**2. Determination of the fluvial sensitivity index in the Amazon**

Since the beginning of the 1990s, Petrobras has verified that the methodology of drawing up maps of environmental sensitivity to oil spills, adopted by the National Oceanic and Atmospheric Administration (NOAA), adequately adapts to the Brazilian reality, due to its great acceptance and utilization in many countries, as well as the ease of operational implementation [8].


#### **Table 1.**

*Current Practice in Fluvial Geomorphology - Dynamics and Diversity*

requiring interdisciplinary studies for risk assessment [3, 4].

environmental conservation in the areas under its influence [5, 6].

ography, and patterns of human occupation [1].

with the typical Amazonian seasonality.

main contributor responsible for the productivity of Amazonian floodplains, in terrestrial and aquatic systems alike [2]. The resulting annual deposition of such sediments defines the fauna and flora, the geomorphology of the floodplain, bioge-

The discovery and exploitation of oil and gas in the Central Amazon rainforest is a major challenge for sustainable development. Hydrocarbon transportation is an industrial enterprise that involves various potential environmental impacts, thus

The study area includes the flow route of crude oil, liquefied petroleum gas (LPG), and natural gas produced by Petrobras in the Petroleum Province of Urucu. Three pipelines are used to bring crude oil, LPG, and natural gas from Urucu to a terminal located in Coari. From there, crude oil and LPG are transported to Manaus by river, while natural gas is taken via a terrestrial gas pipeline. Thus, this region is susceptible to possible damages due to oil activities, which are a potential threat to

The major oil companies have intensified their programs of excellence in environmental management and operational safety in order to reduce the risk of accidents in the operations of exploration, production, and transportation of petroleum and its derivatives. Nevertheless, these accidents can occur in rivers or at sea, due to product spills during procedures of oil tanker reservoir cleaning or loading in terminals, which require standardized response procedures for such emergencies [7]. Thus, given that the Amazon region presents considerable environmental sensitivity to oil spills, there is a need to respond proactively to possible accidents. In order to do this, it was necessary to examine the list of features of its rivers and lakes and their corresponding sensitivity in more detail, in order to hierarchize them in terms of potential impacts [8]. As a result, Araújo et al. [9] defined the fluvial sensitivity index to oil spills, adapted to the corresponding features and consistent

Among the factors that influence the sensitivity of habitats to oil spills, the most important are (1) the degree to which affected areas are exposed to processes of natural removal, (2) biological productivity and recovering capability after oil impacts, (3) existing land-use practices, and (4) ease of oil spill cleaning [10]. The overall sensitivity of natural habitats to oil spillage is ranked according to the aforementioned factors in the context of the environmental sensitivity index (ESI).

The most sensitive habitats in the Amazon region are flooded forests. In fact, inundation causes seasonal differences in the water level and changes the landscape, which creates the need for production of a specific sensitivity index map for each season: low water, high water, receding water, and rising water. The areas occupied by flooded forests change with time, such that continuous monitoring is required through the collection, processing, and analysis of remote sensing data. These areas were first systematically studied in the last decade using LHH SAR images based on

The global weather-independent coverage provided by the synthetic aperture radar (SAR) system onboard the JERS-1 satellite allowed end users to monitor the rapidly changing conditions in cloud-covered rainforest regions. This L-band, HH polarization system is best suited for flood mapping in rainforest-covered areas due

The JERS-1 satellite orbital arrangement favors the continuous monitoring of the Amazonian hydrological cycle. To do so, contiguous orbits on consecutive days are used. Such a procedure allows temporally homogeneous images to be acquired on a continental scale [13]. Consequently, JERS-1 SAR data were instrumental in mapping inundation variation in space and time over large forested floodplain regions [14–18].

The use of ESI is fundamental for oil spill contingency planning.

the multi-seasonal coverage of the JERS-1 satellite [11].

to its capability to penetrate dense vegetation [12].

**64**

*Amazon region riverine features.*


#### **Table 2.**

*Fluvial sensitivity index to oil spills of the Amazon region.*

NOAA's environmental sensitivity classification system is based on the knowledge of geomorphological characteristics of intertidal regions or river and lake limits. In the case of the Amazon basin, because of the need to map the areas under the influence of petroleum production and transportation, it was necessary to hierarchize its ecosystems, after listing its fluvial features. This is necessary because the yearly water level variation results in the flooding of a large portion of the alluvial plain [19, 20]. This area comprises a complex ecosystem composed of lakes, flooded forest, macrophytes, and other habitats (**Table 1**) [21].

Subsequently, taking into account local characteristics of specific features, Araújo et al. [21] defined the different degrees of fluvial sensitivity to oil spills of the Amazon region (10b is the most sensitive; **Table 2**).

## **3. Study area**

The study area embraces the Urucu Petroleum Province; the vicinity of the city of Coari, located near the petroleum terminal (TESOL); the Solimões River stretched up to the Petrobras refinery in Manaus (Reman), in which transportation of crude oil and LPG takes place; and part of the area occupied by the terrestrial gas pipeline (**Figure 1**).

Because it is a region devoid of infrastructure that is subject to a strong seasonal variation of the water level in the river plain, travel between Manaus and the adjacent municipalities is possible only by air or waterway. The main waterways present in the study area include the Tefé, Urucu, Coari, Manacapuru, Purus, and Solimões rivers, in the stretch from Coari to the confluence of the Rio Negro in Manaus (**Figure 1**). Some lakes are present in this region; the most expressive, in account of its size and strategic location, is Lake Coari.

#### **Figure 1.**

*Study area location on the JERS-1 SAR LHH image msaic (high water). Red rectangle corresponds to Figure 8; red circle is Coari city.*

**67**

*Overview of Hydrological Dynamics and Geomorphological Aspects of the Amazon Region…*

Environmental sensitivity index maps should reflect the impact of landscape change as a result of flooding. The JERS-1 SAR image mosaics in the low water and high water seasons [22] are input to the unsupervised semivariogram textural classifier (USTC). This algorithm performs image classification, thus recognizing upland forest, flooded forest, flooded vegetation (low biomass above water), and water bodies. A by-product of the classification procedure is the map depicting classes of change, which expresses the difference between dry and

When texture is more important than spectral information, remote sensing image classification must rely on spatial structure. This is the case for the JERS-1 SAR system, which operated using single frequency (L-band) and single polarization (HH). Therefore, the classifier should take into account a pixel in the context of its neighborhood. One possible approach is to analyze texture by means of the

JERS-1 SAR data have already been successfully submitted to semivariogram classification as an aid to vegetation mapping [17, 24]. The unsupervised semivariogram textural classifier (USTC) considers the spatial structure of remote sensing data to carry out image classification. Both textural and radiometric information are

Consider an image dataset X = {x(t) ∈ R,t = 1,…,N}, where each element is a pixel that represents the radiometric information conveyed by the Frost filter digital number (DNdsp) value. Textural information is captured by the semivariogram

(*xh*(*t*,*i*) − *x*(*t*))<sup>2</sup> (1)

2

, *t* = 1,…,N}.

(*t*)] (2)

<sup>2</sup>*<sup>n</sup>* ∑ *i*=1 *n*

the pixel x(t); and *n* is the number of pixels of the neighborhood.

**z**(*t*) = [*x*(*t*), γ(*t*, 1), γ(*t*, 2)…, γ(*t*,*h*),σ*<sup>h</sup>*

unsupervised classification is the set *<sup>Z</sup>* <sup>=</sup> {*z*(*t*) <sup>∈</sup> *<sup>R</sup><sup>h</sup>*+2

(*t*) is the variance of the area *xh*(*t*,*i*),*i* = 1…*n*.

where x(t) is the pixel value; *h* is a parameter that controls the extent of the neighborhood, such that the pixels x*h*(*t,i*) lie inside the circle of radius *h* centered at

The value x*h*(*t,i*) in (1) is the value of the neighborhood pixel for i = 1…*n*, in a neighborhood of *h* defined by the circular semivariogram function γ(t,h) as the texture descriptor. Each pixel in the image is thus transformed into the *h* + 2

The classification procedure is accomplished based on all components of this h + 2 dimensional vector, calculated for each pixel location. The training set for the

The clustering algorithm known as ISODATA [25] is utilized to perform the unsupervised classification of this set of vectors. At last, clustering results are interactively merged together to define aggregates of one or more classes capable of

*DOI: http://dx.doi.org/10.5772/intechopen.86592*

**4.1 Fluvial oil spill sensitivity index maps**

**4. Thematic products**

flooded seasons.

*4.1.1 The USTC classifier*

semivariogram function [23].

combined in this algorithm.

function γ(t,h) (Eq. (1)):

dimensional vector (Eq. (2)):

bearing thematic significance.

where σ*<sup>h</sup>* 2

γ(*t*,*h*) = \_\_\_1

*Overview of Hydrological Dynamics and Geomorphological Aspects of the Amazon Region… DOI: http://dx.doi.org/10.5772/intechopen.86592*
