**4. Remote sensing and coastal environmental sensitivity in Amazon**

The coastal zone of the Brazilian Amazon is composed by tree states: Amapá, Pará and Maranhão. According to the IBGE (2011), the total population of Amapá State is 669,526 distributed in 16 municipalities; the state capital, Macapá, concentrates 59% of this population. The state of Pará has a total population of 7,581,051 distributed in 143 municipalities; Belém comprises 18%. Maranhão state has a total population of 6,574,789 distributed in 217 municipalities; São Luis comprises 15% of this population. The population density in capital cities is over 100 hab/km², while in other coastal municipalities vary from 10 to 50 hab/km².

Until 21st century most of the coastal zone of the north of Brazil had sectors virtually devoid of information, or where data was available, it was non-systematized and both temporally and spatially non-continuous. The most important environmental dataset is related to the large continuous and well-developed mangroves - *Rhizophora mangle*, *Avicennia germinans* and *Laguncularia racemosa* (Szlafsztein, 2000). The mangroves have ecological and socioeconomic importance due to communities' livelihoods, and they are considered a protected ecosystem. The main activities are fishing, collecting shrimp and crabs (Andrade et al., 2010; Andrade et al., 2009).

However, port complexes and industries have been established alongside residential, protected areas and fishing grounds. For example, in Piatam Mar context the principal ports chosen to develop oil mapping were "Santana" (State of Amapá); "Itaqui" (State of Maranhão); "Outeiro", "Miramar" and "Vila do Conde" (State of Pará). The biological information was

7 Exposed tidal flats Exposed sandy tidal flats; low

Table 3. ESI comparison between NOAA (2002) and Ministry of the Environment (MMA,

The coastal zone of the Brazilian Amazon is composed by tree states: Amapá, Pará and Maranhão. According to the IBGE (2011), the total population of Amapá State is 669,526 distributed in 16 municipalities; the state capital, Macapá, concentrates 59% of this population. The state of Pará has a total population of 7,581,051 distributed in 143 municipalities; Belém comprises 18%. Maranhão state has a total population of 6,574,789 distributed in 217 municipalities; São Luis comprises 15% of this population. The population density in capital cities is over 100 hab/km², while in other coastal municipalities vary from

Until 21st century most of the coastal zone of the north of Brazil had sectors virtually devoid of information, or where data was available, it was non-systematized and both temporally and spatially non-continuous. The most important environmental dataset is related to the large continuous and well-developed mangroves - *Rhizophora mangle*, *Avicennia germinans* and *Laguncularia racemosa* (Szlafsztein, 2000). The mangroves have ecological and socioeconomic importance due to communities' livelihoods, and they are considered a protected ecosystem. The main activities are fishing, collecting shrimp and crabs (Andrade

However, port complexes and industries have been established alongside residential, protected areas and fishing grounds. For example, in Piatam Mar context the principal ports chosen to develop oil mapping were "Santana" (State of Amapá); "Itaqui" (State of Maranhão); "Outeiro", "Miramar" and "Vila do Conde" (State of Pará). The biological information was

**4. Remote sensing and coastal environmental sensitivity in Amazon** 

Gravel beaches; dendritic limestone coast; platform with

Sheltered scarps in bedrock (permeable and non permeable); Scarps and steep slopes in sand; permeable sheltered man-made

Sand tidal flats; sheltered mud

Delta and vegetated sand bars; sheltered wetlands; salt marshes saline wetlands; mangroves

lateritic concretion

structures (riprap)

tidal flats; coral reefs

tide platform

**number Color NOAA (2002) MMA (2002)** 

Sheltered: scarps in bedrock, mud or

(impermeable/permeable), solid manmade structures, riprap, rocky rubble

Salt and brackish water marshes; Freshwater marshes; Swamps; Scrubshrub wetlands: mangroves; Inundated

<sup>6</sup>Gravel beaches; Riprap gravel beaches (cobbles and boulders)

clay, rocky shores

shores; Peat shoreline

<sup>9</sup>Sheltered tidal flats; Vegetated low banks; hypersaline tidal flats

low-lying tundra

**ESI** 

8

10

2002) classification.

10 to 50 hab/km².

et al., 2010; Andrade et al., 2009).

intensively identified in "Lago Piratuba biological reserve" (Amapá); "Soure extractive reserve" (Pará) and "Ilha dos Caranguejos Environmental Protection Area" (Maranhão). According to Souza Filho et al., (2009a) these conservation units work as control areas, given both their well-preserved conditions and their proximity to transportation routes due to proximity to protected areas along the ports mentioned above (Figure 3).

Fig. 3. Principal ports and environmental protected areas in the Amazon coast (source: Souza Filho et al., 2009a).

Oil spills are a potential risk around these port areas which can affect the environment, human population infrastructure and livelihood, resulting from the transportation process, as well as tank cleaning and oil storage procedures within the area of the port (Noernberg & Lana, 2002). To comprehend the oil impact, it is necessary to analyze the coastal Amazonian environment as a whole. PETROBRAS established and financed nine projects to deal with this subject, among them, the "Environmental Sensitivity Map to Oil Spill in Guajará Bay (PA)" (2001 – 2003), the "JERS-1, RADARSAT-1 and ALOS/PALSAR application in monitoring and mapping Amazon coastal environments: an approach for multi-temporal environmental sensitivity maps to oil spill" (2004 - 2006), PIATAM MAR (2004 - 2010) and currently "Elaboration of ESI maps for Pará-Maranhão and Barreirinhas basin" (2012 until 2014).

The PIATAM MAR project was implemented in Northern Brazil and was led by the Federal University of Pará, the Federal University of Rio de Janeiro and PETROBRAS. The general aims proposed are: the consolidation of a multidisciplinary researcher's network that are active in the Amazonian coastal zone; the development of technological tools and infrastructure to support local monitoring and environmental management; and ESI maps construction (Souza Filho et al., 2009a).

Remote Sensing and Environmental Sensitivity for Oil Spill in the Amazon, Brazil 323

**ESI Amazon Coastal Environment** 

1A Exposed rocky shores

6 Riprap

7 Exposed tidal flats

8C Sheltered riprap 8D Peat shorelines 9A Sheltered tidal flats 9B Vegetated low banks 9C Hypersaline tidal flats

10C Intertidal mangrove 10D Supratidal mangrove

2005a).

and cleaning efforts.

1B Exposed, solid man-made structures

3B Scarps and steep slopes in sand 4 Coarse-grained sand beaches

1C Exposed rocky cliffs with boulder talus base 2 Exposed scarps and steep slopes in clay 3A Fine to medium grained sand beaches

5 Mixed sand and gravel banks and beaches

8A Sheltered scarps in bedrock, mud, or clay 8B Sheltered, solid man-made structures

10A Salt, and brackish-water marshes

10B Freshwater marches, aquatic vegetation

(2004) based on the proposals of NOAA (2002) and Araújo et al. (2002).

Table 4. ESI shoreline classification for the Amazon Coast, modified by Souza Filho et al.

flooded mangrove forests. This environment is considered to be the most oil-sensitive habitat described in Table 4 - ESI Ranking specification = 10c and 10d (Souza Filho et al.,

High resolution images, such as Ikonos, were used for operational scale mapping. The resolution of 1 m provides a detailed geomorphic map, and it's also possible to map the potentially hazardous industrial structures stratified by type of hazard. However, the use of Ikonos images is limited when cloud cover is higher than 25%. As a result the images are mostly inadequate between March and June (Andrade et al., 2010). On the other hand, biological and socioeconomic resources, risk areas and oil spill hazard zones of storage and platform transportation can be better identified and delimitated (Rodrigues & Szlafsztein, 2010; Andrade et al., 2009). This location contributes to planning and management strategies

Initially, the researchers of PIATAM MAR project compiled environmental data and other information available on the Amazon coastal zone. The results are integrated in the book, "Bibliography of the Amazon Coastal Zone: Brazil" (Souza Filho et al., 2005a). Meanwhile, a computational database system using the MYSQL language was developed and used as a basis for the development of a geographic information system called SIGmar.

All of these initial steps support the subsequent aim of PIATAM MAR: the construction of ESI maps. From 2006 to 2010 socio-economic and environmental data were integrated in ESI maps. These maps have been developed through the SIGmar and the extensive use of remote sensing. ESI maps create an operational alternative for the monitoring and mapping of the Amazonian coastal zone and provide guidelines for the use of the InfoPAE (Computerized Emergency Action Plan Support) System on the Amazonian coast (Souza Filho et al., 2009a).

Two considerations should be taken into account when mapping and monitoring oil spills in the Amazonian coastal environments. First, the unique complex environmental dynamics of the Amazon basin have demanded an adaptation of ESI classification with values from 1 (low) to 10 (high) sensitivity (Souza Filho et al., 2004) (Table 4). Second, the Amazonian coast is situated in the intertropical convergence zone (ITCZ) that is located near the equator and has a broad area of low atmospheric pressure. Therefore, there is a huge cloud cover between December and May which limits the use of some kinds of sensors.

Coastal ESI mapping for the Amazon uses remote sensing as an indispensable and very powerful tool. Oil spill and environmental sensitivity to oil spills in the Amazon were mostly mapped during the PIATAM MAR project. Table 5 shows the most important scientific results in this study area. The perspectives of the ESI adaptation proposed by Souza Filho et al. (2004) were extensively used.

The whole Amazon coastal zone was mapped with spatial resolution of 90 m based on the processing and images mosaics of SRTM images and 30 m of RADARSAT-1 Wide 1 images and mosaics of JERS-1 SAR. This sensor was chosen given the six months of unfavorable climatic conditions; radar sensors (Synthetic Aperture Radar – SAR) are used for strategic scale.

On a tactical scale, multi-sensor data fusion between microwaves sensors and optical sensors are considered to be the most important source of spatial data for geomorphologic recognition and basic coastline characteristics. The commons sensors fusion are made in general with low resolution data from RADARSAT-1 Wide 1 and JERS-1 SAR mosaic, together with moderate spatial resolution data (10–30 km) from Landsat series (MSS, TM and ETM+) and Cbers-2 images (20m). The multi-fusion of optical (Landsat 5 TM) and radar (RADARSAT-1) sensors had a particular emphasis on the evaluation of the new hybrid sensor product combining PCA (Principal Component Analysis) and IHS components. In areas with little or no data, this fusion method from multi sensors to orbital images, together with field data are economically efficient and provide a good environment sensitivity characterization (Rodrigues & Souza Filho, 2011).

Hydrological dynamics with flood area delimitation could be differentiated by the use of JERS-1, L band (Santos et al., 2009), which is important in a region dominated by different tidal regimes that can amplify the area affected by oil spills. Methods include visual and automatic classification leading to good results in identifying widespread occurrence of

Initially, the researchers of PIATAM MAR project compiled environmental data and other information available on the Amazon coastal zone. The results are integrated in the book, "Bibliography of the Amazon Coastal Zone: Brazil" (Souza Filho et al., 2005a). Meanwhile, a computational database system using the MYSQL language was developed and used as a

All of these initial steps support the subsequent aim of PIATAM MAR: the construction of ESI maps. From 2006 to 2010 socio-economic and environmental data were integrated in ESI maps. These maps have been developed through the SIGmar and the extensive use of remote sensing. ESI maps create an operational alternative for the monitoring and mapping of the Amazonian coastal zone and provide guidelines for the use of the InfoPAE (Computerized Emergency Action Plan Support) System on the Amazonian coast (Souza

Two considerations should be taken into account when mapping and monitoring oil spills in the Amazonian coastal environments. First, the unique complex environmental dynamics of the Amazon basin have demanded an adaptation of ESI classification with values from 1 (low) to 10 (high) sensitivity (Souza Filho et al., 2004) (Table 4). Second, the Amazonian coast is situated in the intertropical convergence zone (ITCZ) that is located near the equator and has a broad area of low atmospheric pressure. Therefore, there is a huge cloud cover

Coastal ESI mapping for the Amazon uses remote sensing as an indispensable and very powerful tool. Oil spill and environmental sensitivity to oil spills in the Amazon were mostly mapped during the PIATAM MAR project. Table 5 shows the most important scientific results in this study area. The perspectives of the ESI adaptation proposed by

The whole Amazon coastal zone was mapped with spatial resolution of 90 m based on the processing and images mosaics of SRTM images and 30 m of RADARSAT-1 Wide 1 images and mosaics of JERS-1 SAR. This sensor was chosen given the six months of unfavorable climatic

On a tactical scale, multi-sensor data fusion between microwaves sensors and optical sensors are considered to be the most important source of spatial data for geomorphologic recognition and basic coastline characteristics. The commons sensors fusion are made in general with low resolution data from RADARSAT-1 Wide 1 and JERS-1 SAR mosaic, together with moderate spatial resolution data (10–30 km) from Landsat series (MSS, TM and ETM+) and Cbers-2 images (20m). The multi-fusion of optical (Landsat 5 TM) and radar (RADARSAT-1) sensors had a particular emphasis on the evaluation of the new hybrid sensor product combining PCA (Principal Component Analysis) and IHS components. In areas with little or no data, this fusion method from multi sensors to orbital images, together with field data are economically efficient and provide a good environment sensitivity

Hydrological dynamics with flood area delimitation could be differentiated by the use of JERS-1, L band (Santos et al., 2009), which is important in a region dominated by different tidal regimes that can amplify the area affected by oil spills. Methods include visual and automatic classification leading to good results in identifying widespread occurrence of

conditions; radar sensors (Synthetic Aperture Radar – SAR) are used for strategic scale.

basis for the development of a geographic information system called SIGmar.

between December and May which limits the use of some kinds of sensors.

Souza Filho et al. (2004) were extensively used.

characterization (Rodrigues & Souza Filho, 2011).

Filho et al., 2009a).


Table 4. ESI shoreline classification for the Amazon Coast, modified by Souza Filho et al. (2004) based on the proposals of NOAA (2002) and Araújo et al. (2002).

flooded mangrove forests. This environment is considered to be the most oil-sensitive habitat described in Table 4 - ESI Ranking specification = 10c and 10d (Souza Filho et al., 2005a).

High resolution images, such as Ikonos, were used for operational scale mapping. The resolution of 1 m provides a detailed geomorphic map, and it's also possible to map the potentially hazardous industrial structures stratified by type of hazard. However, the use of Ikonos images is limited when cloud cover is higher than 25%. As a result the images are mostly inadequate between March and June (Andrade et al., 2010). On the other hand, biological and socioeconomic resources, risk areas and oil spill hazard zones of storage and platform transportation can be better identified and delimitated (Rodrigues & Szlafsztein, 2010; Andrade et al., 2009). This location contributes to planning and management strategies and cleaning efforts.

Remote Sensing and Environmental Sensitivity for Oil Spill in the Amazon, Brazil 325

Remote sensing techniques are used for risk identification, assessment and analysis. The technological risk of oil spills needs continuous planning and monitoring actions. The availability of airborne and satellite remote sensing provides a diversity of resolution and sensors required to construct environmental sensitivity maps, using basic information about

Remote sensing and ground confirmation provide accurate information about this basic information. In particular, the coastline is usually mapped in detail with both optical and radar sensors. The multi-sensor data fusion of an optical moderate sensor with radar has been extensively used in the Amazon region to provide basic information about coastal environments. Radar is a very powerful tool once it can operate in difficult weather conditions. It provides detailed information about shoreline irregularities and geomorphic units if the texture and the altitude of this type of images are precise. Optical sensors are used for environmental differentiation once land cover and land use have different spectral

Studies in Brazil regarding oil spills have increased after 2000, and ESI maps have been generated at different scales for different areas along the coast. Remote sensing tools were essential to achieve initial and advanced cartographic information in a context of the diversity of the environment, information and cartographic background. Particularly in the Amazon, little background information about the coastline existed before the PIATAM MAR project. In the context of this project, the Amazon coast was previewed at strategic scale with the use of a moderate sensor. ESI maps were produced at the tactical and operational scales and it was possible to map the coastal environment and organize information about socioeconomic and biological resources. A large, extensive mangrove system coexists with industrial port areas on the Amazon coast with a high sensitivity to oil

spills, which should to be continuously monitored with remote sensing techniques.

Andrade, M.; Souza Filho, P. & Szlafsztein, C. (2009). High Resolution Images for

Andrade, M; Szlafsztein, C., Souza Filho, P., Araújo, A. & Gomes, M. (2010). A

Araújo, S., Silva G. & Muehe, D. (2002). Manual básico para elaboração de mapas de

Araújo, S.; Silva, G. & Muehe, D. (2006). *Mapas de sensibilidade ambiental a derrames de óleo -* 

Araújo, R.; Petermann, R.; Klein, A.; Menezes, J.; Sperb, R. & Gherardi, D. (2007).

Costeiros e Estuarinos. CENPES/Petrobras, Rio de Janeiro, 134 pp.

CENPES/Petrobras, pp.168 Ed. 2ª, ISBN: 8599891014

Recognition of the Susceptibility of Social Economic Resources to Oil Spill in the Itaqui-Bacanga Port Complex, Maranhão, Brazil. *Journal of Integrated Coastal Zone* 

socioeconomic and natural vulnerability index for oil spills in an Amazonian harbor: A case study using GIS and remote sensing. *Journal of Environmental* 

sensibilidade ambiental a derrames de óleo no sistema Petrobras: Ambientes

*Ambiente costeiros, estuarinos e fluviais.* Rio de Janeiro Rio de Janeiro.

Determinação do ìnndice de sensibilidade do litoral (ISL) ao derramamento de óleo

socioeconomic and biological resources and geomorphic characteristics.

**5. Conclusions** 

responses.

**6. References** 

*Management*, vol. 9, pp. 127-133.

*Management*, vol 91, pp. 1972-1980.


Table 5. Results published in a scientific paper related to remote sensing use and sensitivity environment to oil spill in Amazon coast.

### **5. Conclusions**

324 Remote Sensing – Applications

**Resolution Sensor Method Scale Study case** 

Visual interpretation, Field data collection

Visual interpretation, Field data collection

Visual interpretation, Field data collection

Visual interpretation, Field data collection

Automatic classification, multi-fusion sensors, Field data collection

Automatic classification, multi-sensor fusion, field data collection

Visual

Visual interpretation, Field data collection

classification Tactical

Operational

Operational

Operational

Operational

Tactical

Tactical

Operational

Municipality of São Luis (Maranhão)

Municipality Barcarena (Pará)

Municipality of São Luis (Maranhão)

Municipality of Barcarena (Pará)

Municipalities of Maracanã, Santarém Novo, Salinópolis, Cuiarana, São João de PIrabas, Santa Luzia and Primavera (Pará)

Municipality of Belém (Pará)

Municipality of Bragança (Pará)

Municipality of São Luis, (Maranhão)

**Author Map Type Spatial** 

Oil spill

Oil spill hazard representation and susceptible socioeconomic resources

Vulnerability High Ikonos

Oil spill risk High Ikonos

ESI Moderate Spot-5

ESI Moderate

ESI Moderate

al. (2007) ESI Moderate Landsat 5

environment to oil spill in Amazon coast.

et al. (2009) ESI Moderate

High Ikonos

Landsat 7 ETM+, SRTM High, aerial photography

Landsat 7 ETM, Radarsat 1

Landsat 5 TM, Radarsat 1

TM

Table 5. Results published in a scientific paper related to remote sensing use and sensitivity

Andrade et al. (2010)

Rodrigues & Szlafsztein (2010)

Andrade et al. (2009)

Boulhosa & Mendes (2009)

Boulhosa & Souza Filho (2009)

Gonçalves

Souza Filho et al. (2009)

Novaes et

Remote sensing techniques are used for risk identification, assessment and analysis. The technological risk of oil spills needs continuous planning and monitoring actions. The availability of airborne and satellite remote sensing provides a diversity of resolution and sensors required to construct environmental sensitivity maps, using basic information about socioeconomic and biological resources and geomorphic characteristics.

Remote sensing and ground confirmation provide accurate information about this basic information. In particular, the coastline is usually mapped in detail with both optical and radar sensors. The multi-sensor data fusion of an optical moderate sensor with radar has been extensively used in the Amazon region to provide basic information about coastal environments. Radar is a very powerful tool once it can operate in difficult weather conditions. It provides detailed information about shoreline irregularities and geomorphic units if the texture and the altitude of this type of images are precise. Optical sensors are used for environmental differentiation once land cover and land use have different spectral responses.

Studies in Brazil regarding oil spills have increased after 2000, and ESI maps have been generated at different scales for different areas along the coast. Remote sensing tools were essential to achieve initial and advanced cartographic information in a context of the diversity of the environment, information and cartographic background. Particularly in the Amazon, little background information about the coastline existed before the PIATAM MAR project. In the context of this project, the Amazon coast was previewed at strategic scale with the use of a moderate sensor. ESI maps were produced at the tactical and operational scales and it was possible to map the coastal environment and organize information about socioeconomic and biological resources. A large, extensive mangrove system coexists with industrial port areas on the Amazon coast with a high sensitivity to oil spills, which should to be continuously monitored with remote sensing techniques.

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**14** 

*Mexico* 

**Satellite Remote Sensing of** 

*1Centro de Investigación en Geografía y Geomática* 

*"Jorge L. Tamayo" A.C., CentroGeo* 

*Uso de la Biodiversidad, CONABIO* 

*3Comisión Nacional para el Conocimiento y* 

**Coral Reef Habitats Mapping in** 

F. Omar Tapia-Silva1,2 and Sergio Cerdeira-Estrada3

*2Universidad Autónoma Metropolitana, Unidad Iztapalapa* 

**Shallow Waters at Banco Chinchorro** 

Ameris Ixchel Contreras-Silva1, Alejandra A. López-Caloca1,

**Reefs, México: A Classification Approach** 

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

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

of these communities will disappear in a relatively short period of time.

coral reef system, using satellite images (Landsat 7-ETM+).

**1. Introduction** 

