirección General Adjunta de Oceanografía, Hidrografía y Meteorología. We would like to also thank the Secretaría de Marina and Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación for the images granted under license of Spot Image, S.A. We also thank F. Omar Tapia Silva for his revisions, suggestions and methodological contributions, in the same way we thank José Manuel Madrigal, Sergio Álvarez Hernández and Camilo Caudillo for their methodological contributions during this work.

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