Vegetation indices that were used derived from the images.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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\r\n\t
Bolted joints are widely used in engineering structures such as aerospace and civil structures. Significant advantages of bolted joints are that they can be easily assembled and disassembled and the possibility of bearing large load. In practical applications, bolted joints are subjected to a variety of failure modes including self-loosening, slippage, shaking apart, fatigue cracks, and breaking [1]. Self-loosening is the most common issue among them due to inappropriate preloads during installation, time varying external loads during service, or other environment factors. Bolts loosening may lead to the failure of the entire structure. Therefore, it is critical to monitor bolt preload to ensure the safety and reliability of structures.
Structural health monitoring (SHM) is generally referred to the process of acquiring and analyzing data from on-board sensors to determine the health of a structure [2]. Several SHM approaches have been reported for the detection of bolt loosening in different structural systems, such as vibration, electromechanical impedance, and guided wave-based techniques. In vibration-based techniques, global dynamic properties, like resonant frequencies, modal shapes, and frequency response functions are utilized for the detection of bolt loosening [3]. However, since an assembled structure usually comprises many bolts and joint interfaces which are known as local structural elements, global structural dynamic properties do not change significantly due to bolt preload loosening at a local position [4]. Consequently, vibration-based SHM techniques are relatively insensitive to changes in bolt preloads and thus lead to poor prognostic capability. Impedance-based techniques monitor variations in mechanical impedance due to damage, which is coupled with electrical impedance of piezoelectric transducers (PZTs) [5]. Previous studies have shown the feasibility of using impedance-based approaches for the detection of bolt loosening [6, 7, 8]. A piezoelectric transducer (PZT) is attached to a target bolt-jointed structure, and bolt preload can be identified by monitoring the change of the measured electrical impedance [7]. Although this technique is sensitive to minor changes in the bolt preload, its detection area is limited to the near field of the piezoelectric active sensor [9] and an expensive high-precision impedance analyzer with a high-sampling frequency is required [10].
Guided wave-based damage detection techniques have been intensively developed over the last two decades [11, 12]. Due to their sensitivity to small structural damages and large sensing range [13], guided wave techniques have been increasingly used for structural health monitoring. In recent years, bolt preload detection methods using guided wave have received much interest. In this chapter, bolt preload monitoring methods based on guided waves and the relevant theories are reviewed. The objective is to understand the current technology gaps, future research directions, and areas requiring attention of the researchers. This chapter is organized as follows. Section 2 presents the theoretical backgrounds and numerical modeling approach of guided wave-based SHM methods. Then, linear feature-based detection methods are reviewed and compared in Section 3, which include wave energy dissipation methods and time reversal methods. Section 4 displays nonlinear feature-based methods including contact acoustic nonlinearity (CAN), phase shift, and chaotic ultrasonic excitation methods. Finally, conclusions are summarized in Section 5.
A typical bolted joint is illustrated in Figure 1. It can be seen that a bolted joint usually consists of one bolt, one nut, and two contact parts. From the view of a micro-scale, the joint interface can be considered to be covered with a large amount of asperities. The real contact area is the summation of the contact area of each asperity. As the bolt preload increases, the contact pressure at the interface increases. Correspondingly, the real contact area increases as well. When a wave travels through a lap joint, only a part of the incident wave energy can be transmitted, and the other part is reflected and dissipated. Based on Hertz contact theory and the sinusoidal wavy surface model, Yang and Chang [3] establish the relationship between real contact area and contact pressure at a joint interface. Their results show that the energy of transmitted guided wave is proportional to the real contact area of joint interface which increases with bolt preload. Although the topographies of rough contact surfaces are not strictly sinusoidal and the plastic deformation of contact asperities are not considered, Yang’s theoretical analysis agrees well with experimental observation. After that, the transmitted wave energy is widely used as the tightness index for bolt-loosening detection. However, based on the theory of rough contact mechanics, the real contact area at an interface reaches a saturation value when the applied contact pressure reaches a certain value [14]. Accordingly, the transmitted energy also saturates when the externally applied load reaches a certain value. In this case, the sensitivity of the transmitted wave energy-based damage detection strategy is reduced considerably.
Guided wave transmitted across a bolted joint.
Nonlinear features of acoustic waves can also be extracted and linked to bolt loosening. Among approaches based on nonlinear features, contact acoustic nonlinearity (CAN) is drawing increasing attention. When the bolt is loosening and the joint is stimulated by acoustic waves or vibration under certain amplitude, joint interface undergoes a certain extent of tension and compression and it opens and closes periodically. This induces asymmetry in the contact restoration forces. Consequently, those forces cause a parametric change of stiffness and lead to structural dynamic nonlinearity, known as contact acoustic nonlinearity [15, 16]. Since the guided wave amplitude excited by a piezoelectric element is generally small, it is difficult to stimulate the nonlinearity of the structure itself. Therefore, impact modulation (IM) and vibro-acoustic modulation (VAM) are two major implementations of CAN-based modulation [17]. The major difference between them is that IM adopts an impact force to excite the natural vibration modes of the inspected structure, while VAM applies a stable vibration to the structure using a harmonic force. The essence of the modulation methods resides on the interaction of the jointed interface with a mixed excitation, like a vibration and a wave. When all the bolts in a jointed structure are fully fastened, the acquired signal spectrum exhibits two peaks at the vibration and wave frequencies, respectively. When bolts are loosening, there would be additional frequency components around the wave frequency in the spectrum, termed as left and right sidebands. The magnitudes of the sidebands, which are determined by the intensity of CAN, can be linked quantitatively to the bolt preload [18].
In order to quantitatively describe the relation between sidebands of signal spectral features and the residual bolt preload, Zhang et al. [18] established a theoretical modeling of CAN in a joint, as shown in Figure 2a. The analysis based on the model demonstrates that the magnitude of the sideband is proportional linearly to the nonlinear contact stiffness K2 which is dependent on the contact pressure at the jointed interface. The above model is a simplified model with single degree of freedom (DOF). Subsequently, Zhang et al. [19] presented a two-DOF nonlinear model to analyze the physical phenomenon of subharmonics and their generation conditions, as shown in Figure 2b. On this basis, analytical prediction was carried out to verify the validity of the loosening detection method for bolted joint structures using the subharmonic resonance.
Theoretical modeling of CAN in a joint: (a) single degree of freedom [18] and (b) two degrees of freedom [19].
To understand how guided waves interact with bolted lap joints exactly, theoretical models are essential to describe the propagation behavior of guided wave. Apparently, the above simplified single or two DOF models are not enough. Since the bolted structure is inhomogeneous in the direction of wave propagation, it cannot be modeled by analytical or semi-analytical methods. Finite element method (FEM) can be applied to a variety of complex geometries and has become the most common wave propagation analysis method. Therefore, Clayton et al. [20] established a three-dimensional finite element model of guided wave propagation in bolted joints, but interface contact was not considered in order to reduce computational cost. Then, Doyle et al. [21], and Bao and Giurgiutiu [22] used the same method to establish finite element analysis models. However, they found that these models could not reflect the variation of the guided wave under different bolt preloads. Therefore, in order to consider contact behaviors, Bao et al. [23] added contact elements to the finite element model. The improved model can effectively reflect the variation of the guided wave under different preloads, but the wave variations and the measurement results were quite different. The main reason might be that the contact surfaces in the above models are smooth, while the real contact surfaces are rough. In 2016, Parvasi et al. [10] tried to consider rough contact surfaces in finite element model by randomly adjusting node position at the contact surfaces, as shown in Figure 3. The simulation results are closer to the experimental measurement results, but the mesh size (1.8 mm) of the contact area is much larger than the size of micro-asperities on rough surfaces.
Multi-physics FEM model of bolted lap joint considering rough contact surfaces [10].
The above FEM models are mainly used to analyze the relationship between bolt preload and transmitted guided wave energy. Shen et al. [24] built anther 3D multiphysics transient dynamic finite element model to analyze the relationship between CAN and bolt load, as shown in Figure 4a. The nonlinear higher harmonics (second-order harmonic and third-order harmonic) can be observed clearly in the simulation signal, as shown in Figure 4b. The simulation results also displayed that as the bolt preload increases, the ratio of the spectral amplitude at the second harmonic to that at the excitation frequency decreases.
Transient dynamic finite element model and frequency spectrum of simulation signal [24].
Because ultrasonic wave energy through the bolt joint is strongly tied to the contact status of bolted interface, the transmitted guided wave energy is widely used as tightness index. This type of method is also known as wave energy dissipation (WED) method. In order to detect fastener integrity in thermal protection panels in space vehicles, Yang and Chang [3, 25] used the energy and attenuation speed of guided wave transmitted across jointed interface to assess preload levels and locations of loosening bolt. Subsequently, Wang et al. [26] used only the transmitted guided wave energy to monitor bolt preload. The schematic of the bolt joint monitoring system is displayed in Figure 5. The experimental results show that the transmitted energy is basically proportional to torque level. However, the energy does not change with bolt torque when the applied torque reaches a certain value and this is referred to as saturation phenomenon, as shown in Figure 6a. Similarly, Amerini and Meo [27] calculated the energy of the transmitted guided wave in frequency domain to assess the tightening state of a bolt lap joint, as shown in Figure 6b. Yang et al. [28] extended the WED method to composite bolted joints. With a scanning laser ultrasound system, Haynes et al. [29] acquired the full-field wave data and calculated the wave energy before and after the lap joint to monitor bolt torque levels. Unfortunately, saturation phenomena are also observed in all the above experimental studies. On the other hand, due to multi-mode, dispersion, and boundary reflection of guided waves, the response signal at a joint structure is quite complex [27]. Hence, Kędra et al. [30] investigated the effects of excitation frequency, the time range of received signal, and the position of sensor on the preload detection accuracy of the WED method. They pointed out that these parameters have to be carefully selected.
Schematic of the bolt joint monitoring system [26].
Results of WED methods with saturation phenomenon: (a) result from reference [26] and (b) result from reference [27].
The above bolt preload detection methods are limited to a flat lap joint with a single bolt. However, in real structures, bolted joints with complex geometry or multiple bolts are more common. In this case, complex signal-processing methods are always needed. In order to monitor the preload of L-shaped bolt joints, Jalalpour et al. [31] proposed a preload monitoring method using fast Fourier transform, cross-correlation, and fuzzy pattern recognition to process transmitted wave. Nevertheless, the fuzzy sets of torque level were limited. Montoya et al. [32] assessed the rigidity of L-shaped bolt joint using transmitted wave energy. Subsequently, Montoya et al. [33] further extended the method to bolt loosening and preload monitoring of satellite panels jointed by a right angle bracket. Their experimental results display that some measurement parameters, such as the time window of the received signal, have a significant effect on the sensitivity and repeatability of the measurement [33]. With respect to bolt-loosening monitoring in multi-bolt-jointed structures, Mita et al. [34] proposed to use support vector machine to recognize different loosening patterns. Their results show that the proposed method could identify the location and the level of preload of loosened bolts. Moreover, Liang and Yuan [35] developed a decision fusion system for multi-bolt structure, as shown in Figure 7. This system consists of individual classification, classifier selection, and decision fusion. The results demonstrate that the proposed method can accurately and rapidly identify the bolt loosening by analyzing the acquired wave signal.
Sensor layout and joint failure position on the specimen [35].
Since guided wave signals are always very complex because of multi-mode, dispersion, and scattering at any discontinuity, Fink et al. [36] extended time reversal concept (TR) to a guided wave monitoring technique. In time reversal approach, a received signal is reversed and reemitted as an excitation signal, and then a reconstruction of the input signal can be obtained at the source position. Hence, the time reversal method can effectively reduce the influences of dispersion and multi-modal of the guided wave. In recent years, time reversal-based guided wave monitoring methods have been widely applied to damage detection in various structures, such as metallic plates [37], composite plates [38, 39, 40], and rebar-reinforced concrete beams [41]. Recently, Parvasi et al. [10] proposed to use time reversal method to focus guided wave energy transmitted through bolted joint, and then the refocused amplitude peak was selected as the tightness index for preload detection. The experimental results show that the proposed tightness index increases with bolt torque. The TR method for bolt preload monitoring can be divided into four steps, which is shown in Figure 8. Step 1, a tone burst input e(t) is applied to transducer A, which activates wave propagation in the plate. Step 2, a wave response signal u(t) is captured by transducer B. Step 3, the recorded signal u(t) is reversed in time domain and is restimulated using transducer B. Step 4, a guided wave signal is captured by transducer A again, and the original signal is reconstructed. Finally, the reconstructed signal peak is used as the tightness index for preload detection [10]. One of the main advantages of TR method is that there is no need to take efforts to select time window of received signal as the WED method.
Illustration of the time reversal method in a lap jointed beam.
Actually, the refocused amplitude peak is strongly related to the transmitted wave energy. Hence, when bolt preload is relatively high and the real contact area does not increase with preload, the focused signal peak amplitude changes very slowly. Therefore, Tao et al. [42] experimentally investigated the saturation phenomenon of TR method for bolted preload detection. The results demonstrate that with the increase of the surface roughness of bolted interface, the saturation phenomenon becomes insignificant. Huo et al. [43] studied guided wave propagation across contact interface based on fractal contact theory and finite element method. They concluded that the saturation phenomenon is linked to the plastic deformation of interacting asperities under a heavy axial load.
Until now, the difference in monitoring sensitivities of WED and TR methods is not clear. Hence, the monitoring sensitivities of the two methods are compared in this section. The experimental setup and specimens are displayed in Figure 9. The metallic bolted lap joint consists of two flat aluminum 2024-T3 beams, one M6 bolt, one nut, and two washers. The length of each beam is 400 mm, the width is 50 mm, and the thickness 2 mm. The normal torque is selected to be 10 Nm. A torque wrench with a resolution of 0.2 Nm is used to apply bolt preload. A data acquisition (DAQ) system NI USB-6366 with a sampling frequency of 2 MHz is used to generate wave excitation and record responses. A program is built in the LabVIEW environment to control the process of data acquisition. A high voltage amplifier PINTEK HA-400 is used to amplify the excitation signal and provides voltage to PZT actuators. In addition, the specimen is mounted on a foam support to simulate a free-free boundary condition. Two PZT patches are bonded on the specimen. The patch on the left beam, 100 mm, away from the bolt is numbered as PZT 1
Experimental setup and specimens.
The bolt preload is evaluated by both WED and TR methods at the same time. Figure 10 presents the results of tightness indexes TIΩ(WED) and TIΩ(TR) obtained by
Preload detection results of the WED and TR methods.
Contact acoustic nonlinearity (CAN) is shown to increase with the decrease in contact load, so the second-order harmonics, subharmonic, and spectral sidebands caused by CAN have also been used for bolt preload detection. Usually, the second-order harmonic and subharmonic can be generated by a single frequency excitation, and spectral sidebands are generated by both low- and high-frequency excitations. For the second-order harmonic-based method, the ratio between the second harmonic amplitude and the carrier frequency signal amplitude provided a reliable index for bolt load assessment. Under multi-frequency excitation, the loosening/tightening index proposed is the difference in dB between the carrier frequency amplitude and a mean of the two sideband amplitudes [27]. Zhang M et al. [19] presented a subharmonic resonance method for the detection of bolt looseness, and the bolted joint was excited by a single frequency-guided wave. CAN features are more likely to be excited by adding vibration excitation. Thereby, Zhang Z et al. [17, 18] proposed a vibro-acoustic modulation (VAM)-based method and developed a sideband index for metal and composite bolted joints. The experimental setup and the corresponding detection results for composite bolted joints are shown in Figure 11.
Preload monitoring of bolted composite joint using VAM method [17]: (a) experimental setup for VAM method and (b) comparison of VAM and WED methods.
In Figure 11, the label β is the sideband index in VAM method, and the label energy is the transmitted energy of Lamb waves in WED method. Zhang Z et al. [17] compared the proposed VAM method with WED-based linear method, and the results show that the proposed sideband index β effectively enhanced measurement sensitivity. In addition, Amerini and Meo [27] developed both second-order harmonics index and sideband index to assess the tightening state of a bolted structure, and the assessment results of the two methods are similar. On the other hand, Zhang Z et al. [16] also compared the high-order harmonics and sideband methods and demonstrated that the stability of spectral sideband-based method is better. Spectral sideband can also be generated by impact modulation. Meyer and Adams [44] proposed an impact modulation-based method to detect bolt loosening in an aluminum joint. However, the sideband amplitudes are sensitive to test parameters including impact amplitude and location, probing force amplitude and frequency, and sensor location. One common disadvantage of these above spectral sideband methods is that it needs two different actuators and one sensor for each joint monitored [27]. Meanwhile, the saturation phenomena have not been completely removed, and the detection sensitivity still needs to be improved at the early stage of bolt loosening.
Apart from transmitted wave energy and CAN, the phase shift of guided wave has also been used for quantifying bolt torques. Zagrai et al. [45] estimated bolt torques by measuring delays of guided wave transmitted across bolt joint. Their experimental results demonstrated that bolt torque is proportional to phase shift of the guided waves, as shown in Figure 12.
Guided wave signals recorded at different bolt torques: (a) full records and (b) zoomed-in segments [45].
In addition, Zagrai et al. [45] tried to explain the experiment results by acousto-elastic theory and presented a simplified theoretical approach to calculate phase shift of the propagating elastic wave. However, their approach gives approximately an order of magnitude underestimation for pulse delays. Subsequently, Doyle et al. [46, 47] further studied phase shift of guided wave propagating in a complex structure analogous to a typical satellite panel containing 49 bolt joints using an array of piezoelectric sensors sparsely distributed. The results show that the time at which this shift occurs is related to the distance between the location of loosening bolt and the primary wave propagation path. Thereby, using only two or three possible paths, it is possible to obtain a realistic estimate of the location of damage in the form of single bolt loosening [47]. On this basis, Zagrai et al. [48] tried to develop a baseline-free method utilizing signals of different initial phases to assess bolt loosening. Unfortunately, it does not work in structures with complicated geometries and large number of bolts. Furthermore, changes of the phase shift induced by a bolted joint are rather small and require sensitive equipment with advanced signal-processing capabilities [46]. In addition, because received guided waves are very complex, it is difficult to select the correct time window and the corresponding wave speed to calculate phase shift and the distance between wave path and damage.
In addition to stimulate the nonlinear characteristics of the jointed structure, another research idea is to directly use nonlinear ultrasonic excitation. At this time, artificially introducing a nonlinear component in the ultrasonic excitation signal can be used to sensitively estimate the change of structural parameters caused by loosened bolts. Chaotic signal is a well-known nonlinear signal, but chaotic signals generated by most well-known chaotic systems are unsuitable for guided wave monitoring which is more sensitive to small-scale damage. Clayton et al. [20] proposed a bolt preload monitoring approach combining a chaotic excitation method with ultrasonic guided waves. In this method, the chaotic signal is upconverting to an ultrasonic frequency band, and the ultrasound signal with chaotic characteristics is generated to stimulate the bolted structure. The response signal is reconstructed to analyze the phase space, and the nonlinear characteristic quantitatively representing the bolt looseness is extracted. Fasel et al. [49, 50] used similar methods to identify bolt preload in simulations and experiments on single and multi-bolt structures. Recently, based on the chaotic ultrasonic excitation method, Wu and Xu [51] take both Lyapunov dimension and the ratio of averaged local attractor variance (ALAVR) as looseness indexes, which can be used to characterize an attractor’s whole features and local features. Experimental results show that ALAVR is better for bolt preload monitoring, as displayed in Figure 13.
Looseness indexes versus bolt preload [51]: (a) Lyapunov dimension and (b) ALAVR.
Ultrasonic guided wave is an effective technique to monitor the preload of bolts. The research status of this field is reviewed in this chapter. At present, considerable advancements have been made in this area in the past two decades. Both linear and nonlinear features of guided waves introduced by bolted joints have been used for bolt preload monitoring. In particular, the transmitted wave energy as a linear feature is the most extensively used for preload monitoring in single bolt and multi-bolt structures. For this reason, the wave energy dissipation method (WED) based on the above features is experimentally compared with time reversal method (TR). The results show that the detection sensitivity of WED method is not very good, especially at the early stage of bolt loosening, and the TR method is more sensitive to bolt loosening. Meanwhile, this chapter also reviews a variety of monitoring methods based on nonlinear features, including contact acoustic nonlinearity (CAN), phase shift caused by acoustic-elastic, and chaotic ultrasound. The above methods can improve the detection sensitivity, but there are also several disadvantages. For example, both acoustic and vibrational excitations are always required for CAN-based methods, and high-frequency sampling frequencies are required for phase shift-based method. The open areas of research, which might need attention, are outlined as follows:
Accurate and efficient numerical models should be further developed to simulate wave propagation in bolted joints. For example, acoustic-elastic are currently believed to cause the phase shift of transmitted guided-wave signal. However, the current simplified model based on acoustic-elastic cannot effectively explain the phase shift phenomenon. In the meantime, it is very difficult to consider the micro-topography of contact surfaces in FEM models now. Therefore, the establishment of a more accurate and efficient numerical model is expected to fully study the interaction between jointed interface and guided wave theoretically.
Improving bolt preload monitoring method is still required. Although bolt preload monitoring methods such as TR and VAM methods can effectively improve the preload detection sensitivity, the detection sensitivity of these methods is not still very good at the early stage of bolt loosening. Moreover, almost all the methods currently require baseline signals from healthy structures. Therefore, the establishment of a baseline free monitoring method with a high detection sensitivity is an important step for moving toward the goal of real-life in-service implementation.
Bolt-loosening detection methods considering environmental factors for multi-bolt structures should be further developed. Current research limited to a flat lap joint with a single bolt. However, bolted joints with complex structure and multiple bolts are more common in real structures. Meanwhile, little attention has been paid to preload monitoring considering environmental factors which have significant effect on guided wave monitoring. Hence, loosening detection method considering environmental factors for multi-bolt structures is also very important for realizing the application of bolt preload monitoring in real engineering structures.
This study is supported by the National Natural Science Foundation of China (Grant Nos. 51705422 and 11372246). This study is also supported by China NSAF Project (Grant No. U1530139) and Fundamental Research Funds for the Central Universities (Grant No. 3102017OQD004).
ALAVR | ratio of averaged local attractor variance |
CAN | contact acoustic nonlinearity |
DOF | degree of freedom |
FEM | finite element method |
IM | impact modulation |
K1, K2 | contact stiffness |
PZT | piezoelectric transducers |
SHM | structural health monitoring |
TR | time reversal |
TIΩ | tightness index |
VAM | vibro-acoustic modulation |
WED | wave energy dissipation |
β | sideband index |
ΔD | tightness index based on Lyapunov dimension |
Mangroves act as frontiers that protect the coastal land against destruction of ocean waves, tsunamis and storms. Mangroves also provide habitat for various aquatic life forms and function as natural filter, which improves the quality of water. Mangroves also play important roles as a significant carbon sink in coastal environment. It is interesting fact that despite only 0.05% of plant biomass stored in the ocean and coastal areas out of the total plant biomass on land, it can absorb a comparable amount of carbon every year. A study demonstrated that primary productivity in mangroves is higher than other types of forests. Biomass carbon in mangroves stands is among the highest in the tropics. Mangroves can store up to four times more carbon (C) as compared to other tropical forests around the world [1].
\nA mangroves ecosystem has an ability to absorb carbon dioxide (CO2) and store carbon 40% more than the dry land forest ecosystem. Due to this ability, the total carbon deposited in a square kilometer of mangrove ecosystem is 50 times faster than those of the same area in a dryland tropical forest ecosystem. The absorbed CO2 is stored not only in the plants, but in layers of soils underneath [2]. Therefore, mangroves are playing a crucial role in global carbon budgets and thus mitigating climate change.
\nHowever, despite being realized the importance of mangroves in the global carbon cycle and climate change, the extents of mangroves have inevitably declined since the last few decades. Unfortunately, the declines have been resulting mainly from human activities such as aquaculture expansion, coastal development, and over-harvesting [3]. Malaysia is one of the countries in South East Asia that has among the largest extents of mangroves. Despite its extensive distribution of mangrove ecosystem, this forest is inevitable from threats by various land use activities. The total area of mangrove forest was approximately 2% (650,000 ha) of the total land area in Malaysia in the 1990s [4].
\nHowever, the mangroves in Malaysia have been gradually diminishing, where the total area of mangrove forest has reduced to approximately 580,000 ha in the last decade [5]. Other reports indicated that the extent of mangrove areas in Malaysia is decreasing, from about 700,000 ha in 1975 to 572,000 ha in 2000 due to the intensive harvesting and natural wave actions [6, 7]. Globally, mangroves have also declined from 18.8 million ha to 15.6 million ha between years 1980 and 2005 [8]. Overall Asia was the largest net loss of mangroves since 1980, with about 1.9 million ha have loss, mainly due to conversion of mangrove forest to other land uses. However, there has been a slowdown in the annual rate of mangrove loss, from about 187,000 ha in the 1980s to 102,000 ha between 2000 and 2005. This reflects an increased awareness and an improved management system in mangroves ecosystem.
\nMajor threats towards the mangroves that are triggered by human activities can generalized into six [9], which are (i) conversion to other uses, (ii) overharvesting, (iii) overfishing, (iv) pollution, (v) sedimentation and (vi) alteration of flow regimes. Direct conversion to other uses was identified as the major factor that changes the world’s mangroves. This includes conversions to (i) urban and industrial areas, (ii) aquaculture, and (iii) agriculture. Additionally, natural phenomena such as coastal erosion, storm and lightning strikes are also the natural impacts that kill mangroves in Peninsular Malaysia, including the tragic tsunami on 24 December 2004.
\nDespite widespread concern and numerous case studies describing local issues and challenges, comprehensive information on the global extent of mangroves and trends of deforestation is largely lacking [10]. It is because determining the precise area of mangroves is not always easy. Measurement is affected by varying definitions of what constitutes mangroves; inclusion only on the basis of official recognition such as gazetted forest reserves; scattered or sparse areas considered too inconsequential for inclusion; and the accuracy of the returns made by the responsible authorities. Each of these can create uncertainty and produce significant variation depending on the timing and purpose of the assessment exercise.
\nRecently, RS satellites have been widely used for mangrove monitoring. They greatest reasons why is because the RS can (i) acquire information over large areas, (ii) produce repeated measurement over a place, and (iii) make full use of electromagnetic spectrum for quantitative and qualitative measurements over mangroves [11]. Satellites also provide information on spatial distribution and temporal changes of mangrove forests. When this information is gathered over decades, the mangrove monitoring over the large area will become possible. There are studies on the assessment of mangroves changes and identifying threats, for example in Terengganu [12], Selangor [13], and Peninsular Malaysia [14]. However, these studies are unable to represent the holistic conditions at national level. Therefore, this study was conducted to provide the information pertaining status of mangroves and changes that occurred since the last three decades.
\nThe study area covers the entire mangroves ecosystem in Malaysia, which can be divided into two regions, which are Peninsular Malaysia and East Malaysia (i.e. Malay Borneo). Forests in these regions can be divided into three major types, which are inland dipterocarps (dryland), peat swamp, mangrove forests (wetlands). The mangrove forest is a unique ecosystem and the second largest wetland forest type after the peat swamp forest. Ecologically based on elevation the mangrove forest is located at the lowest elevation, which is equivalent to the sea level. The mangrove forest is generally found along sheltered coasts where it grows abundantly in saline soil and brackish water dominated mainly by trees from the Rhizophoraceae family. Mangroves are fringing the coastlines (up to 5 km landward) and major estuaries of the regions and they reside on wetlands ecosystem of not more than 20 m land altitude.
\nImages from Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper (ETM+), and Landsat-8 Operational Land Imager (OLI) satellite were used in this study. Images from three different epochs, which are 1990, 2000 and 2017 were acquired to conduct the work. For the respective years were utilized in this study. All images are available at
Landsat scenes that were used for the classification. Numbers within the scene boundary indicates path/row ID of Landsat satellites.
Cloud cover is inevitable on the images acquired by the satellites. However, cloud patching process can eliminate the cloud covers that appear on a single-date observation data. Images of particular scenes that were acquired on different dates were used for cloud patching process as shown in Figure 2. F_mask algorithm was used to perform this process [15, 16]. Seamless mosaics product (i.e. images without cloud covers and atmospherically corrected) were used as input for subsequent processes.
\nCloud detection and removal process. Individual Landsat scene that was captured on 26 January 2017 (a) was merged with that captured on 14 June 2017 (b), where both produced a cloud-free images for the year 2017 (c).
Appropriate enhancement techniques were applied to the images to make the mangroves appear better on the images [17]. In addition to the individual spectral bands of Landsat images, vegetation indices such as Normalized Different Vegetation Index (NDVI), Green Atmospherically Resistant Index (GARI), and Normalized Difference Infrared Index (NDII) were also derived from the images to improve quality of classification. The vegetation indices that were used in this study are summarized in Table 1.
\nVegetation indices that were used derived from the images.
Note: NIR = near infrared, G = green, B = blue, R = red, and MidIR = middle wave infrared channels.
Most spectral-based image classifications are performed using traditional methods such as maximum likelihood, linear discriminant analysis, and spectral angle mapper classifiers. These methods are applied to the spectral bands to produce a classified feature in images [18]. Instead of using these approaches, this study attempted a new approach to classify the images. R Package, which is free, open source software with the RandomForest algorithm [19] was used.
\nRandomForest implements Breiman’s RandomForest algorithm, based on Breiman and Cutler’s original FORTRAN code for classification and regression [20]. It can also be used for assessing proximities among data points without necessarily a training set. All sampling points that were collected on the ground were connected to the corresponding pixels on the image through this algorithm. Classification was done by searching the most important variables i.e. which spectral bands are used in decision tree approach [21, 22, 23]. RandomForest applies four major steps of looking at the importance of variables as follow:
Step 1: to determine the significance of the mth variable. In the left out cases for the kth tree, randomly permute all values of the mth variable. Put these new covariate values down the tree and get classifications.
Steps 2 and 3: for the nth case in the data, its margin at the end of a run is the proportion of votes for its true class minus the maximum of the proportion of votes for each of the other classes. The 2nd measure of importance of the mth variable is the average lowering of the margin across all cases when the mth variable is randomly permuted as in Step 1. Step 3 then count the margins that was shrank.
Step 4: the splitting criterion used in RandomForest is the Gini criterion, a mechanism that can measure the most to least importance of variables used in decision tree. At every split, one of the mth variables is used to form the split and there is a resulting decrease in the Gini. The sum of all will decrease the forest due to a given variable, normalized by the number of trees.
All images have been classified to distinguish mangroves from the other land uses. The classification results were transformed into vector shapefile for further refinement and editing. The accuracy of the classification results were assessed by using a number of ground truth points. The GIS platform was used to carry out post-classification analysis. Post-classification analysis is usually used for quantifying changes of land uses. Changes of mangroves were identified from the conversions of mangroves to other landuse classes, which are (i) urban, settlement, and industrial areas, (ii) agricultural, (iii) aquaculture activities, and (iv) coastal erosion.
\nCarbon dioxide (CO2) is defined as natural, colorless and odorless greenhouse gas that is emitted when fossil fuels (i.e. natural gas, oil, coal, etc.) are burnt. In this study, the CO2 emission is expressed as C loss, assuming that the gas is emitted when deforestation occur. The units of metric tons C was converted to CO2 by multiplying the ratio of the molecular weight of carbon dioxide to that of carbon (44/12 = 3.67) [24].
\nThe CO2 resulted from deforestation is one of the important elements in greenhouse gases emissions. Therefore, it is also essential to quantify the contribution of mangrove deforestation towards the CO2 emission. Net emission as resulted from deforestation of mangroves can be estimated based stock-difference method, which can be expressed as Eq. (1) as follow [24];
\nwhere ∆C is changes in carbon stock (Mg C yr−1), Ct1 and Ct2 (Mg C) is carbon stock at time t1 and t2 (year), respectively. In this case, the Ct1 and Ct2 was quantified from the changes analysis that have been carried out earlier this study.
\nF mask algorithm successfully removed almost 100% of cloud covers and their shadows on the images. The algorithm also managed to detect thin, low temperature clouds in the high altitude by thermal sensors onboard the Landsat TM, ETM+ and OLI. The algorithm somehow failed to detect small scattering clouds that occurred in small patches on the images. Nevertheless, the algorithm has facilitated the cloud removal process and make the mangroves mapping and monitoring work at landscape-level practical. Figure 2 shows a portion of mangroves on two different images that were captured on different dates with clouds. These images were used to produce seamless mosaic of images without cloud covers.
\nThe study indicated that the suitable spectral bands for species discrimination varied with scale. However, near-infrared (700–1327 nm) bands were consistently important spectrum across all scales and the visible bands (437–700 nm) were more important at pixel and crown scales. By using the RandomForest algorithm, the most important bands in the classification were represented by a mean decrease Gini values. The most important bands in mangroves discrimination, from most to least, are; MidIR, NIR-2, NIR, Green, Blue, Red. Spectral profile of the images also showed that the NIR channels separate the mangroves from the other land covers very well (Figure 3). On the other hands, the vegetation indices that were used in this study played similar important role in mangroves classification.
\nSpectral profiles of several land covers extracted from the images. Channel 1 through 6 on the y-axis are blue, green, red, NIR, NIR-2 and MidIR, respectively.
The image classification approach that has been applied in this study was found to be effective only at large coverage of mangroves. The accuracy for all classifications were ranging from 83 to 91%, which were acceptable and reliable for monitoring purpose. Mangroves are normally appear dark on any combination of spectral bands of multispectral image. This is due to the natural ecosystem of mangroves, which is covered by swamps and sometimes inundated by tidal water. The chlorophyll content of the mangrove leaves, which is higher than those of trees and crops, tends to make them appear darker on satellite images [25], as depicted in Figure 4. Each mangrove species has a unique configuration of trunks, prop roots and pneumatophores that works as a different drag force therefore resulting in a different reduction rate of sea waves (Figure 5). Not only this, the wet floor of the forest gives special spectral characteristics on satellites images that can be differentiated easily from other features (Figure 6).
\nImages showing (a) combination of bands 5, 6 and 4 of Landsat-8 OLI and (b) combination of vegetation indices, NDVI, GARI and NDII. These images were selected for the classification process.
Roots and successive stands of Rhizophora apiculata in a common mature mangrove forest.
Mangroves as they appeared on Landsat-8 image. The dark green areas represent the mangrove areas. The image classification process, either automated or manual digitizing, is usually easier for mangrove areas than for other vegetation. The image is displayed using a combination of bands 543 (RGB) over the Kapar area in Klang, Selangor. The central bottom is Klang port complex and the bottom left is Pulau Klang, which is predominantly covered by mangroves.
The classification results were further edited to refine the shapes and accuracy. This process was conducted manually on the vector shapefile by visual interpretation on GIS platform. Finally the spatial distribution of the mangroves were mapped properly (Figure 7). The mangroves in Malaysia were mostly found in Sabah (60%), followed by Sarawak (22%) and Peninsular Malaysia (18%). Table 2 summarizes the total extents of mangroves in the respective regions that have been produced from the classification. It is notable that the total extents of mangroves have been decreasing throughout the monitoring period. Figure 8 shows spatially explicit map of mangroves distribution in Malaysia as of year 2017. Mangroves are found mainly along the west coast of Peninsular Malaysia, west coast of Sarawak and the east coast of Sabah.
\nMangroves appear dark green on the original image (left) and the classified mangroves, indicated as red polygons (right).
Region | \nMangroves 1990 (ha) | \nMangroves 2000 (ha) | \nMangroves 2017 (ha) | \n
---|---|---|---|
Peninsular Malaysia | \n116,746 | \n114,353 | \n110,953 | \n
Sabah | \n385,630 | \n382,448 | \n378,195 | \n
Sarawak | \n147,936 | \n145,263 | \n139,890 | \n
Total | \n650,311 | \n642,063 | \n629,038 | \n
Extents of mangroves in Malaysia.
Distribution of mangroves in Malaysia over the year 2017.
Table 3 reports the changes of mangroves that occurred over the 27 years of monitoring period. The total loss of mangroves was about 21,274 ha where majority of the mangroves loss were outside the Permanent Forest Reserve or within the stateland areas. These areas are actually the land bank for the states developments, which are principally included in the State’s Structural Planning. Example of mangroves changes detected from the multi-temporal mapping process is shown in Figure 9. From this information, it can be concluded that the annual decrease rate of mangroves was about 788 ha per year or about 0.13% per annum since year 1990. Major factors that contributed to these changes have been identified as: (i) direct conversion to other land uses (Figure 10), predominantly for commercial-scale agriculture (Figure 11) and aquaculture (Figure 12), and (ii) coastal erosion (Figure 13). The other factors such as overharvesting and pollution affect the mangroves to a lesser degree.
\nRegion | \nMangrove loss 1990–2000 (ha) | \nMangrove loss 2000–2017 (ha) | \nRate of deforestation 1990–2000 (ha yr−1) | (% yr−1) | \nRate of deforestation 2000–2017 (ha yr−1) | (% yr−1) | \nAverage rate of deforestation 1990–2017 (ha yr−1) | (% yr−1) | \n
---|---|---|---|---|---|
Peninsular Malaysia | \n2393 | \n3400 | \n239 | 0.20 | \n200 | 0.17 | \n215 | 0.19 | \n
Sabah | \n3182 | \n4253 | \n318 | 0.08 | \n250 | 0.07 | \n275 | 0.07 | \n
Sarawak | \n2673 | \n5373 | \n267 | 0.18 | \n316 | 0.22 | \n298 | 0.21 | \n
Total | \n8227 | \n13,190 | \n823 | 0.13 | \n776 | 0.12 | \n793 | 0.13 | \n
Mangroves deforestation in Malaysia between years 1990 and 2017.
Changes of mangroves that occurred between 1990 and 2017 overlaid on GIS platform.
Land developments on mangroves. Reddish color represents newly opened areas for development purposes that were cleared from the original mangroves areas (dark green color).
Expansion of oil palm plantation on mangroves. Reddish color represents newly opened plantations from the original mangroves areas (dark green color). The bright green represents existing plantations.
Expansion of aquaculture industries on mangroves. Dark blue patches represents newly opened aquaculture ponds from the original mangroves areas (dark green color).
Shoreline changes that resulted from coastal erosion along the coast of south Pontian, Johor. The study indicated that 14.2 km stretches have been facing serious coastal erosion within the last two decades with the rate of erosion ranging from 3.2 to 12.5 m per year.
Although coastal erosion was identified as one of the factors of mangroves loss, there were some accretions occurred in some other places. Erosion and accretion is a dynamic process and takes place along the coastlines and major estuaries, where suspended sediments are likely to settle. These phenomena also lead to species succession when the existing plant species die due to unsuitable soil and new species emerge. Besides, mangrove roots can act as wave breaker and promote flocculation and sedimentation, eventually forming mudflats that allow positive accretion (Figure 14). Coastal erosion occurs when the waves hit perpendicular to the coastlines and when the rapid flow of sea currents wash away the sand or soil particles. The frequency and height of waves hitting the coastlines contribute to the harshness of coastal erosion. Thus, the presence of mangroves can reduce the coastal erosion significantly. This condition is obvious particularly in the areas facing the sea [26, 27].
\nPositive accretion of mangroves at estuaries. The new formations at the river mouths were colonized by mangroves trees forming a naturally generated forest.
A study has indicated that the average C stock (aboveground and belowground) in mangroves in Malaysia is about 181 Mg C ha−1 [28]. The extents of mangroves loss for each epoch were multiplied by this average carbon stocks. The study demonstrated that the total loss of carbon due to the loss of mangroves was about 2.6 million Mg C. Subsequently, this has led to the CO2 emission at about 14.2 million Mg CO2, with an average of about 0.5 million Mg CO2 emission per year, along the monitoring period. Table 4 summarizes the impact of mangroves loss in terms of CO2 emission. Although the figures are generally crude, the study provided some ideas for further studies, especially which related to carbon cycles and climate change.
\nRegion | \nMangrove loss (ha) | \nCarbon loss (Mg C) | \nCO2 emission (Mg CO2) | \nRate of CO2 emission (Mg CO2 yr−1) | \n
---|---|---|---|---|
Peninsular Malaysia | \n5793 | \n1,048,567 | \n3,848,242 | \n142,527 | \n
Sabah | \n7435 | \n1,345,672 | \n4,938,617 | \n182,912 | \n
Sarawak | \n8046 | \n1,456,288 | \n5,344,578 | \n197,947 | \n
Total | \n21,417 | \n3,876,409 | \n14,226,422 | \n526,905 | \n
CO2 emission resulted from mangroves loss between years 1990 and 2017.
This study has successfully assessed the current state of mangroves and determined the rate of mangroves loss in Malaysia since the last decade. Total mangroves in Malaysia has decreased from 650,311 ha in 1990 to 629,038 ha in 2017. Total deforestation was accounted at 21,274 ha or 3.3% with the annual rate of deforestation of 788 ha yr−1 or 0.13% yr−1, between 1990 and 2017. The study also quantified the C stock changes and estimated CO2 emission due to the loss of mangroves in Malaysia. Total emission caused by the mangroves deforestation was accounted at about 14 million Mg CO2 with annual emission rate of around 0.5 million Mg CO2 yr−1.
\nThe study found that the Landsat-based mapping and monitoring of mangroves was very practical. It provides a reliable information on mangroves distribution, both qualitatively and quantitatively. Landsat missions provide a very useful RS tool for monitoring changes of mangroves over time. The study suggests that appropriate actions should be taken by the Government of Malaysia to protect the mangroves and keep their ecosystem intact forever. The most effective way to conserve the mangroves is to gazette the remaining stateland forest as Permanent Reserved Forests (PRFs). These PRFs should then be maintained as amenity for current and future generations, while contributing to the mitigation of climate change impacts at the local level. Any development in PRFs should be prohibited or implemented with caution.
\nOverall, there is great potential in the application of Landsat-based data with appropriate GIS technique for mapping and monitoring of mangroves in Malaysia. Although there are cloud covers problems on some of the images, this has not hindered the assessment of mangroves at landscape and regional levels. The accuracy and precision also vary depending on the objective of the application. However, the ability to detect major changes in the ecosystem that can cause profound and irreversible damage far outweighs a perfectly or highly accurate and precise RS based method at this point.
\nCurrently, Malaysia has reserved about 85% (~535,000 ha) out of the total areas of mangroves as Permanent Forest Reserve and State/National Parks. The remaining 15% is under the state-lands and alienated lands. By far, the most effective way to preserve these mangroves is through gazzeting into permanent forest reserves.
\nThis work has been carried out under the Research and Development Committee on Mangroves (JTRD) led by FRIM. Special thanks for the Forestry Department Peninsular Malaysia (JPSM), Sabah Forestry Department (SFD), and Forest Department Sarawak (FDS) for the supports on the ground data collection activities.
\nThe authors declare no ‘conflict of interest’ for this chapter.
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