1. Introduction

Wetlands are defined based on the Canadian Wetland Classification System as land that is saturated with water long enough to promote wetland or aquatic processes as indicated by poorly drained soils, hydrophytic vegetation and various kinds of biological activity which are adapted to a wet environment [1]. Wetlands are important ecological systems which play a critical role in hydrology and act as water reservoirs, affecting water quality and controlling runoff rate [2]. Also, they are amongst the most productive ecosystems, providing food,

© 2016 The Author(s). Licensee InTech. 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 eproduction in any medium, provided the original work is properly cited. © 2018 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.

construction materials, transport, and coastline protection. They provide many important environmental functions and habitat for a diversity of plant and animal species [2]. Furthermore, wetlands bring economic value with social benefits for people, providing significant tourism opportunities and recreation that can be a key source of income. For these reasons, the continuous and accurate monitoring of wetlands is necessary, especially for better urban planning and improved natural resources management [3]. The formation of wetlands requires the presence of the appropriate hydrological, geomorphological and biological conditions [2].

The Canadian Wetland Classification System divides wetlands into five classes based on their developmental characteristics and the environment in which they exist [1]. As shown in Figure 1, these classes are: bogs, fens, marches, swamps, and shallow water. Bogs (Figure 2a) are peatlands with a peat layer of at least 40 cm thickness, consisting partially decomposed plants. Bogs surface is usually higher relatively to the surrounding landscape and characterized by evergreen trees and shrubs and covered by sphagnum moss. The only source of water and nutrients in this type of wetlands is the rainfall [4]. Bogs are extremely low in mineral nutrients and tend to be strongly acidic [1].

Like bogs, fens (Figure 2b) are also peatlands that accumulate peats. Fens occurs in regions where the ground water discharges to the surface [1]. This type of wetlands is usually covered by grasses, sedges, reeds, and wildflowers. Typically, fens have more nutrients than bogs, and the water is less acidic [4]. Marshes (Figure 2c) are wetlands that are periodically or permanently flooded with standing or slowly moving water and hence are rich in nutrients [4]. Some marshes accumulate peats, though many do not. Marshes are characterized by non-woody vegetation, such as cattails, rushes, reeds, grasses and sedges [1]. Similar to marshes, swamps (Figure 2d) are wetlands that are subject to relatively large seasonal water level fluctuations [4]. Swamps are characterized by woody vegetation, such as dense coniferous or deciduous forest and tall shrubs. Some marshes accumulate peats, though many do not [1]. Shallow open water wetlands (Figure 2e) are ponds of standing water bodies, which represent a transition

Figure 2. Wetland classes as defined by the Canadian Wetland Classification System: (a) bog, (b) fen, (c) marsh, (d)

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swamp and (e) shallow open water.

Figure 1. Wetland classes hierarchy.

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construction materials, transport, and coastline protection. They provide many important environmental functions and habitat for a diversity of plant and animal species [2]. Furthermore, wetlands bring economic value with social benefits for people, providing significant tourism opportunities and recreation that can be a key source of income. For these reasons, the continuous and accurate monitoring of wetlands is necessary, especially for better urban planning and improved natural resources management [3]. The formation of wetlands requires the presence of the appropriate hydrological, geomorphological and biological conditions [2]. The Canadian Wetland Classification System divides wetlands into five classes based on their developmental characteristics and the environment in which they exist [1]. As shown in Figure 1, these classes are: bogs, fens, marches, swamps, and shallow water. Bogs (Figure 2a) are peatlands with a peat layer of at least 40 cm thickness, consisting partially decomposed plants. Bogs surface is usually higher relatively to the surrounding landscape and characterized by evergreen trees and shrubs and covered by sphagnum moss. The only source of water and nutrients in this type of wetlands is the rainfall [4]. Bogs are extremely low in mineral

Like bogs, fens (Figure 2b) are also peatlands that accumulate peats. Fens occurs in regions where the ground water discharges to the surface [1]. This type of wetlands is usually covered by grasses, sedges, reeds, and wildflowers. Typically, fens have more nutrients than bogs, and the water is less acidic [4]. Marshes (Figure 2c) are wetlands that are periodically or permanently flooded with standing or slowly moving water and hence are rich in nutrients [4]. Some marshes accumulate peats, though many do not. Marshes are characterized by non-woody vegetation, such as cattails, rushes, reeds, grasses and sedges [1]. Similar to marshes, swamps (Figure 2d) are wetlands that are subject to relatively large seasonal water level fluctuations [4]. Swamps are characterized by woody vegetation, such as dense coniferous or deciduous forest and tall shrubs. Some marshes accumulate peats, though many do not [1]. Shallow open water wetlands (Figure 2e) are ponds of standing water bodies, which represent a transition

nutrients and tend to be strongly acidic [1].

62 Wetlands Management - Assessing Risk and Sustainable Solutions

Figure 1. Wetland classes hierarchy.

Figure 2. Wetland classes as defined by the Canadian Wetland Classification System: (a) bog, (b) fen, (c) marsh, (d) swamp and (e) shallow open water.

stage between lakes and marshes. This type of wetlands is free of vegetation with a depth of less than 2 m [1].

signal. A dual polarized SAR system is a SAR system which transmits one horizontally or vertically polarized signal and receives both the horizontal and vertical polarized components of the returned signal. A single or dual polarized SAR system acquires partial information with respect to the full polarimetric state of the radar target. A fully polarimetric SAR system transmits alternatively horizontally and vertically polarized signal and receives returns in both orthogonal polarizations, allowing for complete information of the radar target [6, 8]. While full polarimetric SAR systems provide complete information about the radar target, the coverage of these systems is half of the coverage of single or dual polarized SAR systems. Also, the energy required by the satellite for the acquisition of full polarimetric SAR imagery and the pulse repetition frequency of the SAR sensor are twice the single or dual polarized SAR

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A new SAR configuration named compact polarimetric SAR is currently being implemented in SAR systems, where a circular polarized signal (Figure 4) is transmitted and two orthogonal polarizations (horizontal and vertical) are coherently received [9]. Thus, the relative phase between the two receiving channels is preserved and calibrated, but the swath coverage is not

In comparison to the full polarimetric SAR systems, compact polarimetric SAR operates with half pulse repetition frequency, reducing the average transmit power and increasing the swath width. Consequently, this SAR configuration is associated with low-cost and low-mass constraints of the spaceborne polarimetric SAR systems. The wider coverage of the compact SAR system reduces the revisit time of the satellite, making this system operationally viable [10]. These advantages come with an associated cost in the loss of full polarimetric information. Hence, generally, a compact polarimetric SAR system cannot be "as good as" a full polarimetric system [11]. Such SAR architecture is already included in the current Indian Radar Imaging Satellite-1 (RISAT-1) and the Japanese Advanced Land Observing Satellite-2 (ALOS-2) carrying the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). Also, compact polarimetric SAR will be included in the future Canadian RADARSAT Constellation Mission (RCM).

systems.

reduced.

Figure 4. Circular polarized radar signal.

Spaceborne remote sensing technology is necessary for effective monitoring and mapping of wetlands. The use of this technology provides a practical monitoring and mapping approach of wetlands, especially for those located in remote areas [5].

### 2. Basic SAR concepts

Wetlands are usually located in remote areas with limited accessibility. Thus, remote sensing technology is attractive for mapping and monitoring wetlands. Synthetic Aperture Radar (SAR) systems are active remote sensing systems independent of weather and sun illumination. SAR systems transmit electromagnetic microwave from their radar antenna and record the backscattered signal from the radar target [6]. The sensitivity of SAR sensors is a function of the: (1) band, polarization, and incidence angle of the transmitted electromagnetic signal and (2) geometric and dielectric properties of the radar target [7]. Radar targets can be discriminated in a SAR image if their backscattering components are different and the radar spatial resolution is sufficient to distinguish between targets [6]. Conventional SAR systems are linearly polarized radar systems which transmit horizontally and/or vertically polarized radar signal and receive the horizontal and/or vertical polarized components of the backscattered signal (Figure 3). In SAR systems, polarization is referred to the orientation of the electrical field of the electromagnetic wave.

A single polarized SAR system is a SAR system which transmits one horizontally or vertically polarized signal and receives the horizontal or vertical polarized component of the returned

Figure 3. Horizontally and vertically polarized radar signal.

signal. A dual polarized SAR system is a SAR system which transmits one horizontally or vertically polarized signal and receives both the horizontal and vertical polarized components of the returned signal. A single or dual polarized SAR system acquires partial information with respect to the full polarimetric state of the radar target. A fully polarimetric SAR system transmits alternatively horizontally and vertically polarized signal and receives returns in both orthogonal polarizations, allowing for complete information of the radar target [6, 8]. While full polarimetric SAR systems provide complete information about the radar target, the coverage of these systems is half of the coverage of single or dual polarized SAR systems. Also, the energy required by the satellite for the acquisition of full polarimetric SAR imagery and the pulse repetition frequency of the SAR sensor are twice the single or dual polarized SAR systems.

A new SAR configuration named compact polarimetric SAR is currently being implemented in SAR systems, where a circular polarized signal (Figure 4) is transmitted and two orthogonal polarizations (horizontal and vertical) are coherently received [9]. Thus, the relative phase between the two receiving channels is preserved and calibrated, but the swath coverage is not reduced.

In comparison to the full polarimetric SAR systems, compact polarimetric SAR operates with half pulse repetition frequency, reducing the average transmit power and increasing the swath width. Consequently, this SAR configuration is associated with low-cost and low-mass constraints of the spaceborne polarimetric SAR systems. The wider coverage of the compact SAR system reduces the revisit time of the satellite, making this system operationally viable [10]. These advantages come with an associated cost in the loss of full polarimetric information. Hence, generally, a compact polarimetric SAR system cannot be "as good as" a full polarimetric system [11]. Such SAR architecture is already included in the current Indian Radar Imaging Satellite-1 (RISAT-1) and the Japanese Advanced Land Observing Satellite-2 (ALOS-2) carrying the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). Also, compact polarimetric SAR will be included in the future Canadian RADARSAT Constellation Mission (RCM).

Figure 4. Circular polarized radar signal.

stage between lakes and marshes. This type of wetlands is free of vegetation with a depth of

Spaceborne remote sensing technology is necessary for effective monitoring and mapping of wetlands. The use of this technology provides a practical monitoring and mapping approach

Wetlands are usually located in remote areas with limited accessibility. Thus, remote sensing technology is attractive for mapping and monitoring wetlands. Synthetic Aperture Radar (SAR) systems are active remote sensing systems independent of weather and sun illumination. SAR systems transmit electromagnetic microwave from their radar antenna and record the backscattered signal from the radar target [6]. The sensitivity of SAR sensors is a function of the: (1) band, polarization, and incidence angle of the transmitted electromagnetic signal and (2) geometric and dielectric properties of the radar target [7]. Radar targets can be discriminated in a SAR image if their backscattering components are different and the radar spatial resolution is sufficient to distinguish between targets [6]. Conventional SAR systems are linearly polarized radar systems which transmit horizontally and/or vertically polarized radar signal and receive the horizontal and/or vertical polarized components of the backscattered signal (Figure 3). In SAR systems, polarization is referred to the orientation of the electrical

A single polarized SAR system is a SAR system which transmits one horizontally or vertically polarized signal and receives the horizontal or vertical polarized component of the returned

of wetlands, especially for those located in remote areas [5].

64 Wetlands Management - Assessing Risk and Sustainable Solutions

less than 2 m [1].

2. Basic SAR concepts

field of the electromagnetic wave.

Figure 3. Horizontally and vertically polarized radar signal.

#### 2.1. Polarimetric scattering vector

Fully polarimetric SAR systems measure the complete polarimetric information of a radar target in the form of a scattering matrix [S]. The scattering matrix [S] is an array of four complex elements that describes the transformation of the polarization of a wave pulse incident upon a reflective medium to the polarization of the backscattered wave and has the form [6]:

$$\mathbf{[S]} = \begin{bmatrix} \mathbf{HH} & \mathbf{HV} \\ \mathbf{VH} & \mathbf{VV} \end{bmatrix} \tag{1}$$

where h i … denotes a spatial ensemble averaging assuming homogeneity of the random scattering medium and \* the complex conjugate. Analogously, the so-called polarimetric coherency matrix [T] is formed by the complex product of the Pauli scattering vector Kp with its complex

The relationship between the covariance matrix [C] and the coherency matrix [T] is linear. Both matrices are full rank, hermitian positive semidefinite and have the same real non-negative eigenvalues, but different eigenvectors. Moreover, both matrices contain the complete information about variance and correlation for all the complex elements of the scattering matrix [S] [12]. A compact polarimetric SAR system transmits a right- or left-circular polarized signal, provid-

where R refers to a transmitted right-circular polarized signal. A four-element vector called Stokes vector [g] can be calculated from the measured compact polarimetric scattering vector,

where Re and Im are the real and imaginary parts of a complex number. The first Stokes element g0 is associated with the total power of the backscattered signal while the fourth Stokes vector is associated with the power in the right-hand and left-hand circularly polarized component [13]. The elements of the Stokes vector can be used to derive an average coherency

Radar backscattering is a function of the radar target properties (dielectric properties, roughness, target geometry) and the radar system characteristics (polarization, band, incidence angle). Three major backscattering mechanisms can take place during the backscattering process. These are the surface, double bounce and volume scattering mechanism (Figure 5).

<sup>h</sup> j j RH <sup>2</sup> <sup>þ</sup> j j RV <sup>2</sup> j j RH <sup>2</sup> � j j RV <sup>2</sup> 2Re RHRV<sup>∗</sup> ð Þ �2Im RHRV<sup>∗</sup> ð Þ

g0 þ g1 g2 þ ig3 g2 � ig3 g0 � g1

<sup>2</sup> <sup>h</sup> j j HH <sup>þ</sup> VV <sup>2</sup> ð Þ HH <sup>þ</sup> VV ð Þ HH � VV <sup>∗</sup> 2 HH ð Þ <sup>þ</sup> VV HV<sup>∗</sup> ð Þ HH � VV ð Þ HH <sup>þ</sup> VV <sup>∗</sup> j j HH � VV <sup>2</sup> 2 HH ð Þ � VV HV<sup>∗</sup> 2HV HH ð Þ <sup>þ</sup> VV <sup>∗</sup> 2HV HH ð Þ � VV <sup>∗</sup> 4 HV j j<sup>2</sup>

Kc <sup>¼</sup> ½ � RH RV <sup>T</sup> (6)

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� � (8)

<sup>5</sup><sup>i</sup> (7)

3 7 5 i (5) 67

<sup>p</sup> and takes the form [6]:

½ �¼ g

g0 g1 g2 g3

Tc ½ �¼ <sup>1</sup> 2

conjugate transpose K<sup>∗</sup><sup>T</sup>

<sup>p</sup> <sup>¼</sup> <sup>1</sup>

2 6 4

ing a scattering vector of two elements:

matrix, which takes the form [14]:

2.2. Polarimetric scattering mechanisms

½ �¼ <sup>T</sup> Kp:K<sup>∗</sup><sup>T</sup>

as follow [11]:

where H and V refer to horizontal and vertical polarized signals, respectively. The elements of the scattering matrix [S] are complex scattering amplitudes. For most natural targets including wetlands, the reciprocity assumption holds where HV = VH. The diagonal elements HH and VV are called co-polarized elements, while the off-diagonal elements HV and VH are called cross-polarized elements. Two polarimetric scattering vectors can be extracted from the target scattering matrix, which are the lexicographical scattering vector and the Pauli scattering vector [12]. Assuming the reciprocity condition, the lexicological scattering vector has the form:

$$\mathbf{K}\_{\mathrm{l}} = \begin{bmatrix} \mathbf{H} \mathbf{H} \mathbf{V} \mathbf{V} \mathbf{2} \mathbf{H} \mathbf{V} \end{bmatrix}^{\mathrm{T}} \tag{2}$$

where the superscript T denotes the vector transpose. The multiplication of the crosspolarization with 2 is to preserve the total backscattered power of the returned signal. The Pauli scattering vector can be obtained from the complex Pauli spin matrices [6] and, assuming the reciprocity condition, has the form:

$$\mathbf{K\_{p}} = \frac{1}{\sqrt{2}} \left[ \mathbf{HH} + \mathbf{VV} \,\mathbf{HH} - \mathbf{VV} \,\mathbf{2HV} \right]^{\mathrm{T}} \tag{3}$$

Deterministic scatterers can be described completely by a single scattering matrix or vector. However, for remote sensing SAR applications, the assumption of pure deterministic scatterers is not valid. Thus, scatterers are non-deterministic and cannot be described with a single polarimetric scattering matrix or vector. This is because the resolution cell is bigger than the wavelength of the incident wave. Non-deterministic scatterers are spatially distributed. Therefore, each resolution cell is assumed to contain many deterministic scatterers, where each of these scatterers can be described by a single scattering matrix [Si]. Therefore, the measured scattering matrix [S] for one resolution cell consists of the coherent superposition of the individual scattering matrices [Si] of all the deterministic scatterers located within the resolution cell [6, 12].

An ensemble average of the complex product between the lexicological scattering vector Kl and K<sup>∗</sup><sup>T</sup> <sup>l</sup> leads to the so-called polarimetric covariance matrix [C], which has the form [6]:

$$\mathbf{[C]} = \mathbf{K}\_{\text{l}} \mathbf{K}\_{\text{l}}^{\*T} = \begin{pmatrix} \left| \mathbf{H} \mathbf{H} \right|^{2} & \mathbf{H} \mathbf{H} \mathbf{V} \mathbf{V}^{\*} & \sqrt{2} \mathbf{H} \mathbf{H} \mathbf{H} \mathbf{V}^{\*}\\ \mathbf{V} \mathbf{V} \mathbf{H} \mathbf{H}^{\*} & \left| \mathbf{V} \mathbf{V} \right|^{2} & \sqrt{2} \mathbf{V} \mathbf{V} \mathbf{H} \mathbf{V}^{\*}\\ \sqrt{2} \mathbf{H} \mathbf{V} \mathbf{H} \mathbf{H}^{\*} & \sqrt{2} \mathbf{H} \mathbf{V} \mathbf{V} \mathbf{V}^{\*} & 2 \left| \mathbf{H} \mathbf{V} \right|^{2} \end{pmatrix} \tag{4}$$

where h i … denotes a spatial ensemble averaging assuming homogeneity of the random scattering medium and \* the complex conjugate. Analogously, the so-called polarimetric coherency matrix [T] is formed by the complex product of the Pauli scattering vector Kp with its complex conjugate transpose K<sup>∗</sup><sup>T</sup> <sup>p</sup> and takes the form [6]:

$$\mathbf{I}[\mathbf{T}] = \mathbf{K}\_{\mathbf{P}} \mathbf{K}\_{\mathbf{p}}^{\*T} = \frac{1}{2} \begin{bmatrix} \left| \mathbf{HH} + \mathbf{VV} \right|^2 & (\mathbf{HH} + \mathbf{VV})(\mathbf{HH} - \mathbf{VV})^\* & 2(\mathbf{HH} + \mathbf{VV})\mathbf{HV}^\* \\ (\mathbf{HH} - \mathbf{VV})(\mathbf{HH} + \mathbf{VV})^\* & \left| \mathbf{HH} - \mathbf{VV} \right|^2 & 2(\mathbf{HH} - \mathbf{VV})\mathbf{HV}^\* \\ 2\mathbf{HV}(\mathbf{HH} + \mathbf{VV})^\* & 2\mathbf{HV}(\mathbf{HH} - \mathbf{VV})^\* & 4|\mathbf{HV}|^2 \end{bmatrix} \tag{5}$$

The relationship between the covariance matrix [C] and the coherency matrix [T] is linear. Both matrices are full rank, hermitian positive semidefinite and have the same real non-negative eigenvalues, but different eigenvectors. Moreover, both matrices contain the complete information about variance and correlation for all the complex elements of the scattering matrix [S] [12].

A compact polarimetric SAR system transmits a right- or left-circular polarized signal, providing a scattering vector of two elements:

$$\mathbf{K}\_{\mathbf{c}} = \begin{bmatrix} \mathbf{RH} \mathbf{RV} \end{bmatrix}^{\mathrm{T}} \tag{6}$$

where R refers to a transmitted right-circular polarized signal. A four-element vector called Stokes vector [g] can be calculated from the measured compact polarimetric scattering vector, as follow [11]:

$$\mathbf{[g]} = \begin{bmatrix} \mathbf{g}\_0 \\ \mathbf{g}\_1 \\ \mathbf{g}\_2 \\ \mathbf{g}\_3 \end{bmatrix} = \begin{bmatrix} \left| \mathbf{R} \mathbf{H} \right|^2 + \left| \mathbf{R} \mathbf{V} \right|^2 \\ \left| \mathbf{R} \mathbf{H} \right|^2 - \left| \mathbf{R} \mathbf{V} \right|^2 \\ 2 \text{Re}(\mathbf{R} \mathbf{H} \mathbf{R} \mathbf{V}^\*) \\ -2 \text{Im}(\mathbf{R} \mathbf{H} \mathbf{R} \mathbf{V}^\*) \end{bmatrix} \tag{7}$$

where Re and Im are the real and imaginary parts of a complex number. The first Stokes element g0 is associated with the total power of the backscattered signal while the fourth Stokes vector is associated with the power in the right-hand and left-hand circularly polarized component [13]. The elements of the Stokes vector can be used to derive an average coherency matrix, which takes the form [14]:

$$\mathbf{T}\_{\mathbf{c}}[\mathbf{T}\_{\mathbf{c}}] = \frac{1}{2} \begin{bmatrix} \mathbf{g}\_{0} + \mathbf{g}\_{1} & \mathbf{g}\_{2} + i \mathbf{g}\_{3} \\ \mathbf{g}\_{2} - i \mathbf{g}\_{3} & \mathbf{g}\_{0} - \mathbf{g}\_{1} \end{bmatrix} \tag{8}$$

#### 2.2. Polarimetric scattering mechanisms

2.1. Polarimetric scattering vector

66 Wetlands Management - Assessing Risk and Sustainable Solutions

the reciprocity condition, has the form:

½ �¼ <sup>C</sup> Kl:K<sup>∗</sup><sup>T</sup>

2 6 4

tion cell [6, 12].

and K<sup>∗</sup><sup>T</sup>

Kp <sup>¼</sup> <sup>1</sup> ffiffiffi 2

Fully polarimetric SAR systems measure the complete polarimetric information of a radar target in the form of a scattering matrix [S]. The scattering matrix [S] is an array of four complex elements that describes the transformation of the polarization of a wave pulse incident upon a

> HH HV VH VV � �

Kl <sup>¼</sup> ½ � HH VV 2HV <sup>T</sup> (2)

<sup>p</sup> ½ � HH <sup>þ</sup> VV HH � VV 2HV <sup>T</sup> (3)

3 7 5

<sup>i</sup> (4)

where H and V refer to horizontal and vertical polarized signals, respectively. The elements of the scattering matrix [S] are complex scattering amplitudes. For most natural targets including wetlands, the reciprocity assumption holds where HV = VH. The diagonal elements HH and VV are called co-polarized elements, while the off-diagonal elements HV and VH are called cross-polarized elements. Two polarimetric scattering vectors can be extracted from the target scattering matrix, which are the lexicographical scattering vector and the Pauli scattering vector [12]. Assuming the reciprocity condition, the lexicological scattering vector has the form:

where the superscript T denotes the vector transpose. The multiplication of the crosspolarization with 2 is to preserve the total backscattered power of the returned signal. The Pauli scattering vector can be obtained from the complex Pauli spin matrices [6] and, assuming

Deterministic scatterers can be described completely by a single scattering matrix or vector. However, for remote sensing SAR applications, the assumption of pure deterministic scatterers is not valid. Thus, scatterers are non-deterministic and cannot be described with a single polarimetric scattering matrix or vector. This is because the resolution cell is bigger than the wavelength of the incident wave. Non-deterministic scatterers are spatially distributed. Therefore, each resolution cell is assumed to contain many deterministic scatterers, where each of these scatterers can be described by a single scattering matrix [Si]. Therefore, the measured scattering matrix [S] for one resolution cell consists of the coherent superposition of the individual scattering matrices [Si] of all the deterministic scatterers located within the resolu-

An ensemble average of the complex product between the lexicological scattering vector Kl

<sup>l</sup> leads to the so-called polarimetric covariance matrix [C], which has the form [6]:

<sup>l</sup> <sup>¼</sup> <sup>h</sup> j j HH <sup>2</sup> HHVV<sup>∗</sup> <sup>√</sup>2HHHV<sup>∗</sup> VVHH<sup>∗</sup> j j VV <sup>2</sup> <sup>√</sup>2VVHV<sup>∗</sup> <sup>√</sup>2HVHH<sup>∗</sup> <sup>√</sup>2HVVV<sup>∗</sup> 2 HV j j<sup>2</sup>

(1)

reflective medium to the polarization of the backscattered wave and has the form [6]:

½ �¼ S

Radar backscattering is a function of the radar target properties (dielectric properties, roughness, target geometry) and the radar system characteristics (polarization, band, incidence angle). Three major backscattering mechanisms can take place during the backscattering process. These are the surface, double bounce and volume scattering mechanism (Figure 5).

In the case of surface scattering mechanism (Figure 5), the incident radar signal features one or an odd number of bounces before returns back to the SAR antenna. In this case, a phase shift of 180o occurs between the transmitted and the received signal [6]. However, a very smooth surface could cause the radar incident signal to be reflected away from the radar antenna, causing the radar target to appear dark in the SAR image. In this case, scattering is called specular scattering. An example of such surfaces is the open water in wetlands [12]. In the case of double bounce scattering mechanism (Figure 5), the incident radar signal hits two surfaces, horizontal and adjacent vertical forming a dihedral angle, and almost all of incident waves return back to the radar antenna. Thus, the scattering from radar targets with double bounce scattering is very high. The phase difference between the transmitted and the received signal is equal to zero. Double bounce scattering mechanism is frequently observed in open wetlands, such as bog and marsh, as the results of the interaction of the radar signal between the standing water and vegetation [15]. In the case of volume scattering mechanism (Figure 5), the radar signal features multiple random scattering within the natural medium. Usually, a large portion of the transmitted signal is returned back to the SAR sensor, causing rise to cross polarizations (HV and VH). Thus, illuminated radar targets with volume scattering appear bright in a SAR image. Volume scattering is commonly observed in flooded vegetation wetlands due to multiple scattering in the vegetation canopy.

In general, the penetration capabilities and the attenuation depth of radar signal in a medium, such as flooded vegetation, increases with the increasing of the wavelength [6, 12]. Figure 6 presents the penetration of radar signals for different bands. As shown in Figure 6, X-band SAR has a short wavelength signal with limited penetration capability, while L-band SAR has long wavelength signal with higher penetration capability. C-band SAR is assumed as a good compromise between X- and L-band SAR systems. As shown in Figure 6, the scattering mechanism of a radar target could be affected by the penetration depth of the radar signal. Thus, dense flooded vegetation could present volume scattering mechanism in X- or C-band SAR (return from canopy), but double bounce scattering mechanism in L-band due to scattering process from trunk-water interaction (Figure 6) [12].

This method is incoherent decomposition method based on the eigenvector and eigenvalue analysis of the coherency matrix [T]. Given that [T] is hermitian positive semidefinite matrix, it can always be diagonalized using unitary similarity transformations. That is, the coherency

> 2 6 4

where [Λ] is the diagonal eigenvalue matrix of [T], λ<sup>1</sup> ≥ λ<sup>2</sup> ≥ λ<sup>3</sup> ≥ 0 are the real eigenvalues and [U] is a unitary matrix whose columns correspond to the orthogonal eigenvectors of [T]. Based on the Cloude-Pottier decomposition, three parameters can be derived [17]. The polarimetric

λ<sup>1</sup> 0 0 0 λ<sup>2</sup> 0 0 0 λ<sup>3</sup>

<sup>i</sup>¼<sup>1</sup> <sup>λ</sup>i. This parameter is an indicator of the number of effective scattering

3 7

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<sup>5</sup>½ � <sup>U</sup> <sup>∗</sup><sup>T</sup> (9)

(11)

Pi log 3Pi (10)

½ �¼ <sup>T</sup> ½ � <sup>U</sup> ½ � <sup>Λ</sup> ½ � <sup>U</sup> <sup>∗</sup><sup>T</sup> <sup>¼</sup> ½ � <sup>U</sup>

entropy H (0 ≤ H ≤ 1) is defined by the logarithmic sum of the eigenvalues

describes the proportions between the secondary scattering mechanisms

<sup>H</sup> ¼ �<sup>X</sup> 3

i¼1

mechanisms which took place in the scattering process [6]. The anisotropy A (0 ≤ A ≤ 1)

<sup>A</sup> <sup>¼</sup> <sup>λ</sup><sup>2</sup> � <sup>λ</sup><sup>3</sup> λ<sup>2</sup> þ λ<sup>3</sup>

The anisotropy A provides additional information only for medium values of H because in this case secondary scattering mechanisms, in addition to the dominant scattering mechanism,

matrix can be given as

Figure 6. The radar signal penetration for different bands.

where Pi ¼ λi=

P<sup>3</sup>

Different decomposition methods have been proposed to derive the target scattering mechanisms for both full polarimetric [6, 16–24] and compact polarimetric [11, 25] SAR data. One of the earliest and widely used decomposition methods is the Cloude-Pottier decomposition [17].

Figure 5. The three major scattering mechanisms: surface, double bounce and volume.

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Figure 6. The radar signal penetration for different bands.

In the case of surface scattering mechanism (Figure 5), the incident radar signal features one or an odd number of bounces before returns back to the SAR antenna. In this case, a phase shift of 180o occurs between the transmitted and the received signal [6]. However, a very smooth surface could cause the radar incident signal to be reflected away from the radar antenna, causing the radar target to appear dark in the SAR image. In this case, scattering is called specular scattering. An example of such surfaces is the open water in wetlands [12]. In the case of double bounce scattering mechanism (Figure 5), the incident radar signal hits two surfaces, horizontal and adjacent vertical forming a dihedral angle, and almost all of incident waves return back to the radar antenna. Thus, the scattering from radar targets with double bounce scattering is very high. The phase difference between the transmitted and the received signal is equal to zero. Double bounce scattering mechanism is frequently observed in open wetlands, such as bog and marsh, as the results of the interaction of the radar signal between the standing water and vegetation [15]. In the case of volume scattering mechanism (Figure 5), the radar signal features multiple random scattering within the natural medium. Usually, a large portion of the transmitted signal is returned back to the SAR sensor, causing rise to cross polarizations (HV and VH). Thus, illuminated radar targets with volume scattering appear bright in a SAR image. Volume scattering is commonly observed in flooded vegetation wet-

In general, the penetration capabilities and the attenuation depth of radar signal in a medium, such as flooded vegetation, increases with the increasing of the wavelength [6, 12]. Figure 6 presents the penetration of radar signals for different bands. As shown in Figure 6, X-band SAR has a short wavelength signal with limited penetration capability, while L-band SAR has long wavelength signal with higher penetration capability. C-band SAR is assumed as a good compromise between X- and L-band SAR systems. As shown in Figure 6, the scattering mechanism of a radar target could be affected by the penetration depth of the radar signal. Thus, dense flooded vegetation could present volume scattering mechanism in X- or C-band SAR (return from canopy), but double bounce scattering mechanism in L-band due to scattering

Different decomposition methods have been proposed to derive the target scattering mechanisms for both full polarimetric [6, 16–24] and compact polarimetric [11, 25] SAR data. One of the earliest and widely used decomposition methods is the Cloude-Pottier decomposition [17].

lands due to multiple scattering in the vegetation canopy.

68 Wetlands Management - Assessing Risk and Sustainable Solutions

process from trunk-water interaction (Figure 6) [12].

Figure 5. The three major scattering mechanisms: surface, double bounce and volume.

This method is incoherent decomposition method based on the eigenvector and eigenvalue analysis of the coherency matrix [T]. Given that [T] is hermitian positive semidefinite matrix, it can always be diagonalized using unitary similarity transformations. That is, the coherency matrix can be given as

$$\mathbf{[T]} = [\mathbf{U}][\boldsymbol{\Lambda}][\mathbf{U}]^{\ast T} = [\mathbf{U}] \begin{bmatrix} \lambda\_1 & \mathbf{0} & \mathbf{0} \\ \mathbf{0} & \lambda\_2 & \mathbf{0} \\ \mathbf{0} & \mathbf{0} & \lambda\_3 \end{bmatrix} [\mathbf{U}]^{\ast T} \tag{9}$$

where [Λ] is the diagonal eigenvalue matrix of [T], λ<sup>1</sup> ≥ λ<sup>2</sup> ≥ λ<sup>3</sup> ≥ 0 are the real eigenvalues and [U] is a unitary matrix whose columns correspond to the orthogonal eigenvectors of [T]. Based on the Cloude-Pottier decomposition, three parameters can be derived [17]. The polarimetric entropy H (0 ≤ H ≤ 1) is defined by the logarithmic sum of the eigenvalues

$$\mathbf{H} = -\sum\_{i=1}^{3} \mathbf{P}\_i \log\_3 \mathbf{P}\_i \tag{10}$$

where Pi ¼ λi= P<sup>3</sup> <sup>i</sup>¼<sup>1</sup> <sup>λ</sup>i. This parameter is an indicator of the number of effective scattering mechanisms which took place in the scattering process [6]. The anisotropy A (0 ≤ A ≤ 1) describes the proportions between the secondary scattering mechanisms

$$\mathbf{A} = \frac{\lambda\_2 - \lambda\_3}{\lambda\_2 + \lambda\_3} \tag{11}$$

The anisotropy A provides additional information only for medium values of H because in this case secondary scattering mechanisms, in addition to the dominant scattering mechanism, play an important role in the scattering process [6]. The alpha angle α (0 ≤ α ≤ 90o ) provides information about the type of scattering mechanism

$$\alpha = \sum\_{i=1}^{3} \mathbf{P}\_i \alpha\_i \tag{12}$$

where Pd, Pv, and Ps refer to even bounce, volume, and odd bounce scattering mechanisms,

Wetland Monitoring and Mapping Using Synthetic Aperture Radar

http://dx.doi.org/10.5772/intechopen.80224

71

The accurate, effective, and continuous identification and tracking of changes in wetlands is necessary for monitoring human, climatic and other effects on these ecosystems and better understanding of their response. Wetlands are expected to be even more dynamic in the future with rapid and frequent changes due to the human stresses on environment and the global warming [27]. Different methodologies can be adopted to detect and track changes in wetlands using SAR imagery, depending on the type of the change and the available polarization option. For example, a change in the surface water level of a wetland area due to e.g. heavy rainfall could extend the wetland water surface, causing flooding in the surrounding areas. Such a change can be easily detected using SAR amplitude images before and after the event acquired with similar acquisition geometry. The specular scattering of the radar signal can highlight the open water areas (dark areas due to low returned signal). Spatiotemporal changes in wetlands as dynamic ecosystems could be interpreted using SAR amplitude imagery only. This is because changes within wetlands could change the surface type illuminated by the radar. Sometimes, the change could be more complex with alternations in surface water, flooded vegetation and upland boundaries. In this case, the additional polarimetric information from full or compact polarimetric SAR is necessary for the detection and interpretation of changes

As shown in Figure 7, a change within a wetland from wet soil with a high dielectric constant to open water is usually accompanied with a change in the radar backscattering from surface scattering with a strong returned signal (Figure 7a) to specular reflection with a weak returned signal (Figure 7b). The change in wetland could also be due to its seasonal development over time. Hence, intermediate marsh with large vegetation stems properly oriented could allow for double bounce scattering mechanism (Figure 7c). As the marsh develops, the strong observed double bounce scattering mechanism gradually decreases in favor of the volume scattering (Figure 7d) from the dense canopy of the fully developed marsh [28]. Thus, polarimetric decomposition methods enable the identification of wetland classes (e.g. flooded vegetation) and monitoring changes within these classes by means of the temporal change in the backscattering mechanisms. The role of decomposition methods for identification and monitoring of wetlands was highlighted in a number of recent studies [26, 29–31]. Another way of monitoring changes within wetlands could be through polarimetric change detection methodologies using full [10], compact [10, 32], or even coherent dual [33] polarized SAR imagery. These methodologies are based on polarimetric coherency/covariance matrices. Herein, changes are flagged without information about the scattering mechanisms, which occurred during the scattering process. Test statistics, such as those proposed in [34, 35], were proven effective for

respectively.

3. SAR wetland applications

3.1. Change detection

within wetlands.

polarimetric change detection over wetlands.

where cos(αi) in the magnitude of the first component of the coherency matrix eigenvector ei (i = 1, 2, 3).

Another widely used polarimetric decomposition method is the Freeman-Durden method [18]. Contrary to the Cloude-Pottier decomposition, which is a purely mathematical construct, the Freeman-Durden decomposition method is a physically model-based incoherent decomposition based on the polarimetric covariance matrix. It relies on the conversion of a covariance matrix to a three-component model. The results of this decomposition are three coefficients corresponding to the weights of different model components. A polarimetric covariance matrix [C] can be decomposed to a sum of three components, corresponding to volume, surface, and double bounce scattering mechanisms [18]:

$$\mathbf{[C]} = \mathbf{f}\_{\mathbf{v}}[\mathbf{C}]\_{\mathbf{v}} + \mathbf{f}\_{\mathbf{s}}[\mathbf{C}]\_{\mathbf{s}} + \mathbf{f}\_{\mathbf{d}}[\mathbf{C}]\_{\mathbf{d}} \tag{13}$$

where fv, fs, and fd are the three coefficients corresponding to volume, surface, and double bounce scattering, respectively. The Freeman-Durden decomposition is particularly well adapted to the study of vegetated areas [18]. Thus, it is widely used for multitemporal wetland monitoring to track changes of shallow open water to flooded vegetation [26].

Scattering mechanism information can also be obtained using compact polarimetric SAR data. Two decomposition methods are commonly used. The first is the m-δ decomposition method [11], which is based on the degree of polarization of the backscattered signal m <sup>¼</sup> ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

$$
\sqrt{\mathbf{g}\_1^2 + \mathbf{g}\_2^2 + \mathbf{g}\_3^2}/\mathbf{g}\_0 \text{ and the relative phase } \delta = \operatorname{atan}(\mathbf{g}\_3/\mathbf{g}\_2) \text{ and has the form [11]:}
$$

$$
\begin{bmatrix}
\mathbf{V\_d} \\
\mathbf{V\_v} \\
\mathbf{V\_s}
\end{bmatrix} = \begin{bmatrix}
\sqrt{\mathbf{g\_0}\mathbf{m}\frac{(1-\sin\delta)}{2}} \\
\sqrt{\mathbf{g\_0}(1-\mathbf{m})} \\
\sqrt{\mathbf{g\_0}\mathbf{m}\frac{(1+\sin\delta)}{2}}
\end{bmatrix} \tag{14}
$$

where Vd, Vv, and Vs refer to double bounce, volume, and surface scattering mechanisms, respectively. The second decomposition method is the m-χ decomposition [25], which is based on the degree of polarization m and the ellipticity χ ¼ asin �g3=mg0 � �=2, and has the form [25]:

$$
\begin{bmatrix} \mathbf{P\_d} \\ \mathbf{P\_v} \\ \mathbf{P\_s} \end{bmatrix} = \begin{bmatrix} \sqrt{\mathbf{g\_0} \mathbf{m} \frac{(1 + \sin 2\chi)}{2}} \\ \sqrt{\mathbf{g\_0}(1 - \mathbf{m})} \\ \sqrt{\mathbf{g\_0} \mathbf{m} \frac{(1 - \sin 2\chi)}{2}} \end{bmatrix} \tag{15}
$$

where Pd, Pv, and Ps refer to even bounce, volume, and odd bounce scattering mechanisms, respectively.
