*4.1.2 Results of UTSC classification*

Results of the USTC image classification for the dry (low water) and flooded (high water) seasons are shown, respectively, in **Figures 2** and **3**. Pixels are represented as follows: upland forest in green, water bodies in blue, flooded forest in yellow, and flooded vegetation in light blue.

After definition of the dual season USTC-classified products, a postclassification change detection algebraic calculation generated a multi-temporal landscape change map, corresponding to half hydrological cycle in the Central Amazon region (i.e., from dry to flooded seasons).

The landscape change map depicted in **Figure 4** represents all possible combinations of classes detected using the USTC method. Some of them are rare or would seem impossible to occur in the transition from dry to flooded seasons, such as C15 (flooded forest/upland forest), while others describe most of the pixels such as C16 (upland forest/upland forest). Some classes such as C12 (upland forest/ flooded forest) should be highlighted because they indicate increased environmental sensitivity to oil spills from low water to high water in river plains. This map is an important input for generation of a temporal environmental sensitivity index. In this study, such a product refers to the transition from low water in September 1995 to high water in May 1996, when orbital images were obtained for JERS-1 SAR mosaic composition.

#### **Figure 2.**

*USTC result for the dry season (October 1995; low water). Modified from [11].*

**69**

**Table 3.**

**matrix**

**Figure 4.**

*4.2.1 The risk matrix*

Environmental sensitivity to oil spill

(4) graphical editing of the risk matrix.

*(see Table 3 for the definition of classes of change (C1 to C16).*

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

**4.2 An analytical method using linguistic rules for the construction of a risk** 

**Risk matrix Probability of hydrological change Extremely unlikely**

*The risk matrix for the impact of an oil spill in Amazonian environments subject to changes due to flooding.*

Catastrophic 0.20 0.40 0.60 0.80 1.00 Critic 0.15 0.30 0.45 0.60 0.75 Marginal 0.10 0.20 0.30 0.40 0.50 Negligible 0.05 0.10 0.15 0.20 0.25

**Unlikely Rare Likely Frequent**

*Landscape change map from October 1995 (low water) to May 1996 (high water). Modified from [11]* 

One way to qualitatively or semi-quantitatively assess risk is to create a ranking with a risk matrix [3, 26]. According to Markowski and Mannan [27], a risk matrix (RM) is a tool that subjectively allows for the assessment of different analytical processes. The basis for defining a RM is the association of severity or possible consequences in each scenario with the frequency with which certain event occurs. A RM is developed through the following steps: (1) characterization and scaling of the severity of the consequences and the frequency of an event, (2) characterization and ranking of the risk, (3) establishment of the basic rules focused on the risk, and

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

**Figure 3.** *USTC result for the flooded season (may 1996; high water). Modified from [11].*

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

#### **Figure 4.**

*Current Practice in Fluvial Geomorphology - Dynamics and Diversity*

Results of the USTC image classification for the dry (low water) and flooded (high water) seasons are shown, respectively, in **Figures 2** and **3**. Pixels are represented as follows: upland forest in green, water bodies in blue, flooded forest in

The landscape change map depicted in **Figure 4** represents all possible combinations of classes detected using the USTC method. Some of them are rare or would seem impossible to occur in the transition from dry to flooded seasons, such as C15 (flooded forest/upland forest), while others describe most of the pixels such as C16 (upland forest/upland forest). Some classes such as C12 (upland forest/ flooded forest) should be highlighted because they indicate increased environmental sensitivity to oil spills from low water to high water in river plains. This map is an important input for generation of a temporal environmental sensitivity index. In this study, such a product refers to the transition from low water in September 1995 to high water in May 1996, when orbital images were obtained for JERS-1 SAR

After definition of the dual season USTC-classified products, a postclassification change detection algebraic calculation generated a multi-temporal landscape change map, corresponding to half hydrological cycle in the Central

*4.1.2 Results of UTSC classification*

mosaic composition.

yellow, and flooded vegetation in light blue.

Amazon region (i.e., from dry to flooded seasons).

**68**

**Figure 3.**

**Figure 2.**

*USTC result for the flooded season (may 1996; high water). Modified from [11].*

*USTC result for the dry season (October 1995; low water). Modified from [11].*

*Landscape change map from October 1995 (low water) to May 1996 (high water). Modified from [11] (see Table 3 for the definition of classes of change (C1 to C16).*

#### **4.2 An analytical method using linguistic rules for the construction of a risk matrix**

#### *4.2.1 The risk matrix*

One way to qualitatively or semi-quantitatively assess risk is to create a ranking with a risk matrix [3, 26]. According to Markowski and Mannan [27], a risk matrix (RM) is a tool that subjectively allows for the assessment of different analytical processes. The basis for defining a RM is the association of severity or possible consequences in each scenario with the frequency with which certain event occurs. A RM is developed through the following steps: (1) characterization and scaling of the severity of the consequences and the frequency of an event, (2) characterization and ranking of the risk, (3) establishment of the basic rules focused on the risk, and (4) graphical editing of the risk matrix.


**Table 3.**

*The risk matrix for the impact of an oil spill in Amazonian environments subject to changes due to flooding.*

The number of hierarchical levels should be determined as needed by the problem. Considering the environmental sensitivity to oil spills, they may, for example, be designated as catastrophic, critical, marginal, and negligible. The graphical representation of the risk matrix should be simple in order to easily convey the risks involved in a set of rules.

A RM was developed to assess the environmental risk for oil spills in the Amazonian landscape within half hydrological cycle based on the following predefined rules: (1) landscape changes that exist are reliable within half hydrological cycle (from drought to flood), and (2) the environmental impacts of an oil spill depend on variations of the hydrological regime. The combination of these two sets of rules can provide the environmental risk involved in oil pipeline and fluvial transportation for each combination of landscape change (**Tables 3** and **4**).

#### *4.2.2 Fuzzy modeling of the environmental sensitivity*

The use of fuzzy logic allows for the capture and integration of scientific and local expert knowledge about the phenomenon being studied using heuristic "if-then" rules [26]. Fuzzy logic provides a powerful approach for classifying and monitoring environmental conditions related to flooding and describing the nature and severity of changes occurring over time.

The fuzzy modeling employs a symbolic representation of those classes, which is used to estimate the risk of each type of landscape change, based on the linguistic interpretation of the symbols and the RM defined in Section 4.2.1. The fuzzy modeling computes the function that converts the classes of change into levels of risk, as defined by the rule set in Eq. (3). In the present study, n = 16 classes of change will be mapped into m = 3 classes of risk. The fuzzy model is conveniently computed as a TS model (Eq. (4)), where risk is represented by a continuous parameter vector


**71**

**Figure 5.**

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

∑*n k*=1

the grade that membership functions overlap, usually 1.2 ≤ γ ≤ 2.0:

and γ is the parameter that adjusts the fuzziness of the membership function, i.e.,

*Ck* → *Ll*, *k* = 1,…*n l* = 1,…,*m* (4)

The temporal environmental index is computed from the input variable defined

, which represents the backscatter space of the dry and flooded season data, following the work of Hess et al. [28]. The input variable is the vector x(t) = [x\_D (t), x\_F (t)], where x\_D (t) is the coefficient of backscatter LHH in the dry season (dB)

The fuzzy model computes a smooth approximation of the risk values of each class of change according to the membership vector of each pixel to the classes of

The map depicting temporal environmental sensitivity index (TESI) values is presented in **Figure 5**. They range in the interval [0,1] in order to avoid abrupt transitions between contiguous landscape change classes. The achieved results portray a broad spectrum of flooded forest with TESI values between 0.6 and 0.7, as well as

The TESI values obtained by the proposed methodology were compared with field checks at the Coari terminal (TESOL). **Figure 6** presents the map of the 16 landscape change classes and the TESI map. Inserted in each panel of **Figure 6** are the location of each field checkpoint and profiles of different values of the TESI

At PT06, people live on the bank of the river, occupying a small space on stilts surrounded by different flooded and dried plant species (**Figure 13h**). The landscape change map assigns PT06 to class C4, which corresponds to upland forest that

and x\_F (t) is the coefficient of backscatter LHH in the flooded season (dB).

, in which each component represents the environmental sensitivity index of

‖**x**(*t*) − *k*‖

\_\_\_2 <sup>γ</sup>−1 \_\_\_\_\_\_\_\_\_\_\_\_\_

is the coordinates of the center of each class of change *k*, *k* = 1, ...n,

\_\_\_2 γ−1 **,** (3)

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

*uk*(*t*) <sup>=</sup> ‖**x**(*t*) <sup>−</sup> *k*‖

*4.2.3 The temporal environmental sensitivity index map*

several minor regions with higher values.

obtained by the fuzzy modeling approach.

*Results of the temporal environmental sensitivity index (TESI).*

each class of landscape change:

where ϖ<sup>k</sup> є R<sup>2</sup>

θ ∈ Rn

in R2

change.

#### **Table 4.**

*Level of risk for each class of change and the respective model parameters.*

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

θ ∈ Rn , in which each component represents the environmental sensitivity index of each class of landscape change:

 *uk*(*t*) <sup>=</sup> ‖**x**(*t*) <sup>−</sup> *k*‖ \_\_\_2 <sup>γ</sup>−1 \_\_\_\_\_\_\_\_\_\_\_\_\_ ∑*n k*=1 ‖**x**(*t*) − *k*‖ \_\_\_2 γ−1 **,** (3)

where ϖ<sup>k</sup> є R<sup>2</sup> is the coordinates of the center of each class of change *k*, *k* = 1, ...n, and γ is the parameter that adjusts the fuzziness of the membership function, i.e., the grade that membership functions overlap, usually 1.2 ≤ γ ≤ 2.0:

$$\mathbf{C}\_{k} \to L\_{l}, \ k = \mathbf{1}, \dots \\ n \quad l = \mathbf{1}, \dots, m \tag{4}$$

The temporal environmental index is computed from the input variable defined in R2 , which represents the backscatter space of the dry and flooded season data, following the work of Hess et al. [28]. The input variable is the vector x(t) = [x\_D (t), x\_F (t)], where x\_D (t) is the coefficient of backscatter LHH in the dry season (dB) and x\_F (t) is the coefficient of backscatter LHH in the flooded season (dB).

The fuzzy model computes a smooth approximation of the risk values of each class of change according to the membership vector of each pixel to the classes of change.

#### *4.2.3 The temporal environmental sensitivity index map*

The map depicting temporal environmental sensitivity index (TESI) values is presented in **Figure 5**. They range in the interval [0,1] in order to avoid abrupt transitions between contiguous landscape change classes. The achieved results portray a broad spectrum of flooded forest with TESI values between 0.6 and 0.7, as well as several minor regions with higher values.

The TESI values obtained by the proposed methodology were compared with field checks at the Coari terminal (TESOL). **Figure 6** presents the map of the 16 landscape change classes and the TESI map. Inserted in each panel of **Figure 6** are the location of each field checkpoint and profiles of different values of the TESI obtained by the fuzzy modeling approach.

At PT06, people live on the bank of the river, occupying a small space on stilts surrounded by different flooded and dried plant species (**Figure 13h**). The landscape change map assigns PT06 to class C4, which corresponds to upland forest that

**Figure 5.** *Results of the temporal environmental sensitivity index (TESI).*

*Current Practice in Fluvial Geomorphology - Dynamics and Diversity*

*4.2.2 Fuzzy modeling of the environmental sensitivity*

and severity of changes occurring over time.

*Level of risk for each class of change and the respective model parameters.*

involved in a set of rules.

**Class of change**

The number of hierarchical levels should be determined as needed by the problem. Considering the environmental sensitivity to oil spills, they may, for example, be designated as catastrophic, critical, marginal, and negligible. The graphical representation of the risk matrix should be simple in order to easily convey the risks

A RM was developed to assess the environmental risk for oil spills in the Amazonian landscape within half hydrological cycle based on the following predefined rules: (1) landscape changes that exist are reliable within half hydrological cycle (from drought to flood), and (2) the environmental impacts of an oil spill depend on variations of the hydrological regime. The combination of these two sets of rules can provide the environmental risk involved in oil pipeline and fluvial

transportation for each combination of landscape change (**Tables 3** and **4**).

The use of fuzzy logic allows for the capture and integration of scientific and local expert knowledge about the phenomenon being studied using heuristic "if-then" rules [26]. Fuzzy logic provides a powerful approach for classifying and monitoring environmental conditions related to flooding and describing the nature

The fuzzy modeling employs a symbolic representation of those classes, which is used to estimate the risk of each type of landscape change, based on the linguistic interpretation of the symbols and the RM defined in Section 4.2.1. The fuzzy modeling computes the function that converts the classes of change into levels of risk, as defined by the rule set in Eq. (3). In the present study, n = 16 classes of change will be mapped into m = 3 classes of risk. The fuzzy model is conveniently computed as a TS model (Eq. (4)), where risk is represented by a continuous parameter vector

C1 Water Water Low **0.25** C2 Flooded veget. Water Intermediate **0.4** C3 Flooded forest Water Intermediate **0.4** C4 Upland forest Water Low **0.3** C5 Water Flooded veget. Low **0.3** C6 Flooded veget. Flooded veget. High **1** C7 Flooded forest Flooded veget. High **0.8** C8 Upland forest Flooded veget. Intermediate **0.6** C9 Water Flooded forest Low **0.3** C10 Flooded veget. Flooded forest Intermediate **0.4** C11 Flooded forest Flooded forest High **1** C12 Upland forest Flooded forest Intermediate **0.6** C13 Water Dry forest Low **0.1** C14 Flooded veget. Dry forest Low **0.1** C15 Flooded forest Dry forest Low **0.1** C16 Dry forest Dry forest Low **0.25**

**Class name: dry season Class name: flooded season Level of risk θ**

**70**

**Table 4.**

#### **Figure 6.**

*Area in the vicinities of Coari, with field checkpoint location. (A) Landscape change map; (B) the map of TESI.*

is covered by water (i.e., completely drowned) in the flood season. For this point, TESI = 0.5810; the local environmental conditions suggest that this value agrees well with the intermediate risk assigned to this area.

In PT07, near TESOL (**Figure 13g**), it is possible to clearly see the closed canopy of the flooded forest, with only the tree tops and a few trunks above the waterline. The C8 class indicates that the upland forest is flooded here in the high water season. The TESI value shows that PT07 has an intermediate risk if oil is spilled there.

## **4.3 A computational method to represent fluctuations of the seasonal inundation**

The following data were used in this part of the study:


It is worth mentioning that under the dense vegetation conditions of the Amazonian environment, there are limitations of the SRTM altimetry, which refer to the crown of the trees in the C bands. Therefore, it is necessary to this DEM with other sources of information, in order to produce a proper interpretation.

**73**

and 985 cm.

between 932 and 985 centimeters.

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

Using SRTM data, it was possible to develop the following cartographic

10b (flooded vegetation) and 10a (aquatic macrophytes) (**Table 2**).

• **The elevation map** (**Figure 8**), showing the altimetric classes derived from the contour lines, allows to point out features that are not very prominent in the landscape, but which configure steep stretches along the shores of Coari and Mamiá lakes. An expressive drainage network was developed in the most elevated areas, which are not, however, subject to flooding. On the other hand, the removal of the vegetation cover allows the hydrographic network to be exposed more clearly in the periphery of the urban center of Coari. The distribution of the altimetric classes reveals the presence of characteristic features of the investigated area, such as plateaus and floodplains with many depressions [6].

• **The slope map** (**Figure 9**), in which flat relief corresponds to slopes varying from 0 to 3.7%, gentle undulations range from 3.7 to 9.3%, and moderate undulations whose present slopes above 10%. In this product, significant terrain features are observed, such as the plateaus (cyan) that are not restricted to a single hypsometric unit, since they occur in different altitudes. In addition, there are

very steep river banks, which are naturally more susceptible to erosion.

In addition to the cartographic products, the water-level historical series of Coari was examined (**Figure 10**). This permitted to infer the different phases of the hydrological cycle. Thus, in the flood period, rivers can reach up to approximately 18 meters, while in the most intense droughts, the water can reach values less than 2 meters. The lowest water level was recorded in October 1998, reaching 1.86 m;

Considering that the acquisition of orbital data for the SRTM mission took place between February 10 and 20, 2000, the analysis of the historical series also made it possible to gauge the apportionments in that period for the water level in Coari,

The water level during the SRTM mission (February/2000) ranged between 932

After performing the procedures mentioned above, it was necessary to implement algorithms on MatLab to use a morphological operator, known as watershed,

the largest flood occurred in July 1999 with the water level at 17.68 m.

• **Topographic contour lines**, which show the low altitudinal amplitude characteristic of the region, as a very unfavorable condition to the analysis, aggravated by the SRTM DEM ambiguity, as mentioned above. This reflects the limited penetration in the canopy of the radar pulse, because the Amazonian biome is predominantly composed of dense forest (trees with an average height of 20 meters). Therefore, it is necessary to interpret the contour lines together with the USTC-classified JERS-1 SAR mosaics. Such a procedure allowed verifying that the limits of potentially floodable areas coincide with the 40 m contour line. Furthermore, in the terrain located below this topographic level, there are flooded forests and macrophyte banks. It was also verified, locally, that the greater penetration in the vegetation of the JERS-1 SAR L-band signal allowed the delineation of flooded forests even above 40 meters (**Figure 7**). The criterion used to consider the terrain below 40 m as constituted by flooded forests and macrophyte banks, when the maximum water level in Coari occurs (approximately 20 m) and also taking into account the average tree height in the Amazon region (20 m). According to the fluvial sensitivity index to oil spills [5], the aforementioned areas, during the flood season, correspond to

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

products:

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

Using SRTM data, it was possible to develop the following cartographic products:


In addition to the cartographic products, the water-level historical series of Coari was examined (**Figure 10**). This permitted to infer the different phases of the hydrological cycle. Thus, in the flood period, rivers can reach up to approximately 18 meters, while in the most intense droughts, the water can reach values less than 2 meters. The lowest water level was recorded in October 1998, reaching 1.86 m; the largest flood occurred in July 1999 with the water level at 17.68 m.

Considering that the acquisition of orbital data for the SRTM mission took place between February 10 and 20, 2000, the analysis of the historical series also made it possible to gauge the apportionments in that period for the water level in Coari, between 932 and 985 centimeters.

The water level during the SRTM mission (February/2000) ranged between 932 and 985 cm.

After performing the procedures mentioned above, it was necessary to implement algorithms on MatLab to use a morphological operator, known as watershed,

*Current Practice in Fluvial Geomorphology - Dynamics and Diversity*

is covered by water (i.e., completely drowned) in the flood season. For this point, TESI = 0.5810; the local environmental conditions suggest that this value agrees

*Area in the vicinities of Coari, with field checkpoint location. (A) Landscape change map; (B) the map of TESI.*

In PT07, near TESOL (**Figure 13g**), it is possible to clearly see the closed canopy of the flooded forest, with only the tree tops and a few trunks above the waterline. The C8 class indicates that the upland forest is flooded here in the high water season. The TESI value shows that PT07 has an intermediate risk if oil is spilled there.

**4.3 A computational method to represent fluctuations of the seasonal inundation**

• SRTM C-band data, located in the border portion of Sheet SB.20-V-B, as a basis

• SWBD mask (SRTM Water Body Data), which is a by-product of the digital elevation model (DEM) generated by SRTM, with the objective of portraying bodies of water that meet the minimum criteria of capture, which resulted in the identification and delimitation of lakes and rivers existing in the region of

• Data of fluviometric measurements acquired through the National Water Agency (ANA), where a historical series of water level in Coari was obtained, from 1982 to 2010, which made it possible to infer the main moments of the water regime of the area investigated, characterized by low water, high water,

It is worth mentioning that under the dense vegetation conditions of the Amazonian environment, there are limitations of the SRTM altimetry, which refer to the crown of the trees in the C bands. Therefore, it is necessary to this DEM with

other sources of information, in order to produce a proper interpretation.

• JERS-1 SAR mosaics submitted to the USTC classification procedure.

well with the intermediate risk assigned to this area.

The following data were used in this part of the study:

for the extraction of altimetric information.

Coari, in the shapefile vector format.

receding water, and rising water.

**72**

**Figure 6.**

described by Meyer [29] and with an algorithm elaborated by Gonzalez [30]. The objective was to simulate a flood process on the hypsometric map extracted from SRM DEM. This technique interprets the gray-scale image as the expression of the

#### **Figure 7.**

*Illustrative diagram using 40-m contour lines, superimposed on the USTC-classified JERS-1 SAR mosaic at the high-water period (a), resulting in the interpretation of (b). The inserted time series shown in (c) indicates the maximum and minimum water levels in the Coari region. The areas in yellow represent flooded forests in USTC classification of JERS-1 SAR images. Letters (g) and (h) refer to flooded forest stretches in areas higher than the 40-m level, with restricted spatial distribution. Source: Silva et al. [6].*

#### **Figure 8.**

*Elevation or hypsometric map of Coari with altimetric classes ranging from 0 to 80 m (see Figure 1 for location). Compare with the location of flooded forest areas in Figure 8. In a, b, c, and d, there are examples of steep scarps on the margins of the Coari and Mamia lakes. In e, f, and g, the relief seems to be structurally controlled by geologic features oriented roughly E-W. Source: Silva et al. [6].*

**75**

**Figure 10.**

**Figure 9.**

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

*Slope map, in which the gradient is calculated as a percentage. Source: Silva et al. [6].*

*Water-level time series in Coari, indicating the maximum high- and low-water periods, as well as the levels below observations carried out in the SRTM data acquisition period (red rectangle). Source: Silva et al. [6].*

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

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