Very High Resolution satellites (1999-2006), with their spatial resolutions and spectral bands. Note that there is not any available satellite with VHR in multispectral bands.
\\n\\n
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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Inventories on forest communities are performed with the objective of providing support to the management and conservation activities in rural or urban forests or even in tree plantations. The traditional method of obtaining information on forest communities is to use systematic or random sampling or by sampling stands, so that the final parameters for the population are obtained on the basis of statistical extrapolation [1, 2]. Usually, the following parameters are determined for each tree included in the sampling: location, diameter at breast height (DBH), basal area (BA), height, identification of the species, crown size, and crown closure. Based on these measurements, other parameters such as volume of wood and biomass can be derived for the community stand. This renders the field survey techniques for forest inventories expensive, time consuming, and unsuited for large areas.
Remote sensing with high spatial resolution is a cost-effective and reliable way to obtain information about trees. It may be the only practical manner to assure sustainable management of forests with the necessary information, such as biochemical and biophysical data on the vegetation in a synoptic and repetitive manner for large areas and over long periods of time [3]. The tree crown is the basis of the data required for the inventory, for it allows to determine not only its size but also its position, crown closure, and, in some cases, the species. It also allows the derivation of parameters such as the density of the population, the health condition of the trees, the volume, the biomass, and the carbon sequestration rates [3–6]. This information is crucial to a series of applications such as the inventory and management of forested areas as well as in parks and urban forests. It can also be used for counting and monitoring trees in orchards or under power lines to prevent damage and accidents.
The study of individual trees with remote sensing started with the use of aerial photography with very high spatial resolution (scale greater than 1:10.000), driven mainly by the use of stereoscopy techniques. The task was performed by photointerpreters trained to recognize individual tree species, extract a series of measurements, or evaluate different types of damage [2]. The use of orbital optical remote sensing data for forest studies began in the 1970s, with the development of techniques to separate forested from non-forested areas [7]. The spatial resolution of these satellite images was the main limiting factor for more detailed studies about the forests, and as a result, the studies remained focused on the disturbances affecting forests (such as land clearing, burning, diseases, and pest) or to estimate some biophysical parameters of the vegetation [3,8]. It was only toward the end of the 1990s that orbital remote sensing began to provide very high resolution (VHR) data with a spatial resolution under 1 m, allowing the study of individual trees. Launched in 1999, Ikonos was the first of what is now a series of VHR satellites (Table 1), consolidating the use of orbital data for the study of individual trees. However, the increase in spatial resolution was not always accompanied by an increase in spectral resolution for VHR data which is often restricted to a single panchromatic band.
The increase in spatial resolution changed the focus of many remote sensing studies, which started to analyze not only classes of objects but also each object individually [9]. Branches and irregularities within the crowns became visible, and as a result, the spectral response of a tree is influenced by variations in the shape of the crown (differential illumination) and background effects. This causes an increase in the intra-class variance and often results in a reduced accuracy when using conventional pixel-based classification [10]. This had a significant effect on the image processing techniques for forest studies and generated the development of new forms of information extraction.
Within the study of individual objects, the automatic detection and delineation of tree crowns using remote sensing VHR imagery have attracted much attention from researchers in forestry and computer vision [4,7]. Researchers have developed several automatic and semiautomatic methods for extracting individual trees and their characteristics using digital aerial photos of various types and VHR satellite images [11]. The applications range from the identification of tree crowns to their delineation and classification and are often based on image segmentation algorithms and other advanced image processing and analysis techniques [9,12]. Most of these algorithms were specifically developed for the detection and delineation of trees in temperate forests based on the assumption that the trees are cone shaped and round (mostly coniferous) in the images, with the apex of the tree having the highest reflectance of the crown area [4].
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
\n\t\t\t\t | \n\t\t\t1999 | \n\t\t\t0.82 | \n\t\t\t3.2 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2001 | \n\t\t\t0.65 | \n\t\t\t2.62 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2007 | \n\t\t\t0.46 | \n\t\t\t- | \n\t\t\t- | \n\t\t
\n\t\t\t\t | \n\t\t\t2008 | \n\t\t\t0.46 | \n\t\t\t1.84 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2009 | \n\t\t\t0.46 | \n\t\t\t1.85 | \n\t\t\tCoastal, Blue, Green, Yellow, Red, Red Edge, Near IR, Near IR2 (8) | \n\t\t
\n\t\t\t\t | \n\t\t\t2011 | \n\t\t\t0.5 | \n\t\t\t2.0 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2012 | \n\t\t\t0.5 | \n\t\t\t2.0 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2012 | \n\t\t\t0.7 | \n\t\t\t2.8 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2013 | \n\t\t\t0.9 | \n\t\t\t2.0 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2014 | \n\t\t\t0.31 | \n\t\t\t1.24 | \n\t\t\tCoastal, Blue, Green, Yellow, Red, Red Edge, Near IR, Near IR2 (8) | \n\t\t
\n\t\t\t\t | \n\t\t\t2014 | \n\t\t\t0.9 | \n\t\t\t2.0 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2015 | \n\t\t\t0.55 | \n\t\t\t2.2 | \n\t\t\tBlue, Green, Red, Near IR (4) | \n\t\t
\n\t\t\t\t | \n\t\t\t2016 | \n\t\t\t0.34 | \n\t\t\t1.36 | \n\t\t\tNot available at time of printing | \n\t\t
Very High Resolution satellites (1999-2006), with their spatial resolutions and spectral bands. Note that there is not any available satellite with VHR in multispectral bands.
* Panchromatic (Px) and Multispectral (Mx) resolution at nadir.
The analysis of individual trees based on remote sensing images is a complex problem. Images of trees with varied crown size increase the difficulty of the analysis. What is detected as a single object may in fact represent a separate branch or even a group of trees [10]. Other sources of error are caused by the proximity between neighboring trees, trees located under other trees, trees in the shade, or trees that have a low spectral contrast with the background [13]. Consequently, high-level complex algorithms are necessary to exploit this contextual information [1].
This chapter provides an overview of the state of the art in individual tree crown detection based on optical VHR remote sensing data. An original method developed by the authors is also presented as an alternative approach to the problem of tree crown detection. In Part I, we present the main algorithms developed for the detection of individual trees, be it for tree identification or delineation. The principle of each approach is presented as well as its potential and limitations. Part II is dedicated to outlining the original MPP–TM approach, a hybrid method that combined two methods used in pattern recognition: marked point process and template matching. The results are shown for tree detection and delineation in an urban environment and for tree counting in orchards.
We present six of the main algorithms used in individual tree detection in high spatial resolution images. The algorithms are summarily described individually, but it should be noted that many approaches use hybrid methods for the detection and delineation of tree crowns. For instance, some authors might use one algorithm for detecting the trees and another to delineate them; some may even use one approach as a first approximation and another to fine-tune the results.
Local maxima (LM) filtering is a technique used for identifying tree crowns in high spatial resolution imagery which is based on the recognition of the points with the greatest brightness within a search window that scans the entire image [4,14]. The search window, with a fixed size, defines which pixel has the greatest reflectance compared to all the other pixels inside the window. The pixels with the highest digital number are identified as possible tree locations. This method is adequate for trees which have the greatest reflectance at their top, surrounded by lower intensity pixels, and due to its concept, it is widely used for detecting conifers.
When the kernel window passes over the image, it does not take into account the presence of trees with different crown sizes, and the success of the LM tree recognition depends on the careful selection of the size of the search window. If it is too small, errors of commission occur by selecting nonexistent trees or multiple radiance peaks for an individual tree crown; if it is too big, the algorithm is likely to miss some trees (omission errors) [13].
The identification of trees by LM is affected by false bright pixels, which are not part of the brightest part of the crown. An effective method for dealing with the problem is to apply a Gaussian filter to the image. This allows the low-pass filter function to grant more weight to the crown center pixels (surrounded by much lower values) compared to those located toward the crown edge which might belong to other bright objects or noise. Applying a Gaussian filter directly affects the number of local maxima identified and causes the smoothing off of the brightness values on the tree crown edges [15].
In order to minimize the problem of the window size with LM, reference [13] used windows of varying sizes based on the assessment of the spatial structure of the image obtained by analyzing the local semi-variogram with different pixel lags and different window sizes. This results in a personalized window for each pixel, leading to greater accuracy when compared to using a single fixed window size. Reference [16] used LM to identify the centroid of eucalyptus trees in Australia. The search for the trees is carried out based on the maxima in four linear kernels pertaining to the four main directions (0°, 45°, 90°, and 135°) of the image and by summing the individual maxima found in each pass (Figure 1).
Examples of the surface produced by applying a LM kernel operator: (a) original image, (b) the local maxima appearing in the third dimension are associated with the presence of trees, (c) application of local maxima filter in four linear cumulative kernels (0°, 45°, 90°, and 135°).
Template matching (TM) is a technique used for object recognition widely cited in the specialized literature which uses quantitative descriptors, such as length, area, and texture to describe recurring patterns in an image [17,18]. Based on a synthetic model or a sample extracted from the image, the correlation coefficient between the model and the image is calculated in order to determine the strength of the match between the two matrices. The object is assumed to be located where the measurement of the match reaches a maximum [4].
For tree crown detection, the study of reference [19] was the first to propose an elliptical 3D model for tree crowns based on an ellipsoid of generalized revolution (Equation 1).
where
Because it is based on a physical model (rather than a complex mathematical concept), TM is considered a user-friendly method. Its limitation is mainly due to the need to use a library of models if many types of trees are present in the image, which may involve a complex phase for generating the models. Figure 2 shows examples of synthetic tree models and an application in a orchard.
References [20 and 21] used this technique to identify tree crowns in aerial images. Other researchers used this technique to recognize individual tree crowns, using templates made from small sub-images of the actual scene to identify the trees [22, 23]. Reference [24] proposed an improved version by generating separate models for trees and their shade in VHR images of unmanned aerial vehicles (UAV). The authors explored the relation between the tree and shade models separately and then joined them to generate a more powerful object detector.
Left: examples of synthetic tree models to different tree crown shape. Right: identification of trees in an apple orchard showing the model used at the upper right corner.
Valley-following (VF) is a crown delineation method which identifies the shaded areas between the trees. This methodology was initially described in reference [25] and makes an analogy with topographic data, where the shades of gray of the pixels represent local lows in the third dimension. In this analogy, the bright tree crowns would be the hills and the darker zones around the trees the valleys (Figure 3). This darker zone is the one which typically helps human interpreters to separate one tree crown from the other. In this approach the shaded areas are eliminated, making it possible to separate the trees in the image. This was not sufficient to separate all of the trees, so the authors developed an approach based on a series of rules (e.g., no discontinuity, checking directions, context, gap filling, etc.) to accurately describe the boundaries of each tree, one at a time [26].
This approach performed well in images with a combination of low solar elevation angle and conical trees. Conversely, the approach failed to produce good results when the canopy was composed of trees of very different sizes, or when the tree crowns were very large and have internal shadows. The latter case resulted in subdividing the individual tree into two or more parts. Smaller trees, in contrast, tended to be grouped together. Reference [27] found that this approach causes many false positives (FPs) in open areas (clearings). As a solution, they suggested the exclusion of these areas by retaining only the high-value pixels in the normalized difference vegetation index (NDVI).
Results of the valley-following applied on a forest image in Canada (source: Gougeon and Leckie, 2003; reproduced with permission from Natural Resources Canada).
Like VF, the watershed segmentation (WS) is a technique related to thresholding that uses the gray levels in the images as if it were a topographic surface [28]. It is used not only for the delineation of individual tree crowns but also for generic segmentation of images. The watershed concept is based on a 3D image representation, with the third dimension being provided by the intensity of gray. The main objective of the watershed algorithm is to find the “drainage” divide lines. The “relief” in the image is inverted (high gray values become valleys) and progressively filled with a virtual liquid, and when the liquid is almost overflowing from one basin to another, a virtual dam is built, to create the watershed. These lines are considered the limits of each segment. The simplest approach to the construction of the dam is the use of morphological dilation of the minima, without merging the regions [17].
The images are usually preprocessed before the WS is applied. In fact, this segmentation is frequently applied to the gradient of an image, and not to the image itself. This is due to the relative homogeneity of the gray values of objects that do not provide sufficient contrast for an effective segmentation. In this formulation, the regional minimum value of the catchment basins usually correlates well with the lower gradient values that match the contours of the objects of interest [17,28]. The direct application of the WS algorithm generally leads to over-segmentation due to noise or other local irregularities of the gradient (Figure 4a). One of the approaches used to limit the number of regions is to use markers. The selection of markers can be based on simple procedures, intensity and connectivity between pixels, or even complex descriptors, such as size, format, location, relative distances, texture, and others. The use of markers provides prior knowledge to support the segmentation process [17].
The approaches that use WS for the delineation of the tree crowns normally use markers representing the center of the tree crown, to assist the segmentation process. For instance, reference [29] used WS to detect and delineate tree crowns in a VHR forest image in Canada but divided the approach into two phases, namely using LM to detect the crown and applying WS for the delineation. The LM image with the detected crowns was produced by using a Laplacian of Gaussian edge detection operator. The tree crowns were modeled based on their geometry and radiometry, resulting in an image of markers. This image then served to guide the WS in delineating the crowns. Reference [30] developed a bitemporal procedure for the automatic segmentation and reconciliation of groups of pixels (called blobs) within the forest using WS. By using two dates, they were able to increase the probability of properly defining the tree contours. Many problems were encountered in the segmentation process of the individual trees. For instance, trees with spread branches were sometimes split into two or more segments or contrarily by including several crowns in the same segment when trees were not sufficiently separated.
Region growing (RG) is another segmentation technique that groups pixels or groups of pixels based on predefined growth criteria in an attempt to separate and recognize objects in the image [4]. Like WS, RG is used as a generic segmentation method and can be adapted for the delineation of individual tree crowns (Figure 4b). Starting with some seed pixels (which can be random if no other information is provided), the neighboring pixels are examined one by one and added to the growth region if their predefined properties are similar to those of the seeds (such as specific intervals of intensity or color) [17]. When no more pixels can be added or some predefined limit is reached (e.g., number of pixels), these pixels are labeled as belonging to the specific region of the seed pixel. Additional criteria can increase the power of an RG algorithm by introducing a higher concept like size and similarity between candidate pixels and the pixels selected or even the format of the region [17,31].
Reference [16] used RG integrated with LM to identify and delineate tree crowns in Australia. The LM method served to find the center of potential trees, which were then used as seeds for the RG. Reference [6] tested two different types of segmentation by RG, one by Brownian motion and the other by random walk, to detect conifers in a boreal forest. The methods were capable of detecting about 80% of the illuminated portion of the crowns, with a better performance found in larger crowns (Figure 5).
Comparison between two segmentation algorithms: (a) watershed and (b) region growing on a WorldView-2 image (panchromatic band with a 50 cm resolution). The WS was applied to the gradient image without using markers and resulted in the over-segmentation of the tree crowns. In (b), the RG segmentation was performed within an object-oriented classification (GEOBIA) approach, where the correct delineation of the tree crowns is noteworthy (source: Gomes and Maillard, 2013).
Example of RG segmentation to delineate tree crowns in a boreal forest. (a) The original image (with a spatial resolution of 3 cm), (b) with the results using Brownian motion, (c) and random walk (Source: Erikson, 2004).
The marked point process refers to a probabilistic method which has been used in recent years for the recognition of objects in high spatial resolution imagery [5,11,32–35]. In an MPP, sets of random points in a given space (
The laws of density and probability distinguish various types of point processes, which can be Poisson, Strauss, Markov, or Gibbs, among others. The Markov or Gibbs point processes have been used for the recognition of tree crowns by a number of authors [5,32,33]. These processes are defined by a density function using a form of energy expressed as a sum of the a priori energy and the local energy. The process seeks to minimize the global energy of the model, by iterating it with some optimization scheme (Markov random fields, algorithm of multiple births and deaths, and Monte Carlo-Monte Carlo simulations).
Example of the application of MPP for tree crown recognition on a poplar plantation. The original image is shown at the top and the results at the bottom (Source: Perrin, 2006).
Reference [5] proposed two different models to serve as marks in an MPP, one in 2D for detection of trees in densely forested zones (Figure 6) and the other in 3D for scattered or isolated zones, based on aerial photos of high spatial resolution in the infrared band. The MPP was integrated with a reversible jump Markov Chain Monte Carlo in a simulated annealing method. Reference [32] used an MPP to automatically detect the tree crowns in high spatial resolution images, based on the modeling of the crowns as 2D circles on high-resolution spatial images. The method was successfully tested on mangrove forests and eucalyptus plantations.
In the previous section, we have presented some of the most common algorithms used in the detection of individual trees, be it for their identification, delineation, or both. Table 2 presents a summary of these principles through their main characteristics and limitations.
Trees may differ in shape, size, spectral properties, height, foliage type, and density, and their spatial context varies with illumination, ground type, and inclination. They can also be surrounded by many other objects, especially in an urban setting. As such, the task is not trivial and can become highly complex depending on the number of parameters involved. Conversely, in planted forest and orchards where trees have the same age and species, tree crown extraction can take advantage of their relative uniformity.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
\n\t\t\t\t \n\t\t\t\t | \n\t\t\tIdentification of tree crown | \n\t\t\tIdentification of brightest points locally as corresponding to the apex of a treetop within a search window. | \n\t\t\tWulder Culvenor (2002) Pouliot (2002), Wang | \n\t\t\tAppropriate for conifers, with a conical shape and high reflectance point at the top of the tree. Simple method to use. Results are affected by the spatial distribution of trees, variation of tree crowns size, search window size (increased omission errors in larger windows and commission errors in smaller windows). | \n\t\t
\n\t\t\t\t \n\t\t\t\t | \n\t\t\tPattern recognition | \n\t\t\tQuantitative descriptors used to describe patterns. Calculate the correlation between the image and the model. Model may be a sample extracted from the image or not. | \n\t\t\tPollock (1996) Larsen (1997) Larsen and Rudemo (1998) Quackenbush Erikson (2004) Hung | \n\t\t\tEnables analysis of the tree crown from its spectral, textural and structural characteristics. Allows neighborhood analysis of the tree crown by considering its shadow. User-friendly method. Needs a template library, making it unpractical in complex forests. Recognition errors increase with irregularity of the tree crowns. Easier to detect larger trees than smaller ones. Performance reduced in very dense environments. | \n\t\t
\n\t\t\t\t | \n\t\t\tDelineation of tree crown | \n\t\t\tDerives from an analogy with a topographical surface, programmed to identify the shaded portion between the tree crowns (valleys). | \n\t\t\tGougeon (1995, 1999) Leckie and Gougeon (1998) Gougeon and Leckie (2003) Erickson (2004) Gougeon and Leckie (2006) | \n\t\t\tAppropriate for trees with conical shape that create shadow areas between individuals. Most successful to delineate populations of the same age without intertwined tree crowns. Best performance for images in mid-low solar elevation angle. Performance reduced when trees are asymmetrical, of from different species, with different tree crown sizes or when shadows of trees protrude over each other. Tendency to group smaller trees together and split larger trees into multiple segments. | \n\t\t
\n\t\t\t\t \n\t\t\t\t | \n\t\t\tDelineation of tree crown | \n\t\t\tPerformed from the image gradient. Image is seen as an inverted topographic surface flooded to determine watershed divides. Commonly uses markers to limit the number of segments. | \n\t\t\tWang Lamar | \n\t\t\tPerforms best when applied after selection of markers to control segmentation process. More suitable for conifers, which allow preselection of treetops by using another approach (usually LM). Over-segmentation occurs when applied directly to the image or without the use of markers. Can separate tree crowns in different segments when the branches are too spread, or may include several trees in the same segment when there is no spatial separation between them. | \n\t\t
\n\t\t\t\t \n\t\t\t\t | \n\t\t\tDelineation of tree crown | \n\t\t\tGroups pixels or sub-regions based on predefined criteria for the growth of region in order to separate and recognize objects in the image. | \n\t\t\tCulvenor (2002) Pouliot et al. (2002) Erikson (2004) Bunting and Lucas (2006) Pu and Landry (2012) | \n\t\t\tMore complex shapes of trees are better delineated. Method more complex as it requires different rules for different environments. Tends to create more than one segment when the tree has branches with dark portions, and tends to group different trees if they are very similar. | \n\t\t
\n\t\t\t\t \n\t\t\t\t | \n\t\t\tPattern recognition | \n\t\t\tStochastic process in which unordered points in a space are provided with marks. Marks are modeled from geometric and radiometric characteristics of objects. | \n\t\t\tPerrin (2006) Zhou (2012) Larsen (2012) | \n\t\t\tPerforms best with plantations of trees of same species and age and in images of isolated trees. It is less effective to detect trees in more complex environments. | \n\t\t
Summary of Local Maxima, Template Matching, Valley Following, Watershed, Region Growing and Marked Point Processes algorithms used to individual tree crown detection. The principles, main researches and main characteristics and limitations are presented.
Reference [11] compared six different algorithms (valley-following, region growing, template matching, scale-space theory, marked point processes, and Markov random fields) in six different aerial images, ranging from a homogeneous plantation and an area with isolated tree crowns to an extremely dense deciduous forest type. The authors found that none of the algorithms can by itself reach a high rate of success in all of the tested images and concluded that there is no single optimum algorithm for all types of images and forests. They also emphasized that for complex types of forests, monoscopic images are insufficient for a consistent detection of tree crowns, even for human interpreters.
As shown in our brief review, many methods have been developed for trees in temperate forest environments. In an exploratory research [23], three algorithms in urban tropical environments were tested: region growing, watershed, and template matching. Better results were generally obtained by combining region-growing segmentation and geographic object-based image analysis (GEOBIA) for classification. Although highly effective, the approach requires much parameter setting and experience and is not especially dedicated to the problem of tree crown detection.
Studies that use marked point processes have triggered our attention and made us consider that they could benefit from using marks modeled from 3D objects in a different approach than from that developed by reference [5]. We propose to use a geometrical-optical tree model in a manner resembling that of template matching that uses some form of correlation between image and model to identify candidate pixels. An MPP taking advantage of a geometrical optical 3D model and measurements of similarity to seek tree crowns could represent a significant improvement to using simpler marks. Considering such a hypothesis, we developed an algorithm for tree crown detection that combines elements from MPP, TM, and tree crown geometrical-optical modeling for the automatic detection and (simplified) delineation of trees in VHR satellite imagery. We have named our algorithm MPP–TM.
In our approach, the TM did not scan the whole image like it was initially conceived but rather uses an MPP approach to select random locations within the image. Additionally, the 3D marks receive a random diameter between a predetermined range depending on the type of environment. The geometrical-optical model includes both the sunlit and shaded areas of the crown and a portion of the projected shadow to allow a better match between model and image. Some statistical and spectral parameters were also included in the model-matching phase.
MPP-based algorithm for pattern recognition usually alternates between phases of birth and death during which the objects are created (placed) and destroyed when they do not comply with the matching rules. This is also a characteristic of MPP–TM, but we have somewhat deviated from the original concept where the destruction phase also incorporated a random process.
The following subsections are devoted to describe the construction process of the 3D geometrical-optical model and the functioning of the algorithm.
The parameters that determine the radiance pattern of a tree crown are direct and indirect radiation, shape of tree, branch pattern, leaf reflectance, multiple reflectances within the canopy, etc. [36]. In creating a valid 3D geometrical-optical model, we have chosen a simplified version in which the crown is represented by a dome of varying skewness, a Lambertian reflectance model with ambient light, and a projected shadow on the ground (or on another tree). Equations 2 and 3 give the formulation of our model in which each pixel is treated as a singular surface.
where
where
Illustration of the geometrical-optical model of tree crown as seen in the same sun azimuth (32°) but in two different solar elevation angles: (left) 20° and (right) 45°
A parameter of projected shadow clipping has also been added to account for the fact that it was not beneficial to use the whole shadow in situations where it was projected onto another tree and not on the ground. The height of the tree also affects the size of the shadow so that it did not appear wise to set the height to a fixed value. To illustrate this, Figure 8 shows a comparison between the tree model and an actual tree from the image both with whole and clipped shadows.
Comparison between an isolated tree from the (a) WorldView-2 image and (b) the geometrical-optical 3D model. A clipping factor of about 80% was applied to the same images in (c) and (d) to enable the use of only a portion of the shadow in cases where that shadow is not projected on the ground but on another object.
According to reference [32], using MPP to extract objects consists in searching for the “best” possible object configuration in a scene, the one that will respect a certain number of properties both of the objects being sought and the radiometric properties of the image. In our algorithm, the “best” configuration be it geometric or radiometric is given by the model.
The process consists in alternating phases of birth and death. The MPP starts with a birth phase during which tree crowns represented by circles of varying size (a randomized interval) are inserted on a matrix of equal size to the image being processed. Tree crowns are only inserted where no other crowns are present. Once all the circles have been inserted (determined by a density parameter
The
where
In Equation 5, the cross-correlation is calculated between the model matrix (
In the death phase, tree crowns are kept if their similarity is larger or equal to a pre-set threshold. Because we found that such a threshold represented a weak element in our algorithm, we implemented a strategy by which it needs not be predetermined with a fixed value but rather adjusts itself as the number of iterations grows. The threshold is set very high at the beginning but then starts to decay when a certain number of iterations do not find any "new" tree crown (typically 100 iterations). Additionally, if more than a certain amount of iterations (say 1000) still does not add any new tree crown, then the process is stopped. Ultimately, it will be stopped if the maximum number of iterations is reached. A flowchart of our algorithm is presented in Figure 9 and schematically described in Table 3.
Flowchart of the MPP–TM algorithm.
\n\t\t\t\t \n\t\t\t\t | \n\t\t||
\n\t\t\t | \n\t\t\t\t \n\t\t\t\t \n\t\t\t\t \n\t\t\t\t | \n\t\t|
\n\t\t\t\t | \n\t\t||
\n\t\t\t | a. While the number of searched trees is not achieved or some of the interruption process (total iterations or minimum threshold for similarity). | \n\t\t|
\n\t\t\t\t | \n\t\t||
\n\t\t\t | a. Randomly pick a radius within model catalogue b. Randomly pick c. Check if crown is already present d. If not: | \n\t\t|
\n\t\t\t | \n\t\t\t | i. Fill area with circle of radius ii. Extract corresponding area in the image matrix iii. Compare image and model matrices iv. Calculate and store values: i,j, average, standard deviation and | \n\t\t
\n\t\t\t\t | \n\t\t||
\n\t\t\t | a. Input parameters: birth image matrix; crown statistics ( b. While smcrown < smthreshold: | \n\t\t|
\n\t\t\t | \n\t\t\t | i. Zero crown pixels in birth image matrix | \n\t\t
\n\t\t\t | c. While δ crown < min δ threshold and δ crown > max δ threshold: | \n\t\t|
\n\t\t\t | \n\t\t\t | i. Zero crown pixels in birth image matrix | \n\t\t
\n\t\t\t\t \n\t\t\t\t \n\t\t\t\t | \n\t\t
Description of MPP-TM algorithm.
Figure 10a shows the state of the crown matrix after a single birth phase with 163 circles of random radius (between 3 and 15 m) and randomly located within the image matrix. After the death phase, using a similarity threshold of 0.98, only one tree crown was kept (Figure 10b).
Illustration of the (a) birth and (b) death phases of the MPP–TM algorithm. In this example, of the 163 randomly positioned crowns, only one had a similarity value larger than the threshold of 0.98.
Because trees in orchards are often individually distinguishable and have similar shape and size, they are perfect candidates for TM with a 3D geometrical-optical model. By using a GOM, the effects of varying illumination (sun elevation and azimuth) become an advantage rather than an obstacle especially when the background is homogeneous. In terms of data, VHR image data such as a large proportion of Google Earth images have sufficient resolution for identifying orchard trees. In this case, however, illumination parameters are not readily available and must be determined.
The objective of this modified approach is to introduce an adaptation of the algorithm described earlier to detect and count trees in orchards of different types. Because it was aimed at a more regional or even global application, Google Earth images were used in an attempt to simulate a generic operational framework. The modified approach uses a similarity measurement between the GOM and the image to calculate the probability of being the center of tree and then places trees in nonoverlapping positions (unless some overlapping is allowed). The algorithm also incorporates a module to determine the illumination parameters from a sample.
The algorithm is based on three principles. First, it assumes that the trees have a dome-like shape approximated with a GOM and the right illumination parameters. Second, there is little or no overlapping between trees, and third, the pixel with the highest similarity represents the most likely central position of the tree.
The GOM is a simple dome model for which the height is estimated at 1.5 times the diameter of the crown, and to simplify the problem we have assumed a unique diameter for all trees in the orchard (this can easily be modified to incorporate a range of diameters). The algorithm responsible for the detection of trees are best explained through a list of steps.
Urban trees play an important role in the welfare and quality of life in cities. They contribute to improving air and water quality, mitigate the carbon dioxide and other pollutants, moderate the microclimate and air temperature, help control soil erosion, reduce the flow of rainwater, and provide biodiversity [37–39]. A good knowledge of the species planted in cities and their health contributes to the inventory and management of these trees. To fulfill their role in the urban environment, trees need to be looked after through maintenance practices such as pruning and monitoring them for pests and diseases.
A WorldView-2 (WV-2) image of the campus of the Universidade Federal de Minas Gerais (UFMG) (and surroundings) in Belo Horizonte, Brazil, was used as our test data (Figure 11). The scene was already orthorectified and radiometrically corrected. Although WV-2 offers nine different spectral bands, only the panchromatic band (
Location of study area. The image on the right is a WorldView-2 false color composite
Three WV-2 sub-images were selected to test the performance of MPP–TM algorithm (Figure 10). These images were chosen from different contexts with both isolated and grouped trees and with other objects present in the scene. A wide variety of crown radii is also present in these images. The first two images (Figure 12a and b) are from the university campus of UFMG, and the last is from an urban park (Figure 12c).
Sub-images selected from the WV-2 image.
To assess the quality of the results produced by MPP–TM, validation was done by comparing our results with a visual interpretation of the trees in the image. For these, only tree counting was used as validation. For the crown counting validation, we considered the following situations: 1) true positives (TP) for found trees, 2) false positives (FP) when a detected object is not a tree, and 3) false negatives (FN) for trees not encountered. The success score was computed as follows (Equation 7):
where
These results are shown below with their respective overall similarity and standard deviation graphs (Figure 13). The validation results are presented in Table 4.
MPP–TM results obtained with the three WV-2 image 1–3 (left) and their graphs of global similarity (center) and standard deviation (right). The yellow circles correspond to correctly identified trees (true positive or TP), the objects marked with a yellow "A" are false negatives (FN) and the objects marked with a yellow "B" are false positives (FP).
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
\n\t\t\t\t | \n\t\t\t47 | \n\t\t\t43 | \n\t\t\t3 | \n\t\t\t8 | \n\t\t\t72.73 | \n\t\t
\n\t\t\t\t | \n\t\t\t50 | \n\t\t\t59 | \n\t\t\t8 | \n\t\t\t5 | \n\t\t\t92.73 | \n\t\t
\n\t\t\t\t | \n\t\t\t175 | \n\t\t\t161 | \n\t\t\t5 | \n\t\t\t20 | \n\t\t\t80.00 | \n\t\t
Validation of the MPP-TM results with the three WV-2 images.
In the two images of the campus, the program was able to find 73% and 93% of the trees, respectively, with very few errors in isolated trees (Figure 13a and b). The presence of other objects (buildings, streets, and sidewalks) did not hinder the identification of trees and few false positives (3 and 8, respectively) were found. In both images, MPP–TM was able to find most grouped trees, but the crown diameter was often slightly off. It should be noted that some cases are even difficult to correctly identify and delineate visually. Mostly, the errors came from dividing a single crown into two, or including two different crowns as a single object.
The WV-2 image 3 is from a protected urban park area with predominantly isolated trees and relative homogeneous crown size of about 6 m (Figure 13 c). A total of 161 objects were detected with only 5 false positives and 20 false negatives for an overall success of 80%. Although most deciduous trees were selected, the crown size was often incorrect but given the highly irregular shape of many of these trees, this was somewhat expected, and similar problems have been reported by reference [4].
The behavior of the overall similarity during the iterations tend to increase as the image is progressively occupied by found trees and this is why the overall similarity increases. The standard deviation, however, is very different for each image and is mostly related to the amount of contrast in the original image. Images with highly contrasting objects (e.g., building tops) will tend to show a progressively decreasing standard deviation. Images of low contrast will tend to see it increasing as the trees are progressively added because of the double illumination nature of the trees.
Orchards are collections of individual trees often arranged regularly for which the MPP–TM algorithm could easily be adapted. Tree counting in orchards can be very useful for inventory and management purposes. For instance, the European Union (EU) Common Agricultural Policy (CAP) regulations (EC 73/2009) provide support for permanent crops such as hazelnuts, almonds, walnuts, and fruits in general [40–42]. Eligible orchards need to have a certain size and tree density depending on the type of crop. It has been estimated that orchard fruit production represents approximately 3– 4% of the total arable land [43], so the task of estimating fruit production needs tools for counting trees in a timely fashion. Furthermore, the task can take advantage of the near-global high-resolution image cover provided by Google (Google Earth and Google Map) and other Internet-based image services.
Orchards are plantation of trees of the same species and often of the same age. Consequently, trees of orchards usually have similar size and shape and are regularly spaced. Image processing can easily be adapted to such a task providing VHR images are available. To illustrate the adapted MPP–TM algorithm (which no longer is a real MPP), we have tested over three different types of orchards: a mango plantation in Brazil near Juazeiro, a walnut plantation in France near Grenoble, and an olive plantation in Italy near Bracciano. The three images were directly extracted from Google Earth and had a relatively bad quality as they appeared to have been enhanced for sharpness. To validate the results, we have asked three geography students to manually interpret and mark the trees belonging to orchards for the three test images, and we have evaluated the results in the following way:
the total number of trees (NT) was determined by the interpreters;
matched trees were computed as true positive and are defined by the number of trees found by the algorithm minus the false positives;
unmatched trees (present on the image but absent from the results) were computed as false negative (FN);
trees marked by the algorithm but not by the interpreters were marked as false positive;
the final accuracy was computed as TP / (NT + FN).
To be fair, the interpreters were told not to mark the trees that seem too small or too big for the orchards. In addition, valid trees that were found by the algorithm but did not pertain to an orchard were not computed as false positive. As a further improvement, restricting the search within the boundaries of the orchards would increase the accuracy and enable the similarity parameter to be relaxed. The addition of other spectral bands should also improve the results.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
\n\t\t\t\t | \n\t\t\t2435 | \n\t\t\t(2358-69)= 2289 | \n\t\t\t69 | \n\t\t\t103 | \n\t\t\t90.19% | \n\t\t
\n\t\t\t\t | \n\t\t\t837 | \n\t\t\t(1071-264)=807 | \n\t\t\t264 | \n\t\t\t29 | \n\t\t\t93.19% | \n\t\t
\n\t\t\t\t | \n\t\t\t2534 | \n\t\t\t(2555-114)= 2441 | \n\t\t\t114 | \n\t\t\t93 | \n\t\t\t92.92% | \n\t\t
Results of the tree counting algorithm for the three regions (France, Italy and Brazil).
Table 5 shows an overview of the results for the three test images, and Figure 14 shows the graphical results. The top row shows the original images, the center row shows the results of the tree identification (as well as false positives and negatives), and the bottom row displays a detailed section of the image on which the results were overlaid. The Grenoble test image (Figure 14 left column) was characterized by densely arranged walnut trees, which have a large round crown so that the model was well correlated with trees on the image, but the fact that the trees are close to one another produced a relatively large number of “miss” (103). This forced to relax the similarity threshold and caused a few false positives (69). In the case of the Bracciano image (Figure 14 center column), the olive trees are more ill- shaped than the walnut trees, and the relaxation of the similarity threshold caused a large number of false positives, especially in the nearby forested areas. Conversely, very few trees were missed. Finally, the last test image from Juazeiro (Figure 14 right column) is populated by mango trees that, like the walnut trees, have large round crowns. Still, the algorithm produced a fair amount of both false positives and false negatives mainly because of the variation of tree crown size and the particular situation of the dirt road at the top of the image that created a pattern of light and shade similar to the trees (approximately one-third of the false positives came from that road). The three very different images still produced similar accuracy results between 90 and 93%.
Illustration of the results of the tree counting for the three test images: Grenoble (left column), Briacciano (center column), and Juazeiro (right column). The empty circles represent the trees that were found, “
The detection of individual tree crown in images of very high resolution is a growing and challenging field of research within the remote sensing community. In addition to the structural complexity of the forest, many other factors such as the characteristics of the scene (topography, illumination, and other environmental variables) and forest type (season and biodiversity) make the task difficult. To reference [16], the ability to achieve individual tree crown delineation of all trees in a forest was recognized as an unrealistic expectation.
In an effort to provide the reader with an overview of the current state of the research in tree crown detection, Part I presented a brief review of some of the most common computerized techniques for detecting and delineating trees in optical VHR images. Part II describes the concepts and implementation of a novel approach based on two mathematical/pattern recognition concepts integrated to improve performance. MPP–TM was developed based on concepts from marked point processes and template matching for the former to take advantage of a mark built from a geometrical-optical model.
MPP–TM was highly effective in finding trees in urban environment with images from the WorldView-2 satellite (ground resolution of 50 cm). A total of 263 trees out of 272 were found (96%), and taking false positives into account, a success rate over 90% was still achieved. The algorithm was also adapted for a tree counting application such as is often needed in large orchards. To count trees in orchards, the approach works very well when the trees are easily distinguishable. Results from three datasets of different crops show an average success better than 90%. Out of 5806 trees, 5537 were found excluding all false positives.
The growing availability of VHR images from commercial satellites or even from web mapping services opens a wide field of applications especially that VHR multispectral images are becoming increasingly common. Multi-temporal studies will further strengthen these applications for monitoring purposes.
Finally, we should mention that Lidar (light detection and ranging) data are also becoming widely available, and its integration with VHR images promises to further improve the results of tree detection algorithm. By adding a third dimension to the images, Lidar reduces the probability of errors by strengthening the evidence around the digital representation of trees.
We are grateful to François Gougeon, Donald Leckie, Guillaume Perrin and Mats Erikson for having kindly provided the rights of reproduction of their figures.
There are three major components of the turbine engine; the compressor, the combustor, the turbine blade and the nozzle. In recent years, the aeronautic trade demands the advancements of new material for the aero-engine components characterized by thrust, weight, safety, fuel utilization, life cycle costs and environmental necessities [1]. Contemporary innovative advances and evolution in the aerospace industry require improvement and application of structural materials that would provide higher performance and will be cost-effective in fabrication and maintenance compared with existing parts. The choice of material relies upon the working conditions and an ideal alloy that could withstand an environment with extreme temperatures while lightweight. Consequently, the aero engine material distribution comprises steels, titanium alloys, nickel superalloys, aluminum alloys and more recently high entropy alloys [2].
\nThe avionic trade utilizes low-alloy steels, maraging steels and highly alloyed secondary stainless steels in commercial and military aircraft for their resistance from erosion, oxidation and the capacity to withstand high temperatures. However, a lot of strength is required for the steel to reduce the weight of its parts and the fracture mechanism of the material are not well understood reducing its usage for aerospace applications. Aluminum alloys were at a point fascinating to use on aero engines due to its low density but the inability to withstand elevated temperatures limited its applications. On the other hand, Ti-6Al-4V and other titanium alloys including nickel superalloys, chromium super alloys, tantalum and rare earth metallic alloys are used for their ability to withstand high temperatures, low density and lightweight [3]. However, failure of these materials has been inevitable because of extreme operating temperatures, cyclic and translational movement of the part, mode of fabrication of parts and nature of the material. Material improvements and technological advancements in the mode of synthesis led to the discovery of high entropy alloys.
\nSome key benefits of high entropy alloys are:
With a hoisted strength-to-weight ratio, good oxidation resistance, fatigue resistance, hot consumption opposition, elevated temperature strength, lightweight, wear and creep resistance. High entropy alloys are excellent materials for compressors, combustion chambers, exhaust nozzle and gas turbine case applications within the gas turbine engine [4].
With excellent creep resistance, less radioactive waste, elevated temperature strength and ion irradiation resistance, high entropy alloys are great auxiliary materials used for nuclear reactors applications [5].
With excellent mechanical, electrical, electrochemical and anticorrosion properties, high entropy alloys are alternative alloys for biomedical applications [6].
High entropy alloys (HEAs) are alloys with at least five metallic components and every one of these components has a molar atomic concentration somewhere between 5 and 35% [4]. Reports on most HEAs show the amalgams contain a basic face-centered cubic (FCC) or body-centered cubic (BCC) or hexagonal closed pack (HCP) solid solution phases without intermetallic phases because of their high-entropy impact [5]. These solid solution phases empower high entropy alloys combinations to have remarkable properties, for example, increased hardness, high fracture strength, yield stress, and plastic strain. HEAs exhibit good ductility, they have a superb work hardenability and high-temperature oxidation resistance [6]. They contain particular attractive magnetic properties, have high wear resistance and exhibit good erosion opposition [7]. As a result of these properties, HEAs are remarkable refractory materials, fatigue resistant materials, and have a corrosion-resistant surface layers and diffusion obstruction layers for various structural jet engine applications. However, most HEAs were fabricated using conventional techniques and an enhancement in the manufacturing process of the alloys will upgrade the mechanical properties of these alloys.
\nOther materials used for aerospace applications are:
\nMaterial | \nProperties | \nShort comings | \n
---|---|---|
Metals: ferrous metals, e.g., stainless 15-5PH and non-ferrous metals, e.g., aluminum, e.g., aluminum 6062, aluminum 7050, aluminum 7075, Al-Li and magnesium | \nGood electric and thermal conductivity, high strength and corrosion resistance, light weight and ductility, hardness, high heat resistance | \nMagnesium is exceedingly combustible; steel is non-receptive to warm treatment. There is a surprising expense of delivering aluminum parts and thin aluminum cannot be compound bent while conveying load | \n
Heat resistant super alloys, e.g., nickel 718, Ti6Al4V, Ti-5553, TiAl-based alloys | \nElevated temperature strength and corrosion resistance | \nTitanium and nickel alloys and some ceramic materials are grouped under heat-resistant alloys; in any case, they are hard to machine meaning shorter life expectancy and decreased process security | \n
Composite materials | \nLow weight, high tensile strength and compression resistance | \nThe most common composite material used in the aerospace is fiberglass and it is expensive to fix while resins used as matrix discharges poisonous fumes | \n
When designing high entropy alloys, the components in the blend need not be equivalent or near equal. The structure does not separate a minor or major element in its composition rather, the major consideration for designing high entropy alloys amalgams is; the elements in the mix ought to be at least 5 in number and additionally, their atomic concentrations should be between 5 and 35%. Higher configurational entropy is achieved by having no less than five elements in the composition because an increment in the number of components increases the mixing entropy and best explains the high entropy effect which is an important factor to the designing of high entropy alloys [8, 9, 10].
\nWhen the Gibbs free energy (∆Gmix) is at its base, the system is said to be at equilibrium.
\nwhere the enthalpy of mixing is the ∆Hmix, ∆Smix is the entropy of mixing and T is the temperature. From the Boltzmann hypothesis on the entropy of mixing, the molar configuration entropy design (∆SConf) gives more prominent outcomes of forming a multi-element solid solution phase through statistical thermodynamics determined by:
\nwhere \n
High entropy alloys form stable solid solutions because of the crystal structure of the elements which is unaltered when different elements are included. Furthermore, they are stable when the chemical components remain in a single homogeneous phase. This happens when the elements in the composition are firmly packed together on the periodic table.
\nThere is a relationship between the entropy of fusion and the phase transition metals used in the compositional design of high entropy alloys. At the point when the number of elements in a composition is increased, the framework will be progressively stable.
\nFor instance, from Eq. (5),
\nWhen the number of elements in a system
Cantor et al. [11] demonstrated the number of alloys that have been studied including unitary, binary, ternary and high entropy alloy in a system with the total number of different possible alloys
With 60 elements in the alloying range of the periodic table, at a material specification of 1, he gave the conservative number of possible alloys design as ≈10177.
\nThe property, microstructure and design of high entropy alloys are dependent on some core effects, the phase composition and technique of fabrication respectively [12].
\nThe high configurational entropy impact hinders the phase transformation influencing the thermodynamics of the system yet builds the formation of solid solutions. Despite having numerous meta-stable states, the equilibrium state has the most reduced free energy of mixing in solids from the second law of thermodynamics. This suggests that combining five or more components in equimolar or near equimolar concentrations may bolster two phases; the solid solution phase and the intermetallic phase. Intermetallic phase is a stoichiometric compound with super-lattices that give the high entropy alloy amalgams ominous properties while single-phase solid solution phases show a system with a total blend of elements forming simple (BCC, FCC, HCP) crystal structures over intermetallic compounds. The presence of a prolonged range order isolates the intermetallic phase for the solid solution phases. At elevated temperatures the higher the number of elements in a disordered state, the less the possibility of forming intermetallic phases.
\nThis uses the Boltzmann equation:
\nTo show the configurational entropy S, of an ideal solution with N, number of elements as it only regards configurational entropy each at an equimolar concentration and
Sluggish diffusion involves the kinetics of the system; low diffusion rate, increases thermal stability, an increase in recrystallization temperature, slows down grain growth, slows down phase separation and improves creep resistance which might benefit the microstructure. The presence of nanocrystals in as-cast material and amorphous materials in sputter-deposited thin films and high recrystallization temperatures enlivened the sluggish diffusion theory. Cross-diffusion happens when the focus angle of one element prompts or changes the motion of another element. This occurs when one component changes the chemical attributes of other elements in the alloy system. Increasing the number of the composition of the elements in an alloy may make the diffusion become sluggish and reduce the temperature of the system.
\nLattice distortion manages the structure of the high entropy alloy system. A noteworthy contrast in the atomic radii; the movement of large and small atoms causes’ lattice strain making the entire framework to have a distorted lattice. Lattice distortion prompts solid solution strengthening by restricting dislocation, the displacement occurring at each lattice spot relies on the atom dwelling at that spot.
\nCocktail effect affects the properties of the system designed. It includes the compositional mixtures of elements where the consequence of the blend is both unpredictable and unexpected because of the distinctive properties the individual element provides. Adding an element to the mixture with properties realized will enhance the combination through the cocktail effect. High entropy alloys may exhibit properties dependent on the reaction between elements in the system. On the off chance that a high-temperature high entropy alloy is desired, elements with elevated temperature strength ought to be used.
\nIn recent times, a few techniques for anticipating the phase(s) high entropy alloys will possess have emerged. The phases have been predicted most often, however not continually, using the calculation of phase diagram [13], the valence electron concentration or utilizing the thermodynamic and geometry effect.
\nAccording to Gibbs rule:
\nwhere C is the number of elements in the system, F is the degree of freedom and P is the maximum number of phases at equilibrium. This standard proposes that high entropy alloys can exhibit multiple phases, nonetheless, high entropy alloys (HEAs) are usually a single phase or double phase system but rarely having multiple phases regardless of containing multiple elements. Solid solution high entropy alloys form FCC, BCC or HCP phases due to their mixing entropies. BCC structured HEAs have high yield strengths, low ductility, limited plasticity and are brittle while the FCC structured HEAs have a low yield strength, inferior cast ability, compositional segregation high plasticity and ductility [14]. The ductility of HEAs decreases as the yield stress and comprehensive strength increases and the blend of BCC and FCC phase produce mechanical properties with high strength and good ductility producing balanced alloys [15]. However, the combination of more BCC elements will show more BCC phases while combinations of elements with more FCC elements will show more FCC phases. Although entropy is not the only criteria for phase formation, both entropy and enthalpy must be considered. The crystal structures of elements used and the number of times the elements are used in an alloy system influences the phases found in that alloy system [16]. The hardness and yield strength of HEAs with FCC phases is smaller than the BCC phase; therefore, hardness increases due to the increase in the BCC phase. The BCC phase is more grounded than the FCC phase because of the structure and solution hardening and sometimes adding an element to the mixture can change the phase completely from BCC to FCC and vice versa [17].
\nPreparation method of HEAs can be divided into three major routes Liquid mixing, solid mixing and gaseous mixing. The liquid mixing includes arc melting, electric resistance melting, inductive melting, Bridgman solidification and laser additive manufacturing [18]. In research, most HEAs were manufactured using Arc melting, which occurs in a vacuum sealed argon environment where the molten alloy is cast. The alloys to be fabricated are liquefied using a vacuum arc melter. The melter is fitted with a button-crucible. Melting is accomplished using a consumable tungsten electrode utilizing metal pellets as a charge striking the arc. A turbo-molecular and roughing pump is then used to pump the chamber to obtain a pressure of about 3 × 10−4 Torr [19]. Argon is filled in the chamber to reduce the pressure a little facilitating the plasma formation when the arc strikes. Then the melt pool is stirred by the plasma through the convention. Then the process is repeated several times to achieve homogeneity of the composition.
\nIn any case, the challenges of heating the components together have the tendency to form a hypoeutectic that isolates itself from the rest of the elements due to slow cooling rates, the shape and sizes of bulk ingots are limited and fabrication of high entropy alloys also in bulk using this technique is relatively expensive. The solid mixing route involves mechanical alloying and subsequent consolidation process. Some studies have shown that mechanical alloying produces homogenous and stable nano-crystalline microstructure. While the gas mixing route includes molecular beam epitaxy, sputter deposition, pulse-laser deposition (PLD), vapor phase deposition and atomic layer deposition [20].
\nIn recent times, the era of manufacturing technologies are automated, mechanized and computer integrated. Thus, additive manufacturing (AM) is a preferred alternative to conventional manufacturing technologies. AM enables industries to create products utilizing fewer parts and fabricate items that are less vulnerable to mileage pores and blowholes. It reduces new product cost by 70% and promoting time by 90% by utilizing the rapid prototyping and related assembling techniques [21]. Once the shape and dimensional resistance of a component or product are made as an automated 3-D image, a solid reproduction will be created in hours anywhere in the world. AM reduces life-cycle impacts and the heaviness of the final product. AM is versatile, flexible and customizable making it a preferred choice by most sectors of production. There is no need for storage as AM parts can be made and on demand from a computer-aided design file and along these lines, there is no compelling reason to change the production line to make one part. The AM parts are fabricated layer by layer reducing excesses [22] while human production errors are insignificant. More complex parts are produced in shorter time spans and it likewise guarantees higher product quality since parts created are without residual porosity. Thus, additive manufacturing can be solid, liquid or powder-based. The powder-based processes are better utilized in lieu of other processes because additive manufacturing of segments using a laser and powder together helps create complex structures assuring quality and strength to the finished parts. AM technologies of solid structures are realized by the successive deposition of layers of flowing powders, making the powder-base additive manufacturing methods practical and attractive.
\nAdditive manufacturing via powder based melting is a technique utilized in most metal rapid frameworks which makes use of the continuous supply of metallic materials in powder shape and an energy source, dissolving the material while forming a melt pool which solidifies rapidly into metal layers. This rapid solidification or high cooling rate will produce fine microstructures making the final part fabricated have enhanced mechanical properties. Laser additive manufacturing via powder based melting process includes the Laser Engineering Net Shaping (LENS) [23] and selective laser melting (SLM) [24]. The SLM uses a cold powder bed technique while LENS uses a blown powder method by a laser beam through nozzles for particle deposition. SLM and LENS liquefying technique is adaptable, achieves accuracy in geometry and there is a better tendency to form fine grains, non-equilibrium phases and new chemical compounds in both SLM and LENS technique which results in improved mechanical properties of the material with minimal/zero defects.
\nThe fabrication of high entropy alloy Al-Co-Cr-Cu-Fe-Ni using laser additive manufacturing will be discussed. It should be noted that this is the most studied high entropy alloy system with limited information on the fabrication of this system via LAM technique; however, this system is in great proportion an extension of the common superalloys used as aero-engine materials.
\nIn view of this, the powder characterization of the high entropy alloy Al-Co-Cr-Cu-Fe-Ni is achieved to check the morphology of the powders used.
\nLENs substrates can be made out of 25 × 25 × 4.5 mm stainless steels plate were subjected to a compressor used to apply pressure jet of air to blast an abrasive material, etching the surface of the plates in a process called sandblasting, the process was done in an enclosed cabinet designed to contain and recycle the abrasive grit at a high air pressure to make the blasting faster. A small nozzle size helped the blast make a fine and uniform pattern at a close blasting distance with a slighted blast angle to create the desired effect.
\nAfterwards, continuous wave Nd:YAG laser processing system fitted with an off-axis nozzle with a dual hopper plasma spray powder feeder system is used to deposit the alloy system. The laser and powder stream move over the surface to create layers; two or more layers can be made to create a three-dimensional deposit. The LENS process then begins with a computer-aided design [25] file transferred to the laser Optomec system which in turn slices the information on the file into layers of the desired height. The CAD file is converted into a stereolithography file and parameters such as the hatch space and layer rotation are set. This stereo-lithography file is then converted to a motor control file and the travel speed is set. The laser power is set and the federate is also set. Once all parameters are set, the process can begin automatically. The powders must be spherical in shape to flow smoothly through the hoppers while deposition takes place in an argon-filled chamber. The high entropy alloys fabricated is deposited on the substrate as the oxygen level of the chamber is constantly monitored.
\nSLM is a powder bed fusion process [26]. It uses a laser beam which melts and then fuses the metal powders together as a thin layer of powder is deposited over the substrate plate then the laser beam fuses the powder particles selectively as dictated by the computer aided design data. Process parameters must be taken into consideration in order to fabricate a defect-free-part [27, 28]. The process parameters are laser power, laser scan speed, hatch distance, hatch overlaps, hatch style etc. and these parameters all affect the mechanical properties and influences the microstructures of the parts [29, 30].
\nLight amplification by stimulated emission of radiation is simply a device that generates an intense beam of coherent monochromatic light by stimulated emissions of photons from excited atoms or molecules. Lasers can be classified as; gas lasers, diode lasers, liquid (dye) lasers, fiber lasers and solid state lasers. The rate of energy input with respect to time is called the laser power. The intensity of the laser beam increases with an increase in time, therefore, to know the influence of the laser power on high entropy alloys, different laser power needs to be observed.
\nFor instance, the influence of laser power on high entropy alloy CrMnFeCoNi deposited via laser melting deposition was studied by Xiang et al. [31]. The authors observed that the laser power influences the densification behavior of the alloy. They also observed that by changing the laser power, the proportion of equiaxed and columnar grains could be adjusted which affects the solidification and heat flux direction of the process.
\nLaser scan speed is the velocity of deposition carried out by the laser beam along the track created. It is the time rate at which the deposition is created when the laser beam is passed along the surface of the substrate is called the laser scan speed. Zhang et al. [32] observed that decreasing the scanning speed leads to a higher temperature of the melt pool. The laser scan speed offers the laser powers enough heating energy to melt the high entropy alloy powders and slower scan speed ensures a longer period to melt the powder layer completely. Therefore, the laser scan speed and the laser power will determine the energy density within the melt pool [33].
\nThe length at which the laser beam covers a focal distance in millimeters while creating a layer is called the beam size or beam diameter. The beam creates a melt pool as it moves along the track with an oval, thus, the major axis of the melt pool created is dependent on the scan speed. A decrease in the beam diameter increases the energy density which leads to a deeper depth at a constant powder feed rate.
\nDuring laser deposition, the high entropy alloy powders are carried through a feed tube by a carrier gas usually argon at a speed called the powder feed rate. The thickness of the layers is directly proportional to an increase in the powder feed rate. However, deposition of thick layers may result in a poor bond between layers as well as a high energy consumption which negatively increases the thermal stress and distortion of the high entropy alloy component.
\nHatch spacing refers to the distance and overlap between two consecutive scan vectors. An overlap is required between the successive hatch lines to avoid pores and the spacing is usually less than the beam diameter. Zhou et al. [34] studied the influence of hatch spacing on high entropy alloy Al0.5CoCrFeNi prepared by selective laser melting (SLM) and the authors reported that the hatch spacing influences the relative density of the alloy as the relative density increases with an increase in the energy density. Notably, the porosity decreases with an increase in the hatch spacing and vice versa.
\nThis is also known as the powder density and it is the energy responsible for the melting of the powder on the substrate, therefore, the height of a single layer is dependent on the energy density. The energy density is directly proportional to the dilution; therefore, when the energy density is low, the dilution is low and no fusion bond can be formed amongst the high entropy alloy system.
\nwhere \n
The laser scan strategies are used to reduce residual thermal stresses and fill a single cross section that can be subdivided into smaller sectors with scan lines. The lines can follow patterns such as spiral, zigzag, parallel, chessboard or paintbrush. When the laser scan speed is reduced thermal gradients and solidification may lead to cracks, however, when the scan speed is increased, the power has to be increased, therefore, knowing the right scan strategy to use in fabricating high entropy alloys is important to achieve a homogeneous system.
\nThe type of laser and the process parameters of a laser additive manufacturing technique are not included as the scan strategies. The scan strategies show a pattern that influences independent variables during the LAM process, therefore, the scan strategies must first be defined before another parameter optimization is achieved.
\nScan strategies can be divided into the layer and vector scan strategies and these strategies not only control the properties of the material but also are an important factor used to control the grain location and texture of the high entropy alloy microstructure [35].
\nHelix vector scan strategy is most suitable for producing complex parts and it reduces deformation caused by steep thermal gradients in the parts produced. A Voronoi diagram is used to build each layer and a tool path algorithm applies to the diagram and generates the recursive helix scan path for every layer (\nFigure 1\n).
\nHelix scan strategy; adapted from [
This is a strategy that tries to remove thermal residual stresses and this is achieved by putting separating exposed areas in a track into smaller sections called islands and this is usually 5 × 5 mm by default. The islands are then scanned in a random sequence with short scan tracks eliminating localized heating of the larger sections and subsequently reducing the thermal gradients and residual stresses (\nFigure 2\n).
\nIsland strategy; adapted from [
Layer scan strategies comprise an orthogonal scan strategy and inter-layer stagger strategy. An orthogonal scan strategy is used to reduce porosity and stresses building up along the scan track by changing the direction of the scan after each layer is built. This is achieved when consecutive layers are scanned orthogonally to each other. The inter-layer or knitting strategy is used to repair defects observed in previously scanned layers through overlapping. The defects are corrected by melting all the powder in the overlapping zone causing a strong bond between the layers (\nFigure 3\n).
\nLayer scan strategies; adapted from [
The vector scan strategy consists of the progressive and ‘raster scan strategies. The ‘raster scan strategy alternates the vector track after every scan. The laser scans from the beginning to the end of a vector before moving to the next vector beginning with the next vector at close range to the end of the previous vector. While the progressive scan strategy as the word progressive states is a scan strategy that does not stop but continues from one vector to another (\nFigure 4\n).
\nVector scan strategies; adapted from [
Aero engines comprise different parts and those parts are composed of several materials; aluminum alloys, steels, titanium alloys, nickel superalloys, ceramics, composites and intermetallics to name a few, however, most of these materials have limiting properties. High entropy alloys fabricated using laser additive manufacturing through research and development show promising properties; elevated temperature strength, oxidation resistance, favorable compressive yield strength, advantages over other materials used in the jet engines despite its challenges [41].
\nThe high entropy alloy system Al
Although this high entropy system shows variation in its corrosion properties from favorable to not favorable in both NaCl and H2SO4 solutions it has been reported to be susceptible to pitting corrosion in chloride environments, which is increased by anodizing in H2SO4.
\nThe aluminum and chromium content in the high entropy alloy system has shown to improve the oxidation properties of the alloy. Aluminum achieves this by creating a protective aluminum oxide (Al2O3) layer on the surface while chromium also creates a protective chromium oxide (Cr2O3) layer on the surface [43].
\nThe fatigue resistance of the alloy has been reported to be favorable between 540 and 950 MPa. However, there is a need to improve the fatigue resistance of the alloy as recent studies have shown that Al0.5CoCrCuFeNi high entropy alloy is sensitive to defects, such as micro-cracks, introduced using the conventional manufacturing techniques [44]. These manufacturing defects arise and contribute to a reduced fatigue life of the material and an increase in the cost of reproduction, therefore, the removal of these defects and an increase in the fatigue resistance of the material will cause improvements of the technology of production. SLM and LENS melting technique are versatile and achieve accuracy in geometry. SLM uses a powder bed and LENS uses a blown powder method by the laser beam. Formation of fine grains, non-equilibrium phases and new chemical compounds result in improved mechanical properties
\nHigh entropy alloys (HEAs) possesses superior mechanical, thermal and oxidation properties exceeding that of pure metals. Attributed to the core effects; high mixing entropy, lattice distortion, slow diffusion and cocktail effect. HEAs have outstanding strength is comparable to some metallic glasses and that of structural ceramics attributed to the simple solid solutions they form. Valence electron concentration, CALPHAD and using the thermodynamic and geometry effect are suggested means of discovering the phase HEAs.
\nHigh entropy alloys have shown good wear and corrosion resistance with their thermal conductivity lower than that of pure metals. The alloys have remarkable superconductivity and have been reported to be applicable in high temperature and low-density refractory for the aero engine components. However, defects have been reported to limit the fatigue resistance of high entropy alloys using arc melting; the most widely used technique of fabricating high entropy alloy until recently. Many techniques have been substituted over the conventional process of manufacturing high entropy alloys; mechanical alloying, sputter deposition, molecular beam epitaxy (MBE), pulse-laser deposition (PLD), atomic layer deposition (ALD) and vapor phase deposition. However, none of these techniques is versatile, flexible and customizable. None use a computer-aided design file eliminating the need to change the production line just to make one part. None of the techniques mentioned above is built layer by layer reducing excesses while human production errors are minimal. None have more complex parts produced in shorter time frames and which assures higher product quality because parts developed are without residual porosity than the laser additive manufacturing technique.
\nTherefore, there are limitless possibilities in using high entropy alloys fabricated using laser additive manufacturing for aero engine applications. Not only are high entropy alloys similar to nickel-based superalloys currently in use but also a cheaper alternative.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. 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Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null}]}},subseries:{item:{id:"28",type:"subseries",title:"Animal Reproductive Biology and Technology",keywords:"Animal Reproduction, Artificial Insemination, Embryos, Cryopreservation, Conservation, Breeding, Epigenetics",scope:"The advances of knowledge on animal reproductive biology and technologies revolutionized livestock production. Artificial insemination, for example, was the first technology applied on a large scale, initially in dairy cattle and afterward applied to other species. Nowadays, embryo production and transfer are used commercially along with other technologies to modulate epigenetic regulation. 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