Tsunami Damage Estimate

**Chapter 2**

*and Xun Wang*

**Abstract**

**1. Introduction**

**13**

Monitoring of Tsunami/

Earthquake Damages by

Sensing Technique

Polarimetric Microwave Remote

Polarization characterizes the vector state of EM wave. When interacting with polarized wave, rough natural surface often induces dominant surface scattering; building also presents dominant double-bounce scattering. Tsunami/earthquake causes serious destruction just by inundating the land surface and destroying the building. By analyzing the change of surface and double-bounce scattering before and after disaster, we can achieve a monitoring of damages. This constitutes one basic principle of polarimetric microwave remote sensing of tsunami/earthquake. The extraction of surface and double-bounce scattering from coherency matrix is achieved by model-based decomposition. The general four-component scattering power decomposition with unitary transformation (G4U) has been widely used in the remote sensing of tsunami/earthquake to identify surface and double-bounce scattering because it can adaptively enhance surface or double-bounce scattering. Nonetheless, the strict derivation in this chapter conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for an extended G4U (EG4U). Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative

monitoring and quantitative evaluation of tsunami/earthquake damages.

**Keywords:** disaster monitoring, damage evaluation, tsunami, earthquake, microwave remote sensing, synthetic aperture radar (SAR), polarimetric SAR (PolSAR), polarimetric decomposition, scattering model, unitary transformation

Tsunami and earthquake seriously endanger people's lives and properties. Efficient and accurate monitoring and assessment are of crucial importance for the fast response, management, and mitigation of the disasters [1–3]. Compared with the optical remote sensing, microwave remote sensing technology such as synthetic aperture radar (SAR) has been widely applied to monitoring natural and human-

induced disasters for its all-day and all-weather working capacity [4].

*Dong Li, Yunhua Zhang, Liting Liang, Jiefang Yang*

#### **Chapter 2**

## Monitoring of Tsunami/ Earthquake Damages by Polarimetric Microwave Remote Sensing Technique

*Dong Li, Yunhua Zhang, Liting Liang, Jiefang Yang and Xun Wang*

#### **Abstract**

Polarization characterizes the vector state of EM wave. When interacting with polarized wave, rough natural surface often induces dominant surface scattering; building also presents dominant double-bounce scattering. Tsunami/earthquake causes serious destruction just by inundating the land surface and destroying the building. By analyzing the change of surface and double-bounce scattering before and after disaster, we can achieve a monitoring of damages. This constitutes one basic principle of polarimetric microwave remote sensing of tsunami/earthquake. The extraction of surface and double-bounce scattering from coherency matrix is achieved by model-based decomposition. The general four-component scattering power decomposition with unitary transformation (G4U) has been widely used in the remote sensing of tsunami/earthquake to identify surface and double-bounce scattering because it can adaptively enhance surface or double-bounce scattering. Nonetheless, the strict derivation in this chapter conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for an extended G4U (EG4U). Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative monitoring and quantitative evaluation of tsunami/earthquake damages.

**Keywords:** disaster monitoring, damage evaluation, tsunami, earthquake, microwave remote sensing, synthetic aperture radar (SAR), polarimetric SAR (PolSAR), polarimetric decomposition, scattering model, unitary transformation

#### **1. Introduction**

Tsunami and earthquake seriously endanger people's lives and properties. Efficient and accurate monitoring and assessment are of crucial importance for the fast response, management, and mitigation of the disasters [1–3]. Compared with the optical remote sensing, microwave remote sensing technology such as synthetic aperture radar (SAR) has been widely applied to monitoring natural and humaninduced disasters for its all-day and all-weather working capacity [4].

Polarization is an essential property of the electromagnetic wave [5–8]. The polarization state of wave will change when interacting with ground object. For example, rough natural surface such as land and water often induces the strong Bragg surface scattering, while building often presents the dominant double-bounce scattering because of the dihedral corner reflectors formed by ground and the vertical wall of building. Therefore, by analyzing the polarization of the scattering wave, we can acquire the physical and geometrical information regarding the object. This is the main task of SAR polarimetry (PolSAR) [9–11].

and DG4U for an extended G4U (EG4U). Experiments on the PolSAR images of Miyagi Prefecture, Japan, acquired by the L-band spaceborne ALOS-PALSAR system before and after the March 11, 2011, Off-Tohoku 9.0 tsunami/earthquake demonstrate not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the monitoring of tsunami/earthquake disaster.

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

principle of PolSAR and the polarization descriptors first. The advanced fourcomponent scattering power decompositions are then described in Section 3 to develop the EG4U. By decomposing the ALOS-PALSAR datasets of the 2011 great Tohoku tsunami/earthquake using EG4U, Section 4 evaluates and analyzes the polarimetric monitoring of disaster damages further. The chapter is eventually

**2. SAR polarimetry and polarization descriptors**

incident electric filed *EI* into the scattered electric filed *E<sup>S</sup>* [31]:

*<sup>r</sup>* ½ � *<sup>S</sup> EI* ! *ES*

is obtained by first transmitting *H*-polarized wave (*EI*

*<sup>E</sup><sup>S</sup>* <sup>¼</sup> *<sup>e</sup>*�*jkr*

and then transmitting *V*-polarized wave (*EI*

the second-order moment of the fluctuations [9]:

2 6 4

h i ½ � *<sup>T</sup>* <sup>¼</sup> *kk*† � � <sup>¼</sup>

**15**

concluded in Section 5.

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

earth surface [9].

The remainder of this chapter is arranged as follows. Section 2 presents the basic

SAR is an active microwave remote sensing technique dedicated to acquire the large-scaled 2D coherent image of the earth's surface reflectivity [9]. It transmits microwave pulses and receives the backscattering from the illuminated terrain to synthesize a high spatial resolution image. Such an active operation enables SAR an all-day working capacity independent of solar illumination. In addition, operating in the microwave region of electromagnetic spectrum avoids the effects of rain and clouds, which allows SAR an almost all-weather continuous monitoring of the

Polarization characterizes the vector state of the electromagnetic wave. The polarization state of wave will change when interacting with a ground object. By processing and analyzing such change of polarization, we can obtain the material, roughness, shape, and orientation information regarding the object. The core of this change is the (Sinclair) scattering matrix ½ � *S* of the object, which transforms the

> *H ES V*

*H* - and *V*-polarization simultaneously to measure the first column *SHH* and *SVH*

polarization simultaneously for the second column *SHV* and *SVV*. In reciprocal backscattering, we have *SHV* ¼ *SVH* and matrix ½ � *S* covers five DoF then.

> *T*<sup>11</sup> *T*<sup>12</sup> *T*<sup>13</sup> *T*<sup>21</sup> *T*<sup>22</sup> *T*<sup>23</sup> *T*<sup>31</sup> *T*<sup>32</sup> *T*<sup>33</sup>

3 7 <sup>5</sup>, *<sup>k</sup>* <sup>¼</sup> <sup>1</sup>

ffiffi 2 p

2 6 4

Generally, almost all the ground scatterers are situated in the dynamically changing environment and subjected to spatial and*=*or temporal variations [32]. Such scatterer is called the distributed target, and we can no longer model its scattering with a determined scattering matrix ½ � *S* . The 3 � 3 coherency matrix h i ½ � *T* is then constructed as the statistical average of the acquired scatterings to describe

<sup>¼</sup> *<sup>e</sup>*�*jkr r*

where *r* denotes the distance from radar to ground object, *k* is the wave number, and subscript *H* or *V* represents the horizontal or vertical polarization. Matrix ½ � *S*

*SHH SHV SVH SVV* � � *EI*

*H EI V*

*<sup>H</sup>*) and receiving scatterings in

(1)

" #

*<sup>V</sup>*) and also receiving in *H*- and *V*-

*SHH* þ *SVV SHH* � *SVV* 2*SHV*

3 7

<sup>5</sup> (2)

" #

Tsunami is often accompanied by earthquake and flooding [1–3]. It damages and inundates the buildings and causes the collapse of the ground-wall dihedral structures as well as the enhancement of the direct surface scatterers. Therefore, by analyzing the power of double-bounce scattering and surface scattering before and after the event, we can achieve an efficient monitoring of the disasters. This simple strategy has been successfully adopted in the polarimetric microwave remote sensing of tsunami/earthquake [12–21].

Nonetheless, the extraction of double-bounce scattering and surface scattering from PolSAR image is not so straightforward because each pixel in PolSAR is a 3 � 3 complex coherency matrix h i ½ � *T* with nine degrees of freedom (DoF). A widely used approach to achieve this is to decompose h i ½ � *T* on the canonical scattering models [22]. The first such decomposition was devised by Freeman and Durden [23] which expands h i ½ � *T* on the surface scattering, double-bounce scattering, and volume scattering (describes the complex scattering in vegetation area). This threecomponent decomposition, however, is responsible for only five DoF of h i ½ � *T* because of the symmetric reflection assumption. This assumption was tackled by Yamaguchi et al. [24] by introducing a fourth helix component and two additional models of volume scattering. The resulted four-component decomposition (Y4O) then only leaves three DoF unaccounted: the 1, 3 ð Þ element of h i ½ � *T* , i.e., *T*13, and the real part of the 2, 3 ð Þ element of h i ½ � *T* , i.e., Re f g *T*<sup>23</sup> . A same target will present differently by a simple rotation about the line of sight of radar. Deorientation should be first conducted on h i ½ � *T* to eliminate the influence [25]. As a result, Re f g *T*<sup>23</sup> changes to zero and Y4O with rotation (Y4R) accounts for seven DoF [26]. Based on Y4R, Sato et al. [27] further proposed to add a new model to characterize volume scattering generated by even-bounce structure. However, Sato's extended Y4R (S4R) still leaves *T*<sup>13</sup> unaccounted. To solve this, Singh et al. [28] in 2013 proposed a general four-component decomposition (G4U) based on a special unitary matrix. G4U enables *T*<sup>13</sup> included in the accounted models by conducting unitary transformation to the rotated version of h i ½ � *T* . Singh et al. [28] claimed that G4U could make full use of polarimetric parameters. As a result, in comparison with the fourcomponent decompositions such as S4R and Y4R, G4U could enhance doublebounce scattering power over urban area while enhancing surface scattering contribution over an area where surface scattering is preferable [28]. This makes G4U very suitable to the remote sensing of tsunami/earthquake [16, 20] and establishes G4U the state-of-the-art four-component scattering power decomposition [29, 30].

This chapter is dedicated to enable an extension to G4U for better monitoring of tsunami/earthquake disaster. It is indicated that the unitary transformation in G4U adds a *T*13-related but redundant balance equation to the original selfcontained equation system in Y4R and S4R. Then *T*<sup>13</sup> is accounted for by G4U, but we obtain no exact solution to the system but the approximate ones. The general expression of the approximate solutions enables a generalized G4U (GG4U), while G4U and S4R represent two special forms. A dual G4U (DG4U) is also obtained. The general solution indicates that G4U cannot always enhance the double-bounce scattering power over urban area nor strengthen the surface scattering power over the area where surface scattering is dominant unless we adaptively integrate G4U

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

and DG4U for an extended G4U (EG4U). Experiments on the PolSAR images of Miyagi Prefecture, Japan, acquired by the L-band spaceborne ALOS-PALSAR system before and after the March 11, 2011, Off-Tohoku 9.0 tsunami/earthquake demonstrate not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the monitoring of tsunami/earthquake disaster.

The remainder of this chapter is arranged as follows. Section 2 presents the basic principle of PolSAR and the polarization descriptors first. The advanced fourcomponent scattering power decompositions are then described in Section 3 to develop the EG4U. By decomposing the ALOS-PALSAR datasets of the 2011 great Tohoku tsunami/earthquake using EG4U, Section 4 evaluates and analyzes the polarimetric monitoring of disaster damages further. The chapter is eventually concluded in Section 5.

#### **2. SAR polarimetry and polarization descriptors**

SAR is an active microwave remote sensing technique dedicated to acquire the large-scaled 2D coherent image of the earth's surface reflectivity [9]. It transmits microwave pulses and receives the backscattering from the illuminated terrain to synthesize a high spatial resolution image. Such an active operation enables SAR an all-day working capacity independent of solar illumination. In addition, operating in the microwave region of electromagnetic spectrum avoids the effects of rain and clouds, which allows SAR an almost all-weather continuous monitoring of the earth surface [9].

Polarization characterizes the vector state of the electromagnetic wave. The polarization state of wave will change when interacting with a ground object. By processing and analyzing such change of polarization, we can obtain the material, roughness, shape, and orientation information regarding the object. The core of this change is the (Sinclair) scattering matrix ½ � *S* of the object, which transforms the incident electric filed *EI* into the scattered electric filed *E<sup>S</sup>* [31]:

$$\mathbf{E}^{\mathbf{S}} = \frac{e^{-jkr}}{r} [\mathbf{S}] \mathbf{E}^{\mathbf{I}} \rightarrow \begin{bmatrix} E\_H^{\mathbf{S}} \\ E\_V^{\mathbf{S}} \end{bmatrix} = \frac{e^{-jkr}}{r} \begin{bmatrix} \mathbf{S}\_{HH} & \mathbf{S}\_{HV} \\ \mathbf{S}\_{VH} & \mathbf{S}\_{VV} \end{bmatrix} \begin{bmatrix} E\_H^{\mathbf{I}} \\ E\_V^{\mathbf{I}} \end{bmatrix} \tag{1}$$

where *r* denotes the distance from radar to ground object, *k* is the wave number, and subscript *H* or *V* represents the horizontal or vertical polarization. Matrix ½ � *S* is obtained by first transmitting *H*-polarized wave (*EI <sup>H</sup>*) and receiving scatterings in *H* - and *V*-polarization simultaneously to measure the first column *SHH* and *SVH* and then transmitting *V*-polarized wave (*EI <sup>V</sup>*) and also receiving in *H*- and *V*polarization simultaneously for the second column *SHV* and *SVV*. In reciprocal backscattering, we have *SHV* ¼ *SVH* and matrix ½ � *S* covers five DoF then.

Generally, almost all the ground scatterers are situated in the dynamically changing environment and subjected to spatial and*=*or temporal variations [32]. Such scatterer is called the distributed target, and we can no longer model its scattering with a determined scattering matrix ½ � *S* . The 3 � 3 coherency matrix h i ½ � *T* is then constructed as the statistical average of the acquired scatterings to describe the second-order moment of the fluctuations [9]:

$$
\langle [T] \rangle = \langle \mathbf{k} \mathbf{k}^{\dagger} \rangle = \begin{bmatrix} T\_{11} & T\_{12} & T\_{13} \\ T\_{21} & T\_{22} & T\_{23} \\ T\_{31} & T\_{32} & T\_{33} \end{bmatrix}, \mathbf{k} = \frac{\mathbf{1}}{\sqrt{2}} \begin{bmatrix} \mathbf{S}\_{HH} + \mathbf{S}\_{VV} \\ \mathbf{S}\_{HH} - \mathbf{S}\_{VV} \\ \mathbf{2S}\_{HV} \end{bmatrix} \tag{2}
$$

Polarization is an essential property of the electromagnetic wave [5–8]. The polarization state of wave will change when interacting with ground object. For example, rough natural surface such as land and water often induces the strong Bragg surface scattering, while building often presents the dominant double-bounce scattering because of the dihedral corner reflectors formed by ground and the vertical wall of building. Therefore, by analyzing the polarization of the scattering wave, we can acquire the physical and geometrical information regarding the

Tsunami is often accompanied by earthquake and flooding [1–3]. It damages and inundates the buildings and causes the collapse of the ground-wall dihedral structures as well as the enhancement of the direct surface scatterers. Therefore, by analyzing the power of double-bounce scattering and surface scattering before and after the event, we can achieve an efficient monitoring of the disasters. This simple strategy has been successfully adopted in the polarimetric microwave remote

Nonetheless, the extraction of double-bounce scattering and surface scattering from PolSAR image is not so straightforward because each pixel in PolSAR is a 3 � 3 complex coherency matrix h i ½ � *T* with nine degrees of freedom (DoF). A widely used approach to achieve this is to decompose h i ½ � *T* on the canonical scattering models [22]. The first such decomposition was devised by Freeman and Durden [23] which expands h i ½ � *T* on the surface scattering, double-bounce scattering, and volume scattering (describes the complex scattering in vegetation area). This threecomponent decomposition, however, is responsible for only five DoF of h i ½ � *T* because of the symmetric reflection assumption. This assumption was tackled by Yamaguchi et al. [24] by introducing a fourth helix component and two additional models of volume scattering. The resulted four-component decomposition (Y4O) then only leaves three DoF unaccounted: the 1, 3 ð Þ element of h i ½ � *T* , i.e., *T*13, and the real part of the 2, 3 ð Þ element of h i ½ � *T* , i.e., Re f g *T*<sup>23</sup> . A same target will present differently by a simple rotation about the line of sight of radar. Deorientation should be first conducted on h i ½ � *T* to eliminate the influence [25]. As a result, Re f g *T*<sup>23</sup> changes to zero and Y4O with rotation (Y4R) accounts for seven DoF [26]. Based on Y4R, Sato et al. [27] further proposed to add a new model to characterize volume scattering generated by even-bounce structure. However, Sato's extended Y4R (S4R) still leaves *T*<sup>13</sup> unaccounted. To solve this, Singh et al. [28] in 2013 proposed a general four-component decomposition (G4U) based on a special unitary matrix. G4U enables *T*<sup>13</sup> included in the accounted models by conducting unitary transformation to the rotated version of h i ½ � *T* . Singh et al. [28] claimed that G4U could make full use of polarimetric parameters. As a result, in comparison with the fourcomponent decompositions such as S4R and Y4R, G4U could enhance doublebounce scattering power over urban area while enhancing surface scattering contribution over an area where surface scattering is preferable [28]. This makes G4U very suitable to the remote sensing of tsunami/earthquake [16, 20] and establishes G4U the state-of-the-art four-component scattering power decomposition [29, 30]. This chapter is dedicated to enable an extension to G4U for better monitoring of tsunami/earthquake disaster. It is indicated that the unitary transformation in G4U adds a *T*13-related but redundant balance equation to the original self-

contained equation system in Y4R and S4R. Then *T*<sup>13</sup> is accounted for by G4U, but we obtain no exact solution to the system but the approximate ones. The general expression of the approximate solutions enables a generalized G4U (GG4U), while G4U and S4R represent two special forms. A dual G4U (DG4U) is also obtained. The general solution indicates that G4U cannot always enhance the double-bounce scattering power over urban area nor strengthen the surface scattering power over the area where surface scattering is dominant unless we adaptively integrate G4U

object. This is the main task of SAR polarimetry (PolSAR) [9–11].

sensing of tsunami/earthquake [12–21].

*Tsunami - Damage Assessment and Medical Triage*

**14**

*Tsunami - Damage Assessment and Medical Triage*

where h i� and superscript † represent the operations of ensemble average and conjugate transpose and *k* denotes the Pauli vector. The spatial*=*temporal depolarization pushes the DoF of h i ½ � *T* to nine. Therefore, different from the conventional SAR image, each pixel in PolSAR image is not a complex number but a 3 � 3 coherency matrix h i ½ � *T* .

The coherency matrix h i ½ � *T* in Eq. (2) is expressed in the *H*-*V* polarization basis; we can also formulate it in some other orthonormal basis by simply taking the unitary transformation of h i ½ � *T* :

$$\text{Unitary}(\langle [T] \rangle) \stackrel{\text{def}}{=} [U\_3] \langle [T] \rangle [U\_3]^\dagger \tag{3}$$

**3.1 Y4R and S4R**

where *T*<sup>0</sup>

*S* � � � � , *T*<sup>0</sup>

components; *β* and *α* in *T*<sup>0</sup>

Hence, there always exists a *T*<sup>0</sup>

*T*0 *V*

**17**

**Figure 1.**

scattering model, respectively:

*T*0 *S* � � � � <sup>¼</sup>

> *T*0 *V* � � � � <sup>¼</sup>

Y4R and S4R decompose the target by linearly expanding matrix *T*<sup>0</sup> h i ½ � on the

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

*D* � � � � <sup>þ</sup> *<sup>f</sup> <sup>V</sup> <sup>T</sup>*<sup>0</sup>

the double-bounce scattering model, the volume scattering model, and the helix

3 7 <sup>5</sup>, *<sup>T</sup>*<sup>0</sup> *D* � � � � <sup>¼</sup>

3 7 <sup>5</sup>, *<sup>T</sup>*<sup>0</sup> *C* � � � � <sup>¼</sup> <sup>1</sup>

Parameters *f <sup>S</sup>*, *f <sup>D</sup>*, *f <sup>V</sup>*, and *f <sup>C</sup>* in Eq. (8) represent the contributions of the four

*D*

*The canonical models involved in the four-component model-based scattering power decompositions.*

� � � � are real constants satisfying *<sup>a</sup>* <sup>þ</sup> *<sup>b</sup>* <sup>þ</sup> *<sup>c</sup>* <sup>¼</sup> 1, which involve in four volume scattering models and are adaptively selected according to the branch conditions [27, 28]. Combining Eqs. (8) and (9), the S4R*=*Y4R scattering balance equation

*C*

*V* � � � � <sup>þ</sup> *<sup>f</sup> <sup>C</sup> <sup>T</sup>*<sup>0</sup>

> 2 6 4

2 6 4

2

*C*

3 7 5,

*j*

3 7

<sup>5</sup>*:* (9)

� � � � denote the surface scattering model,

j j *<sup>α</sup>* <sup>2</sup> *<sup>α</sup>* <sup>0</sup> *α*<sup>∗</sup> 1 0 0 00

000 0 1 � 0 ∓*j* 1

� � � � are complex parameters; *a*, *b*, *c*, and *d* in

<sup>11</sup> � 1Þ

<sup>12</sup> � 2Þ

<sup>22</sup> � 3Þ

<sup>13</sup> -related unaccounted residue in Y4R and S4R.

*:* (10)

<sup>13</sup> in Eq. (10).

<sup>2</sup> <sup>¼</sup> *<sup>T</sup>*<sup>0</sup>

<sup>23</sup> � � � <sup>4</sup><sup>Þ</sup>

<sup>33</sup> � 5Þ

� � � � (8)

four canonical scattering models, as illustrated in **Figure 1**:

*S* � � � � <sup>þ</sup> *<sup>f</sup> <sup>D</sup> <sup>T</sup>*<sup>0</sup>

*V* � � � � , and *T*<sup>0</sup>

1 *β* 0 *<sup>β</sup>* <sup>∗</sup> j j *<sup>β</sup>* <sup>2</sup> <sup>0</sup> 0 00

> *a d* 0 *d b* 0 0 0 *c*

*T*<sup>0</sup> h i¼ ½ � *f <sup>S</sup> T*<sup>0</sup>

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

*D* � � � � , *T*<sup>0</sup>

> 2 6 4

> > 2 6 4

*S* � � � � and *T*<sup>0</sup>

> �*j f C*

8

>>>>>>>>>>>>><

>>>>>>>>>>>>>:

*<sup>f</sup> <sup>V</sup><sup>c</sup>* <sup>þ</sup> *<sup>f</sup> <sup>C</sup>*

system on unknowns *f <sup>S</sup>*, *f <sup>D</sup>*, *f <sup>V</sup>*, *f <sup>C</sup>*, *α*, and *β* is formulated [26, 27]:

*<sup>f</sup> <sup>S</sup>* <sup>þ</sup> *<sup>f</sup> <sup>D</sup>*j j *<sup>α</sup>* <sup>2</sup> <sup>þ</sup> *<sup>f</sup> <sup>V</sup><sup>a</sup>* <sup>¼</sup> *<sup>T</sup>*<sup>0</sup>

*f <sup>S</sup>β* þ *f <sup>D</sup>α* þ *f <sup>V</sup>d* ¼ *T*<sup>0</sup>

*<sup>f</sup> <sup>S</sup>*j j *<sup>β</sup>* <sup>2</sup> <sup>þ</sup> *<sup>f</sup> <sup>D</sup>* <sup>þ</sup> *<sup>f</sup> <sup>V</sup><sup>b</sup>* <sup>þ</sup> *<sup>f</sup> <sup>C</sup>*

<sup>2</sup> <sup>¼</sup> *<sup>j</sup>*Im *<sup>T</sup>*<sup>0</sup>

<sup>2</sup> <sup>¼</sup> *<sup>T</sup>*<sup>0</sup>

Nevertheless, we obtain no scattering balance equation on *T*<sup>0</sup>

where ½ � *U*<sup>3</sup> is the special unitary matrix that describes the relationship between *H*-*V* and the new orthonormal basis. Target deorientation is just based on the real rotation matrix [25]:

$$\begin{aligned} \begin{bmatrix} U\_3(\theta) \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 \\ 0 & \cos 2\theta & \sin 2\theta \\ 0 & -\sin 2\theta & \cos 2\theta \end{bmatrix}, 2\theta = \frac{1}{2} \tan^{-1} \left( \frac{2 \operatorname{Re} \{ T\_{23} \}}{T\_{22} - T\_{33}} \right) \end{aligned} \tag{4}$$

Combining Eq. (4) into Eq. (3), the deoriented coherency matrix *T*<sup>0</sup> h i ½ � is

$$\langle [T'] \rangle = [U\_3(\theta)] \langle [T] \rangle [U\_3(\theta)]^\dagger = \begin{bmatrix} T'\_{11} & T'\_{12} & T'\_{13} \\ T'\_{21} & T'\_{22} & j \text{Im} \{ T'\_{23} \} \\ T'\_{31} & j \text{Im} \{ T'\_{32} \} & T'\_{33} \end{bmatrix}. \tag{5}$$

Deorientation makes *T*<sup>0</sup> <sup>23</sup> become purely imaginary and reduces DoF from nine to eight. In order to eliminate the imaginary part further, Singh et al. developed an imaginary rotation matrix [28]:

$$[U\_3(\rho)] = \begin{bmatrix} 1 & 0 & 0 \\ 0 & \cos 2\rho & j \sin 2\rho \\ 0 & j \sin 2\rho & \cos 2\rho \end{bmatrix}, 2\rho = \frac{1}{2} \tan^{-1} \left( \frac{2 \text{Im} \{ T\_{23}' \}}{T\_{22}' - T\_{33}'} \right). \tag{6}$$

A coherency matrix *T*<sup>00</sup> h i ½ � with zero *T*<sup>00</sup> <sup>23</sup> is then achieved:

$$
\langle \langle T' \rangle \rangle = [U\_3(\rho)] \langle [T'] \rangle [U\_3(\rho)]^\dagger = \begin{bmatrix} T''\_{11} & T''\_{12} & T''\_{13} \\ T''\_{21} & T''\_{22} & \mathbf{0} \\ T''\_{31} & \mathbf{0} & T''\_{33} \end{bmatrix}. \tag{7}
$$

#### **3. Advanced four-component scattering power decompositions**

Polarimetric incoherent decomposition plays an important role in the discrimination and recognition of the distributed target [22]. It pursues the scattering mechanism of the unknown target by extracting the dominant or average target (such as the Huynen-type phenomenological dichotomies [7, 32] and the eigenvalue*=*eigenvector-based target decompositions [9, 33]) from h i ½ � *T* or expanding h i ½ � *T* on the canonical models (such as the model-based target decompositions [23–28]). Among these decompositions, the four-component scattering power decompositions such as Y4R, S4R, and G4U have been a hot topic recently [29].

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

#### **3.1 Y4R and S4R**

where h i� and superscript † represent the operations of ensemble average and conjugate transpose and *k* denotes the Pauli vector. The spatial*=*temporal depolarization pushes the DoF of h i ½ � *T* to nine. Therefore, different from the conventional SAR image, each pixel in PolSAR image is not a complex number but a 3 � 3

The coherency matrix h i ½ � *T* in Eq. (2) is expressed in the *H*-*V* polarization basis;

where ½ � *U*<sup>3</sup> is the special unitary matrix that describes the relationship between *H*-*V* and the new orthonormal basis. Target deorientation is just based on the real

> 3 7 <sup>5</sup>, 2*<sup>θ</sup>* <sup>¼</sup> <sup>1</sup> 2

Combining Eq. (4) into Eq. (3), the deoriented coherency matrix *T*<sup>0</sup> h i ½ � is

2 6 4

*T*0

*T*0

*T*0

to eight. In order to eliminate the imaginary part further, Singh et al. developed an

3 7 <sup>5</sup>, 2*<sup>φ</sup>* <sup>¼</sup> <sup>1</sup> 2

<sup>11</sup> *T*<sup>0</sup>

<sup>21</sup> *T*<sup>0</sup>

<sup>31</sup> *j*Im *T*<sup>0</sup>

def ½ � *<sup>U</sup>*<sup>3</sup> h i ½ � *<sup>T</sup>* ½ � *<sup>U</sup>*<sup>3</sup>

† (3)

(4)

tan �<sup>1</sup> 2 Re f g *<sup>T</sup>*<sup>23</sup> *T*<sup>22</sup> � *T*<sup>33</sup> � �

<sup>12</sup> *T*<sup>0</sup>

32 � � *T*<sup>0</sup>

<sup>23</sup> become purely imaginary and reduces DoF from nine

<sup>23</sup> is then achieved:

*T*<sup>00</sup> <sup>11</sup> *T*<sup>00</sup>

2 6 4

*T*<sup>00</sup> <sup>21</sup> *T*<sup>00</sup>

*T*<sup>00</sup>

<sup>22</sup> *j*Im *T*<sup>0</sup>

tan �<sup>1</sup> 2Im *<sup>T</sup>*<sup>0</sup>

*T*0 <sup>22</sup> � *T*<sup>0</sup> 33

<sup>12</sup> *T*<sup>00</sup> 13

<sup>22</sup> 0

33

<sup>31</sup> 0 *T*<sup>00</sup>

13

33

23 � �

23 � �

> 3 7

� �

3 7

<sup>5</sup>*:* (5)

*:* (6)

<sup>5</sup>*:* (7)

we can also formulate it in some other orthonormal basis by simply taking the

Unitaryð Þ h i ½ � *T* =

10 0 0 cos 2*θ* sin 2*θ* 0 � sin 2*θ* cos 2*θ*

10 0 0 cos 2*φ j*sin 2*φ* 0 *j*sin 2*φ* cos 2*φ*

*<sup>T</sup>*<sup>00</sup> h i ½ � <sup>¼</sup> ½ � *<sup>U</sup>*3ð Þ *<sup>φ</sup> <sup>T</sup>*<sup>0</sup> h i ½ � ½ � *<sup>U</sup>*3ð Þ *<sup>φ</sup>* † <sup>¼</sup>

**3. Advanced four-component scattering power decompositions**

nation and recognition of the distributed target [22]. It pursues the scattering mechanism of the unknown target by extracting the dominant or average target (such as the Huynen-type phenomenological dichotomies [7, 32] and the eigenvalue*=*eigenvector-based target decompositions [9, 33]) from h i ½ � *T* or expanding h i ½ � *T* on the canonical models (such as the model-based target decompositions [23–28]). Among these decompositions, the four-component scattering power decompositions such as Y4R, S4R, and G4U have been a hot

Polarimetric incoherent decomposition plays an important role in the discrimi-

coherency matrix h i ½ � *T* .

rotation matrix [25]:

½ �¼ *U*3ð Þ*θ*

Deorientation makes *T*<sup>0</sup>

imaginary rotation matrix [28]:

2 6 4

A coherency matrix *T*<sup>00</sup> h i ½ � with zero *T*<sup>00</sup>

½ �¼ *U*3ð Þ *φ*

topic recently [29].

**16**

2 6 4

*<sup>T</sup>*<sup>0</sup> h i ½ � <sup>¼</sup> ½ � *<sup>U</sup>*3ð Þ*<sup>θ</sup>* h i ½ � *<sup>T</sup>* ½ � *<sup>U</sup>*3ð Þ*<sup>θ</sup>* † <sup>¼</sup>

unitary transformation of h i ½ � *T* :

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Y4R and S4R decompose the target by linearly expanding matrix *T*<sup>0</sup> h i ½ � on the four canonical scattering models, as illustrated in **Figure 1**:

$$
\langle\langle T' \rangle\rangle = f\_S \langle \left[ T'\_S \right] \rangle + f\_D \langle \left[ T'\_D \right] \rangle + f\_V \langle \left[ T'\_V \right] \rangle + f\_C \langle \left[ T'\_C \right] \rangle \tag{8}
$$

where *T*<sup>0</sup> *S* � � � � , *T*<sup>0</sup> *D* � � � � , *T*<sup>0</sup> *V* � � � � , and *T*<sup>0</sup> *C* � � � � denote the surface scattering model, the double-bounce scattering model, the volume scattering model, and the helix scattering model, respectively:

$$
\langle \left[ T\_S' \right] \rangle = \begin{bmatrix} \mathbf{1} & \boldsymbol{\beta} & \mathbf{0} \\ \boldsymbol{\beta}^\* & |\boldsymbol{\beta}|^2 & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & \mathbf{0} \end{bmatrix}, \langle \left[ T\_D' \right] \rangle = \begin{bmatrix} |a|^2 & a & \mathbf{0} \\ a^\* & \mathbf{1} & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & \mathbf{0} \end{bmatrix},
$$

$$
\langle \left[ T\_V' \right] \rangle = \begin{bmatrix} a & d & \mathbf{0} \\ d & b & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & c \end{bmatrix}, \langle \left[ T\_C' \right] \rangle = \frac{1}{2} \begin{bmatrix} \mathbf{0} & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \mathbf{1} & \pm j \\ \mathbf{0} & \mp j & \mathbf{1} \end{bmatrix}. \tag{9}
$$

#### **Figure 1.**

*The canonical models involved in the four-component model-based scattering power decompositions.*

Parameters *f <sup>S</sup>*, *f <sup>D</sup>*, *f <sup>V</sup>*, and *f <sup>C</sup>* in Eq. (8) represent the contributions of the four components; *β* and *α* in *T*<sup>0</sup> *S* � � � � and *T*<sup>0</sup> *D* � � � � are complex parameters; *a*, *b*, *c*, and *d* in *T*0 *V* � � � � are real constants satisfying *<sup>a</sup>* <sup>þ</sup> *<sup>b</sup>* <sup>þ</sup> *<sup>c</sup>* <sup>¼</sup> 1, which involve in four volume scattering models and are adaptively selected according to the branch conditions [27, 28]. Combining Eqs. (8) and (9), the S4R*=*Y4R scattering balance equation system on unknowns *f <sup>S</sup>*, *f <sup>D</sup>*, *f <sup>V</sup>*, *f <sup>C</sup>*, *α*, and *β* is formulated [26, 27]:

$$\begin{cases} f\_S + f\_D |a|^2 + f\_V a = T\_{11}' & -1 \\ f\_S \theta + f\_D a + f\_V d = T\_{12}' & -2 \\ f\_S |\theta|^2 + f\_D + f\_V b + \frac{f\_C}{2} = T\_{22}' & -3 \\ \quad \pm j \frac{f\_C}{2} = j \text{Im} \left\{ T\_{23}' \right\} & -4 \\ f\_V c + \frac{f\_C}{2} = T\_{33}' & -5 \end{cases} \tag{10}$$

Nevertheless, we obtain no scattering balance equation on *T*<sup>0</sup> <sup>13</sup> in Eq. (10). Hence, there always exists a *T*<sup>0</sup> <sup>13</sup> -related unaccounted residue in Y4R and S4R.

#### **3.2 G4U**

To model *T*<sup>0</sup> 13, G4U uses ½ � *U*3ð Þ *φ* to conduct unitary transformation to both sides of Eq. (10) first and then eliminates the influence of *φ* [28]. As a result, an additional balance equation is brought into G4U, and we obtain the following scattering balance equation system [30]:

$$\begin{cases} f\_S + f\_D |a|^2 + f\_V a = T\_{11}' & -1 \\ f\_S \theta + f\_D a + f\_V d = T\_{12}' + T\_{13}' \\ f\_S \theta + f\_D a + f\_V d = T\_{12}' - T\_{13}' \\ f\_S |\theta|^2 + f\_D + f\_V b + \frac{f\_C}{2} = T\_{22}' & -3 \\ \pm j \frac{f\_C}{2} = j \text{Im} \{ T\_{23}' \} & -4 \\ f\_V c + \frac{f\_C}{2} = T\_{33}' & -5 \end{cases} \tag{11}$$

*BC*>0 ) dominant surface scattering ) *α* ¼ 0 *BC*≤0 ) dominant double � bounce scattering ) *β* ¼ 0

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

*PS* <sup>¼</sup> *<sup>f</sup> <sup>S</sup>* <sup>1</sup> <sup>þ</sup> j j *<sup>β</sup>* <sup>2</sup> � � <sup>¼</sup>

*PD* <sup>¼</sup> *<sup>f</sup> <sup>D</sup>* <sup>1</sup> <sup>þ</sup> j j *<sup>α</sup>* <sup>2</sup> � � <sup>¼</sup>

<sup>22</sup> þ *T*<sup>0</sup>

*C* ¼ *C*<sup>1</sup> ¼ *T*<sup>0</sup>

*C* ¼ *C*<sup>2</sup> ¼ *T*<sup>0</sup>

<sup>2</sup> <sup>¼</sup> *<sup>T</sup>*<sup>0</sup>

Hence, the essential difference between S4R and G4U just lies in the different definition of parameter *C* in Eqs. (16) and (18). The unitary transformation is just

*<sup>C</sup>* <sup>¼</sup> *<sup>C</sup>*<sup>1</sup> <sup>þ</sup> *<sup>C</sup>*<sup>2</sup>

*PC* ¼ *f <sup>C</sup>H T*<sup>0</sup>

*PV* <sup>¼</sup> <sup>1</sup> 2*c* 2*T*<sup>0</sup> <sup>33</sup> � *PC* � �

<sup>11</sup> þ *T*<sup>0</sup>

where *BC* ¼ *S* � *D*. Combining Eqs. (13) and (14), we can then simply obtain the scattering power of each of the four components, i.e., the surface scattering power *PS*, the double-bounce scattering power *PD*, the volume scattering power *PV*,

> 8 >>>><

> >>>>:

8 >>>><

>>>>:

23 � � � � � � � �

<sup>33</sup> � Im *T*<sup>0</sup>

where *H*ð Þ� denotes the Heaviside step function, which is used here to adjust the value of *PC* for nonnegative *PV* ruling [27]. It can be easily validated that

By taking appropriate value to *μ*, we can have some different decompositions, which are denoted as Gð Þ *μ* . Here we are particularly interested to the following

<sup>12</sup> þ *T*<sup>0</sup>

This is just the parameter *C* used in G4U. GG4U changes to G4U in this case.

<sup>12</sup> � *T*<sup>0</sup>

This acts as the complement of case (1); thus we name it the dual G4U (DG4U).

This is the parameter *C* used in S4R, i.e., S4R also shows a special form of GG4U.

*<sup>S</sup>* <sup>þ</sup> j j *<sup>C</sup>* <sup>2</sup>

*<sup>S</sup>* � j j *<sup>C</sup>* <sup>2</sup>

*<sup>D</sup>* � j j *<sup>C</sup>* <sup>2</sup>

*<sup>D</sup>* <sup>þ</sup> j j *<sup>C</sup>* <sup>2</sup>

*<sup>S</sup>* , *BC* <sup>&</sup>gt;<sup>0</sup>

*<sup>D</sup>* , *BC*≤<sup>0</sup>

*<sup>S</sup>* , *BC* <sup>&</sup>gt;<sup>0</sup>

*<sup>D</sup>* , *BC* <sup>≤</sup><sup>0</sup>

33. Thus GG4U gives a decomposition of

<sup>13</sup> � *f <sup>V</sup>d* ¼ *C*G4U*:* (16)

<sup>13</sup> � *f <sup>V</sup>d:* (17)

<sup>12</sup> � *f <sup>V</sup>d* ¼ *C*S4R*:* (18)

(14)

(15)

(

and the helix scattering power *PC*:

8

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

>>>>>>>>>>>>>>>>>>>>>>><

>>>>>>>>>>>>>>>>>>>>>>>:

*PS* þ *PD* þ *PV* þ *PC* ¼ *T*<sup>0</sup>

**3.4 Special decompositions**

Case (1): G þð Þ1 ≔ G4U

Case (2): G �ð Þ1 ≔ DG4U

Case (3): Gð Þ 0 ≔ S4R

**19**

scattering power.

special cases of Gð Þ *μ* .

Comparing Eq. (11) with Eq. (10), we can find that Eq. (11–2) gives a dichotomy to Eq. (10–2). The redundancy makes Eq. (11) have no such exact solution like Eq. (10) but some approximate ones. In G4U, Singh et al. preferred the first equation of (11–2) only.

#### **3.3 GG4U: generalization of G4U**

Obviously, Eq. (11) provides us a generalized G4U (GG4U). Here we focus on the general solution to (11) for the unknowns *f <sup>S</sup>*, *f <sup>D</sup>*, *f <sup>V</sup>*, *f <sup>C</sup>*, *α*, and *β*. Let

$$\begin{cases} \mathbf{S} = T\_{11}' - f\_V a \\ \mathbf{C}\_1 = T\_{12}' + T\_{13}' - f\_V d \\ \mathbf{C}\_2 = T\_{12}' - T\_{13}' - f\_V d \\ D = T\_{22}' - f\_V b - \frac{f\_C}{2} \\ \mathbf{C} = \frac{1+\mu}{2} \mathbf{C}\_1 + \frac{1-\mu}{2} \mathbf{C}\_2 \end{cases} \tag{12}$$

where *μ* is a real constant. Then Eq. (11) can be rearranged as

$$\begin{cases} \left. f\_S + f\_D \middle| a \right|^2 = \mathcal{S} & -\mathbf{1} \\ \left. f\_S \theta + f\_D a = C & -\mathbf{2} \right) \\ \left. f\_S \middle| \theta \right|^2 + f\_D = D & -\mathbf{3} \\ \left. f\_C = 2 \middle| \text{Im} \left\{ T\_{23}' \right\} \right| & -\mathbf{4} \end{cases} \tag{13}$$
 
$$\begin{cases} \left. f\_V = \frac{1}{2c} \left( 2T\_{33}' - f\_C \right) \right. \end{cases} \tag{14}$$

Eq. (13) comprises of five equations and six unknowns. Following Freeman-Durden [23] and Yamaguchi et al. [24], we can fix *α* or *β* in terms of the sign of *S* � *D* for the superior between surface scattering and double-bounce scattering: *Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

$$\begin{cases} \begin{aligned} BC > 0 &\Rightarrow \text{dominant surface scattering} \Rightarrow a = 0\\ BC \le 0 &\Rightarrow \text{dominant double} - \text{bounce scattering} \Rightarrow \beta = 0 \end{aligned} \tag{14}$$

where *BC* ¼ *S* � *D*. Combining Eqs. (13) and (14), we can then simply obtain the scattering power of each of the four components, i.e., the surface scattering power *PS*, the double-bounce scattering power *PD*, the volume scattering power *PV*, and the helix scattering power *PC*:

$$\begin{cases} P\_S = f\_S \left( 1 + |\beta|^2 \right) = \begin{cases} S + \frac{|C|^2}{S}, BC > 0 \\\\ S - \frac{|C|^2}{D}, BC \le 0 \end{cases} \\\\ P\_D = f\_D \left( 1 + |a|^2 \right) = \begin{cases} D - \frac{|C|^2}{S}, BC > 0 \\\\ D + \frac{|C|^2}{D}, BC \le 0 \end{cases} \\\\ P\_C = f\_C H (T\_{33}' - \left| \mathrm{Im} \left\{ T\_{23}' \right\} \right|) \\\\ P\_V = \frac{1}{2c} (2T\_{33}' - P\_C) \end{cases} \tag{15}$$

where *H*ð Þ� denotes the Heaviside step function, which is used here to adjust the value of *PC* for nonnegative *PV* ruling [27]. It can be easily validated that *PS* þ *PD* þ *PV* þ *PC* ¼ *T*<sup>0</sup> <sup>11</sup> þ *T*<sup>0</sup> <sup>22</sup> þ *T*<sup>0</sup> 33. Thus GG4U gives a decomposition of scattering power.

#### **3.4 Special decompositions**

**3.2 G4U**

To model *T*<sup>0</sup>

equation of (11–2) only.

**18**

**3.3 GG4U: generalization of G4U**

scattering balance equation system [30]:

*Tsunami - Damage Assessment and Medical Triage*

8

>>>>>>>>>>>>>>>><

�*j f C*

>>>>>>>>>>>>>>>>:

*<sup>f</sup> <sup>V</sup><sup>c</sup>* <sup>þ</sup> *<sup>f</sup> <sup>C</sup>*

13, G4U uses ½ � *U*3ð Þ *φ* to conduct unitary transformation to both sides

<sup>12</sup> þ *T*<sup>0</sup> 13

<sup>12</sup> � *T*<sup>0</sup> 13

<sup>2</sup> <sup>¼</sup> *<sup>T</sup>*<sup>0</sup>

� � � <sup>4</sup><sup>Þ</sup>

<sup>33</sup> � 5Þ

<sup>11</sup> � 1Þ

)

<sup>22</sup> � 3Þ

� 2Þ

*:* (11)

(12)

*:* (13)

of Eq. (10) first and then eliminates the influence of *φ* [28]. As a result, an additional balance equation is brought into G4U, and we obtain the following

*<sup>f</sup> <sup>S</sup>* <sup>þ</sup> *<sup>f</sup> <sup>D</sup>*j j *<sup>α</sup>* <sup>2</sup> <sup>þ</sup> *<sup>f</sup> <sup>V</sup><sup>a</sup>* <sup>¼</sup> *<sup>T</sup>*<sup>0</sup>

*f <sup>S</sup>β* þ *f <sup>D</sup>α* þ *f <sup>V</sup>d* ¼ *T*<sup>0</sup>

*f <sup>S</sup>β* þ *f <sup>D</sup>α* þ *f <sup>V</sup>d* ¼ *T*<sup>0</sup>

*<sup>f</sup> <sup>S</sup>*j j *<sup>β</sup>* <sup>2</sup> <sup>þ</sup> *<sup>f</sup> <sup>D</sup>* <sup>þ</sup> *<sup>f</sup> <sup>V</sup><sup>b</sup>* <sup>þ</sup> *<sup>f</sup> <sup>C</sup>*

23

to Eq. (10–2). The redundancy makes Eq. (11) have no such exact solution like Eq. (10) but some approximate ones. In G4U, Singh et al. preferred the first

Comparing Eq. (11) with Eq. (10), we can find that Eq. (11–2) gives a dichotomy

Obviously, Eq. (11) provides us a generalized G4U (GG4U). Here we focus on

<sup>11</sup> � *f <sup>V</sup>a*

<sup>12</sup> þ *T*<sup>0</sup>

<sup>12</sup> � *T*<sup>0</sup>

<sup>22</sup> � *<sup>f</sup> <sup>V</sup><sup>b</sup>* � *<sup>f</sup> <sup>C</sup>*

*C*<sup>1</sup> þ

*<sup>f</sup> <sup>S</sup>* <sup>þ</sup> *<sup>f</sup> <sup>D</sup>*j j *<sup>α</sup>* <sup>2</sup> <sup>¼</sup> *<sup>S</sup>* � <sup>1</sup><sup>Þ</sup> *f <sup>S</sup>β* þ *f <sup>D</sup>α* ¼ *C* � 2Þ *<sup>f</sup> <sup>S</sup>*j j *<sup>β</sup>* <sup>2</sup> <sup>þ</sup> *<sup>f</sup> <sup>D</sup>* <sup>¼</sup> *<sup>D</sup>* � <sup>3</sup><sup>Þ</sup>

> 23 � � � � �

Eq. (13) comprises of five equations and six unknowns. Following Freeman-Durden [23] and Yamaguchi et al. [24], we can fix *α* or *β* in terms of the sign of *S* � *D* for the superior between surface scattering and double-bounce scattering:

� � 4Þ

<sup>13</sup> � *f <sup>V</sup>d*

<sup>13</sup> � *f <sup>V</sup>d*

2

1 � *μ* 2 *C*2

the general solution to (11) for the unknowns *f <sup>S</sup>*, *f <sup>D</sup>*, *f <sup>V</sup>*, *f <sup>C</sup>*, *α*, and *β*. Let

*S* ¼ *T*<sup>0</sup>

8

>>>>>>>>>><

>>>>>>>>>>:

8

>>>>>>>>>><

>>>>>>>>>>:

*C*<sup>1</sup> ¼ *T*<sup>0</sup>

*C*<sup>2</sup> ¼ *T*<sup>0</sup>

*D* ¼ *T*<sup>0</sup>

*<sup>C</sup>* <sup>¼</sup> <sup>1</sup> <sup>þ</sup> *<sup>μ</sup>* 2

where *μ* is a real constant. Then Eq. (11) can be rearranged as

*f <sup>C</sup>* ¼ 2 Im *T*<sup>0</sup>

*<sup>f</sup> <sup>V</sup>* <sup>¼</sup> <sup>1</sup> 2*c* 2*T*<sup>0</sup> <sup>33</sup> � *f <sup>C</sup>* � � � <sup>5</sup><sup>Þ</sup>

<sup>2</sup> <sup>¼</sup> *<sup>j</sup>*Im *<sup>T</sup>*<sup>0</sup>

<sup>2</sup> <sup>¼</sup> *<sup>T</sup>*<sup>0</sup>

By taking appropriate value to *μ*, we can have some different decompositions, which are denoted as Gð Þ *μ* . Here we are particularly interested to the following special cases of Gð Þ *μ* .

Case (1): G þð Þ1 ≔ G4U

$$\mathbf{C} = \mathbf{C}\_1 = T\_{12}' + T\_{13}' - f\_V d = \mathbf{C}\_{\text{G4U}}.\tag{16}$$

This is just the parameter *C* used in G4U. GG4U changes to G4U in this case. Case (2): G �ð Þ1 ≔ DG4U

$$\mathbf{C} = \mathbf{C}\_2 = T\_{12}' - T\_{13}' - f\_V d. \tag{17}$$

This acts as the complement of case (1); thus we name it the dual G4U (DG4U). Case (3): Gð Þ 0 ≔ S4R

$$\mathbf{C} = \frac{\mathbf{C}\_1 + \mathbf{C}\_2}{2} = T\_{12}' - f\_\mathbf{v} d = \mathbf{C}\_{\text{S4R}}.\tag{18}$$

This is the parameter *C* used in S4R, i.e., S4R also shows a special form of GG4U. Hence, the essential difference between S4R and G4U just lies in the different definition of parameter *C* in Eqs. (16) and (18). The unitary transformation is just

to enable the *T*<sup>0</sup> <sup>13</sup> entry contained in *C*G4U and finally in *PS* and *PD*. Parameter *C* defined in Eq. (12) is a generalization of *C*G4U and *C*S4R.

#### **3.5 Theoretical evaluation of S4R and G4U**

S4R can improve Y4R by strengthening the double-bounce scattering in urban area [27]. Singh et al. [28] indicated that G4U could further improve S4R in this aspect by strengthening surface scattering in the area where surface scattering is preferable to double-bounce scattering, while increasing the double-bounce scattering in the urban area where the double-bounce scattering is preferable to surface scattering. By combining the ruling in Eq. (14), we can formulate these observations as

$$\begin{cases} P\_S^{\text{G4U}} \ge P\_S^{\text{S4R}}, BC > 0\\ P\_D^{\text{G4U}} \ge P\_D^{\text{S4R}}, BC \le 0 \end{cases} \tag{19}$$

*P*G4U

8 >>><

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

>>>:

*P*G4U

We can immediately obtain from Eq. (23) that

max *P*G4U

max *P*G4U

max *P*G4U

max *P*G4U

EG4U ≔ G �ð Þ¼ 1

*<sup>S</sup>* , *P*DG4U *S* � �≥ *P*S4R

*<sup>D</sup>* , *P*DG4U *D* � �≥ *P*S4R

(

From Eq. (24) we obtain

8

>>>>>>>><

>>>>>>>>:

*P*EG4U

(

*P*EG4U

*<sup>S</sup>* <sup>¼</sup> max *<sup>P</sup>*G4U

*<sup>D</sup>* <sup>¼</sup> max *<sup>P</sup>*G4U

of EG4U is outlined in **Algorithm 1**.

02: Conduct deorientation to h i ½ � *T* for *T*<sup>0</sup> h i ½ � 03: Compute helix power *PC* ¼ 2 Im *T*<sup>0</sup>

06: Obtain volume scattering power *PV* ¼ 2*T*<sup>0</sup>

08: Implement *SPAN* reservation ruling based on *S* þ *D*

04: Calculate branch condition *BC*

**Algorithm 1**: EG4U

01: Input: h i ½ � *T*

09: if *S* þ *D* > 0

**21**

8 < :

> 8 <

*<sup>S</sup>* � *<sup>P</sup>*DG4U

*<sup>D</sup>* � *<sup>P</sup>*DG4U

max *P*G4U

max *P*G4U

*<sup>S</sup>* , *P*DG4U *S* � � <sup>¼</sup> *<sup>P</sup>*G4U

*<sup>D</sup>* , *P*DG4U *D* � � <sup>¼</sup> *<sup>P</sup>*G4U

*<sup>S</sup>* , *P*DG4U *S* � � <sup>¼</sup> *<sup>P</sup>*DG4U

*<sup>D</sup>* , *P*DG4U *D* � � <sup>¼</sup> *<sup>P</sup>*DG4U

where *BC*<sup>1</sup> ¼ j j *C*<sup>1</sup> � j j *C*<sup>2</sup> . Eq. (26) just lays the foundation for EG4U:

(

: , *BC*<sup>1</sup> <sup>≤</sup><sup>0</sup>

As the adaptive combination of G4U and DG4U, EG4U is also a special case of GG4U. So we denote it as G �ð Þ1 . By bringing *μ* ¼ þ1 or *μ* ¼ �1 into Eqs. (12) and (15) based on the branch condition *BC*1, we can achieve the scattering powers of four components in EG4U. Furthermore, from Eqs. (25) to (27), we have

Compared with S4R and G4U, EG4U increases surface scattering in area where surface scattering is superior to double-bounce scattering and strengthens doublebounce scattering in area where double-bounce scattering is preferable to surface scattering. Therefore, EG4U achieves not only a nice improvement to S4R, but also an effective extension to G4U. This may make EG4U more suitable to the remote sensing of tsunami/earthquake. We will investigate this in Section 4. The procedure

> 23 � � � � �

05: Determine volume scattering model based on branch condition

07: Compute parameters *S*, *D*, *C*1, and *C*2, as well as branch condition *BC*<sup>1</sup>

�*H T*<sup>0</sup>

<sup>33</sup> � Im *T*<sup>0</sup>

<sup>33</sup> � *PC* � �*=*2*c*

23 � � � � � � � �

*<sup>S</sup>* <sup>¼</sup> j j *<sup>C</sup>*<sup>1</sup>

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

*<sup>D</sup>* <sup>¼</sup> j j *<sup>C</sup>*<sup>1</sup>

*<sup>S</sup>* , *P*DG4U *S* � �≥*P*S4R

*<sup>D</sup>* , *P*DG4U *D* � �≥*P*S4R

<sup>2</sup> � j j *<sup>C</sup>*<sup>2</sup> 2 *<sup>S</sup>* , *BC*><sup>0</sup>

<sup>2</sup> � j j *<sup>C</sup>*<sup>2</sup> 2 *<sup>D</sup>* , *BC*≤<sup>0</sup>

*<sup>S</sup>* , *BC*>0

*<sup>D</sup>* , *BC*≤0

, *BC*<sup>1</sup> >0

*<sup>S</sup>* , *BC*>0

*<sup>D</sup>* , *BC*≤0

*<sup>S</sup>* , *BC*>0

*<sup>D</sup>* , *BC*≤0

G þð Þ¼ 1 G4U, *BC*<sup>1</sup> > 0 G �ð Þ¼ 1 DG4U, *BC*<sup>1</sup> ≤0

*<sup>S</sup>* , *P*G4U

*<sup>D</sup>* , *P*G4U

*<sup>S</sup>* , *P*DG4U *S* � �, *BC*>0

*<sup>D</sup>* , *P*DG4U *D* � �, *BC*≤0

*:* (24)

*:* (25)

(26)

*:* (27)

*:* (28)

In terms of the general expression of *PS* and *PD* in (15), here we give a simple validation to Eq. (19) by combining *μ* ¼ 0 and *μ* ¼ 1 into Eqs. (12) and (15):

$$\begin{cases} \begin{aligned} P\_S^{\text{G4U}} &= \text{S} + \frac{\left| \mathbf{C}\_1 \right|^2}{\mathbf{S}}\\ P\_S^{\text{S4R}} &= \text{S} + \frac{\left| \mathbf{C}\_1 + \mathbf{C}\_2 \right|^2}{4 \mathbf{S}} \end{aligned}, \begin{aligned} BC &> \text{0}; \end{aligned} \end{cases} \begin{aligned} P\_D^{\text{G4U}} &= D + \frac{\left| \mathbf{C}\_1 \right|^2}{D} \\ P\_D^{\text{S4R}} &= D + \frac{\left| \mathbf{C}\_1 + \mathbf{C}\_2 \right|^2}{4D} \end{aligned} \tag{20}$$

From Eq. (20) we have

$$\begin{cases} P\_S^{\text{G4U}} - P\_S^{\text{S4R}} = \frac{|2C\_1|^2 - |C\_1 + C\_2|^2}{4\mathcal{S}}, BC > 0\\ P\_D^{\text{G4U}} - P\_D^{\text{S4R}} = \frac{|2C\_1|^2 - |C\_1 + C\_2|^2}{4D}, BC \le 0 \end{cases} \tag{21}$$

Then Eq. (19) will hold if 2j j *C*<sup>1</sup> <sup>2</sup> � j j *<sup>C</sup>*<sup>1</sup> <sup>þ</sup> *<sup>C</sup>*<sup>2</sup> <sup>2</sup> ≥ 0. Obviously, this condition is not always tenable. Hence, despite better performance in some areas, G4U cannot improve S4R for every target area. To tackle this, the extended G4U (EG4U) will be developed in the following as an adaptive combination of G4U and DG4U.

#### **3.6 EG4U: adaptive combination of G4U and DG4U**

Combining *μ* ¼ �1 into Eqs. (12) and (15), DG4U surface and double-bounce scattering powers can be formulated as

$$\begin{cases} \begin{aligned} P\_S^{\text{DG4U}} &= \mathcal{S} + \frac{\left| \mathcal{C}\_2 \right|^2}{\mathcal{S}}, BC > \mathbf{0} \\\\ P\_D^{\text{DG4U}} &= D + \frac{\left| \mathcal{C}\_2 \right|^2}{D}, BC \le \mathbf{0} \end{aligned} \end{cases} . \tag{22}$$

Combining Eqs. (20) and (22), after some simple deduction, we obtain

$$\begin{cases} \frac{P\_S^{\text{G4U}} + P\_S^{\text{DG4U}}}{2} - P\_S^{\text{S4R}} = \frac{|C\_1 - C\_2|^2}{4\mathcal{S}} \ge 0, BC > 0\\ \frac{P\_D^{\text{G4U}} + P\_D^{\text{DG4U}}}{2} - P\_D^{\text{S4R}} = \frac{|C\_1 - C\_2|^2}{4D} \ge 0, BC \le 0 \end{cases} \tag{23}$$

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

$$\begin{cases} P\_S^{\text{G4U}} - P\_S^{\text{DG4U}} = \frac{|\mathcal{C}\_1|^2 - |\mathcal{C}\_2|^2}{\mathcal{S}}, BC > 0 \\\\ P\_D^{\text{G4U}} - P\_D^{\text{DG4U}} = \frac{|\mathcal{C}\_1|^2 - |\mathcal{C}\_2|^2}{D}, BC \le 0 \end{cases} \tag{24}$$

We can immediately obtain from Eq. (23) that

$$\begin{cases} \max\left\{P\_S^{\text{G4U}}, P\_S^{\text{DG4U}}\right\} \ge P\_S^{\text{S4R}}, BC > 0\\ \max\left\{P\_D^{\text{G4U}}, P\_D^{\text{DG4U}}\right\} \ge P\_D^{\text{S4R}}, BC \le 0 \end{cases} \tag{25}$$

From Eq. (24) we obtain

8

>>>>>>>><

>>>>>>>>:

to enable the *T*<sup>0</sup>

*P*G4U

8 >>><

>>>:

*P*S4R

*<sup>S</sup>* <sup>¼</sup> *<sup>S</sup>* <sup>þ</sup> j j *<sup>C</sup>*<sup>1</sup>

From Eq. (20) we have

*<sup>S</sup>* <sup>¼</sup> *<sup>S</sup>* <sup>þ</sup> j j *<sup>C</sup>*<sup>1</sup> <sup>þ</sup> *<sup>C</sup>*<sup>2</sup>

8 >>><

>>>:

Then Eq. (19) will hold if 2j j *C*<sup>1</sup>

scattering powers can be formulated as

*P*G4U

8 >>><

>>>:

**20**

*P*G4U

2 *S*

4*S*

*P*G4U *<sup>S</sup>* � *<sup>P</sup>*S4R

*P*G4U *<sup>D</sup>* � *<sup>P</sup>*S4R

**3.6 EG4U: adaptive combination of G4U and DG4U**

8 >>><

>>>:

*<sup>S</sup>* <sup>þ</sup> *<sup>P</sup>*DG4U *S* <sup>2</sup> � *<sup>P</sup>*S4R

*<sup>D</sup>* <sup>þ</sup> *<sup>P</sup>*DG4U *D* <sup>2</sup> � *<sup>P</sup>*S4R

*P*DG4U

*P*DG4U

2

<sup>13</sup> entry contained in *C*G4U and finally in *PS* and *PD*. Parameter *C*

*<sup>S</sup>* , *BC*>0

*:* (19)

, *BC*≤0*:* (20)

*:* (21)

*<sup>D</sup>* , *BC*≤0

*<sup>D</sup>* <sup>¼</sup> *<sup>D</sup>* <sup>þ</sup> j j *<sup>C</sup>*<sup>1</sup>

<sup>2</sup> � j j *<sup>C</sup>*<sup>1</sup> <sup>þ</sup> *<sup>C</sup>*<sup>2</sup>

<sup>2</sup> � j j *<sup>C</sup>*<sup>1</sup> <sup>þ</sup> *<sup>C</sup>*<sup>2</sup>

*<sup>D</sup>* <sup>¼</sup> *<sup>D</sup>* <sup>þ</sup> j j *<sup>C</sup>*<sup>1</sup> <sup>þ</sup> *<sup>C</sup>*<sup>2</sup>

2 *D*

4*D*

2 <sup>4</sup>*<sup>S</sup>* , *BC*<sup>&</sup>gt; <sup>0</sup>

2 <sup>4</sup>*<sup>D</sup>* , *BC*<sup>≤</sup> <sup>0</sup>

2

<sup>2</sup> ≥ 0. Obviously, this condition is not

*:* (22)

(23)

In terms of the general expression of *PS* and *PD* in (15), here we give a simple

*P*G4U

*P*S4R

validation to Eq. (19) by combining *μ* ¼ 0 and *μ* ¼ 1 into Eqs. (12) and (15):

8 >>><

>>>:

S4R can improve Y4R by strengthening the double-bounce scattering in urban area [27]. Singh et al. [28] indicated that G4U could further improve S4R in this aspect by strengthening surface scattering in the area where surface scattering is preferable to double-bounce scattering, while increasing the double-bounce scattering in the urban area where the double-bounce scattering is preferable to surface scattering. By

combining the ruling in Eq. (14), we can formulate these observations as

*P*G4U *<sup>S</sup>* ≥*P*S4R

(

*P*G4U *<sup>D</sup>* ≥*P*S4R

, *BC*> 0;

*<sup>S</sup>* <sup>¼</sup> j j <sup>2</sup>*C*<sup>1</sup>

*<sup>D</sup>* <sup>¼</sup> j j <sup>2</sup>*C*<sup>1</sup>

<sup>2</sup> � j j *<sup>C</sup>*<sup>1</sup> <sup>þ</sup> *<sup>C</sup>*<sup>2</sup>

Combining *μ* ¼ �1 into Eqs. (12) and (15), DG4U surface and double-bounce

2 *<sup>S</sup>* , *BC*><sup>0</sup>

2 *<sup>D</sup>* , *BC*≤<sup>0</sup>

*<sup>S</sup>* <sup>¼</sup> j j *<sup>C</sup>*<sup>1</sup> � *<sup>C</sup>*<sup>2</sup>

*<sup>D</sup>* <sup>¼</sup> j j *<sup>C</sup>*<sup>1</sup> � *<sup>C</sup>*<sup>2</sup>

2 <sup>4</sup>*<sup>S</sup>* <sup>≥</sup>0, *BC*<sup>&</sup>gt; <sup>0</sup>

2 <sup>4</sup>*<sup>D</sup>* <sup>≥</sup>0, *BC*<sup>≤</sup> <sup>0</sup>

*<sup>S</sup>* <sup>¼</sup> *<sup>S</sup>* <sup>þ</sup> j j *<sup>C</sup>*<sup>2</sup>

*<sup>D</sup>* <sup>¼</sup> *<sup>D</sup>* <sup>þ</sup> j j *<sup>C</sup>*<sup>2</sup>

Combining Eqs. (20) and (22), after some simple deduction, we obtain

always tenable. Hence, despite better performance in some areas, G4U cannot improve S4R for every target area. To tackle this, the extended G4U (EG4U) will be

developed in the following as an adaptive combination of G4U and DG4U.

defined in Eq. (12) is a generalization of *C*G4U and *C*S4R.

**3.5 Theoretical evaluation of S4R and G4U**

*Tsunami - Damage Assessment and Medical Triage*

$$\begin{cases} \max\left\{P\_S^{\text{G4U}}, P\_S^{\text{DG4U}}\right\} = P\_S^{\text{G4U}}, BC > 0\\ \max\left\{P\_D^{\text{G4U}}, P\_D^{\text{DG4U}}\right\} = P\_D^{\text{G4U}}, BC \le 0\\ \max\left\{P\_S^{\text{G4U}}, P\_S^{\text{DG4U}}\right\} = P\_S^{\text{DG4U}}, BC > 0\\ \max\left\{P\_D^{\text{G4U}}, P\_D^{\text{DG4U}}\right\} = P\_D^{\text{DG4U}}, BC \le 0 \end{cases} \tag{26}$$

where *BC*<sup>1</sup> ¼ j j *C*<sup>1</sup> � j j *C*<sup>2</sup> . Eq. (26) just lays the foundation for EG4U:

$$\text{EG4U} \coloneqq \mathcal{G}(\pm \mathbf{1}) = \begin{cases} \mathcal{G}(+\mathbf{1}) = \mathbf{G}4\mathbf{U}, BC\_1 > \mathbf{0} \\ \mathcal{G}(-\mathbf{1}) = \text{DG4U}, BC\_1 \le \mathbf{0} \end{cases} \tag{27}$$

As the adaptive combination of G4U and DG4U, EG4U is also a special case of GG4U. So we denote it as G �ð Þ1 . By bringing *μ* ¼ þ1 or *μ* ¼ �1 into Eqs. (12) and (15) based on the branch condition *BC*1, we can achieve the scattering powers of four components in EG4U. Furthermore, from Eqs. (25) to (27), we have

$$\begin{cases} P\_S^{\text{EG4U}} = \max\left\{ P\_S^{\text{G4U}}, P\_S^{\text{DG4U}} \right\} \ge \left\{ P\_S^{\text{S4R}}, P\_S^{\text{G4U}}, P\_S^{\text{DG4U}} \right\}, \text{BC} > \mathbf{0} \\\ P\_D^{\text{EG4U}} = \max\left\{ P\_D^{\text{G4U}}, P\_D^{\text{DG4U}} \right\} \ge \left\{ P\_D^{\text{S4R}}, P\_D^{\text{G4U}}, P\_D^{\text{DG4U}} \right\}, \text{BC} \le \mathbf{0} \end{cases} \tag{28}$$

Compared with S4R and G4U, EG4U increases surface scattering in area where surface scattering is superior to double-bounce scattering and strengthens doublebounce scattering in area where double-bounce scattering is preferable to surface scattering. Therefore, EG4U achieves not only a nice improvement to S4R, but also an effective extension to G4U. This may make EG4U more suitable to the remote sensing of tsunami/earthquake. We will investigate this in Section 4. The procedure of EG4U is outlined in **Algorithm 1**.

#### **Algorithm 1**: EG4U

01: Input: h i ½ � *T*


09: if *S* þ *D* > 0

10: Adaptively select between G4U and DG4U based on *BC*<sup>1</sup>

11: if *BC*<sup>1</sup> > 0 12: *C* ¼ *C*<sup>1</sup> 13: else 14: *C* ¼ *C*<sup>2</sup> 15: end if 16: Calculate surface scattering power *PS* and double-bounce scattering power *PD* according to *BC* 17: if *BC*>0 18: *PS* <sup>¼</sup> *<sup>S</sup>* <sup>þ</sup> j j *<sup>C</sup>* <sup>2</sup> *<sup>=</sup>S*, *PD* <sup>¼</sup> *<sup>D</sup>* � j j *<sup>C</sup>* <sup>2</sup> *=S* 19: else 20: *PS* <sup>¼</sup> *<sup>S</sup>* � j j *<sup>C</sup>* <sup>2</sup> *<sup>=</sup>D*, *PD* <sup>¼</sup> *<sup>D</sup>* <sup>þ</sup> j j *<sup>C</sup>* <sup>2</sup> *=D* 21: end if 22: Implement nonnegative *PS* and *PD* ruling 23: else 24: *PS* ¼ *PD* ¼ 0, *PV* ¼ *T*<sup>0</sup> <sup>11</sup> þ *T*<sup>0</sup> <sup>22</sup> þ *T*<sup>0</sup> <sup>33</sup> � *PC* 25: end if 26: Output: *PS*, *PD*, *PV*, *PC*

#### **4. Monitoring of disaster by EG4U decomposition of ALOS-PALSAR images of 2011 Tohoku tsunami/earthquake**

As indicated in Subsection 3.4, G4U and S4R represent two special forms of GG4U of equal status. Hence, G4U cannot fully improve S4R only if we ascend the status of G4U by combining the duality of G4U, i.e., DG4U and G4U together for EG4U. EG4U can adaptively strengthen the surface scattering and double-bounce scattering. Therefore, it may improve the competence and performance of G4U in the remote sensing of damages caused by earthquake/tsunami disaster. We demonstrate these in the following by decomposing the ALOS-PALSAR images of the 2011 great Tohoku tsunami/earthquake using EG4U.

To demonstrate the capability of polarimetric remote sensing for damage monitoring, we choose two quad-polarization single-look complex-level 1.1 (ascending orbit) datasets acquired around Miyagi Prefecture, Japan, before and after the earthquake/tsunami with 138 days' temporal baseline, as summarized in **Table 1**.

*The ground-range resolution is defined as the slant-range resolution/sin(incidence angle) [9], while the slant-range*

*Location of the great Tohoku tsunami/earthquake epicenter ( ) and the ALOS-PALSAR footprint of the two*

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

**Azimuth resolution**

*selected fully polarimetric datasets (red rectangle, pre-event; blue rectangle, post-event).*

**Incidence angle<sup>1</sup>**

ALPSRP257090760 2010-11-21 23.802° 4.5 m 23.5 m ALPSRP277220760 2011-04-08 23.836° 4.5 m 23.5 m

The flowchart of EG4U-based monitoring and evaluation of damages caused by tsunami/earthquake disaster is illustrated in **Figure 3**. We first co-register the two datasets based on the image features [37–40]. The boxcar filtering [9] is then carried out to both datasets to suppress the speckles. To ensure the pixel size in both image directions comparable, the window size for ensemble average is chosen as 2 pixels in ground-range direction and 12 pixels in azimuth direction, i.e., we integrate the scattering matrix ½ � *S* of a total of 24 pixels for the estimation of a coherency matrix h i ½ � *T* in Eq. (2). From h i ½ � *T* we calculate the orientation angle *θ* according to Eq. (4) and implement the deorientation operation for the deoriented coherency

]i according to Eq. (5). Finally, EG4U is used to decompose h[*T*<sup>0</sup>

extract scattering powers *PS*, *PD*, *PV*, and *PC* and construct the RGB pseudo-color

This process is executed on each cell of the two datasets until we obtain the complete pre- and post-event scattering power images shown in **Figure 4**, based on which we evaluate EG4U on monitoring of the tsunami/earthquake disaster in the

scattering power visualization result by encoding f g *<sup>R</sup>*, *<sup>G</sup>*, *<sup>B</sup>* with ffiffiffiffiffiffi

]i to

� � <sup>p</sup> .

*PD* <sup>p</sup> , ffiffiffiffiffiffi *PV* <sup>p</sup> , ffiffiffiffiffi *PS*

**Ground-range resolution<sup>2</sup>**

The ALOS-PALSAR footprint of the two datasets is shown in **Figure 2**.

**4.3 Method**

**Figure 2.**

*1*

*2*

**Table 1.**

**Scene ID Acquire**

*resolution of the two datasets is both 9.5 m.*

**data**

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

*The incidence angle here indicates the incidence angle at the scene center.*

*ALOS-PALSAR datasets used in the experiment and their characteristics.*

matrix h[*T*<sup>0</sup>

following.

**23**

#### **4.1 Great Tohoku earthquake and tsunami**

The great Tohoku earthquake is also known as the great Sendai earthquake or the great East Japan earthquake, which was a magnitude 9.0–9.1 (Mw) undersea megathrust earthquake off the coast of northeast Japan (the epicenter is shown in **Figure 2** as " ") that occurred on March 11, 2011, the most powerful earthquake ever recorded in Japan [34]. The earthquake triggered powerful tsunami, which swept the mainland of Japan, killed over 10,000 people (mainly through drowning), and damaged over 1,000,000 buildings (half of them are collapsed and even totally collapsed) [35].

#### **4.2 Datasets**

The Advanced Land Observing Satellite (ALOS) was launched in 2006 by the Japanese Space Agency (JAXA). It has three remote sensing payloads, i.e., the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) for digital elevation mapping, the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) for precise land coverage observation, and the Phased Array type Lband SAR (PALSAR) for all-day/all-weather land observation [36].

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

#### **Figure 2.**

10: Adaptively select between G4U and DG4U based on *BC*<sup>1</sup>

*<sup>=</sup>S*, *PD* <sup>¼</sup> *<sup>D</sup>* � j j *<sup>C</sup>* <sup>2</sup>

*<sup>=</sup>D*, *PD* <sup>¼</sup> *<sup>D</sup>* <sup>þ</sup> j j *<sup>C</sup>* <sup>2</sup>

<sup>11</sup> þ *T*<sup>0</sup>

<sup>22</sup> þ *T*<sup>0</sup>

**4. Monitoring of disaster by EG4U decomposition of ALOS-PALSAR**

As indicated in Subsection 3.4, G4U and S4R represent two special forms of GG4U of equal status. Hence, G4U cannot fully improve S4R only if we ascend the status of G4U by combining the duality of G4U, i.e., DG4U and G4U together for EG4U. EG4U can adaptively strengthen the surface scattering and double-bounce scattering. Therefore, it may improve the competence and performance of G4U in the remote sensing of damages caused by earthquake/tsunami disaster. We demonstrate these in the following by decomposing the ALOS-PALSAR images of the 2011

The great Tohoku earthquake is also known as the great Sendai earthquake or the great East Japan earthquake, which was a magnitude 9.0–9.1 (Mw) undersea megathrust earthquake off the coast of northeast Japan (the epicenter is shown in **Figure 2** as " ") that occurred on March 11, 2011, the most powerful earthquake ever recorded in Japan [34]. The earthquake triggered powerful tsunami, which swept the mainland of Japan, killed over 10,000 people (mainly through drowning), and damaged over 1,000,000 buildings (half of them are collapsed and even

The Advanced Land Observing Satellite (ALOS) was launched in 2006 by the Japanese Space Agency (JAXA). It has three remote sensing payloads, i.e., the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) for digital elevation mapping, the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) for precise land coverage observation, and the Phased Array type L-

band SAR (PALSAR) for all-day/all-weather land observation [36].

22: Implement nonnegative *PS* and *PD* ruling

**images of 2011 Tohoku tsunami/earthquake**

great Tohoku tsunami/earthquake using EG4U.

**4.1 Great Tohoku earthquake and tsunami**

totally collapsed) [35].

**4.2 Datasets**

**22**

16: Calculate surface scattering power *PS* and double-bounce scattering power

*=S*

*=D*

<sup>33</sup> � *PC*

11: if *BC*<sup>1</sup> > 0 12: *C* ¼ *C*<sup>1</sup> 13: else 14: *C* ¼ *C*<sup>2</sup> 15: end if

17: if *BC*>0

19: else

21: end if

23: else

25: end if

18: *PS* <sup>¼</sup> *<sup>S</sup>* <sup>þ</sup> j j *<sup>C</sup>* <sup>2</sup>

20: *PS* <sup>¼</sup> *<sup>S</sup>* � j j *<sup>C</sup>* <sup>2</sup>

24: *PS* ¼ *PD* ¼ 0, *PV* ¼ *T*<sup>0</sup>

26: Output: *PS*, *PD*, *PV*, *PC*

*PD* according to *BC*

*Tsunami - Damage Assessment and Medical Triage*

*Location of the great Tohoku tsunami/earthquake epicenter ( ) and the ALOS-PALSAR footprint of the two selected fully polarimetric datasets (red rectangle, pre-event; blue rectangle, post-event).*


*1 The incidence angle here indicates the incidence angle at the scene center.*

*2 The ground-range resolution is defined as the slant-range resolution/sin(incidence angle) [9], while the slant-range resolution of the two datasets is both 9.5 m.*

#### **Table 1.**

*ALOS-PALSAR datasets used in the experiment and their characteristics.*

To demonstrate the capability of polarimetric remote sensing for damage monitoring, we choose two quad-polarization single-look complex-level 1.1 (ascending orbit) datasets acquired around Miyagi Prefecture, Japan, before and after the earthquake/tsunami with 138 days' temporal baseline, as summarized in **Table 1**. The ALOS-PALSAR footprint of the two datasets is shown in **Figure 2**.

#### **4.3 Method**

The flowchart of EG4U-based monitoring and evaluation of damages caused by tsunami/earthquake disaster is illustrated in **Figure 3**. We first co-register the two datasets based on the image features [37–40]. The boxcar filtering [9] is then carried out to both datasets to suppress the speckles. To ensure the pixel size in both image directions comparable, the window size for ensemble average is chosen as 2 pixels in ground-range direction and 12 pixels in azimuth direction, i.e., we integrate the scattering matrix ½ � *S* of a total of 24 pixels for the estimation of a coherency matrix h i ½ � *T* in Eq. (2). From h i ½ � *T* we calculate the orientation angle *θ* according to Eq. (4) and implement the deorientation operation for the deoriented coherency matrix h[*T*<sup>0</sup> ]i according to Eq. (5). Finally, EG4U is used to decompose h[*T*<sup>0</sup> ]i to extract scattering powers *PS*, *PD*, *PV*, and *PC* and construct the RGB pseudo-color scattering power visualization result by encoding f g *<sup>R</sup>*, *<sup>G</sup>*, *<sup>B</sup>* with ffiffiffiffiffiffi *PD* <sup>p</sup> , ffiffiffiffiffiffi *PV* <sup>p</sup> , ffiffiffiffiffi *PS* � � <sup>p</sup> . This process is executed on each cell of the two datasets until we obtain the complete pre- and post-event scattering power images shown in **Figure 4**, based on which we evaluate EG4U on monitoring of the tsunami/earthquake disaster in the following.

**Figure 3.** *Flowchart of EG4U-based monitoring of tsunami/earthquake disaster.*

#### **4.4 Evaluation and analysis**

For better comparison and analysis, we also display the optical image of the study area obtained from ©Google Earth in **Figure 5**. Our intuitive impression of **Figure 4(a)** and **(b)** is their consistency and nice correspondence to the optical image. The blue color mainly appears in the water and land areas because of the dominant surface scattering there. The red color mainly arises in the urban area, such as the Ishinomaki City and Higashi-Matsushima City, with a large number of buildings. The ground and the vertical walls of buildings constitute the dihedral corner structures, which generally reflect the dominant double-bounce scattering. Mountain presents the green color, i.e., the dominant volume scattering. The welldeveloped branch and crown structures of trees on the mountain complicate the scattering process, depolarize the scattering wave, and show themselves as the complex mixed volume scattering in PolSAR image. Therefore, by color-coding the scattering powers obtained by EG4U, we can achieve a nice discrimination of the

*Color-coded scattering power image of the study area (a) before and (b) after the great Tohoku tsunami/ earthquake disaster. The framed patch regions A, B, and C are extracted for particular analysis.*

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

pre- and post-event scattering power images. A lot of red pixels in **Figure 4(a)** change to blue pixels in **Figure 4(b)**, particularly in the urban areas of Ishinomaki and Higashi-Matsushima, which illustrate the change from the dominant doublebounce scattering to the dominant surface scattering, denote the decrease of the dihedral structures, and indicate the collapse of buildings. Take Ishinomaki City framed in Patch A for instance; it is interesting to observe that the strong change mainly arises in the area by the seaside, while tiny change occurs in the area away from the coast. This finding is also validated by the corresponding optical images acquired before and after the event shown in **Figure 6(a)** and **(b)**. Therefore, the severe damages brought by the Tohoku tsunami/earthquake are probably mainly due to the flooding rather than the earthquake. Flooding from the Onagawa Bay and the Mangokuura Sea also swept the town of Onagawa framed in Patch B,

Despite the consistency, we can also observe the obvious difference between the

ground objects.

**Figure 4.**

**25**

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

#### **Figure 4.**

**4.4 Evaluation and analysis**

*Flowchart of EG4U-based monitoring of tsunami/earthquake disaster.*

*Tsunami - Damage Assessment and Medical Triage*

**Figure 3.**

**24**

For better comparison and analysis, we also display the optical image of the study area obtained from ©Google Earth in **Figure 5**. Our intuitive impression of **Figure 4(a)** and **(b)** is their consistency and nice correspondence to the optical image. The blue color mainly appears in the water and land areas because of the dominant surface scattering there. The red color mainly arises in the urban area, such as the Ishinomaki City and Higashi-Matsushima City, with a large number of buildings. The ground and the vertical walls of buildings constitute the dihedral corner structures, which generally reflect the dominant double-bounce scattering. Mountain presents the green color, i.e., the dominant volume scattering. The welldeveloped branch and crown structures of trees on the mountain complicate the scattering process, depolarize the scattering wave, and show themselves as the complex mixed volume scattering in PolSAR image. Therefore, by color-coding the

*Color-coded scattering power image of the study area (a) before and (b) after the great Tohoku tsunami/ earthquake disaster. The framed patch regions A, B, and C are extracted for particular analysis.*

scattering powers obtained by EG4U, we can achieve a nice discrimination of the ground objects.

Despite the consistency, we can also observe the obvious difference between the pre- and post-event scattering power images. A lot of red pixels in **Figure 4(a)** change to blue pixels in **Figure 4(b)**, particularly in the urban areas of Ishinomaki and Higashi-Matsushima, which illustrate the change from the dominant doublebounce scattering to the dominant surface scattering, denote the decrease of the dihedral structures, and indicate the collapse of buildings. Take Ishinomaki City framed in Patch A for instance; it is interesting to observe that the strong change mainly arises in the area by the seaside, while tiny change occurs in the area away from the coast. This finding is also validated by the corresponding optical images acquired before and after the event shown in **Figure 6(a)** and **(b)**. Therefore, the severe damages brought by the Tohoku tsunami/earthquake are probably mainly due to the flooding rather than the earthquake. Flooding from the Onagawa Bay and the Mangokuura Sea also swept the town of Onagawa framed in Patch B,

#### **Figure 5.**

*Optical image of the study area obtained from ©Google earth. Particular attention is paid to the framed patch regions A, B, and C.*

as shown in **Figure 6(c)** and **(d)** in terms of the pre- and post-event optical images. A large majority of red pixels of Patch B in **Figure 4(a)** change to blue pixels or even green pixels in **Figure 4(b)**, which indicates that nearly all the buildings in Onagawa were badly damaged by the flooding except for a few buildings constructed in high elevation. The collapsed buildings not only present the dominant surface scattering here, but also the dominant volume scattering because of the complex scattering in such mountain area. The biggest change caused by flooding appears in the area along the Kitakami River. Take the town of Kamaya framed in Patch C, for example, as shown in **Figure 6(e)**, besides several buildings, the most part of Kamaya is farmland. This area can be clearly distinguished from the Kitakami River in **Figure 4(a)** before the disaster. However, after the disaster, nearly all the land and buildings in Kamaya are flooded by the water from Kitakami River as shown in **Figure 6(f)**, which present in **Figure 4(b)** as the wide distribution of blue pixels and show the dominant surface scattering here. Therefore, by decomposing the pre- and post-event PolSAR datasets with EG4U to construct the color-coded scattering power images, we can achieve a simple but accurate monitoring of the damages caused by tsunami/earthquake disaster.

From the above analysis, we can obtain that flooding which resulted from tsunami is the main contributor to the severe damages in the 3.11 great Tohoku earthquake. The flooding destroyed the buildings and inundated the lands. All these damages present themselves in the polarization domain as the change of the dominant scattering mechanism from double-bounce scattering to surface scattering and in the image domain as the change of pixel color from red to blue. The boundary condition *BC* has been widely used in model-based decomposition as a crucial feature to discriminate surface scattering and double-bounce scattering [23, 24, 26–28]. As expressed in Eq. (14), *BC*>0 indicates stronger surface scattering than double-bounce scattering, while *BC*≤ 0 denotes stronger double-bounce scattering than surface scattering. Therefore, besides the qualitative evaluation in terms of color, we can further achieve an quantitative evaluation of the damages by analyzing the dominant scattering according to *BC*. **Figure 7(a)** and **(b)** show the binary images of *BC* before and after the disaster, respectively. The white pixel denotes *BC*> 0, i.e., the dominant surface scattering, which mainly occupies the water and land areas, while the black one denotes *BC*≤ 0, i.e., the dominant double-bounce

scattering, which mainly occupies the urban and mountain areas. Before disaster, the black pixels account for 15*:*1641% of the whole image, while this ratio decreases to 13*:*0785% after the disaster, i.e., the dominant scattering mechanism of about 2*:*0856% area of the scene is changed from double-bounce scattering to surface scattering. As shown in **Figure 7**, the change mainly arises in the urban area like the Ishinomaki City and Higashi-Matsushima City, in the land area like the town of Kamaya, as well as in the water area like the Mangokuura Sea, Onagawa Bay, and Kitakami River. This further provides us a consistently quantitative evaluation of the damages. All these demonstrate the importance and value of polarimetric microwave remote sensing technique in the monitoring of tsunami/earthquake

*column, i.e. (b), (d), and (f)) after the 3.11 great Tohoku tsunami/earthquake.*

*Optical images of (first row, i.e. (a) and (b)) patch A, (second row, i.e. (c) and (d)) patch B, and (third row, i.e. (e) and (f)) patch C obtained from ©Google earth (first column, i.e. (a), (c), and (e)) before and (second*

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

Singh et al. [28] indicated that G4U could enhance double-bounce scattering over urban area while strengthen surface scattering contribution over water and land area. This establishes G4U the state-of-the-art four-component scattering

damages.

**27**

**Figure 6.**

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

#### **Figure 6.**

as shown in **Figure 6(c)** and **(d)** in terms of the pre- and post-event optical images. A large majority of red pixels of Patch B in **Figure 4(a)** change to blue pixels or even green pixels in **Figure 4(b)**, which indicates that nearly all the buildings in Onagawa were badly damaged by the flooding except for a few buildings

*Optical image of the study area obtained from ©Google earth. Particular attention is paid to the framed patch*

constructed in high elevation. The collapsed buildings not only present the dominant surface scattering here, but also the dominant volume scattering because of the complex scattering in such mountain area. The biggest change caused by flooding appears in the area along the Kitakami River. Take the town of Kamaya framed in Patch C, for example, as shown in **Figure 6(e)**, besides several buildings, the most part of Kamaya is farmland. This area can be clearly distinguished from the Kitakami River in **Figure 4(a)** before the disaster. However, after the disaster, nearly all the land and buildings in Kamaya are flooded by the water from Kitakami River as shown in **Figure 6(f)**, which present in **Figure 4(b)** as the wide distribution of blue pixels and show the dominant surface scattering here. Therefore, by decomposing the pre- and post-event PolSAR datasets with EG4U to construct the color-coded scattering power images, we can achieve a simple but accurate moni-

From the above analysis, we can obtain that flooding which resulted from tsunami is the main contributor to the severe damages in the 3.11 great Tohoku earthquake. The flooding destroyed the buildings and inundated the lands. All these damages present themselves in the polarization domain as the change of the dominant scattering mechanism from double-bounce scattering to surface scattering and in the image domain as the change of pixel color from red to blue. The boundary condition *BC* has been widely used in model-based decomposition as a crucial feature to discriminate surface scattering and double-bounce scattering [23, 24, 26–28]. As expressed in Eq. (14), *BC*>0 indicates stronger surface scattering than double-bounce scattering, while *BC*≤ 0 denotes stronger double-bounce scattering than surface scattering. Therefore, besides the qualitative evaluation in terms of color, we can further achieve an quantitative evaluation of the damages by analyzing the dominant scattering according to *BC*. **Figure 7(a)** and **(b)** show the binary images of *BC* before and after the disaster, respectively. The white pixel denotes *BC*> 0, i.e., the dominant surface scattering, which mainly occupies the water and land areas, while the black one denotes *BC*≤ 0, i.e., the dominant double-bounce

toring of the damages caused by tsunami/earthquake disaster.

**Figure 5.**

**26**

*regions A, B, and C.*

*Tsunami - Damage Assessment and Medical Triage*

*Optical images of (first row, i.e. (a) and (b)) patch A, (second row, i.e. (c) and (d)) patch B, and (third row, i.e. (e) and (f)) patch C obtained from ©Google earth (first column, i.e. (a), (c), and (e)) before and (second column, i.e. (b), (d), and (f)) after the 3.11 great Tohoku tsunami/earthquake.*

scattering, which mainly occupies the urban and mountain areas. Before disaster, the black pixels account for 15*:*1641% of the whole image, while this ratio decreases to 13*:*0785% after the disaster, i.e., the dominant scattering mechanism of about 2*:*0856% area of the scene is changed from double-bounce scattering to surface scattering. As shown in **Figure 7**, the change mainly arises in the urban area like the Ishinomaki City and Higashi-Matsushima City, in the land area like the town of Kamaya, as well as in the water area like the Mangokuura Sea, Onagawa Bay, and Kitakami River. This further provides us a consistently quantitative evaluation of the damages. All these demonstrate the importance and value of polarimetric microwave remote sensing technique in the monitoring of tsunami/earthquake damages.

Singh et al. [28] indicated that G4U could enhance double-bounce scattering over urban area while strengthen surface scattering contribution over water and land area. This establishes G4U the state-of-the-art four-component scattering

**5. Conclusion**

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

**Acknowledgements**

**Conflict of interest**

The authors declare no conflict of interest.

For more details about the paper, please refer to Reference [30].

CXJJ19B10.

**Notes**

**29**

Flooding is the main contributor to the severe damages in the great Tohoku tsunami/earthquake. It destroyed the buildings and inundated the lands by the seaside. All these damages present themselves in the polarization domain as the change of the dominant scattering mechanism from double-bounce scattering to surface scattering and in the image domain as the change of pixel color from red to blue. The color-coded scattering power image is very useful and powerful in the qualitative evaluation of damages. The boundary condition *BC* further enables a nice quantitative evaluation of disaster. The unitary transformation in G4U adds a *T*13-related but redundant balance equation to the original self-contained equation system. The general solution enables a generalized G4U, while G4U just represents a special form. The strict derivation conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for EG4U. Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative monitoring and quantitative evaluation of tsunami/earthquake damages. Efficient and accurate monitoring and assessment are of crucial importance for the fast response, management, and mitigation of the disasters. The all-day and all-weather working capacity is a significant advantage of microwave remote sensing. Polarimetric remote sensing is an effective

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

technique in the discrimination and recognition of ground objects.

This work was supported in part by the National Natural Science Foundation

Sections 2 and 3 of this chapter are extracted from a journal paper of the authors submitted to IEEE Transactions on Geoscience and Remote Sensing on June 07, 2017. The paper is still under review at the time of publication of this chapter.

of China under Grant No. 41871274 and No. 61971402 and by the Strategic High-Tech Innovation Fund of Chinese Academy of Sciences under Grant

**Figure 7.**

*Binary display of the branch condition BC extracted from (a) pre- and (b) post-event ALOS-PALSAR datasets. The white pixels correspond to BC*>0*, while the black pixels denote BC*≤0*.*

#### **Figure 8.**

*Binary display of the branch condition BC*<sup>1</sup> *extracted from (a) pre- and (b) post-event ALOS-PALSAR datasets. The white pixels correspond to BC*<sup>1</sup> >0*, while the black pixels denote BC*<sup>1</sup> ≤0*.*

power decomposition and enables its wide application to the remote sensing of forestry, agriculture, wetland, snow, glaciated terrain, earth surface, manmade target, environment, and damages caused by earthquake, tsunami, and landslide [29, 30]. Nevertheless, the rigorous derivation in Eq. (21) validates that G4U cannot always enhance the double-bounce scattering nor strengthen the surface scattering power unless we adaptively integrate G4U and its duality, i.e., DG4U, for EG4U based on another boundary condition *BC*1. As expressed in Eq. (27), G4U is selected only when *BC*<sup>1</sup> >0; otherwise, we should turn to DG4U. The binary images **Figure 8(a)** and **(b)** further show the pre- and post-event *BC*1, respectively, where the white pixels (i.e., *BC*<sup>1</sup> >0) indicate the area where G4U operates and the black pixels (i.e., *BC*<sup>1</sup> ≤0) give the area where DG4U operates. The white pixels account for 46*:*4260% of the pre-event image, which conveys that G4U achieves better result than S4R only for 46*:*4260% area. As for the rest 53*:*5740% area, we should resort to DG4U for improvement. The ratio of white pixels increases to 49*:*5247% after the disaster. Nevertheless, there are still half a little more areas where G4U will underestimate the surface or double-bounce scattering. If we adopt G4U in this area to evaluate damages caused by tsunami/earthquake, the reduced double-bounce scattering from G4U may lead to the underestimation of building scale and overestimation of damage level. EG4U can adaptively increase the surface scattering or double-bounce scattering. Hence, it definitely improves the competence and performance of G4U in the remote sensing of damages caused by earthquake/tsunami.

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

#### **5. Conclusion**

Flooding is the main contributor to the severe damages in the great Tohoku tsunami/earthquake. It destroyed the buildings and inundated the lands by the seaside. All these damages present themselves in the polarization domain as the change of the dominant scattering mechanism from double-bounce scattering to surface scattering and in the image domain as the change of pixel color from red to blue. The color-coded scattering power image is very useful and powerful in the qualitative evaluation of damages. The boundary condition *BC* further enables a nice quantitative evaluation of disaster. The unitary transformation in G4U adds a *T*13-related but redundant balance equation to the original self-contained equation system. The general solution enables a generalized G4U, while G4U just represents a special form. The strict derivation conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for EG4U. Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative monitoring and quantitative evaluation of tsunami/earthquake damages. Efficient and accurate monitoring and assessment are of crucial importance for the fast response, management, and mitigation of the disasters. The all-day and all-weather working capacity is a significant advantage of microwave remote sensing. Polarimetric remote sensing is an effective technique in the discrimination and recognition of ground objects.

#### **Acknowledgements**

This work was supported in part by the National Natural Science Foundation of China under Grant No. 41871274 and No. 61971402 and by the Strategic High-Tech Innovation Fund of Chinese Academy of Sciences under Grant CXJJ19B10.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Notes**

power decomposition and enables its wide application to the remote sensing of forestry, agriculture, wetland, snow, glaciated terrain, earth surface, manmade target, environment, and damages caused by earthquake, tsunami, and landslide [29, 30]. Nevertheless, the rigorous derivation in Eq. (21) validates that G4U cannot always enhance the double-bounce scattering nor strengthen the surface scattering power unless we adaptively integrate G4U and its duality, i.e., DG4U, for EG4U based on another boundary condition *BC*1. As expressed in Eq. (27), G4U is

*Binary display of the branch condition BC*<sup>1</sup> *extracted from (a) pre- and (b) post-event ALOS-PALSAR*

*datasets. The white pixels correspond to BC*<sup>1</sup> >0*, while the black pixels denote BC*<sup>1</sup> ≤0*.*

*Binary display of the branch condition BC extracted from (a) pre- and (b) post-event ALOS-PALSAR*

*datasets. The white pixels correspond to BC*>0*, while the black pixels denote BC*≤0*.*

*Tsunami - Damage Assessment and Medical Triage*

selected only when *BC*<sup>1</sup> >0; otherwise, we should turn to DG4U. The binary images **Figure 8(a)** and **(b)** further show the pre- and post-event *BC*1, respectively, where the white pixels (i.e., *BC*<sup>1</sup> >0) indicate the area where G4U operates and the black pixels (i.e., *BC*<sup>1</sup> ≤0) give the area where DG4U operates. The white pixels account for 46*:*4260% of the pre-event image, which conveys that G4U achieves better result than S4R only for 46*:*4260% area. As for the rest 53*:*5740% area, we should resort to DG4U for improvement. The ratio of white pixels increases to 49*:*5247% after the disaster. Nevertheless, there are still half a little more areas where G4U will underestimate the surface or double-bounce scattering. If we adopt G4U in this area to evaluate damages caused by tsunami/earthquake, the reduced double-bounce scattering from G4U may lead to the underestimation of building scale and overestimation of damage level. EG4U can adaptively increase the surface scattering or double-bounce scattering. Hence, it definitely improves the competence and performance of G4U in the remote sensing of damages caused by earth-

quake/tsunami.

**28**

**Figure 7.**

**Figure 8.**

Sections 2 and 3 of this chapter are extracted from a journal paper of the authors submitted to IEEE Transactions on Geoscience and Remote Sensing on June 07, 2017. The paper is still under review at the time of publication of this chapter. For more details about the paper, please refer to Reference [30].

*Tsunami - Damage Assessment and Medical Triage*

**References**

10.5772/573

10.5772/61999

2748234

2015.2464113

LGRS.2015.2484383

**31**

[1] Mokhtari M, editor. Tsunami-A Growing Disaster. London: IntechOpen;

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

2009. p. 422. DOI: 10.1201/97814200

Applications in Remote Sensing. Oxford: Oxford University Press; 2010. p. 453.

[10] Cloude SR. Polarisation

[11] van Zyl JJ, Kim Y. Synthetic Aperture Radar Polarimetry. Hoboken: John Wiley & Sons, Inc.; 2011. p. 312.

DOI: 10.1002/9781118116104

[12] Yamaguchi Y. Disaster monitoring by fully polarimetric SAR data acquired with ALOS-PALSAR. Proceedings of the IEEE. 2012;**100**(10):2851-2860. DOI: 10.1109/JPROC.2012.2195469

[13] Sato M, Chen S-W, Satake M. Polarimetric SAR analysis of tsunami damage following the march 11, 2011 East Japan earthquake. Proceedings of the IEEE. 2012;**100**(10):2861-2875. DOI:

10.1109/JPROC.2012.2200649

tsunami with L-band SAR fullpolarimetric mode. IEEE Geoscience and Remote Sensing Letters. 2012;**9**(3): 472-476. DOI: 10.1109/LGRS.2011.

[15] Li X, Zhang L, Guo H, Sun Z, Liang L. New approaches to urban area change detection using multitemporal RADARSAT-2 polarimetric synthetic aperture radar (SAR) data. Canadian Journal of Remote Sensing. 2012;**38**(3):

253-255. DOI: 10.5589/m12-018

M, Park S-E. Monitoring of the March 11, 2011, off-Tohoku 9.0

[16] Singh G, Yamaguchi Y, Boerner W-

earthquake with super-tsunami disaster by implementing fully polarimetric high-resolution POLSAR techniques.

2182030

[14] Watanabe M, Motohka T, Miyagi Y, Yonezawa C, Shimada M. Analysis of urban areas affected by the 2011 off the Pacific coast of Tohoku earthquake and

DOI: 10.1063/1.3502550

54989

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

[2] Morner N-A, editor. The Tsunami Threat–Research and Technology. London: IntechOpen; 2011. p. 714. DOI:

[3] Mokhtari M, editor. Tsunami. London: IntechOpen; 2016. p. 164. DOI:

[4] Marghany M, editor. Advanced Remote Sensing Technology for

10.5772/intechopen.78525

10.1109/TGRS.2018.2845944

Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure. London: IntechOpen; 2019. p. 167. DOI:

[5] Li D, Zhang Y. Adaptive model-based classification of PolSAR data. IEEE Transactions on Geoscience and Remote Sensing. 2018;**56**(12):6940-6955. DOI:

[6] Li D, Zhang Y. Random similaritybased entropy/alpha classification of PolSAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017;**10**(12): 5712-5723. DOI: 10.1109/JSTARS.2017.

[7] Li D, Zhang Y. Unified Huynen phenomenological decomposition of radar targets and its classification applications. IEEE Transactions on Geoscience and Remote Sensing. 2016; **54**(2):723-743. DOI: 10.1109/TGRS.

[8] Li D, Zhang Y. Random similarity between two mixed scatterers. IEEE Geoscience and Remote Sensing Letters. 2015;**12**(12):2468-2472. DOI: 10.1109/

[9] Lee J-S, Pottier E. Polarimetric Radar Imaging: From Basics to Applications. Boca Raton: CRC Press;

2011. p. 232. DOI: 10.5772/922

#### **Author details**

Dong Li1,2\*, Yunhua Zhang1,2\*, Liting Liang1,2, Jiefang Yang1 and Xun Wang1,2

1 Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing, China

2 University of Chinese Academy of Sciences, Beijing, China

\*Address all correspondence to: lidong@mirslab.cn and zhangyunhua@mirslab.cn

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

#### **References**

[1] Mokhtari M, editor. Tsunami-A Growing Disaster. London: IntechOpen; 2011. p. 232. DOI: 10.5772/922

[2] Morner N-A, editor. The Tsunami Threat–Research and Technology. London: IntechOpen; 2011. p. 714. DOI: 10.5772/573

[3] Mokhtari M, editor. Tsunami. London: IntechOpen; 2016. p. 164. DOI: 10.5772/61999

[4] Marghany M, editor. Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure. London: IntechOpen; 2019. p. 167. DOI: 10.5772/intechopen.78525

[5] Li D, Zhang Y. Adaptive model-based classification of PolSAR data. IEEE Transactions on Geoscience and Remote Sensing. 2018;**56**(12):6940-6955. DOI: 10.1109/TGRS.2018.2845944

[6] Li D, Zhang Y. Random similaritybased entropy/alpha classification of PolSAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017;**10**(12): 5712-5723. DOI: 10.1109/JSTARS.2017. 2748234

[7] Li D, Zhang Y. Unified Huynen phenomenological decomposition of radar targets and its classification applications. IEEE Transactions on Geoscience and Remote Sensing. 2016; **54**(2):723-743. DOI: 10.1109/TGRS. 2015.2464113

[8] Li D, Zhang Y. Random similarity between two mixed scatterers. IEEE Geoscience and Remote Sensing Letters. 2015;**12**(12):2468-2472. DOI: 10.1109/ LGRS.2015.2484383

[9] Lee J-S, Pottier E. Polarimetric Radar Imaging: From Basics to Applications. Boca Raton: CRC Press; 2009. p. 422. DOI: 10.1201/97814200 54989

[10] Cloude SR. Polarisation Applications in Remote Sensing. Oxford: Oxford University Press; 2010. p. 453. DOI: 10.1063/1.3502550

[11] van Zyl JJ, Kim Y. Synthetic Aperture Radar Polarimetry. Hoboken: John Wiley & Sons, Inc.; 2011. p. 312. DOI: 10.1002/9781118116104

[12] Yamaguchi Y. Disaster monitoring by fully polarimetric SAR data acquired with ALOS-PALSAR. Proceedings of the IEEE. 2012;**100**(10):2851-2860. DOI: 10.1109/JPROC.2012.2195469

[13] Sato M, Chen S-W, Satake M. Polarimetric SAR analysis of tsunami damage following the march 11, 2011 East Japan earthquake. Proceedings of the IEEE. 2012;**100**(10):2861-2875. DOI: 10.1109/JPROC.2012.2200649

[14] Watanabe M, Motohka T, Miyagi Y, Yonezawa C, Shimada M. Analysis of urban areas affected by the 2011 off the Pacific coast of Tohoku earthquake and tsunami with L-band SAR fullpolarimetric mode. IEEE Geoscience and Remote Sensing Letters. 2012;**9**(3): 472-476. DOI: 10.1109/LGRS.2011. 2182030

[15] Li X, Zhang L, Guo H, Sun Z, Liang L. New approaches to urban area change detection using multitemporal RADARSAT-2 polarimetric synthetic aperture radar (SAR) data. Canadian Journal of Remote Sensing. 2012;**38**(3): 253-255. DOI: 10.5589/m12-018

[16] Singh G, Yamaguchi Y, Boerner W-M, Park S-E. Monitoring of the March 11, 2011, off-Tohoku 9.0 earthquake with super-tsunami disaster by implementing fully polarimetric high-resolution POLSAR techniques.

**Author details**

**30**

Chinese Academy of Sciences, Beijing, China

*Tsunami - Damage Assessment and Medical Triage*

provided the original work is properly cited.

2 University of Chinese Academy of Sciences, Beijing, China

Dong Li1,2\*, Yunhua Zhang1,2\*, Liting Liang1,2, Jiefang Yang1 and Xun Wang1,2

1 Key Laboratory of Microwave Remote Sensing, National Space Science Center,

\*Address all correspondence to: lidong@mirslab.cn and zhangyunhua@mirslab.cn

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Proceedings of the IEEE. 2013;**101**(3): 831-846. DOI: 10.1109/JPROC.2012. 2230311

[17] Chen S-W, Sato M. Tsunami damage investigation of built-up areas using multitemporal spaceborne full polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing. 2013;**51**(4):1985-1997. DOI: 10.1109/TGRS.2012.2210050

[18] Li N, Wang R, Deng Y, Liu Y, Wang C, Balz T, et al. Polarimetric response of landslides at X-band following the Wenchuan earthquake. IEEE Geoscience and Remote Sensing Letters. 2014;**11**(10):1722-1726. DOI: 10.1109/LGRS.2014.2306820

[19] Chen S-W, Wang X-S, Sato M. Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR data for the 3.11 East Japan earthquake. IEEE Transactions on Geoscience and Remote Sensing. 2016;**54**(12):6919-6929. DOI: 10.1109/TGRS.2016.2588325

[20] Ji Y, Sumantyo JTS, Chua MY, Waqar MM. Earthquake/tsunami damage level mapping of urban areas using full polarimetric SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018;**11**(7):2296-2309. DOI: 10.1109/JSTARS.2018.2822825

[21] Zhai W, Huang C, Pei W. Two new polarimetric feature parameters for the recognition of the different kinds of buildings in earthquake-stricken areas based on entropy and eigenvalues of PolSAR decomposition. Remote Sensing. 2018;**10**(10):1613. DOI: 10.3390/ rs10101613

[22] Cloude SR, Pottier E. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing. 1996; **34**(2):498-518. DOI: 10.1109/36.485127

[23] Freeman A, Durden SL. A threecomponent scattering model for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing. 1998;**36**(3):963-973. DOI: 10.1109/36.673687

four-component scattering power decomposition with unitary

review

230106

transformation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing. 2020:1-18. Under

*DOI: http://dx.doi.org/10.5772/intechopen.91242*

[38] Li D, Zhang Y. A fast offset estimation approach for InSAR image subpixel registration. IEEE Geoscience and Remote Sensing Letters. 2012;**9**(2): 267-271. DOI: 10.1109/LGRS.2011.

[39] Li D, Zhang Y. On the appropriate

registration. Proceedings of SPIE. 2012; **8536**:8536X. DOI: 10.1117/12.970520

[40] Li D, Zhang Y. The appropriate parameter retrieval algorithm for feature-based SAR image registration. Proceedings of SPIE. 2012;**8536**:8536Y.

feature for general SAR image

DOI: 10.1117/12.970522

2166752

*Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing…*

[31] Sinclair G. The transmission and reception of elliptically polarized waves. Proceedings of the IRE. 1950;**32**(2): 148-151. DOI: 10.1109/JRPROC.1950.

[32] Huynen JR. Phenomenological theory of radar targets [thesis]. Delft: Delft University of Technology; 1970

[33] Cloude SR, Pottier E. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transactions on Geoscience and Remote

Sensing. 1997;**35**(1):68-78. DOI:

[34] Wikipedia. 2011 Tohoku Earthquake and Tsunami. 2019. Available from: https://en.wikipedia. org/wiki/2011\_T%C5%8Dhoku\_ earth quake\_and\_tsunami. [Accessed:

[35] National Police Agency of Japan. Police Countermeasures and Damage Situation associated with 2011 Tohoku

[36] JAXA Earth Observation Research Center. About ALOS–Overview and Objectives. 2019. Available from: https://www.eorc.jaxa.jp/ALOS/en/ about/about\_index.htm [Accessed:

[37] Li D, Zhang Y. A novel approach for the registration of weak affine images. Pattern Recognition Letters. 2012; **33**(12):1647-1655. DOI: 10.1016/j.

district-off the Pacific Ocean Earthquake. 2019. Available from: https://www.npa.go.jp/news/other/ earthquake2011/pdf/higaijokyo\_e.pdf [Accessed: 28 December 2019]

10.1109/36.551935

28 December 2019]

28 December 2019]

patrec.2012.04.009

**33**

[24] Yamaguchi Y, Moriyama T, Ishido M, Yamada H. Four-component scattering model for polarimetric SAR image decomposition. IEEE Transactions on Geoscience and Remote Sensing. 2005;**43**(8):1699-1706. DOI: 10.1109/TGRS.2005.852084

[25] Zhu F, Zhang Y, Li D. A novel deorientation method in PolSAR data processing. Remote Sensing Letters. 2016;**7**(11):1083-1092. DOI: 10.1080/ 2150704X.2016.1217438

[26] Yamaguchi Y, Sato A, Boerner W-M, Sato R, Yamada H. Four-component scattering power decomposition with rotation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing. 2011;**49**(6):2251-2258. DOI: 10.1109/TGRS.2010.2099124

[27] Sato A, Yamaguchi Y, Singh G, Park S-E. Four-component scattering power decomposition with extended volume scattering model. IEEE Geoscience and Remote Sensing Letters. 2012;**9**(2):166-170. DOI: 10.1109/LGRS. 2011.2162935

[28] Singh G, Yamaguchi Y, Park S-E. General four-component scattering power decomposition with unitary transformation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing. 2013;**51**(5):3014-3022. DOI: 10.1109/TGRS.2012.2212446

[29] Li D, Zhang Y, Liang L. A Concise Survey of G4U. 2019. Available from: https://arxiv.org/ftp/arxiv/papers/1910/ 1910.14323.pdf [Accessed: 28 December 2019]

[30] Li D, Zhang Y, Liang L. A mathematical extension to the general *Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing… DOI: http://dx.doi.org/10.5772/intechopen.91242*

four-component scattering power decomposition with unitary transformation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing. 2020:1-18. Under review

Proceedings of the IEEE. 2013;**101**(3): 831-846. DOI: 10.1109/JPROC.2012.

*Tsunami - Damage Assessment and Medical Triage*

[23] Freeman A, Durden SL. A threecomponent scattering model for polarimetric SAR data. IEEE

[24] Yamaguchi Y, Moriyama T, Ishido M, Yamada H. Four-component scattering model for polarimetric SAR

image decomposition. IEEE

10.1109/TGRS.2005.852084

2150704X.2016.1217438

2011.2162935

2019]

[25] Zhu F, Zhang Y, Li D. A novel deorientation method in PolSAR data processing. Remote Sensing Letters. 2016;**7**(11):1083-1092. DOI: 10.1080/

[26] Yamaguchi Y, Sato A, Boerner W-M, Sato R, Yamada H. Four-component scattering power decomposition with rotation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing. 2011;**49**(6):2251-2258. DOI: 10.1109/TGRS.2010.2099124

[27] Sato A, Yamaguchi Y, Singh G, Park S-E. Four-component scattering power decomposition with extended volume scattering model. IEEE

Geoscience and Remote Sensing Letters. 2012;**9**(2):166-170. DOI: 10.1109/LGRS.

[28] Singh G, Yamaguchi Y, Park S-E. General four-component scattering power decomposition with unitary transformation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing. 2013;**51**(5):3014-3022. DOI: 10.1109/TGRS.2012.2212446

[29] Li D, Zhang Y, Liang L. A Concise Survey of G4U. 2019. Available from: https://arxiv.org/ftp/arxiv/papers/1910/ 1910.14323.pdf [Accessed: 28 December

mathematical extension to the general

[30] Li D, Zhang Y, Liang L. A

10.1109/36.673687

Transactions on Geoscience and Remote Sensing. 1998;**36**(3):963-973. DOI:

Transactions on Geoscience and Remote Sensing. 2005;**43**(8):1699-1706. DOI:

Transactions on Geoscience and Remote Sensing. 2013;**51**(4):1985-1997. DOI: 10.1109/TGRS.2012.2210050

[17] Chen S-W, Sato M. Tsunami damage investigation of built-up areas using multitemporal spaceborne full polarimetric SAR images. IEEE

[18] Li N, Wang R, Deng Y, Liu Y, Wang C, Balz T, et al. Polarimetric response of landslides at X-band following the Wenchuan earthquake. IEEE Geoscience and Remote Sensing Letters. 2014;**11**(10):1722-1726. DOI:

10.1109/LGRS.2014.2306820

[19] Chen S-W, Wang X-S, Sato M. Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR data for the

3.11 East Japan earthquake. IEEE

[20] Ji Y, Sumantyo JTS, Chua MY, Waqar MM. Earthquake/tsunami damage level mapping of urban areas using full polarimetric SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018;**11**(7):2296-2309. DOI: 10.1109/JSTARS.2018.2822825

[21] Zhai W, Huang C, Pei W. Two new polarimetric feature parameters for the recognition of the different kinds of buildings in earthquake-stricken areas based on entropy and eigenvalues of PolSAR decomposition. Remote Sensing.

2018;**10**(10):1613. DOI: 10.3390/

[22] Cloude SR, Pottier E. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing. 1996; **34**(2):498-518. DOI: 10.1109/36.485127

rs10101613

**32**

10.1109/TGRS.2016.2588325

Transactions on Geoscience and Remote Sensing. 2016;**54**(12):6919-6929. DOI:

2230311

[31] Sinclair G. The transmission and reception of elliptically polarized waves. Proceedings of the IRE. 1950;**32**(2): 148-151. DOI: 10.1109/JRPROC.1950. 230106

[32] Huynen JR. Phenomenological theory of radar targets [thesis]. Delft: Delft University of Technology; 1970

[33] Cloude SR, Pottier E. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing. 1997;**35**(1):68-78. DOI: 10.1109/36.551935

[34] Wikipedia. 2011 Tohoku Earthquake and Tsunami. 2019. Available from: https://en.wikipedia. org/wiki/2011\_T%C5%8Dhoku\_ earth quake\_and\_tsunami. [Accessed: 28 December 2019]

[35] National Police Agency of Japan. Police Countermeasures and Damage Situation associated with 2011 Tohoku district-off the Pacific Ocean Earthquake. 2019. Available from: https://www.npa.go.jp/news/other/ earthquake2011/pdf/higaijokyo\_e.pdf [Accessed: 28 December 2019]

[36] JAXA Earth Observation Research Center. About ALOS–Overview and Objectives. 2019. Available from: https://www.eorc.jaxa.jp/ALOS/en/ about/about\_index.htm [Accessed: 28 December 2019]

[37] Li D, Zhang Y. A novel approach for the registration of weak affine images. Pattern Recognition Letters. 2012; **33**(12):1647-1655. DOI: 10.1016/j. patrec.2012.04.009

[38] Li D, Zhang Y. A fast offset estimation approach for InSAR image subpixel registration. IEEE Geoscience and Remote Sensing Letters. 2012;**9**(2): 267-271. DOI: 10.1109/LGRS.2011. 2166752

[39] Li D, Zhang Y. On the appropriate feature for general SAR image registration. Proceedings of SPIE. 2012; **8536**:8536X. DOI: 10.1117/12.970520

[40] Li D, Zhang Y. The appropriate parameter retrieval algorithm for feature-based SAR image registration. Proceedings of SPIE. 2012;**8536**:8536Y. DOI: 10.1117/12.970522

**Chapter 3**

**Abstract**

**1. Introduction**

**35**

Pakistan Coast

*Ghazala Naeem*

Dealing with Local Tsunami on

Tsunami originating from a local source can arrive at Pakistan coastline within minutes. In the absence of a comprehensive and well-coordinated management plan, the fast-approaching tsunami might wreak havoc on the coast. To combat such a threat, a wide range of short- and long-term mitigation measures are needed to be taken by several government and private sector organizations as well as security agencies. Around 1000-km coastline is divided administratively into two provinces of Baluchistan and Sindh and further into seven districts. Most of the coastal communities were severely affected by an earthquake of magnitude 8+ on 28 November 1945 followed by a devastating tsunami. In contrast to the level of posed hazard and multiple-fold increase in vulnerabilities since then, the risk mitigation efforts are trivial and least coordinated. It is important to provide stakeholders with a set of prerequisite information and guidelines on standardized format to develop their organizational strategies and course of action for earthquake and tsunami risk

mitigation in a well-coordinated manner, from local to the national level.

standardized format, stakeholders' coordination

sustained tsunami resilience efforts.

tsunami-resilient communities in Pakistan.

**Keywords:** local tsunami, hazard and risk assessment, mitigation, preparedness,

Tsunami being a less frequent hazard has not yet gained due attention in the national hazard mitigation and preparedness program within Pakistan. However, disastrous impacts of 1945 Makran Tsunami, which occurred in the Arabian Sea merely 70 years ago, cannot be ignored and urge need of comprehensive and

In recent decades, 2004 Indian Ocean and 2011 Japan Tsunamis have revealed destructing powers of tsunami and the level of unpreparedness with regard to hazard assessment, warning and response planning, public awareness, mitigation,

Since 2006, in the aftermath of 2004 Indian Ocean Tsunami, significant efforts have been made in the country; however, there is much more to do for developing

There are several multi-tiered stakeholders having inter-reliant responsibilities and mandates for earthquake and tsunami risk reduction, working in the coastal region of Pakistan. There is a need to support those stakeholders in dealing with tsunami and earthquake risks in a well-coordinated and comprehensive manner. This chapter recommends policy guidelines for determining strategic significance of

and research, not only of developing but developed countries as well.

#### **Chapter 3**

## Dealing with Local Tsunami on Pakistan Coast

*Ghazala Naeem*

#### **Abstract**

Tsunami originating from a local source can arrive at Pakistan coastline within minutes. In the absence of a comprehensive and well-coordinated management plan, the fast-approaching tsunami might wreak havoc on the coast. To combat such a threat, a wide range of short- and long-term mitigation measures are needed to be taken by several government and private sector organizations as well as security agencies. Around 1000-km coastline is divided administratively into two provinces of Baluchistan and Sindh and further into seven districts. Most of the coastal communities were severely affected by an earthquake of magnitude 8+ on 28 November 1945 followed by a devastating tsunami. In contrast to the level of posed hazard and multiple-fold increase in vulnerabilities since then, the risk mitigation efforts are trivial and least coordinated. It is important to provide stakeholders with a set of prerequisite information and guidelines on standardized format to develop their organizational strategies and course of action for earthquake and tsunami risk mitigation in a well-coordinated manner, from local to the national level.

**Keywords:** local tsunami, hazard and risk assessment, mitigation, preparedness, standardized format, stakeholders' coordination

#### **1. Introduction**

Tsunami being a less frequent hazard has not yet gained due attention in the national hazard mitigation and preparedness program within Pakistan. However, disastrous impacts of 1945 Makran Tsunami, which occurred in the Arabian Sea merely 70 years ago, cannot be ignored and urge need of comprehensive and sustained tsunami resilience efforts.

In recent decades, 2004 Indian Ocean and 2011 Japan Tsunamis have revealed destructing powers of tsunami and the level of unpreparedness with regard to hazard assessment, warning and response planning, public awareness, mitigation, and research, not only of developing but developed countries as well.

Since 2006, in the aftermath of 2004 Indian Ocean Tsunami, significant efforts have been made in the country; however, there is much more to do for developing tsunami-resilient communities in Pakistan.

There are several multi-tiered stakeholders having inter-reliant responsibilities and mandates for earthquake and tsunami risk reduction, working in the coastal region of Pakistan. There is a need to support those stakeholders in dealing with tsunami and earthquake risks in a well-coordinated and comprehensive manner. This chapter recommends policy guidelines for determining strategic significance of posed tsunami threat to the Pakistan coast and ascertaining the underlying risks in comparison to the current capacities and preparedness measures. The chapter also suggests desirable research work, establishing timely warning system and structural and nonstructural mitigation measures including effective outreach to the public level and international coordination to combat local tsunami threat.

**3.1 Potential tsunami sources**

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

*3.1.1 Subduction zone earthquake*

near Indonesia.

**Figure 1.**

**37**

*Heidarzadeh [2].*

basis for such estimations.

*3.1.2 Submarine landslides*

The most important source of earthquake-generated fast-approaching tsunamis

(a local tsunami) in the Arabian Sea is the Makran subduction zone (**Figure 1**) adjacent to the coasts of Iran and Pakistan [1]. Recent event, known as 1945 Makran

Another potential subduction zone lies from the northern tip of the Bay of Bengal, through the western margin of the Andaman Sea, and skirting the southern coasts of Sumatra, Java, and the islands of Lesser Sunda and is underlain geologically as Sunda subduction zone [1]. However, a tsunami generated by a potential earthquake event within Sunda subduction zone may reach Pakistan coast in hours, categorized as distant tsunami. A devastating event like 2004 Indian Ocean tsunami created disturbance on Pakistan coast after several hours of the incident occurred

It is difficult to evaluate the accurate level of tsunami hazard these subduction zones pose for near and distant regions. The record and likelihood of earthquake occurrence in these zones and the implications for tsunami generation are the only

Within the Arabian Sea region, submarine landslides have the potential to produce large, local tsunamis owing to steep seafloor slopes and rapid sedimentation. Tsunami waves (≤1 m) were observed in the Arabian Sea on 24 September 2013 along several beaches in Oman and Pakistan during low tide period. The event was caused by a secondary effect of an earthquake of magnitude 7.7, which occurred

*General location map of the Makran subduction zone (MSZ) at the northwestern Indian Ocean showing locations of past tsunamis in the region. Source tsunami risk, preparedness and warning system in Pakistan by*

Tsunami, was caused by the earthquake in eastern part of this zone.

#### **2. Recommended policy guidelines for local tsunami**

The Arabian Sea region is threatened by earthquake and tsunami hazards, mainly because of the presence of the Makran subduction zone (MSZ). An earthquake of magnitude 8+ had wreaked havoc along the Pakistan coastline on 28 November 1945 followed by a devastating tsunami. In contrast to the level of posed threat and multiple-fold increase in vulnerabilities since then, the risk mitigation efforts are trivial and least coordinated. There is need for stakeholders to provide a set of prerequisite information to develop their organizational strategies and course of action for earthquake and tsunami risk mitigation in a well-coordinated manner, from local to the national level. Most important and immediate tasks include:


The required mitigation measures in a standardized manner are divided into three main categories including hazard assessment, risk evaluation, and mitigating measures to guide national level stakeholders in developing a long-term comprehensive tsunami response and risk reduction plan for Pakistan.

Establishing a technical committee to perform a role of central coordination and advisory under the National Disaster Management Authority can be supportive for stakeholders interested in and mandated for planning and implementation of earthquake and tsunami response and preparedness measures.

#### **3. Assessing tsunami hazard**

This section is based on the review of scientific and historical evidences of various potential sources of tsunamis which have affected and are likely to affect the Arabian Sea region and are described below.

#### **3.1 Potential tsunami sources**

posed tsunami threat to the Pakistan coast and ascertaining the underlying risks in comparison to the current capacities and preparedness measures. The chapter also suggests desirable research work, establishing timely warning system and structural and nonstructural mitigation measures including effective outreach to the public

The Arabian Sea region is threatened by earthquake and tsunami hazards, mainly because of the presence of the Makran subduction zone (MSZ). An earthquake of magnitude 8+ had wreaked havoc along the Pakistan coastline on 28 November 1945 followed by a devastating tsunami. In contrast to the level of posed threat and multiple-fold increase in vulnerabilities since then, the risk mitigation efforts are trivial and least coordinated. There is need for stakeholders to provide a set of prerequisite information to develop their organizational strategies and course of action for earthquake and tsunami risk mitigation in a well-coordinated manner, from local to the national level. Most important and immediate tasks include:

• Develop standardized and coordinated tsunami hazard and risk assessments

• Improve tsunami and seismic sensor data, infrastructure, and standard operating procedures (SOPs) for better tsunami detection and warning.

• Enhance tsunami forecast and warning dissemination capability along the

• Promote the development of model mitigation measures, and encourage communities to adopt resilient construction, critical facilities protection, and

land-use planning practices to reduce the impact of future tsunamis.

• Develop a strategic plan for earthquake- and tsunami-related research

hensive tsunami response and risk reduction plan for Pakistan.

earthquake and tsunami response and preparedness measures.

The required mitigation measures in a standardized manner are divided into three main categories including hazard assessment, risk evaluation, and mitigating measures to guide national level stakeholders in developing a long-term compre-

Establishing a technical committee to perform a role of central coordination and advisory under the National Disaster Management Authority can be supportive for stakeholders interested in and mandated for planning and implementation of

This section is based on the review of scientific and historical evidences of various potential sources of tsunamis which have affected and are likely to affect

development of tsunami response plans.

especially within Arabian Sea region.

the Arabian Sea region and are described below.

**3. Assessing tsunami hazard**

**36**

• Increase outreach to all communities, including all demographics of the at-risk population, to raise awareness, improve preparedness, and encourage the

level and international coordination to combat local tsunami threat.

for all coastal regions of Sindh and Baluchistan provinces.

coastline.

**2. Recommended policy guidelines for local tsunami**

*Tsunami - Damage Assessment and Medical Triage*

#### *3.1.1 Subduction zone earthquake*

The most important source of earthquake-generated fast-approaching tsunamis (a local tsunami) in the Arabian Sea is the Makran subduction zone (**Figure 1**) adjacent to the coasts of Iran and Pakistan [1]. Recent event, known as 1945 Makran Tsunami, was caused by the earthquake in eastern part of this zone.

Another potential subduction zone lies from the northern tip of the Bay of Bengal, through the western margin of the Andaman Sea, and skirting the southern coasts of Sumatra, Java, and the islands of Lesser Sunda and is underlain geologically as Sunda subduction zone [1]. However, a tsunami generated by a potential earthquake event within Sunda subduction zone may reach Pakistan coast in hours, categorized as distant tsunami. A devastating event like 2004 Indian Ocean tsunami created disturbance on Pakistan coast after several hours of the incident occurred near Indonesia.

It is difficult to evaluate the accurate level of tsunami hazard these subduction zones pose for near and distant regions. The record and likelihood of earthquake occurrence in these zones and the implications for tsunami generation are the only basis for such estimations.

#### *3.1.2 Submarine landslides*

Within the Arabian Sea region, submarine landslides have the potential to produce large, local tsunamis owing to steep seafloor slopes and rapid sedimentation.

Tsunami waves (≤1 m) were observed in the Arabian Sea on 24 September 2013 along several beaches in Oman and Pakistan during low tide period. The event was caused by a secondary effect of an earthquake of magnitude 7.7, which occurred

#### **Figure 1.**

*General location map of the Makran subduction zone (MSZ) at the northwestern Indian Ocean showing locations of past tsunamis in the region. Source tsunami risk, preparedness and warning system in Pakistan by Heidarzadeh [2].*

inland in southwestern Pakistan at 11.29.47 UTC (local time is UTC +5) on the same day, but after several hours, the earthquake's epicenter was a couple of hundred kilometers inland. Hoffman et al. [3] suggest the waves must have been triggered by a submarine landslide.

and PDMAs, for example, onshore, offshore surface data, geological and meteorological scientific information, census and building records, and satellite images and archival records. The NDMA being the central focal organization for disaster management can play a vital role to facilitate data and information sharing among organizations and also with researchers.

iv. Tsunami caused by undersea landslides should also be accounted for more

v. All possible impacts of any future tsunami event should also be studied and modeled in details. For example, huge quantities of debris brought onshore

itself can be a major hazard for ports, fishing harbors, and local

Using the outputs from the hazard assessment, disaster, emergency managers, and other relevant organizations (NDMA, PDMAs, and DDMAs of Baluchistan and Sindh Provinces) will need to create a community asset database of maps showing the distribution of population, buildings, infrastructure, and environmental assets in relation to the information on various hazard exposure parameters (inundation limit, run-up, depth of water, proximity to open coast, inundation and drainage flow velocities, etc.) for a particular earthquake and tsunami hazard scenario [1].

i. In addition to following the NDMA's MHVRA guidelines, tsunami

ii. Hazard maps developed under the proposed guidelines of the previous section can be used to incorporate vulnerability maps and finally produce tsunami risk maps, and those should be communicated to all stakeholders

iii. Levels of risk are presented in geospatial ways: maps showing the extents of areas with defined "risk categories" as high, medium, and low levels of

iv. Coastal cities being hub of economic activities having national life line infrastructure like ports are also densely populated. The NDMA and other relevant national and provincial agencies should encourage research institutions and experts to prioritize conduction of vulnerability and risk assessments of urban areas on priority basis and its incorporation in the national database. For example, detailed exposure database of 1- to 3-kmwide coastal belt in urban, semi-urban, and rural settlements can be maintained and updated annually by the provincial and district disaster

v. To assure uniformity and speed in the risk assessment process, the

following data of interest should be maintained for areas in close proximity

categories of risk" from the national to local level.

estimated risks as per the NDMA's MHVRA guidelines.

vulnerability and capacity assessments are to be carried out in detail. Such risk maps should be interpreted in standardized format of "well-defined

reliable hazard assessment process.

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

environment at any specific location.

**4.1 Guidelines for tsunami risk assessment**

in a systematic way.

management authorities.

**39**

**4. Assessing tsunami risk**

#### **3.2 Guidelines for tsunami hazard assessment**

Tsunami hazard assessment at local level is important to understand the locally imposed threat and to deal with the posed hazard accordingly. The creation of local hazard maps is a key step in the tsunami risk assessment procedure and is needed to:


The organizations, institutions, and experts mandated or interested to conduct directly or support to carry out earthquake and/or tsunami hazard assessment along the Pakistan coastline are suggested to follow standard parameters or guidelines, led by any national agency like the National Disaster Management Authority (NDMA) such as:

	- a. Inundation maps
	- b. Flow velocity profiles
	- c. Warning time available for emergency response
	- d. Debris flow profiles at least for ports if possible
	- e. Comprehensive hazard map made by overlaying inundation maps and flow velocity profiles

inland in southwestern Pakistan at 11.29.47 UTC (local time is UTC +5) on the same day, but after several hours, the earthquake's epicenter was a couple of hundred kilometers inland. Hoffman et al. [3] suggest the waves must have been triggered by

Tsunami hazard assessment at local level is important to understand the locally imposed threat and to deal with the posed hazard accordingly. The creation of local hazard maps is a key step in the tsunami risk assessment procedure and is needed to:

• Determine the exposure parameters that will be used in the assessment of vulnerability of the coastal community and of their supporting assets and

The organizations, institutions, and experts mandated or interested to conduct directly or support to carry out earthquake and/or tsunami hazard assessment along the Pakistan coastline are suggested to follow standard parameters or guidelines, led by any national agency like the National Disaster Management Authority (NDMA) such as:

i. Local tsunami hazard maps are usually developed from specified tsunami event scenarios. The parameters defined in Multi-Hazard Vulnerability and Risk Assessment guidelines of the NDMA available at http://ndma.gov.pk/ publications/MHVRA%202017.pdf are used [4]. Other technical details for modeling tsunami hazard, for example, input data sources, modeling tools, and criteria used by different research institutions and experts, can also be reviewed by the NDMA to make integrated tsunami hazard assessment of the Pakistan coastline on a standardized format. Final output products include:

c. Warning time available for emergency response

d. Debris flow profiles at least for ports if possible

e. Comprehensive hazard map made by overlaying inundation maps

ii. The NDMA take the core responsibility of coordination among agencies and implementing partners to develop and facilitate regular update of hazard database. The NDMA collaboration is also required for statistics and information acquisition and sharing among stakeholders, developing and publishing such maps on standardized and easily understandable (for

iii. Interagency coordination for sharing data and information required for comprehensive hazard assessment should be conducted by the NMDA

• Land-use planning within a defined coastal management area

a submarine landslide.

systems

• Develop evacuation plans

a. Inundation maps

general public) format.

**38**

b. Flow velocity profiles

and flow velocity profiles

**3.2 Guidelines for tsunami hazard assessment**

*Tsunami - Damage Assessment and Medical Triage*

and PDMAs, for example, onshore, offshore surface data, geological and meteorological scientific information, census and building records, and satellite images and archival records. The NDMA being the central focal organization for disaster management can play a vital role to facilitate data and information sharing among organizations and also with researchers.


### **4. Assessing tsunami risk**

Using the outputs from the hazard assessment, disaster, emergency managers, and other relevant organizations (NDMA, PDMAs, and DDMAs of Baluchistan and Sindh Provinces) will need to create a community asset database of maps showing the distribution of population, buildings, infrastructure, and environmental assets in relation to the information on various hazard exposure parameters (inundation limit, run-up, depth of water, proximity to open coast, inundation and drainage flow velocities, etc.) for a particular earthquake and tsunami hazard scenario [1].

#### **4.1 Guidelines for tsunami risk assessment**


to the sea (as suggested above, i.e., 1- to 3-km-wide belt along the coast can be surveyed on priority). Detailed survey should be carried out along the coastal areas to collect data such as:

*5.1.1 Current status*

lead time.

tioned below:

techniques.

or concerned PDMAs.

**41**

*5.1.2 Guidelines for effective early warning system*

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

villages including islands and creeks.

to coastal communities (on- and offshore).

The National Seismic Monitoring Tsunami Early Warning Center (NSMTEWC) of the Pakistan Meteorological Department (PMD) is capable of issuing warning bulletins and messages to identified stakeholders including disaster management authorities, concerned provincial and district governments, and media within 13 min as specified in laid down standard operating procedures [5]. However, there is a lack of further downstream time bound SOPs (13 min onward with reference to PMD's SOPs) of other stakeholders (e.g., disaster management authorities, emergency services, provincial and local governments) to ensure the warning information and messages are communicated to all vulnerable coastal communities and, if needed, to adopt evacuation procedures that are timely completed within available

The NDMA being the central coordinating agency of disaster management in Pakistan can take a lead and engage relevant organization including PDMA Sindh,

i. End-to-end time bound synergized SOPs for the dissemination of tsunami warning to be developed involving all stakeholders living in remote coastal

ii. Protocol among all national organizations capable of communication (using one or more communication networks such as satellite, HF/VHF, radio, or any wired or wireless network) should be established and made accessible

iii. Develop and enforce SOPs and procedures to ensure that all tsunami and earthquake detection, forecasting, warning communication, and

iv. Stakeholders and organizations responsible for burden sharing of early warning dissemination can be involved in practicing procedures and equipment operation tests, collectively, at least once a year or on an agreed schedule as a full-scale tsunami exercise in coordination with the NDMA.

v. Individual government organizations involved in early warning chain and emergency response to arrange tabletop and functional test exercises at least twice a year on a feasible schedule, in coordination with NDMA and/

vi. Official early warning bulletins should be adapted as easily understandable public messages by relevant PDMA in coordination with the PMD Tsunami

Center. The NDMA shall provide central coordination to maintain

uniformity and standardization of the public messages.

dissemination network/equipment must be kept in operational condition by the organization in charge of the asset. Such equipment and network should be installed and maintained with earthquake-resistant features and

PDMA Balochistan, Army, Pakistan Navy, Pakistan Coast Guards, Marine Security Agency, port authorities, and police to develop consensus on technical issues, set required protocols, and monitor progress on the policy guidelines men-


The abovementioned information include all required level sof onshore, offshore surface data, geological and meteorological scientific information, census and building records, satellite images, archival records, and organizational capacity (to facilitate and contribute in earthquake and tsunami emergency response and preparedness).

vi. Data and information collection, for reliable and authenticated coastal earthquake and tsunami risks assessment, can be acquired by all the agencies and organization mandated to collect and maintain such database, whereas the NDMA can play a vital role of coordination and support for essential data sharing among agencies and with experts by developing data sharing protocol.

### **5. Managing tsunami risk**

This section covers guidelines for effective earthquake and tsunami risk reduction measure to strengthen coastal communities and infrastructure aiming to reduce impacts of any devastating event in the future.

#### **5.1 Early warning system**

Is it critically important to assess whether the current early warning system and practices are effective for the posed tsunami threat to Pakistan coastal communities [5]? A review of critical issues that hindered the efficient and timely operation of early warning systems has led to the identification of four elements [1]:


#### *5.1.1 Current status*

to the sea (as suggested above, i.e., 1- to 3-km-wide belt along the coast can be surveyed on priority). Detailed survey should be carried out along the

• Census data (population distribution, income, and other statistics such

• Building classification, construction materials and techniques, ground

• Critical infrastructure (roads, water, power, sewerage, emergency

• Economic zones and location (business sectors, industry, ports)

The abovementioned information include all required level sof onshore, offshore surface data, geological and meteorological scientific information,

census and building records, satellite images, archival records, and organizational capacity (to facilitate and contribute in earthquake and

vi. Data and information collection, for reliable and authenticated coastal earthquake and tsunami risks assessment, can be acquired by all the agencies and organization mandated to collect and maintain such database, whereas the NDMA can play a vital role of coordination and support for essential data sharing among agencies and with experts by developing data

This section covers guidelines for effective earthquake and tsunami risk reduction measure to strengthen coastal communities and infrastructure aiming to reduce

Is it critically important to assess whether the current early warning system and practices are effective for the posed tsunami threat to Pakistan coastal communities [5]? A review of critical issues that hindered the efficient and timely operation of

• Implementation of technically oriented early warning systems, without taking

• Weaknesses in monitoring and forecasting of potentially catastrophic events

• Weaknesses in the emission of warnings or in ensuring that warnings reach

• Weaknesses in local capacities to respond to a warning and to a potentially

early warning systems has led to the identification of four elements [1]:

into consideration or without conducting risk assessment

coastal areas to collect data such as:

*Tsunami - Damage Assessment and Medical Triage*

level elevation

facilities)

sharing protocol.

impacts of any devastating event in the future.

**5. Managing tsunami risk**

**5.1 Early warning system**

vulnerable communities

catastrophic event

**40**

as age, occupation, disability, education)

• Environmental services/inventory

tsunami emergency response and preparedness).

The National Seismic Monitoring Tsunami Early Warning Center (NSMTEWC) of the Pakistan Meteorological Department (PMD) is capable of issuing warning bulletins and messages to identified stakeholders including disaster management authorities, concerned provincial and district governments, and media within 13 min as specified in laid down standard operating procedures [5]. However, there is a lack of further downstream time bound SOPs (13 min onward with reference to PMD's SOPs) of other stakeholders (e.g., disaster management authorities, emergency services, provincial and local governments) to ensure the warning information and messages are communicated to all vulnerable coastal communities and, if needed, to adopt evacuation procedures that are timely completed within available lead time.

#### *5.1.2 Guidelines for effective early warning system*

The NDMA being the central coordinating agency of disaster management in Pakistan can take a lead and engage relevant organization including PDMA Sindh, PDMA Balochistan, Army, Pakistan Navy, Pakistan Coast Guards, Marine Security Agency, port authorities, and police to develop consensus on technical issues, set required protocols, and monitor progress on the policy guidelines mentioned below:


#### **5.2 Evacuation planning**

Subject to the assessed level of risk in respect of a tsunami event, disaster management authorities and emergency responders should prioritize establishing and implementing a strategic plan (considering available lead time and resources at local level) for the effective and orderly evacuation of the exposed population.

Evacuation planning in each coastal area is directly related to the:


Vulnerability maps derived from the inundation maps (in Section 4) provide key information for evacuation planning. Either voluntary or mandatory evacuation, both can place a significant burden on the resources and emergency managers in terms of caring for the displaced people (**Figures 2–5**).

*5.2.1 Guidelines for tsunami evacuation planning*

*wave and inundation height. Source: IOC Manuals and Guides 52 [1].*

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

to be addressed:

**Figure 4.**

**Figure 5.**

*Naeem.*

**43**

**Figure 3.**

Disaster management authority of each coastal district, in coordination with concerned stakeholders, should prepare evacuation plans with the following aspects

*Remote coastal communities settled on small islands in the Indus Creek Sindh Province. Photo by Ghazala*

*One of the strategic options for tsunami mitigation is "retreat" with reference to general maximum tsunami*

*Application of engineering solutions of building a protection wall and accommodating the building by raise on stilts to safeguard against maximum tsunami wave and inundation height. Source: IOC Manual 52 [1].*

i. Identify "at-risk" people/communities who may require evacuation (either through risk assessment (Section 4)). It is recommended that authorities

#### **Figure 2.**

*Pedestrian tsunami evacuation route at Gwadar City, Baluchistan Province. (Left) Red arrows show starting and ending point of the evacuation route. (Right) Tsunami evacuation route, more than 600 steps of a stairs designed with landing at several points to facilitate pedestrian evacuation leading to the proposed evacuation site at the top of the Koh-e-Batil (a 450 high mountain) by the district disaster management authority Gwadar. Photo by Ghazala Naeem.*

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

#### **Figure 3.**

vii. Early warning bulletins and messages shall be tested for their level of

areas.

**5.2 Evacuation planning**

• Topography

• Demographics

• Resources available

**Figure 2.**

**42**

*Photo by Ghazala Naeem.*

• Geographical size of the management area

• Assessed hazards and vulnerabilities

*Tsunami - Damage Assessment and Medical Triage*

• Size and density of the population

• Number of agencies involved in the planning process

in terms of caring for the displaced people (**Figures 2–5**).

understanding through a manageable size of survey after each simulation, exercise or drill involving the general public or at least schools in target

viii. Strengthening of information sharing mechanism among Regional Tsunami Watch Providers and National Tsunami Warning Centers to better receive information and advice to complement national data stream including

seismic, sea level, and other geophysical data networks.

Subject to the assessed level of risk in respect of a tsunami event, disaster management authorities and emergency responders should prioritize establishing and implementing a strategic plan (considering available lead time and resources at local level) for the effective and orderly evacuation of the exposed population. Evacuation planning in each coastal area is directly related to the:

Vulnerability maps derived from the inundation maps (in Section 4) provide key information for evacuation planning. Either voluntary or mandatory evacuation, both can place a significant burden on the resources and emergency managers

*Pedestrian tsunami evacuation route at Gwadar City, Baluchistan Province. (Left) Red arrows show starting and ending point of the evacuation route. (Right) Tsunami evacuation route, more than 600 steps of a stairs designed with landing at several points to facilitate pedestrian evacuation leading to the proposed evacuation site at the top of the Koh-e-Batil (a 450 high mountain) by the district disaster management authority Gwadar.* *Application of engineering solutions of building a protection wall and accommodating the building by raise on stilts to safeguard against maximum tsunami wave and inundation height. Source: IOC Manual 52 [1].*

#### **Figure 4.**

*One of the strategic options for tsunami mitigation is "retreat" with reference to general maximum tsunami wave and inundation height. Source: IOC Manuals and Guides 52 [1].*

#### **Figure 5.**

*Remote coastal communities settled on small islands in the Indus Creek Sindh Province. Photo by Ghazala Naeem.*

#### *5.2.1 Guidelines for tsunami evacuation planning*

Disaster management authority of each coastal district, in coordination with concerned stakeholders, should prepare evacuation plans with the following aspects to be addressed:

i. Identify "at-risk" people/communities who may require evacuation (either through risk assessment (Section 4)). It is recommended that authorities

proceed with mapping based on current locally available information and indigenous knowledge and not wait for the perceived required scientific knowledge. Zone boundary definition can then be refined as knowledge improves, over time.

xiii. Procedures for the return of evacuees.

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

signage, and tsunami response actions.

learnt in the overall planning scheme.

siderations including sustainability, over the long-term.

**5.3 Other structural and nonstructural measures**

within such a plan.

community preparedness.

mitigation measures.

belt).

**45**

*5.3.2 Hazard-resilient built environment*

*5.3.1 Coastal engineering solutions*

xiv. A consistent plan to facilitate common public understanding across

communities about tsunami evacuation zones, maps, tsunami evacuation

xv. Maintaining the plan, conduct drills and exercises, and incorporate lessons

Long-term earthquake and tsunami mitigation measures, other than effective early warning system and efficient evacuation plan, are also important to reduce the damage caused on the shores by expected events. There is a wide range of both structural and nonstructural measures, implemented as pre-disaster mitigations to

Within the framework of a coastal area management plan, measures which mitigate the impact of earthquake and tsunami hazard represent a coherent set of interventions. A project monitoring and control system should also be incorporated

This section describes the management of the earthquake and tsunami risk by strategic mitigation, both through the use of structural methods, including the use of natural coastal resources and engineering approaches, and also by nonstructural initiatives, including regulation and land-use, emergency response planning, and

While it is not possible to prevent a tsunami, particularly in tsunami-prone countries, some measures have been implemented and tested to reduce the damage caused on the shores that may have succeeded in slowing down and moderating the impacts of tsunami, for example, construing tsunami in front of populated coastal area, raising ground level for housing near beach by infilling land, and building floodgates and channels to redirect the water from incoming tsunamis. However, their effectiveness has been questioned, as tsunamis are observed there, often higher than the barriers. These engineering options for risk reduction are analyzed, that is, if these are appropriate to the scale of the tsunami threat to the designated coastal area, balancing social and economic pressures against environmental con-

It is important to have a reliable database of the building stock in coastal areas especially in close proximity of the shoreline to have a reliable vulnerability and thus risk assessment. Strong buildings, safe structures, and prudent land-use policies to save lives and reduce property damage are implemented as pre-disaster

i. Relevant disaster management organization and building control

authorities should maintain a GIS-based inventory of already constructed buildings with details of construction type, height, use, age, and structural stability at least for the areas in close proximity to shoreline (as a thumb rule, initially this database can be worked out for 1–3-km-wide coastal

manage earthquake and tsunami risks in the coastal areas of Pakistan.

	- The circumstances of the emergency
	- Transportation (e.g., arrangements in areas where pedestrian evacuation is not possible or for the patients, etc.)
	- Dealing with community that disregards mandatory evacuation
	- The evacuation of specific locations (UC and village level) facilities (ports, hospitals, schools, large industrial setup, atomic reactors, security agencies set-up) and evacuation routes

proceed with mapping based on current locally available information and indigenous knowledge and not wait for the perceived required scientific knowledge. Zone boundary definition can then be refined as knowledge

ii. Safe evacuation sites or buildings should be identified, clearly marked, and communicated to locals based on the perceived hazard analysis, for example, possible earthquake shaking, inundation height and extent, etc. Such sites and buildings should be pre-examined for safety, security, required space, and facilities to cater for the expected number of evacuees.

iii. Maps depicting tsunami evacuation zones, escape routes, and tsunami safe areas should be available for display at workplaces, public gathering areas and buildings, holiday homes, and tourist facilities. Particularly, display in

iv. Well-placed evacuation signage (in nationally agreed standardized format) with local perspective is critically important, for example, safety

instructions and signage (natural signs) for tsunami events, identification of dangerous areas, safe sites, routes to reach evacuation sites, and other

v. Define conditions under which an evacuation may be necessary.

vi. Elaborate command, control, and coordination instructions (including designation of officials who are authorized to order an evacuation).

viii. Procedures for assisting special categories of evacuees (e.g., vulnerable communities with least communication networks, elderly, children, physically challenged people, school students, patients at hospitals, etc.).

• Transportation (e.g., arrangements in areas where pedestrian

• Dealing with community that disregards mandatory evacuation

xi. Welfare support for evacuees; designated reception areas for vulnerable groups like unattended children, elderly, patients, physically challenged

• The evacuation of specific locations (UC and village level) facilities (ports, hospitals, schools, large industrial setup, atomic reactors,

evacuation is not possible or for the patients, etc.)

security agencies set-up) and evacuation routes

x. Means of accounting for evacuees (and registration).

vii. Warning instructions should be issued to the media, public, and businesses.

improves, over time.

*Tsunami - Damage Assessment and Medical Triage*

important messages.

people, etc.

**44**

xii. Security of evacuated areas.

all areas subjected to tsunami risk.

ix. Specific plans and procedures that address:

• The circumstances of the emergency


#### **5.3 Other structural and nonstructural measures**

Long-term earthquake and tsunami mitigation measures, other than effective early warning system and efficient evacuation plan, are also important to reduce the damage caused on the shores by expected events. There is a wide range of both structural and nonstructural measures, implemented as pre-disaster mitigations to manage earthquake and tsunami risks in the coastal areas of Pakistan.

Within the framework of a coastal area management plan, measures which mitigate the impact of earthquake and tsunami hazard represent a coherent set of interventions. A project monitoring and control system should also be incorporated within such a plan.

This section describes the management of the earthquake and tsunami risk by strategic mitigation, both through the use of structural methods, including the use of natural coastal resources and engineering approaches, and also by nonstructural initiatives, including regulation and land-use, emergency response planning, and community preparedness.

#### *5.3.1 Coastal engineering solutions*

While it is not possible to prevent a tsunami, particularly in tsunami-prone countries, some measures have been implemented and tested to reduce the damage caused on the shores that may have succeeded in slowing down and moderating the impacts of tsunami, for example, construing tsunami in front of populated coastal area, raising ground level for housing near beach by infilling land, and building floodgates and channels to redirect the water from incoming tsunamis. However, their effectiveness has been questioned, as tsunamis are observed there, often higher than the barriers. These engineering options for risk reduction are analyzed, that is, if these are appropriate to the scale of the tsunami threat to the designated coastal area, balancing social and economic pressures against environmental considerations including sustainability, over the long-term.

It is important to have a reliable database of the building stock in coastal areas especially in close proximity of the shoreline to have a reliable vulnerability and thus risk assessment. Strong buildings, safe structures, and prudent land-use policies to save lives and reduce property damage are implemented as pre-disaster mitigation measures.

#### *5.3.2 Hazard-resilient built environment*

i. Relevant disaster management organization and building control authorities should maintain a GIS-based inventory of already constructed buildings with details of construction type, height, use, age, and structural stability at least for the areas in close proximity to shoreline (as a thumb rule, initially this database can be worked out for 1–3-km-wide coastal belt).

ii. Building control authorities in collaboration with other stakeholders should review and suggest policies to counter underlying challenges in the development of disaster resilient built environment. For example, lack of regulatory frameworks, unplanned cities and urbanization, old building stocks and at-risk infrastructure, unauthorized structures, weak institutional arrangements, inadequate capacities of local administration, lack of funding, inadequacy of qualified human resources, corruption, and unlawful activities are major challenges in this regard [6].

impossible to be warned in case of approaching local tsunami [5]. Relevant

authorities and organization shall establish appropriate land-use management system to ensure a coastal hazard-resilient and environmentally sensitive land-use pattern along the coastline.

Natural coastal features like high lands, sand dunes, mangroves, and other plantation species have been reportedly protecting the nearby communities in disaster situations. For example, interviews of 1945 Makran Tsunami survivors identified Pasni sand dunes, mangroves in Kalmat village, and Indus Creek system, mountain "Koh-e-Batil" at Gwadar, and tens of feet high rocks at Peshukan and

i. Coastal vegetation can be used to dissipate tsunami energy via turbulent flow through the media. The effectiveness of dissipation is dependent on the density of vegetation, its overall porosity, and its tortuous characteristics of porous matrix. It is important to consider that the vegetation itself is resilient against tsunami propagation and has a root structure that can resist the high velocity regime at the floor bed. Planting mangrove at appropriate

locations can also serve to dissipate extreme wind wave energy.

ii. Sand dunes can provide natural full barriers against tsunami inundation. When overtopped, sand dunes tend to fail progressively by erosion. Dunecladding vegetation provides reinforcement to the dunes, thus impeding

iii. Engineering solution for protection of coastal communities such as offshore breakwaters, dykes, and revetments can be used in hybrid way, i.e., with natural features, harnessing the full potential of coastal ecosystems

including coral reefs, sand dunes, and coastal vegetation such as mangrove

i. Authorities of ports, large- and medium-scale industrial setups, and major facilities, including hospitals, schools, etc., both on- and offshore, need to prepare respective continuity of operation plans in response to estimated

earthquake and tsunami impacts mentioned in Sections 3 and 4.

• Identifying recovery time objectives for business processes.

• Identifying recovery point objective for restoration.

iv. Coastal development authorities, forest department, building control authorities, and local and provincial governments need to maintain a database (preferably GIS based) of such natural safeguard features in coastal belt and develop guidelines and regulation to protect and strengthen

*5.3.4 Continuity of operation plans for ports and other major facilities*

• Conducting business impact analysis.

*5.3.3 Strengthening of natural safeguards*

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

Ganz villages as "savior" [7].

erosion.

forests.

such features.

ii. The plan can include [8]:

**47**

iii. Although cost may be an impediment, the national/provincial/local authorities should choose to adopt tsunami-resistant structures, stronger buildings, and deeper shock-resistant foundations mandatory in areas of high risk. The orientation of buildings with respect to the ocean is another factor for consideration. Mandated organizations should develop guidelines, byelaws, regulations, and codes to encourage coastal earthquake and tsunami-resistant infrastructure and housing in a local context. The overall general design guidelines could be developed from the experience gained from post-tsunami impact and damage assessments from different parts of the world as good practices.

#### *5.3.2.1 Retrofitting of critical public buildings/facilities*


#### *5.3.2.2 Land-use planning*


ii. Building control authorities in collaboration with other stakeholders should review and suggest policies to counter underlying challenges in the development of disaster resilient built environment. For example, lack of regulatory frameworks, unplanned cities and urbanization, old building

institutional arrangements, inadequate capacities of local administration, lack of funding, inadequacy of qualified human resources, corruption, and

authorities should choose to adopt tsunami-resistant structures, stronger buildings, and deeper shock-resistant foundations mandatory in areas of high risk. The orientation of buildings with respect to the ocean is another

guidelines, byelaws, regulations, and codes to encourage coastal earthquake and tsunami-resistant infrastructure and housing in a local context. The overall general design guidelines could be developed from the experience gained from post-tsunami impact and damage assessments from different

communication, water supply, roads, and bridges shall be inspected by the concerned authority for evaluation against estimated earthquake and tsunami impacts. These evaluations shall contribute to the vulnerability and capacity assessments mentioned in Section 4 and completed on priority especially for facilities located within 3-km-wide coastal belt on priority, by

ii. A complete framework for strengthening and retrofitting to be prepared and initiated according to available resources and fund generated through

i. Information contained in the inundation, vulnerability, and risk maps is to

development and to suggest critical measures to make existing land-use

appropriate tools to suggest appropriate measure for land-use planning at any particular location. For example, option of "retreat" with reference to expected inundation extents can be used in land-use planning of high-risk areas [1].

iii. Coastal communities in Pakistan, especially in rural settings, tend to settle right on the beach without any appropriate set back distance from the shoreline. Thus, making these settlements much more vulnerable to coastal hazard at one end and an environmental hazard on the other, by throwing sewerage and garbage disposal directly into sea. Such settled remotely in small islands within the Indus creek system, these dotted communities are

be used as basis to develop policy on land-use planning for new

ii. Hazard maps, particularly inundation maps for tsunami scenarios, are

stocks and at-risk infrastructure, unauthorized structures, weak

iii. Although cost may be an impediment, the national/provincial/local

factor for consideration. Mandated organizations should develop

i. Important public buildings, for example, schools, hospitals, and government offices, and infrastructure like telecommunication,

the concerned government authority/organization.

better resilient for fast-approaching tsunami [1].

unlawful activities are major challenges in this regard [6].

parts of the world as good practices.

*Tsunami - Damage Assessment and Medical Triage*

*5.3.2.1 Retrofitting of critical public buildings/facilities*

public-private partnership schemes.

*5.3.2.2 Land-use planning*

**46**

impossible to be warned in case of approaching local tsunami [5]. Relevant authorities and organization shall establish appropriate land-use management system to ensure a coastal hazard-resilient and environmentally sensitive land-use pattern along the coastline.

### *5.3.3 Strengthening of natural safeguards*

Natural coastal features like high lands, sand dunes, mangroves, and other plantation species have been reportedly protecting the nearby communities in disaster situations. For example, interviews of 1945 Makran Tsunami survivors identified Pasni sand dunes, mangroves in Kalmat village, and Indus Creek system, mountain "Koh-e-Batil" at Gwadar, and tens of feet high rocks at Peshukan and Ganz villages as "savior" [7].


#### *5.3.4 Continuity of operation plans for ports and other major facilities*

	- Conducting business impact analysis.
	- Identifying recovery time objectives for business processes.
	- Identifying recovery point objective for restoration.

2011 Japan tsunami created a sense of realization among national- to local-level organizations and experts to work on tsunami risk assessment and preparedness

boards, booklets, videos, and through radio programs.

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

languages.

*duration."*

**49**

cellular phone networks.

*5.4.2 Public awareness campaign*

tsunami community preparedness and education.

women, etc.) and of every Union Councils (UCs).

Condition of License" of PEMRA Ordinance [9].

Within little more than a decade's period, significant pilot initiatives on community preparedness have been implemented based upon adaptation of international knowledge products and "Information, Education and Communication" (IEC) material. The adaptation strategy included not only the interpretation of those IEC products in national and local languages but also inclusion of indigenous knowledge and social and cultural traces. Tsunami safety tips, guidance for evacuation, observing natural signs of tsunami, protection, and conservation of natural safeguards of coastal region are delimited in handouts, pamphlets, information

i. The NDMA in collaboration with PDMA Baluchistan and Sindh need to maintain a database of all available IEC material for earthquake and

ii. The NDMA to support PDMAs in finalizing standardization of available knowledge products and further adaptation including translation into local

i. At local level, DDMAs need to plan and conduct tsunami awareness campaign on yearly basis through training of various community groups (volunteers, teachers, medical staff, local elected representatives, students,

ii. At national level, NDMA needs to design and implement public awareness campaigns focusing on earthquake and tsunami through national electronic channel and local FM radio channels using the tsunami knowledge database

in the coastal region. The NDMA can collaborate with the Pakistan Electronic Media Regulatory Authority (PEMRA) to broadcast such information on electronic media channels under Section 20 (e) "Terms and

*"Broadcast, if permissible under the terms of its license, programmes in the public interest specified by the Federal Government or the Authority in the manner indicated by the Government or, as the case may be, the Authority, provided that the duration of such mandatory programmes do not exceed ten percent of the total duration of broadcast or operation by a station in twenty-four hours except if, by its own volition, a station chooses to broadcast such content for a longer*

iii. The NDMA in collaboration with the Pakistan Telecommunication

iv. The NDMA, in collaboration with PDMAs and other government and nongovernment organization, can manage (if already available) and develop (if not readily available) knowledge products on a standardized

format for public awareness campaign on the following subjects:

Authority (PTA) can be vital to design and implement tsunami awareness campaigns for general public in coastal region through social media and

measures.


#### *5.3.5 Debris clearance and management plan*

Tsunamis of even small wave heights can bring huge quantities of debris and waste on the coast. Severe public sanitation and environmental concerns are also associated with earthquake and tsunami debris clearance and the management of municipal solid waste:


#### *5.3.6 Emergency response, search, and rescue plan*


#### **5.4 Community preparedness**

#### *5.4.1 Database of tsunami knowledge*

Tsunamis being infrequent phenomena could have gained least focus of all stakeholders in Pakistan; however, mega events of 2004 Indian Ocean tsunami and *Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

• Define business continuity strategies and requirements.

execution of all recovery strategies.

for relocation to alternate work sites.

applications, and data).

*Tsunami - Damage Assessment and Medical Triage*

*5.3.5 Debris clearance and management plan*

international case studies.

*5.3.6 Emergency response, search, and rescue plan*

with reference to Sections 3 and 4.

**5.4 Community preparedness**

**48**

*5.4.1 Database of tsunami knowledge*

consideration and debris clearance plan.

municipal solid waste:

develop continuity of operation plan.

• Work out procedures, resource requirements, and logistics for

• Deciding detailed procedures, resource requirements, and data

iii. National security agencies and port authorities also pay high attention to

Tsunamis of even small wave heights can bring huge quantities of debris and waste on the coast. Severe public sanitation and environmental concerns are also associated with earthquake and tsunami debris clearance and the management of

i. District government and municipal committees to develop tsunami debris clearance and waste recovery plan for expected tsunami derived waste estimation made either through numerical modeling or national and

i. All agencies and organization including civil defense, fire brigade, Rescue 1122, NDMA, PDMAs, Pakistan Army, Navy, Pakistan Coast Guards, Marine Security Agency, and health department mandated and/or capable of emergency response and rescue operations (even only in case of any critical situation) shall develop or adapt (already available) plans, SOPs, manuals, and guides as per estimated hazard of earthquake and tsunami

ii. The abovementioned plan and procedures shall be developed considering lead time availability of only "minutes" before a tsunami can hit the coast. To efficiently act upon the plans, strong coordination (inter and intra department)

is to be assured through practicing envisaged plans and participating in scheduled drills and simulations coordinated by the NDMA and PDMAs.

Tsunamis being infrequent phenomena could have gained least focus of all stakeholders in Pakistan; however, mega events of 2004 Indian Ocean tsunami and

ii. National security agencies' infrastructure and ports need high-level

• Describing detailed procedures, resource requirements, and logistics

restoration plan for the recovery of information technology (networks and required connectivity, servers, desktop/laptops, wireless devices,

2011 Japan tsunami created a sense of realization among national- to local-level organizations and experts to work on tsunami risk assessment and preparedness measures.

Within little more than a decade's period, significant pilot initiatives on community preparedness have been implemented based upon adaptation of international knowledge products and "Information, Education and Communication" (IEC) material. The adaptation strategy included not only the interpretation of those IEC products in national and local languages but also inclusion of indigenous knowledge and social and cultural traces. Tsunami safety tips, guidance for evacuation, observing natural signs of tsunami, protection, and conservation of natural safeguards of coastal region are delimited in handouts, pamphlets, information boards, booklets, videos, and through radio programs.


#### *5.4.2 Public awareness campaign*


*"Broadcast, if permissible under the terms of its license, programmes in the public interest specified by the Federal Government or the Authority in the manner indicated by the Government or, as the case may be, the Authority, provided that the duration of such mandatory programmes do not exceed ten percent of the total duration of broadcast or operation by a station in twenty-four hours except if, by its own volition, a station chooses to broadcast such content for a longer duration."*


or not at all available, in estimated available lead time (subject to the estimated tsunami hazard and risks discussed in Sections 3 and 4).

ii. PDMAs (Baluchistan and Sindh) along with respective DDMAs should design and implement evacuation planning for such communities

a. Identification of feasible evacuation site and routes near each

b. Awareness campaigns and training of local volunteers to receive official warning to disseminate to other villagers and fishermen.

c. Interpretation of bulletins issued by the PMD and DDMAs.

immediate first aid provision to injured, etc.

f. Facilitate and mange evacuation of vulnerable groups.

the village and emergency responders/organization.

h. Knowing, using, and keeping alive the indigenous knowledge.

The NDMA in collaboration with PDMAs (Baluchistan and Sindh) and other organizations should develop a strategy to promote insurance (life and property) for earthquake and tsunami incidents that can play an important role in offering

Understanding disasters and to find appropriate ways to reduce disaster risk are critically important. Scientific, social, and indigenous knowledge-based researches are direly needed to be undertaken, and result sharing with larger audience including communities at risk has a pivotal role in managing disasters. This role of riskbased knowledge sharing has been recognized in international frameworks, i.e., Sendai Framework for disaster risk reduction (SFDRR) 2015–2030 [10].

i. At national level the NDMA may facilitate coordination among academia, research institutions, and private sector to undertake scientific and social research initiatives facilitating overall risk assessment of coastal areas of

a. Earthquake and tsunami hazard analysis

d. Detection of early warning via natural signs such as abnormal behavior of animals, earthquake shaking, and retreat of sea water.

e. Basic emergency response, especially how and where to evacuate,

g. Ways to manage external communication to get help from outside of

i. Knowledge about different categories of threat and how they should

including:

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

individual settlement.

respond to it.

financial protection from the costs of disaster.

**5.6 Research and knowledge sharing**

Pakistan on:

**51**

**5.5 Risk transfer**


The information mentioned above shall be used through electronic, print, and social media campaigns and community training.

v. Evacuation drills must be conducted to ensure training of the community on disciplined evacuation. A regular schedule of conducting drill shall be planned and implemented at local level (UC and village level) once a year with communities settled on the coastline (at least within 3-km-wide coastal belt). PDMAs (Baluchistan and Sindh) shall support respective DDMA to design and implement community-led and sustainable mechanism for monitoring entire processes.

#### *5.4.3 Curriculum development for all levels of academia*


#### *5.4.4 Self-evacuation plan for remote coastal communities*

Fishing villages in coastal Pakistan along tidal creeks of the Indus Delta and Makran region would need to respond quickly to escape a tsunami from nearby parts of the Makran subduction zone.

i. The NDMA in collaboration with PDMA Baluchistan and Sindh should conduct a survey and mapping (using GIS) of all remote coastal communities where means of official warning communication are limited • Observing natural signs of tsunami

*Tsunami - Damage Assessment and Medical Triage*

flooding, and fire impacts

• Evacuation procedures and guidelines

• Receiving and responding official warnings

• Identification and recognizing evacuation centers and routes

• Importance of participating in evacuation drills and training

mangroves, high land, sand dunes, and coral reef.

and social media campaigns and community training.

mechanism for monitoring entire processes.

*5.4.3 Curriculum development for all levels of academia*

also follow the finalized curricula.

*5.4.4 Self-evacuation plan for remote coastal communities*

departments.

**50**

parts of the Makran subduction zone.

• Information about hazard-resistant construction, land-use, byelaws, regulations and codes to ensure safety against earthquake, tsunami,

• Conservation and strengthening of natural safeguards of tsunami like

The information mentioned above shall be used through electronic, print,

v. Evacuation drills must be conducted to ensure training of the community on disciplined evacuation. A regular schedule of conducting drill shall be planned and implemented at local level (UC and village level) once a year with communities settled on the coastline (at least within 3-km-wide coastal belt). PDMAs (Baluchistan and Sindh) shall support respective DDMA to design and implement community-led and sustainable

i. The NDMA in collaboration with PDMAs should lead the process of finalizing curriculum on earthquake, tsunami, flood, cyclone, and fire hazards and preparedness measures for students of all levels. Education departments shall be a part of this process. Private educational institutions

ii. Schedule of evacuation drill (at least once a year) in all level academic institutions, both public and private, located in coastal districts shall be finalized by respective DDMA and district education department.

iii. Teachers' training program to be developed by PDMA Baluchistan and Sindh in collaboration with provincial and coastal districts' education

Fishing villages in coastal Pakistan along tidal creeks of the Indus Delta and Makran region would need to respond quickly to escape a tsunami from nearby

i. The NDMA in collaboration with PDMA Baluchistan and Sindh should conduct a survey and mapping (using GIS) of all remote coastal

communities where means of official warning communication are limited

or not at all available, in estimated available lead time (subject to the estimated tsunami hazard and risks discussed in Sections 3 and 4).

	- a. Identification of feasible evacuation site and routes near each individual settlement.
	- b. Awareness campaigns and training of local volunteers to receive official warning to disseminate to other villagers and fishermen.
	- c. Interpretation of bulletins issued by the PMD and DDMAs.
	- d. Detection of early warning via natural signs such as abnormal behavior of animals, earthquake shaking, and retreat of sea water.
	- e. Basic emergency response, especially how and where to evacuate, immediate first aid provision to injured, etc.
	- f. Facilitate and mange evacuation of vulnerable groups.
	- g. Ways to manage external communication to get help from outside of the village and emergency responders/organization.
	- h. Knowing, using, and keeping alive the indigenous knowledge.
	- i. Knowledge about different categories of threat and how they should respond to it.

#### **5.5 Risk transfer**

The NDMA in collaboration with PDMAs (Baluchistan and Sindh) and other organizations should develop a strategy to promote insurance (life and property) for earthquake and tsunami incidents that can play an important role in offering financial protection from the costs of disaster.

#### **5.6 Research and knowledge sharing**

Understanding disasters and to find appropriate ways to reduce disaster risk are critically important. Scientific, social, and indigenous knowledge-based researches are direly needed to be undertaken, and result sharing with larger audience including communities at risk has a pivotal role in managing disasters. This role of riskbased knowledge sharing has been recognized in international frameworks, i.e., Sendai Framework for disaster risk reduction (SFDRR) 2015–2030 [10].

	- a. Earthquake and tsunami hazard analysis

b. Exposure data, vulnerability and capacity evaluations

information to plan and implement coherent and synergized earthquake and tsunami risk reduction measures. The guidelines for in various sections of the chapter

pattern on priority basis, involving all levels of stakeholders.

ii. Ensuing federal and provincial agencies utilize earthquake monitoring systems, tide gauges, deep ocean buoys, and other capabilities

(international/regional information sharing systems to gather as much information as possible about a potential tsunami). Essential data is then used by forecasting and analysis centers for the assessment of the immediate tsunami threat. Timely and accurate warnings must then be disseminated in clear and actionable terms to emergency managers and a

iii. Identifying mitigation strategies that involve sustained actions taken to reduce or eliminate the long-term risk to human life and property based on

iv. Aiming at tsunami-resilient communities that have plans, enhanced communications, and heightened awareness of the citizens to ensure resilience to earthquake and tsunami events, reduced economic losses, and

v. Encouraging continued broad scientific and social research efforts needed to improve all-purpose understanding of tsunami processes and impacts and then to develop more efficient and effective risk assessment, risk communication, prediction and warning, preparedness, and mitigation

vi. Strengthening partnerships with international organizations and other countries persuade bilateral and multilateral agreements to better

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

understand and reduce the common threat and impact of earthquake and

earthquake and tsunami risk assessments.

shorten recovery periods.

tsunami in the region.

Resilience Group, Islamabad, Pakistan

provided the original work is properly cited.

\*Address all correspondence to: ghazala\_ghq@hotmail.com

i. Determining the earthquake and subsequent local tsunami threat, in terms of hazard and risk assessment all along the coastline on a standardized

are proposed for:

*Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

ready public.

measures.

**Author details**

Ghazala Naeem

**53**


#### **5.7 International cooperation and coordination**

International cooperation on tsunami warning and mitigation is envisaged to assure international compatibility and interoperability for rapid exchange of data and information. Pakistan is actively engaged in exchange of data and resources and capacity building initiatives through bilateral and global commitments. Pakistan is a member state of the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO), established in 1960 as a body with functional autonomy. The Pakistan Meteorological Department is the focal agency mandated to coordinate with UNESCO-IOC, ocean-wide tsunami warning providers for data sharing and capacity building regional and global initiatives.


#### **6. Conclusion**

Coastal area residents in Baluchistan and Sindh provinces can experience a local earthquake—the most common cause of tsunamis—and a local tsunami generated in Arabian Sea can approach the coast within minutes.

Limited information regarding Pakistan coastline's vulnerability is available to assess tsunami risk. Database is not appropriately maintained for social, physical (structural), economic, and environmental dimensions of exposure analysis, making the situation more critical. Since 2006 (in the after math of 2004 Indian Ocean Tsunami), some limited but focused efforts on tsunami hazard and risk assessment, mitigation, and preparedness have been piloted in the country since 2006 serious and consistent efforts of all stakeholders at policy and implementation level.

This chapter suggests earthquake and tsunami risk assessment and mitigation roadmap for Pakistan's coastal areas with a vision of acquiring required deposit of

#### *Dealing with Local Tsunami on Pakistan Coast DOI: http://dx.doi.org/10.5772/intechopen.91279*

b. Exposure data, vulnerability and capacity evaluations

e. Related policies, regulations, guidelines, bylaws, and codes

Oceanography (NIO), PMD, NDMA, PDMA Sindh, and PDMA Baluchistan should explore public-private partnership to encourage researchers to undertake the required studies mentioned in this section as well as ensure that the results are shared and available to the end user (including general public) and are incorporated in policies, regulations, and guidelines from

ii. The Higher Education Commission (HEC), the National Institute of

International cooperation on tsunami warning and mitigation is envisaged to assure international compatibility and interoperability for rapid exchange of data and information. Pakistan is actively engaged in exchange of data and resources and capacity building initiatives through bilateral and global commitments. Pakistan is a member state of the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO), established in 1960 as a body with functional autonomy. The Pakistan Meteorological Department is the focal agency mandated to coordinate with UNESCO-IOC, ocean-wide tsunami warning providers for data sharing and

1.The PMD in coordination with disaster management authorities at national and provincial level should ensure to participate in all capacity building

2.Effective participation in research, knowledge sharing, and capacity building should be ensured, and the PMD being the focal agency should play a lead role.

3.The PMD should also play a lead role in appropriate follow-up of global and

Coastal area residents in Baluchistan and Sindh provinces can experience a local earthquake—the most common cause of tsunamis—and a local tsunami generated

Limited information regarding Pakistan coastline's vulnerability is available to assess tsunami risk. Database is not appropriately maintained for social, physical (structural), economic, and environmental dimensions of exposure analysis, making the situation more critical. Since 2006 (in the after math of 2004 Indian Ocean Tsunami), some limited but focused efforts on tsunami hazard and risk assessment, mitigation, and preparedness have been piloted in the country since 2006 serious and consistent efforts of all stakeholders at policy and implementation level.

This chapter suggests earthquake and tsunami risk assessment and mitigation roadmap for Pakistan's coastal areas with a vision of acquiring required deposit of

c. Indigenous knowledge

*Tsunami - Damage Assessment and Medical Triage*

national to local level.

**5.7 International cooperation and coordination**

capacity building regional and global initiatives.

**6. Conclusion**

**52**

initiatives and ocean-wide simulations and drills.

regional collaborations from national to local level.

in Arabian Sea can approach the coast within minutes.

d. Preparedness and response

information to plan and implement coherent and synergized earthquake and tsunami risk reduction measures. The guidelines for in various sections of the chapter are proposed for:


### **Author details**

Ghazala Naeem Resilience Group, Islamabad, Pakistan

\*Address all correspondence to: ghazala\_ghq@hotmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] IOC Manuals & Guide 52. Tsunami Risk Assessment & Mitigation for the Indian Ocean. Intergovernmental Oceanographic Commission. 2009. pp. 16-17, 33, 45, 58-69. Available at: h ttp://iotic.ioc-unesco.org/images/xplod/ resources/material/tsunami\_risk\_ 270809\_lr.pdf

[2] Heidarzadeh M. Tsunami Risk, Preparedness and Warning System in Pakistan: Chapter 6. In: Atta-Ur-Rahman editor. The Book Disaster Risk Reduction Approaches in Pakistan. Springer; 2015. p. 120. DOI: 10.1007/ 978-4-431-55369-4\_6

[3] Hoffman et al. 2009: An Indian Ocean Tsunami triggered remotely by an onshore earthquake in Baluchistan Pakistan. Geology. 2014;4. DOI: 10.1130/G35756.1

[4] Multi-Hazard Risk and Vulnerability Assessment Guidelines (MHRVA). National Disaster Management Authority Pakistan; 2016. pp. 44-48, 75-91. Available from: http://ndma.gov. pk/publications/MHVRA%202017.pdf

[5] Naeem G, Nawaz J. Challenges and opportunities for reducing losses to fast arriving tsunami in remote villages along the coast of Pakistan. In: Mukhtari M, editor. Tsunami. Intech; 2016. pp. 136-163. ISBN 978-953- 51-2676-8

[6] Malalgoda C et al. Challenges in Creating a Disaster Resilient Built Environment. 2014. pp. 139-144. DOI: 10.1016/S2212-5671(14)00997-6

[7] Remembering 1945 Makran Tsunami booklet: United Nations Educational, Scientific and Cultural Organization (UNESCO) Intergovernmental Oceanographic Commission (IOC). 2015. pp. 17-64. Available from: http://iotic.iocunesco.org/1945makrantsunami/ 1945-makran-tsunami-booklet.pdf

[8] FEMA Business Continuity Plan. 2014. Available from: https://www.fema. gov/media-library-data/1389019980859 b64364cba1442b96dc4f4ad675f552e4/ Business\_ContinuityPlan\_2014.pdf

[9] Pakistan Electronic Media Regulatory Authority (PEMRA) Ordinance 2002; Terms and Conditions of License. p. 18. Available from: http:// 58.65.182.183/pemra/pemgov/wp-conte nt/uploads/2015/08/Ordinance\_2002.pdf

[10] Sendai Framework of Disaster Risk Reduction (2015-30); Priority 1. Available from: https://www.unisdr. org/we/coordinate/sendai-framework

**55**

Section 3

Health Consequences

Section 3
