Vegetation indices that were used derived from the images.
\r\n\tSolar radiation is the radiant energy that originated from the sun in the form of electromagnetic radiation at various wavelengths. Solar radiation is the source of renewable energy and can be captured and converted into various forms of energy (e.g. electricity and heat) using different technologies.
\r\n\tA very vast amount of solar energy reaches the atmosphere and surface of the earth and solar energy has been used for heating purposes for a very long-time and after solar cells’ invention in 1954, solar cells have also been used widely for electricity generation. Solar cells convert the sunlight into electricity by the creation of voltage and electric current through the so-called photovoltaic effect.
\r\n\tPhotovoltaic (PV) solar energy has attracted significant attention in the recent decade as a reliable source for power generation due to various merits such as the free source of energy, abundant materials resources, environmentally friendly and noise-free, longtime service life, requiring low maintenance, technological advancements, market potential, and very importantly, low cost. The growth of using photovoltaic (PV) solar energy as a promising renewable energy technology, is being increased more and more worldwide. Therefore, much further research is needed for possible future developments in the field of solar photovoltaic energy.
\r\n\tThe aim of this book is to provide detailed information about solar radiation as the source of photovoltaic (PV) solar energy for a broad range of readership including undergraduate and postgraduate students, young or experienced researchers and engineers.
\r\n\tThis should be accomplished by addressing the various technical and practical aspects of solar radiation fundamentals, modeling and the measurement for photovoltaic (PV) solar energy applications.
\r\n\tThe majority of this book should describe the basic, modern, and contemporary knowledge and technology of extraterrestrial and terrestrial solar irradiance for photovoltaic (PV) solar energy.
\r\n\tThe book covers the most recent developments, innovation and applications concerning the following topics:
\r\n\t• Fundamental of solar radiation and photovoltaic solar energy
\r\n\t• Solar radiation and photovoltaic solar energy potential
\r\n\t• Solar irradiance measurement: techniques, instrumentation and uncertainty analysis
\r\n\t• Solar radiation modeling for photovoltaic solar energy applications
\r\n\t• Solar monitoring and data quality assessment
\r\n\t• Solar resource assessment and photovoltaic system performance
\r\n\t• Solar energy and photovoltaic power forecasting
\r\n\tThese are accompanied with other useful research topics and material.
",isbn:"978-1-83968-859-1",printIsbn:"978-1-83968-858-4",pdfIsbn:"978-1-83968-860-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"4c3d1319d7286e81bfb15c1f4b20460a",bookSignature:"Dr. Mohammadreza Aghaei",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9862.jpg",keywords:"Solar Radiation Modeling, Solar Data Assessment, Solar Monitoring, Solar Radiation Forecasting, Solar Irradiance Measurements, Solar Instruments, Solar Spectral Distributions, Uncertainty Analysis, Solar Cell Technologies, Photovoltaics (PV), Solar Resource Assessment, Photovoltaics Power Forecasting",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 17th 2020",dateEndSecondStepPublish:"October 15th 2020",dateEndThirdStepPublish:"December 14th 2020",dateEndFourthStepPublish:"March 4th 2021",dateEndFifthStepPublish:"May 3rd 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A senior researcher in the field of photovoltaic solar energy, a postdoctoral scientist at Eindhoven University of Technology (TU/e), Chair of the WG2: reliability and durability of PV in EU COST PEARL PV.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"317230",title:"Dr.",name:"Mohammadreza",middleName:null,surname:"Aghaei",slug:"mohammadreza-aghaei",fullName:"Mohammadreza Aghaei",profilePictureURL:"https://mts.intechopen.com/storage/users/317230/images/system/317230.jpg",biography:"Mohammadreza Aghaei is a senior researcher in the field of photovoltaic solar energy, Eindhoven University of Technology (TU/e), The Netherlands. He is chair of the Working Group 2: reliability and durability of PV in European Cooperation in Science and Technology, COST Action PEARL PV.\nHe received the M.S. degree in electrical engineering from the Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, in 2013, and the Ph.D. degree in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2016.\nHe was a Postdoctoral Scientist with Fraunhofer ISE and Helmholtz-Zentrum Berlin (HZB)-PVcomB, Germany, in 2017 and 2018, respectively. He is a Guest Scientist with the Department of Microsystems Engineering (IMTEK), Solar Energy Engineering, University of Freiburg since 2017. He is currently a Postdoctoral Scientist with the Design of Sustainable Energy Systems Group, Eindhoven University of Technology (TU/e), The Netherlands. He has authored numerous publications in international refereed journals, book chapters, and conference proceedings. The main his research interests include Solar Energy, Photovoltaic systems, PV monitoring, LSC PV, solar cells, machine learning, and UAVs.\nDr. Aghaei is a member of the International Energy Agency, PVPS program-Task 13 and International Solar Energy Society, and also an MC member in EU COST Action PEARL PV.",institutionString:"Eindhoven University of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Eindhoven University of Technology",institutionURL:null,country:{name:"Netherlands"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"16682",title:"A New Methodology to Preliminary Design Structural Components of Re-Entry and Hypersonic Vehicles",doi:"10.5772/17141",slug:"a-new-methodology-to-preliminary-design-structural-components-of-re-entry-and-hypersonic-vehicles",body:'The aim of the hot structure design process is to ensure the structural integrity of the component minimizing two fundamental parameters: mass and thickness. The former influences the total weight of the vehicle, the latter influences the vehicle efficiency (Thornton, 1996;Kelly et al, 1983; Shih et al, 1988).
In order to perform the thermo-structural sizing of the component it is necessary to evaluate the stress and temperature distribution. Usually numeric methods (finite element or finite difference codes) are adopted to estimate those distribution (Daryabeigi, 2002;Poteet et al, 2004). In a preliminary design phase where accurate results are not required, approximate analytic solutions can be used (Kunihiko, 1998).
Analytic solutions, whether exact or approximate, are always useful in engineering analysis, because they provide a better insight into the physical significance of various parameters affecting the problem. When exact analytic solutions are impossible or too difficult to obtain or the resulting analytic solutions are too complicated for computational purposes, approximate analytic solutions provide a powerful alternative approach to handle such problems. There are numerous approximate analytic methods for solving the partial differential equations governing the engineering problems. One of the most powerful method is the Integral one (Crank, 1979; Syed et al, 2010). It is simple to use and gives the opportunity to solve non linear problems such as thermal radiation/conduction ones.
In the frame of thermal structures preliminary design activities the adoption of the integral method together with appropriate assumptions give the possibility to develop analytic/numeric models that allow to solve non linear transient thermal phenomena. Those methods are very useful since they are very simple to use and allow to save a significant amount of time with respect to numeric Finite Element models in a thermal structure preliminary design phase. As a consequence, complex optimization analyses characterized by several design objectives, constraints and variables in a reasonable length of time could be conducted. The proposed approach allows to define a preliminary thermal design of the hot structure. Obviously, in a subsequent design phase structural sizing must be performed starting from the configuration resulted from the previous thermal sizing process.
The present paper describes in detail how the proposed model works. An application of the simplified model on the wing leading edge of a re-entry vehicle is presented.
The integral method is an approximated analytical method since it is based on the assumption that the temperature distribution is described by a chosen expression (polynomial, logarithmic, etc.) (Necati, Ozisik, 1993).
The method is simple, straightforward, and easily applicable to both linear and nonlinear one-dimensional transient boundary value problems of heat conduction for certain boundary conditions. The results are approximate, but several solutions obtained with this method when compared with exact solutions have confirmed that the accuracy is generally acceptable for many engineering applications. In general when the differential equation of heat conduction is solved exactly in a given region subject to specified boundary and initial conditions, the resulting solution is satisfied at each point over the considered region; but with the integral method the solution is satisfied only on the average over the region.
The heat conduction equation is integrated over the spatial domain of the body; the result of the integration is the so called “energy integral equation”. An appropriate expression for the temperature distribution is chosen; the coefficients of the temperature expression are function of the boundary conditions. Then the temperature expression is introduced in the integral equation; its solution gives the temperature variation with time. Once temperature variation with time is available, T(x,t) is known.
The use of the integral method can be divided into two sequential stages.
Approximation of semi-infinite body. It is valid when the thermal layer
Several applications of the integral method have been found in literature. However none of them considers non linear boundary conditions and thermal properties.
In order to take into account the heat flux variation with time and thermal properties variation with temperature it is necessary to divide the trajectory time into a chosen number of time steps. The choice is such that the heat flux variation in a single time step must be less or equal than 1% of the maximum heat flux encountered during the flight trajectory, that is:
Where:
ti is the ith time instant at the beginning of the ith time step
ti+1 is the i+1th time instant at the end of the ith time step
qw is the aerodynamic heating
qw,max is the maximum aerodynamic heating value
The aerodynamic heat flux value is considered constant in the ith time step and equal to the algebraic mean value between the heat flux values encountered at the ith and i+1th time instants:
Thermal conductivity k and specific heat cp values are considered constant and equal to:
Where Ti is the temperature evaluated at the end of the i-1th time step.
In the first stage (approximation of semi-infinite body), radiation contribution is neglected, then linear boundary conditions are applied. In the ith time step the heat conduction differential equation is integrated between
The boundary and initial conditions to be applied are:
Where
Since the time parameter to be evaluated in the integral equation is the thermal layer
The parameters
Figure 1 illustrates the physical significance of the “thermal layer”.
The energy integral equation is:
Physical significance of thermal layer
Where:
Then the temperature distribution T(x,t) is known:
Where:
When
Giving the following energy integral equation:
The origin of the x axis is on the external surface where aerodynamic heating is applied. At x=L adiabatic boundary conditions are considered (see figure 2).
The boundary conditions are:
Schematic representation of the thermal problem
Where Te is the ambient temperature and Ts is the temperature at x=0.
For the same arguments explained above, the temperature distribution chosen is a quadratic one:
As mentioned before the thermal layer has now no physical significance. Then it is necessary to introduce a new time-dependent parameter. Two kind of parameters can be considered:
Ts(t)
Where TL(t) is the temperature at x=L.
In paragraph 2 it will be demonstrated that the second approach gives better results with respect to the first one when compared to one-dimensional finite element model results. In fact, in the second approach the solution of the energy integral equation has an information on the temperature distribution of the previous time step since the parameter
The energy integral equation is:
Where:
When boundary conditions are applied, the integral equation becomes:
For the second case:
The terms
Where
Following the same approach and changing adequately the boundary conditions it is possible to take into account the heat radiation exchange with an internal structure (see figure 3).
In particular, the first expression in (16) becomes:
Where TM represents the external surface temperature of the internal structure. The temperature distribution inside the internal structure is evaluated following the same approach described for the hot structure in a radiative environment; in this case the aerodynamic heating is substituted with the heat radiation flowing from the hot structure. The final expression for the temperature distribution in the hot structure is:
Where
The model can be also applied to double-layer structures.
Schematic representation of the thermal problem-internal radiation
The model described in the previous paragraph has been validated comparing the results of a one-dimensional thermal problem obtained with the proposed model with the results obtained with the Finite Element method. The Finite Element Analyses have been performed on one-dimensional conduction bars LINK32 elements that simulate the heat conduction phenomenon between two nodes (Ansys 12.0). The element is characterized by a single degree of freedom (temperature), the thermal properties and the cross sectional area. The external radiation has been simulated by means of LINK31 elements that are characterized by two nodes, a view factor, emissivity and the Stefan-Boltzmann constant. The net heat radiation between node I and node J is defined by equation (26):
A convergence analysis has been conducted on the mesh of the finite element model.
The benchmark is characterized by the following properties:
ρ=5000 kg/m3
L=0.05 m
Where
UHTC Thermal conductivity vs Temperature
UHTC Specific Heat vs Temperature
The aerodynamic heating variation with time is depicted in figure 6.
Aerodynamic heating vs time
The temperature of the external environment is considered constant and equal to:
Te=300 K
The initial temperature of the structure is:
Ti=300 K
Figure 7 shows the location of the points of the structure that will be considered for the results comparison.
Position of the points considered for the validation
Figures 8 and 9 illustrate T1 and T2 variations with time. The labels “I model” and “II model” refer respectively to the results obtained considering
Temperature T1 vs time
A good accordance between the FEM and the I model results is recorded. The maximum percentage difference is 15.7% (see also figure 9 and 10). On the contrary the second model curve is characterized by significantly greater differences with respect to the FEM results (see figure 10 and 12); this means that the adoption of a parameter that gives precious information regarding the previous temperature distribution is fundamental in order to evaluate the temperature distribution.
Figures 9 and 10 show that the maximum percentage difference occurs after about 50 seconds; more precisely it occurs when the passage from the semi-infinite body approximation to the finite body approximation. This is due to the different temperature distributions chosen for the two solutions (third order polynomial for the semi-infinite body, second order polynomial for the finite body).
Temperature T2 vs time
Percentage difference-Temperature T1 vs time
Percentage difference-Temperature T2 vs time
The same remarks can be made for the temperature T2. The percentage differences are slightly greater (see figure 12.).
Maximum percentage differences for I and II model
Finally, the first model is useful in a preliminary design phase where the one-dimensional approximation can be applied. The model can be used in an optimization tool in order to find the lighter configuration of a given thermo-structure. Since it is very simple and fast, complex optimizations characterized by a relevant number of design and constraint variables can be performed. Then significant amount of computational time can be saved adopting the proposed model instead of the Finite Element method approach.
The described model has been applied to preliminary design the ceramic wing leading edge of a re-entry vehicle. This activity has been performed in the frame of a project founded by the Italian Space Agency (ASI). The aim of the project is to develop advanced reusable structures able to sustain the high thermal loads encountered by re-entry and hypersonic vehicles (Ferraiuolo et al, 2008, 2009; Glass, 2008). Previously, the wing leading edge had been sized adopting a Finite Element commercial code (ANSYS) together with a first order optimization method (Ryszard, 2006; Hackman, Richardson, 2000). In the preliminary design phase, a two-dimensional geometry has been considered since the aero-thermal loads do not vary significantly along the wing.
Scheme of the Test Article
The components of the Test-Article are (see figure 14):
UHTC (Ultra High Temperature Ceramic) wing leading edge,
C/C panel to be joined with the UHTC WLE and the radiative shield,
Steel and Inconel slabs representing the internal structure of the Test Article,
Radiation shield used to minimize the heat transfer between the UHTC and the internal structure.
scheme of the internal structure of the WLE
The design variables considered for the optimization phase are:
UHTC WLE thickness
Radiation Shield Thickness
UHTC material (discrete variable; 3 UHTC material have been considered in the optimization phase).
The constraint variables are:
Limit use temperatures for the UHTC and the radiation shield,
Maximum temperature of the steel slab that must not exceed 430 K (this constraint has been chosen since measurement devices in the steel slab cannot operate at a greater temperature value).
The objective is to minimize the weight of the structure.
The result of the optimization is an almost constant UHTC thickness of 10 mm and a 8 mm thickness for the radiation shield. The time needed to perform that optimization was about 160 hours. Figure illustrates the 3D final configuration of the UHTC wing leading edge.
scheme of the WLE
The model described in paragraph II has been adopted to preliminary design the above described Wing Leading edge. An optimization code (MODEFRONTIER) has been used in order to perform the optimizations. The model has been implemented in Excel and then in MODEFRONTIER.
Figure 16 illustrates where the proposed model has been used. In particular, in the “*1” rectangles the model considering the internal radiation has been adopted; in the “*3” one the model considering only the external radiation has been adopted; the “*4” rectangle refers to the double-layered model and the “*2” one refers to a numeric model able to evaluate the thermal and the structural responses of multi-layered structure subjected to aerodynamic heating (Ferraiuolo et. al, 2008).
The structural sizing has been performed only in rectangles “*2” since they include the junction between The UHTC leading edge and the C/C panel which is more critical from a structural point of view (Blosser, Mcwithey, 1983, Thomas, 2002;Milos, Squire, 1999). Structural sizing on the remaining areas will be performed in a subsequent detailed design phase.
Models adopted for the preliminary design
The optimization phase has been enriched considering the possibility to change the UHTC material. Then, a discrete design variable has been added in MODEFORNTIER. Figure 16 illustrates the scheme built in MODFRONTIER.
Modefrontier optimization scheme
Ten points have been chosen for the optimization of the WLE (see figure 18).
A gradient based method has been chosen to perform the optimization. The time needed to perform the optimization is 20 hours, 8 times smaller than the time needed adopting finite element codes. The outputs of the optimizations are summarized in figures 19, 20 and 21 .
Optimization points
Optimized thicknesses and materials- points 2-7
Optimized thicknesses and materials- points 1 and 8
Optimized thickness – point 9
Interpolating the optimized thicknesses along the wing leading edge, the following configuration is obtained (figure 22):
The final weight is 12% less than the starting configuration. All the optimized thicknesses are less than 10 mm, except for the point 7. The material 3 chosen for the upper portion of the wing leading edge, is characterized by a smaller density and Temperature limit use with
respect to materials 1 and 2. The design of the connection between the UHTC WLE and the C/C panel will be performed in the detailed design phase [19].
Optimized configuration
Figure 23 illustrates the linearized thickness distribution for that starting and the optimized configuration. the black area highlights the weight saving amount obtained by adopting the numeric-analytic model proposed.
Wing leading edge – linearized thickness distribution
A finite element model representing the optimized configuration has been built in order to compare the results obtained by means of the proposed model and the results obtained by means of the Finite Element Method.
Figure 24 contains the temperature values evaluated at the time instant characterized by the maximum heat flux.
FEM and proposed model results-Temperature
Figure 25. contains the principal stress S1 values for points 1 and 8.
FEM and proposed model results – stress values
The proposed model overestimates the temperature values with respect to the Finite element model. The maximum percentage difference is always less than 10%. As a consequence the presented numeric-analytic model is applicable to two-dimensional geometries where the heat transfer contribution in a direction is negligible with respect to the other ones.
A simplified numeric-analytic one-dimensional model able to simulate the thermal behavior of thermal structures has been developed. Radiation and thermal properties non linearities have been taken into account. The model is useful to minimize the computational time needed to perform the preliminary design phase of structural components of re-entry or hypersonic vehicles. The time saving is possible since the proposed model is very simple and does not foresee a spatial discretization of the geometry. Then complex optimizations characterized by several design constraints, variables and objectives can be performed in a reasonable amount of time. The results obtained with the simplified model have been compared with the results obtained by means of the finite element method giving good results (the maximum percentage difference never exceeds 20%).
The model has been applied to preliminary design the ceramic wing leading edge of a re-entry vehicle allowing to save 12% of the weight with a computational time 8 times smaller with respect to the previous optimization performed on a two-dimensional finite element model. A thermostructural finite element analysis performed on the optimized configuration has demonstrated a good accordance between the results obtained with the proposed model and those obtained with the two-dimensional Finite element method.
Finally, the proposed simplified approach is well suited for thermal protection systems and hot structures where the one-dimensional assumption is acceptable. Particular attention must be paid on curved geometries like the wing leading edge where the approach can be used only in certain areas.
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.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
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\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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