Next Article in Journal
The Future of Community-Based Ecotourism (CBET) in China’s Protected Areas: A Consistent Optimal Scenario for Multiple Stakeholders
Previous Article in Journal
Investigation of the Biocontrol Potential of Two Ash Endophytes against Hymenoscyphus fraxineus Using In Vitro Plant–Fungus Dual Cultures
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 12
Issue 12
10.3390/f12121752
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Structural Stability of Urban Trees Using Visual and Instrumental Techniques: A Review

by
Camila S. F. Linhares
1,
Raquel Gonçalves
2,
Luis M. Martins
3 and
Sofia Knapic
1,4,*
1
SERQ–Innovation and Competence Forest Centre, 6100-711 Sertã, Portugal
2
FEAGRI, Unicamp–School of Agricultural Engineering, University of Campinas, Campinas 13083-875, Brazil
3
CITAB (UTAD)–Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
4
ISISE (UC)–Institute for Sustainability and Innovation in Structural Engineering, University of Coimbra, 3030-788 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Forests 2021, 12(12), 1752; https://doi.org/10.3390/f12121752
Submission received: 10 November 2021 / Revised: 3 December 2021 / Accepted: 7 December 2021 / Published: 11 December 2021
(This article belongs to the Section Wood Science and Forest Products)
Browse Figures
Review Reports Versions Notes

Abstract

:
This review focuses on tree health assessment in urban forest, specifically on the methodologies commonly used to detect levels, dimensions, and location of wood deterioration. The acknowledged benefits to the urban forestry area from the application of assessment techniques are also addressed. A summary is presented of the different methodologies, such as visual analyses, acoustic tomography, and digital wood inspection drill, with the underlined importance of the biodeterioration of wood by fungi and termites.

1. Introduction

Trees are living organisms whose characteristics vary with species, age, climate, and growing conditions; thus, it is difficult to accurately predict when and under what conditions they will fail. Thus, a great deal of effort has been devoted to increasing the precision of the analysis of this complex and important problem [1,2,3].
The tree physiology and structure play a strong role in shaping the urban tree ecosystem. Urban forests provide critical social and cultural benefits that may strengthen community resilience to climate change. Street trees or urban parks can hold spiritual value, promote social interaction, and contribute to a sense of place and family for residents [4]. Forested urban areas appear to have potentially stronger and more stable communities [5,6].
Studies have found that the extent of urban forest benefits and ecosystem services depends on several factors including tree structure, health condition, and physiology [7,8,9,10,11], as well as the human population density and the character of the environment, whereby it is important to analyze whether the environment has isolated tree pockets or contiguous tree lined corridors [12]. Natural environmental conditions are also important, whereby it is important to analyze the air temperature, precipitation, relative humidity, wind speed, etc. [13,14,15].
Urban green spaces and their associated benefits are important for sustainable urban development as far as ecological, economic, and social aspects are concerned [16]. The urban forest is a complex ecosystem, closely related to the urban ecosystem [17]; correct planning of green areas provides protection against strong and prevailing winds, being able to modify and direct them through obstruction, deflection, and conduction. Thus, it is possible to improve the local climatic condition and develop a landscape that is accessible to all [18].
The use of vegetation in urban areas will always play an effective role in the psychological and landscape sense. In the context of the urban landscape, each vegetation type has its specific characteristics such as color, shape, and height, as well as leaves and flowers that modify the environment through ornamental properties, making each urban area a pleasant and striking landscape. In the psychological sense, these spaces provide a feeling of wellbeing, improved mood, and social relationship, especially when associated with leisure and sport infrastructures [19].
All the benefits to health and environment provided by urban forests, such as biodiversity protection and mitigation of climate change, are extremely relevant and should always be considered [20]. These benefits cover five major areas that are interconnected: economic, social, health, visual, and aesthetic. These services include air filtration, carbon sequestration, storm water attenuation, energy conservation, reduction in global warming, microclimate maintenance, reduce temperatures through shading, wellbeing improvement, encouragement of outdoor activity, noise absorption, and preservation of biodiversity.
A study demonstrated changes in air quality due to urban trees [21]. The urban forest acts as a filter, removing pollutants and reducing the polluting effect. Trees absorb these gases through their leaves and transform them into biomass. This mechanism has a direct influence on air quality improvement and is characterized as a benefit provided by urban forestry. Furthermore, alongside plant roots, there are bacteria that also absorb polluting gases, degrading them and resulting in nutritious elements available to plants (Figure 1) [22].
The concept of the greenhouse effect emerged in the late 19th century; however, it was in the second half of the 20th century, with growing environmental concern, that it became popular. The concern with the emission of greenhouse gases is the related increase in the Earth’s temperature, changing the climate and compromising the survival of several species [23].
Trees in the urban infrastructure are an important vehicle for carbon sequestration, removing carbon dioxide from the atmosphere. Green spaces remove more carbon than they introduce to the atmosphere [24,25]. Thus, a forest in a green space maximizes carbon sequestration [26].
According to [27], vegetation in the urban environment can greatly improve the urban microclimate, as well as mitigate the heat island effect, by reducing summer air temperatures. Therefore, increasing vegetation in the urban context can be an effective way of mitigating the urban heat island (UHI), thus benefiting urban centers [28]. A decrease in temperature is achieved through trees that provide solar protection, affect air movement and heat exchange, absorb solar radiation, and cool air through evapotranspiration processes. It should be noted that urban parks may extend their cooling potential and decrease ambient temperatures in adjacent urban zones depending on the thermal balance of the overall area under study [29].
A study investigated the role of trees on NOx pollutant dispersion in a real neighborhood in Pamplona (Spain) [30]. Aerodynamic and deposition effects were studied by computational fluid dynamics (CFD) modeling. Different scenarios, changing the tree-foliage, were simulated. The results suggest that the distribution of pollutants is modified by the inclusion of new trees not only in the precise location, but also in nearby locations, which is dependent on each particular case and the surrounding conditions [31].
In biomechanics, the tree is considered a structural element subject to gravitational and wind forces; thus, it is necessary to determine its physical and mechanical properties, in addition to its stability and the normal and shear stresses [32,33]; accordingly, biomechanics researchers have developed or assessed devices and procedures for testing the presence of decay [2,34,35,36,37,38,39,40].
Knowledge of the phytosanitary status of trees helps to prevent accidents and is particularly important in large urban centers where the trees are more likely to fall and cause human and material losses. This knowledge is possible using methods capable of reproducing, with a high degree of confidence, the real health condition of the tree without the use of invasive tests that will harm it [40,41,42,43,44,45,46,47]. In this scenario, it is also important to highlight how pollution of the urban area is harmful to the health of the trees [48].
To better consider the phytosanitary and stability conditions of the tree, it is necessary to consider the predisposing, inciting, and contributing factors that underline the spiral of decline model proposed by [49] (Table 1).
Predisposing factors are those that influence the condition of the tree in the medium or long term, and that are intrinsic to the site or the tree. Inciting factors are those that refer to abiotic or manmade episodes. Contributing factors are those that contribute to accelerating the decline in the phytosanitary conditions of the tree, such as deterioration by biological agents (fungi, virus, nematodes, etc.). These agents take advantage of fragile conditions originating from predisposing and/or inciting factors. As the name suggests, they are preponderant in accelerating the decline and, in most cases, result from multiple causes, not being an inciting factor [49,50,51].
There are technological tools that have been used to assist the inspection of trees. These tools allow obtaining parameters that can be used in the calculation of the risk of fall, contributing to the decision regarding the maintenance or removal of a tree, considering safety of the community and respecting the need to maintain the environmental services provided by trees to cities [4,6,52]. Nondestructive testing has been used as a quality control tool in the forest industry and urban forest. These techniques are used to infer wood physical and mechanical properties [53].
The objective of this paper was to present a review that focuses on tree health assessment in urban areas, specifically on the methodologies commonly used to detect levels, dimensions, and location of wood deterioration.

2. Urban Tree Health Assessment Methodologies

The history of tree risk assessment as documented in the literature is relatively recent, and the interest surrounding tree risk has continued to grow in recent years [53,54,55,56]. Several international research summits have focused on tree risk assessment, the costs associated with not maintaining trees, and the biomechanics of trees as these relate to tree failure potential [57,58,59]. The latter area of research has arguably been the most active, whereby assessment with knowledge gained from biomechanics research is often used to develop guidelines, techniques, and technologies for gauging tree failure potential.
A wide range of methods have been used to assess urban trees. Researchers’ choice of study methods has been influenced by several factors, including scale of the study area, purpose of the study, and availability of information and data. Field surveys, samples, and experiments have primarily been used for analysis where it has been feasible to assess all existing trees. Techniques including GIS, aerial photography, and remote sensing have been used for relatively larger study areas encompassing regions, metropolitan areas, and cross-city, inter-state, and international comparative research [7,60,61,62,63]. Tree statics tests were introduced in the 1980s, with the first example performing a tensile static load test, also known as a pulling test [64]. In [65], many results from pulling tests are gathered and discussed using precursors of this field trial proposal. This method has evolved over time, as has the equipment used to apply the load and to measure tree displacements and inclinations [66,67,68,69,70].
Hence, test methodologies are important to provide knowledge about the health, safety, and structural conditions of trees and improve the measurement accuracy [69,71,72,73,74]. The visual tree assessment (VTA) is the simplest, oldest, and most economical method for tree evaluation, allowing inspection of symptoms, mechanical damage, and presence of moisture, fruiting bodies, or insects [75,76]. However, it does not always allow the detection of such conditions, especially when it is in its initial phase. In this sense, tomography is an important tool for the location of more fragile areas in the stem, since the location of these areas is fundamental for the analysis of the risk of falling [77].
The digital wood inspection drill is another process to detect defects and quantify internal decay in wood tree. When associated with VTA and ultrasound, it allows a precise verification of the biodeterioration processes. The difficulty to recognize and describe patterns associated with the fall risk or final diagnosis of trees condition is still a challenge to the scientists. This review paper provides information and constructs the body of knowledge acquired on the subject by several scientists. Accordingly, it will be possible to enhance the application of knowledge for this association between diagnosis and fall risk.
Management decisions are influenced by the actual, assessed, and perceived risk surrounding trees. Recommendations based on the findings of the assessment are then passed onto people who ultimately make the final decisions [78].

2.1. Visual Analyses

In visual analyses, three standard inputs are usually considered: the failure potential, likelihood of impact, and consequences of failure [75,76,79,80,81]. Over the years, several different visual risk assessment methods have been used by arborists and urban foresters.
The parameters considered in visual tree assessment (VTA) include the structural characteristics or dendrological parameters (diameter of breast height (DBH), height of the tree, height of the canopy, species, etc.), type of surroundings (Table 1), indicative signs or symptoms related to biotic or abiotic agents, and details of the phytosanitary status (Table 2). Arboriculturists consider visual tree assessment essential to provide basic information about tree growth performance and stability [82].
Another visual method is the ISA Tree Hazard Evaluation that acts as a guide for collecting and recording tree risk assessment information. This form is for trees receiving a basic risk assessment [75]. Another example is the United States Department of Agriculture Forest Services Community Tree Risk Evaluation. This visual method is designed to assist communities in designing, adopting, and implementing tree risk management programs, as well as in training field staff to detect, assess, and correct hazardous defects in urban trees [79]. The “Static Integrated Assessment” (SIA) method for assessing tree risk [83] was developed after the VTA (visual tree assessment) method and applies established engineering methods to determine tree breakage safety.
These methods represent some of the more commonly used approaches for visual assessing risk, but it is important to note that additional analyses are required to complement these results, because it is not always possible to assess health conditions, especially when in its initial phase. The superficial definitions from visual assessment give users/arborists the flexibility to use their expertise in the evaluation; thus, it is necessary to identify whether this imprecision reflects the current uncertainty surrounding the tree risk protocol or if it simply adds unnecessary variability to assessments. To this point, instrumental analysis contributes to reducing variability and increases the current understanding in tree risk assessments [54].

2.2. Acoustic Tomography

Although the focus is on acoustic tomography (stress wave and ultrasound) in this work, it is important to emphasize that other methods are also used to obtain internal scanning and imaging in the evaluation of trees.
The idea of applying tomography in the analysis of materials came from medicine, where it is one of the most important diagnostic methods since the discovery of radiographs [84]. In 1978, [85] presented a review of scanning and imaging techniques that, in theory, could be applied to assess deterioration in standing trees. The authors cited microwaves, ultrasound, X-ray and gamma-ray computed tomography, and magnetic resonance imaging as scanning possibilities when specific equipment adapted for use in the field was available. The authors defined X-ray and gamma-ray computed tomography (CT) as very useful to provide two-dimensional density maps related to specific gravity variations that, through interpretation, could detect and differentiate advanced decay, but their main concern, at that moment, was the need for mobile equipment and software with specific requirements. In 1992, [84] described a mobile computed tomography system composed of a radiation source, detectors moving on rails and rotating in the base ring, and a computer controlling the system. This same mobile equipment was proposed by [86] to study the water supply of living trees, in general, and trees damaged by resin extraction, in particular. Computed tomography was the focus of several papers presented at the Symposium on Nondestructive Wood Testing for many years, first directed at standing trees, but later (1989, 1991, 1994) moving on to log, lumber, beams, and poles, probably due to the difficulty of their application for in situ trees. Although CT allows a good internal visualization of the trunk [87], the use of a radiation source has limited practical use and has encouraged the search for other methods that also allow obtaining internal digitization and imaging technologies in the evaluation of rot in standing trees.
As a CT technology, electrical impedance tomography (EIT) was also introduced for medical purposes in 1978 to study pulmonary edema [88]. In recent years, the EIT technique has developed rapidly, and its application has diversified into other areas, such as soil properties, wood deterioration assessment, and plant phenotypic information [89]. EIT has been applied to root evaluations [90,91,92], as well as to trees [93,94].
As an alternative to tomography using radiation sources, acoustic technologies such as ultrasound and stress wave have been the most studied aimed at applications in image construction [93,94,95,96,97]. Since 2002, with the technology bases already consolidated, studies have been focused on improvement and evolution, including equipment, probes, algorithms, and system optimization, with approaches for better understanding the elastic wave propagation in trunks and application of the anisotropy correction factor [98,99,100,101,102].
Acoustic tomography is a nondestructive technique that can be applied to trees to detect the presence of anomalies or deterioration, where the images can be reconstructed from characteristic parameters of the wave, such as propagation time, amplitude, or spectrum frequency [103]. It is an efficient method; hence, wave propagation is affected by materials with different structural characteristics, meaning different acoustic impedance and, therefore, different velocities [103,104]. The velocity of wave propagation in solids with cavities is decreased because the wave surrounds the cavity, causing an increase in the propagation time [105]. In other words, the mechanical and acoustic properties of the wood are vulnerable to the influence of some factors, because it is affected by defects due to conditions that change the wood structure [103], such as the tree age, health condition, and height of the stem [106,107], or defects induced by irregularities of different natural growth patterns (grain deviation, knots, pockets, resin, etc.), generating differences in material and consequent variation in wave propagation velocity and amplitude of the emitted signal. These factors are mainly determined by anatomical characteristics of the wood, such as the proportion of juvenile and late wood and by the angle of the microfibers of conductive elements. The microfibril angle has a strong correlation with density and modulus of elasticity (MOE), and it has an influence on the stiffness, strength, and dimensional stability of wood [108].
Acoustic techniques have been widely used as a nondestructive method to inspect wood and evaluate their internal conditions. Among these techniques, stress and ultrasound waves are the most popular due to the low cost and portability of the required equipment. Technical difficulties encountered in the past have delayed the development of ultrasound tomography. One example is the attenuation of the wave propagating throughout the material, whereby low-power ultrasound equipment is not able to obtain the proper wave propagation signal to analyze large-diameter trees. Stress waves present fewer problems with attenuation, mainly because they use low frequencies. However, high-power ultrasound equipment overcomes these problems with the advantage over stress waves of enabling frequency control. Control of the frequency used in inspections is very important as it enables tailoring the frequency to the size of the defects to be observed, increasing the accuracy of the process [94,109].
Usually, wood inspection is divided into four steps: detection, location, characterization or description, and action to be taken [110,111].
One study proposed a measurement mesh [99] that nowadays is used in different research groups to construct tomographic images (Figure 2). For this mesh, it is logical to imagine that, when increasing the number of measurement points, there will be an increase in the image accuracy. However, this implies substantially increasing the fieldwork. Considering the most frequently adopted diffraction mesh, with n = 8 measurement points (Figure 2), a test made in all directions (Ex: 1–2 and 2–1) will result in 56 routes (8 × 7). With N = 16 measurement points, the number of measurements routes becomes 240 (16 × 15), and so on. Thus, it is important to evaluate a way to obtain an optimal number of measurement points, improving the image accuracy without excessively increasing the field work.
Another study used images generated by ultrasound waves applied to wood logs with artificial holes, whose size and location were known, to compare two different types of measuring grids (reticulated and diffraction grids), concluding that the diffraction grid provided better images than the reticulated grid, when compared to the real image (Figure 3) [112].
With ultrasound tomography, images are generated by associating colors with velocity bands, allowing the visualization of the entire section of the stem under analysis, related to variations of stiffness and/or strength inside the stem. This technique is most sensitive for discs subjected to fungal attacks and is based on the generation of color differentiation images (Figure 4), which represents the wave propagation velocity ranges in the material under inspection. It is important to highlight that, for wood, an orthotropic and heterogeneous material, it is not adequate to use a reference velocity [113,114,115], being more effective to use, as reference, the maximum velocity obtained in the inspected material itself (Figure 4).
The ellipse-based spatial interpolation proposed by [102] seems to improve tomographic images as shown when comparing the images obtained by [116,117]. Moreover, [102] stated that their approach was able to detect two holes, whereas the work of [116,117] failed to do so.
There has been considerable research on improving techniques that can detect and locate internal defects on wood using images obtained from tomography [102,114,116,118], as well as techniques to quantify the accuracy of the acoustic tomograph instead of using only visual analysis of the produced images [119]. Despite the numerous advantages of acoustic tomography mentioned above, the results of this technique cannot be evaluated without considering the parameters used in the measurement (type of mesh and number of measurement points), in the construction of images (software, interpolation algorithms), and related to the reference velocity to identify the areas inside the trunk (sound wood, deteriorated wood, and cavities). This makes the inspection report require the participation of a specialist or that the equipment be previously calibrated for specific analysis conditions. Therefore, among the challenges to be addressed in acoustic tomography is the recognition and description of patterns, considering both the interference generated by the simplified interpolation method used by the software and the recognition of the areas effectively affected by different types of deterioration. With these improvements, it will be possible to increase the precision and rectify the tomographic image interpretation during the tree health diagnosis.

2.3. Digital Wood Inspection Drill

The density is one of the most important properties for forest industries and for obtaining the stiffness coefficient by nondestructive techniques using velocity of wave propagation. According to [120], wood density is an important qualitative parameter for different uses, because it is correlated with several other wood properties. This management involves not only the handling of raw material still in the forest, but also the yield of the cellulose processing. Wood density is considered a qualitative characteristic in tree improvement programs due to its economic value and high degree of genetic control [121]. Currently, the determination of the basic density involves expensive laboratory tests, which require time and specialized personnel.
An increment borer is the primary and most commonly used tool to manually extract cores from trees for analysis of growth trends based on an inspection of the tree’s ring patterns [122,123,124]. In a recent study, [125] evaluated the use of the drill to infer the density of young eucalyptus destined to pulp production for cellulose. The researchers found a clear trend of correlation weakening as the drill penetration depth increased, which could be attributed to the increased friction acting on the drill axis, with the accumulation of chips generated during drilling.
The digital wood inspection drill is a process to detect defects and quantify internal decay in wood, but only defects with advanced stages of decay can be identified reliably [126]. When associated with the VTA and ultrasound, it allows a precise verification of the biodeterioration processes. The drilling resistance measurement is a semi-destructive test, and the measurement is made by the variation of the torque required by the drill bit to advance. The drill needs to have approximately 5 mm diameter, to capture variations in the material’s resistance to perforation without causing damage to the material, whereas it cannot be so thin as to suffer resonance or buckling [127]. As the drill moves through the wood in a linear path, the penetration resistance along its path is measured and recorded. The method has been used in tree inspections and structures since 1988 [128]. The test has been used to identify deteriorated areas in wooden structures [127,129,130], logs, or trees [36,37,131,132].
There is also a growing interest in using the drilling resistance method in genetic improvement programs. A study evaluated the use of drilling resistance for rapid assessment of the relative density of pine wood in progeny tests [133]. A total of 1477 trees were sampled from 14 pine families located at four test sites. They reported weak (R2 = 0.29) to moderate (R2 = 0.65) capacity in explaining the variability of the density by the resistance to drilling. Similar results were also reported in [134,135,136].

3. Biodeterioration of Wood by Fungi and Termites

Xylophages can degrade the wood, leading to tree destabilization and even falls. Many of these attacks occur in the innermost part, making identification, performed solely through visual analysis, inefficient [77]. Termites also cause problems to trees, because of their ability to digest cellulose provided by symbiotic microbiological fauna present in their gut [136,137], but it is important to note that, in Portugal’s environmental conditions, termite damage is not as severe as other countries with warmer environments. Usually, infestation starts at the root, building galleries and destroying the heartwood [138]; thus, the attack is slower and less noticeable in the initial state.
The great majority of termites prefer to attack tissues already damaged by fungi, but there are species that can attack healthy wood. Some factors such as humidity and oxygen availability are determinant for the type of infestation. Different types of events may occur during termite diversification, e.g., the external uptake of symbionts or the horizontal transmission of symbionts within different termite species foraging the same area and resources. The diversification of termites toward an optimization of the digestive process, depending on the environmental conditions and type of resources exploited by the termites, is an important factor explaining the flagellate protist communities associated with certain groups of termites [139]. The termite species that is considered one of the most destructive, both for urban afforestation and crops, is Coptotermes gestroi, which is characterized as an underground termite [140]. This termite is of Asian origin but has been observed today in many tropical and subtropical regions of the world. Underground termites initiate the attack on living trees from the root; often, although the heartwood is destroyed, the tree shows no external signs of attack [138]. A study pointed out that C. gestroi termites can also enter the bark, making pipes and destroying live tree stems [82].
Different studies have contributed to the knowledge of the biology and distribution of these insects, as well as the species with the potential to become pests [141,142]. The wood of the tree stem has its mechanical properties changed upon decay [143]. However, the properties change differently because, in termite attack, galleries are formed surrounded by intact material, whereas, in fungus attack, there are wood modifications dependent on the type of the fungi and on the intensity of the attack. Changes in stiffness and strength are captured by wave velocity propagation variation within the material. Thus, detecting deterioration in trees, particularly when there are no external indicators, is of great interest to forest and urban managers. Therefore, nondestructive methods have become the focus of recent research, and ultrasonic tomography is one of the techniques considered viable, in association with visual analysis, to inspect the phytosanitary state of trees.
Acoustic tomography has proven to be the most effective technique for detecting, locating, and estimating the size and shape of internal defects [144]. One study focused on studying ultrasonic images of wood inoculated with termites, using the variation of the velocity of propagation of the ultrasound waves in wooden discs, as well as the evaluation of their masses during the xylophagous activities [145]. After 195 days of inoculation, it was possible to observe the velocity drop in the wooden discs (Figure 5) caused by the internal galleries generated by the xylophages (termites) [146]. To produce the tomographic images, the minimum velocity obtained in the initial condition was used as a reference to check if there was an increase in the lower-velocity zones [42,145].
Regarding the colonization of fungi in trees, Ref. [147] described five strategies. The first is heart rot, the biggest cause of wood decay in trees. The second is sap rot, involving colonization of unprotected sapwood due to bark damage. The third is caused by plant stress, mainly on branches and roots; ascomycetes and basidiomycetes are recognized by this colonization strategy, e.g., Hypoxylon spp. and Stereum spp. The fourth mechanism is called active pathogenesis, in which some rotten fungi can colonize the sapwood, destroying living wood cells. Lastly, the fifth mechanism is caused by desiccation of the upper part of the tree or by drastic fluctuations in wood moisture, either by disease or by altered physiological activity. Under these conditions, some fungi (e.g., various specialized opportunistic ascomycetes and basidiomycetes) tolerate the low or fluctuating moisture content of wood, making this a selective and strategic advantage for plant colonization. A study found that light is the most important variable affecting the formation of fruiting bodies [148].
Biodeteriorating organisms have special morphological, physiological, and behavioral characteristics that make them able to use wood as substrate, shelter, and food.
Decay caused by fungi is the most recurrent; however, ecologically, some wood decay fungi are useful for stability of the forest [149]. Fungi have a primitive morphological constitution, feeding on existing organic constituents because they are unable to synthesize the nutrients indispensable for their diet [150]. Among fungi, there are three main types of enzymatic activity that promote wood rot: white rot, brown rot, and soft rot [151], with the first two being the most common in living trees [138]. Decay in the stem of trees caused by fungi and/or insects reduces their resistance and can lead to fall. Microscopy analysis for two white rot fungi was conducted by [152], revealing that decay patterns were similar. This information is well-grounded and important, because the fungi that most attack living trees are those with enzymatic activity characterized by white rot [138].
White rot is often related to Armillaria species belonging to the basidiomycetes class. These fungi spoil without distinction polysaccharides and lignin. During the attack, the wood usually appears whitish. The attack of this fungus causes progressive erosion of the cell wall, as well as of the lignin [153]. Although lignin resists attack from most microorganisms, white rot fungi can efficiently degrade lignin [154]. The degradation is especially detected in the parenchyma cell walls and is characterized by a distinct decrease in the UV absorbance values [155].
Brown rot is caused by fungi of the Basidiomycotina subdivision, mainly in softwood. Rotten wood becomes crumbly, brittle, and darker than healthy wood [138]. These fungi basically consume the cellulose and hemicellulose of the cell walls, keeping the residual lignin. Wood tissue attacked by these fungi becomes rigid and breaks with low strength and unpredictable rupture. Soft rot fungi belonging to the Ascomycotina and Deuteromicotina subdivisions preferentially attack cellulose and hemicellulose, rather than lignin, producing cavities in the cell wall. In trees, these fungi are cited as predecessors to the microorganisms that cause the establishment of white or brown rot fungi. Therefore, they are not considered the cause of drastic changes in wood [138].
Ultrasonic tomography can also be used to monitor wood degradation by fungi attack [42]. The authors inoculated Lentinula edodes fungi in Pinus elliottii discs and monitored the evolution of the biodegradation with ultrasound. The tomographic images allowed verifying the evolution of the degradation (Figure 6). As in the case of the research with termites, the minimum velocity obtained in the initial condition was used as a reference to check if there was an increase in the lower-velocity zones [42,145].
Research results have shown that acoustic tomography has the sensitivity to detect inner areas of fungal-decayed trees [37,156,157,158,159]; however, it is also important to note that, under a light microscope, it is possible to verify that the decomposition of naturally decayed wood is greater than that in inoculated wood [160].
The use of ultrasound in the characterization of wood has considerable advantages over conventional compression tests because only one specimen is required to obtain 12 elastic constants, whereas six specimens are required for compression tests [161]. This advantage is even more important in the case of urban trees because obtaining samples from such trees should only be performed when necessary and with the proper authorization. In addition, when it is necessary to obtain branch and root specimens, this question is even more complex, as these parts have small dimensions and are, in general, less assessable [162,163].
Wood characterization through ultrasound is precise in determining the elastic parameters, and it presents correlations with data obtained from destructive static tests. Through ultrasound, it is possible to repeat a test on specimens previously tested and use smaller specimens; moreover, the tests are less expensive and require a reduced number of specimens. The advantages described have motivated several studies in the area [104,163,164,165,166,167,168,169].
The employment of a 26-face polyhedron to characterize wood was initially proposed by [170] as a specimen that would enable the complete characterization of the wood using only one sample. Therefore, it is possible to determine the three elastic moduli (EL, ER, and ET), the three shear moduli (GLR, GLT, and GRT), and the six Poisson ratios (νLR, νLT, νRL, νRT, νTL, and νTR) involved in the complete elastic characterization of the wood.
Ultrasound techniques have been increasingly studied in mechanical sorting applications, wood characterization and inspection [46], and the acoustic tomography of trees [40].

Author Contributions

Conceptualization, L.M.M. and S.K.; investigation, C.S.F.L.; writing—original draft preparation, C.S.F.L.; writing—review and editing, R.G., L.M.M., and S.K.; visualization, R.G., L.M.M., and S.K.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), with the reference UIDB/04029/2020, under the R&D Unit Center for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), with the reference UIDB/04033/2020, and the project F4F—Forest for Future, with the reference CENTRO-08-5864-FSE-000031.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wilcox, W.W. Review of literature on the effects of early stages of decay on wood strength. Wood Fiber Sci. 1978, 4, 252–257. [Google Scholar]
  2. Gilbert, E.A.; Smiley, T. Picus Sonic tomography for the quantification of decay in white oak (Quercus alba) and hickory (Carya sp.). J. Arboric. 2004, 30, 277–281. [Google Scholar]
  3. Fraedrich, B.R. Compartmentalization of Decay in Trees, TR-18; Technical Report; Bartlett Tree Research Laboratories: Charlotte, NC, USA, 1999. [Google Scholar]
  4. Dandy, N. The Social and Cultural Values, and Governance, of Street Trees. In Climate Change & Street Trees Project: Social Research Report; The Research Agency of the Forestry Commission: Surrey, UK, 2010. [Google Scholar]
  5. Klinenberg, E. Heat Wave: A Social Autopsy of Disaster in Chicago; University of Chicago Press: Chicago, IL, USA, 2002; ISBN-13 978-0226443225. [Google Scholar]
  6. Williamson, T.; Dubb, S.; Alperovitz, G. Climate Change, Community Stability, and the Next 150 Million Americans; The Democracy Collaborative: College Park, MD, USA, 2010. [Google Scholar]
  7. Nowak, D. Estimating Leaf Area and Leaf Biomass of Open-Grown Deciduous Urban Trees. For. Sci. 1996, 42, 504–507. [Google Scholar]
  8. Mcpherson, E.; Nowak, D.; Heisler, G.; Grimmond, S.; Souch, C.; Grant, R.; Rowntree, R. Quantifying Urban Forest Structure, Function, and Value: The Chicago Urban Forest Climate Project. Urban Ecosyst. 1997, 1, 49–61. [Google Scholar] [CrossRef]
  9. Nowak, D. Assessing urban forest structure: Summary and conclusions. Arboric. Urban For. 2008, 34, 391–392. [Google Scholar]
  10. Escobedo, F.J.; Nowak, D.J. Spatial heterogeneity and air pollution removal by an urban forest. Landsc. Urban Plan. 2009, 90, 102–110. [Google Scholar] [CrossRef]
  11. Tallis, M.; Taylor, G.; Sinnett, D.; Freer-Smith, P. Estimating the removal of atmospheric particulate pollution by the urban tree canopy of London, under current and future environments. Landsc. Urban Plan. 2011, 103, 129–138. [Google Scholar] [CrossRef]
  12. Ng, E.; Liang, C.; Yingna, W.; Chao, Y. A study on the cooling effects of greening in a high-density city: An experience from Hong Kong. Build. Environ. 2012, 47, 256–271. [Google Scholar] [CrossRef]
  13. Heisler, G. Effects of individual trees on the solar radiation climate of small buildings. Urban Ecol. 1986, 9, 337–359. [Google Scholar] [CrossRef] [Green Version]
  14. Gómez-Muñoz, V.M.; Porta-Gándara, M.A.; Fernández, J.L. Effect of tree shades in urban planning in hot-arid climatic regions. Landsc. Urban Plan. 2010, 94, 149–157. [Google Scholar] [CrossRef]
  15. Hamada, S.; Ohta, T. Seasonal variations in the cooling effect of urban green areas on surrounding urban areas. Urban For. Urban Green. 2010, 9, 15–24. [Google Scholar] [CrossRef]
  16. Jansson, M. Green space in compact cities: The benefits and values of urban ecosystem services in planning. Nord. J. Arch. Res. 2014, 26, 139–160. [Google Scholar]
  17. Christopoulou, O.; Serafeim, P.; Minetos, D. Peri-urban and urban forests in Greece: Obstacle or advantage to urban development? Manag. Environ. Qual. Int. J. 2007, 18, 382–395. [Google Scholar] [CrossRef]
  18. Rivero, R. Arquitetura e Clima: Acondicionamento Térmico Natural; D. C. Luzzatto Editores: Porto Alegre, Brazil, 1986. [Google Scholar]
  19. Givoni, B. Performance and applicability of passive and low-energy cooling systems. Energy Build. 1991, 17, 177–199. [Google Scholar] [CrossRef]
  20. Molla, M. The value of urban green infrastructure and its environmental response in urban ecosystem: A literature review. Int. J. Environ. Sci. 2015, 4, 89–101. [Google Scholar]
  21. Roy, S.; Byrne, J.; Pickering, C. A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For. Urban Green. 2012, 11, 351–363. [Google Scholar] [CrossRef] [Green Version]
  22. Timur, Ö.B.; Karaca, E. Vertical Gardens. In Advances in Landscape Architecture; Özyavuz, M., Ed.; IntechOpen: London, UK, 2013. [Google Scholar] [CrossRef] [Green Version]
  23. Almeida, E.J.M. Desenhar a Verde: Um Estudo Comparativo Entre a Arquitectura Sustentável High-Tech e Low-Tech. Master’s Thesis, Universidade de Coimbra, Coimbra, Portugal, 2012. [Google Scholar]
  24. Nowak, D.; Greenfield, E.; Hoehn, R.; Lapoint, E. Carbon storage and sequestration by trees in urban and community areas of the United States. Environ. Pollut. 2013, 178, 229–236. [Google Scholar] [CrossRef] [Green Version]
  25. Vargas-Hernández, J.G.; Pallagst, K.; Zdunek-Wielgołaska, J. Urban green spaces as a component of an ecosystem. In Handbook of Engaged Sustainability; Dhiman, S., Marques, J., Eds.; Springer: Cham, Switzerland, 2018. [Google Scholar] [CrossRef]
  26. Wolf, K.L. Active living—A literature review. In Green Cities: Good Health; College of the Environment, University of Washington: Seattle, WA, USA, 2010. [Google Scholar]
  27. Dimoudi, A.; Nikolopoulou, M. Vegetation in the urban environment: Microclimatic analysis and benefits. Energy Build. 2003, 35, 69–76. [Google Scholar] [CrossRef] [Green Version]
  28. Georgi, J.N.; Dimitriou, D. The contribution of urban green spaces to the improvement of environment in cities: Case study of Chania, Greece. Build. Environ. 2010, 45, 1401–1414. [Google Scholar] [CrossRef] [Green Version]
  29. Cohen, P.; Potchter, O.; Matzarakis, A. Human thermal perception of coastal Mediterranean outdoor urban environments. Appl. Geogr. 2013, 37, 1–10. [Google Scholar] [CrossRef]
  30. Santiago, J.L.; Rivas, E.; Sanchez, B.; Buccolieri, R.; Martin, F. The Impact of Planting Trees on NOx Concentrations: The Case of the Plaza de la Cruz Neighbourhood in Pamplona (Spain). Atmosphere 2017, 8, 131. [Google Scholar] [CrossRef] [Green Version]
  31. Worsley, W. The Right Tree in the Right Place for a Resilient Future. Urban Tree Manual 2019. Available online: https://www.forestresearch.gov.uk/documents/5318/7111_FC_Urban_Tree_Manual_V15.pdf (accessed on 3 June 2021).
  32. Mattheck, C. Field Guide for Visual Tree Assessment; Forschungzentrum Karlsruhe GmbH: Eggenstein-Leopoldshafen, Germany, 2007; p. 170. [Google Scholar]
  33. Linhares, C.; Gonçalves, R.; Yojo, T. Simplified Methodology for the Inference of Drag Coefficient Applied in Species of Tropical Zone. In Proceedings of the 21st International Nondestructive Testing and Evaluation of Wood: Forest Products Society, Freiburg, Germany, 24–27 September 2019; Forest Research Institute Baden-Württemberg: Freiburg, Germany; p. 657. [Google Scholar]
  34. Rinn, F.; Schweingruber, F.H.; Schar, E. Resistograph and X-ray density charts of wood comparative evaluation of drill resistance profiles and X-ray density charts of different wood species. Holzforschung 1996, 50, 303–311. [Google Scholar] [CrossRef]
  35. Costello, L.R.; Quarles, S.L. Detection of wood decay in blue gum and elm: An evaluation of the RESISTOGRAPH® and the portable drill. J. Arboric. 1999, 25, 311–318. [Google Scholar]
  36. Johnstone, D.; Ades, P.K.; Moore, G.M.; Smith, I.W. Predicting wood decay in eucalypts using an expert system and the IMLRESISTOGRAPH® drill. Arboric. Urban For. 2007, 33, 76–82. [Google Scholar]
  37. Wang, X.; Allison, R.B. Decay detection in red oak trees using a combination of visual inspection, acoustic testing, and resistance micro drilling. J. Arboric. Urban For. 2008, 34, 1–4. [Google Scholar]
  38. Johnstone, D.; Moore, G.; Tausz, T.; Nicolas, M. The measurement of wood decay in landscape trees. Arboric. Urban For. 2010, 36, 121–127. [Google Scholar] [CrossRef]
  39. Johnstone, D.; Moore, G.; Tausz, T.; Nicolas, M. Quantifying wood decay in Sydney bluegum (Eucalyptus saligna) trees. Arboric. Urban For. 2010, 36, 243–253. [Google Scholar] [CrossRef]
  40. Arciniegas, A.; Prieto, F.; Brancheriau, L.; Lasaygues, P. Literature review of acoustic and ultrasonic tomography in standing trees. Trees 2014, 28, 1559–1567. [Google Scholar] [CrossRef]
  41. Balázs, M.; Divos, F. Glue laminated timber structure evaluation by acoustic tomography. General Technical Report FPL-GTR-239. In Proceedings of the 19th International Nondestructive Testing and Evaluation of Wood Symposium, Rio de Janeiro, Brazil, 22–25 September 2015; pp. 462–466. [Google Scholar]
  42. Trinca, A.J.; Guerra, M.R.; Gonçalves, R. Velocity variation in wood as a function of defects. General Technical Report FPL-GTR-239. In Proceedings of the 19th International Nondestructive Testing and Evaluation of Wood Symposium, Rio de Janeiro, Brazil, 22–25 September 2015; pp. 593–599. [Google Scholar]
  43. Linhares, C.; Trinca, A.; Gonçalves, R. Ultrasonic Tomography in Knots Detection. In Proceedings of the 18th International Nondestructive Testing and Evaluation of Wood, Madison, WI, USA, 24–27 September 2013; Forest Products Society: Madison, MI, USA, 2013; p. 664. [Google Scholar]
  44. Sanabria, S.J.; Furrer, R.; Neuenschwander, J.; Niemz, P.; Sennhauser, U. Air-coupled ultrasound inspection of glued laminated timber. Holzforschung 2011, 65, 377–387. [Google Scholar] [CrossRef]
  45. Batista, F.A.F.; Gonçalves, R.; Cerri, D.G.P.; Secco, C.B. Reprodução da condição interna de peças de madeira através de imagens representativas da propagação de ondas. Madeira Arquitetura Eng. 2009, 10, 23–32. (In Portuguese) [Google Scholar]
  46. Brashaw, B.K.; Bucur, V.; Divos, F.; Gonçalves, R.; Lu, J.; Meder, R.; Pellerin, R.F.; Potter, S.; Ross, R.J.; Wang, X.; et al. Nondestructive testing and evaluation of wood: A worldwide research update. For. Prod. J. 2009, 59, 7–14. [Google Scholar]
  47. Socco, L.V.; Sambuelli, L.; Martinis, R.; Comino, E.; NicolottI, G. Feasibility of ultrasonic tomography for nondestructive testing of decay on living trees. Res. Nondestruct. Eval. 2004, 15, 31–54. [Google Scholar] [CrossRef]
  48. Bolund, P.; Hunhammar, S. Ecosystem services in urban areas. Ecol. Econ. 1999, 29, 293–301. [Google Scholar] [CrossRef]
  49. Manion, P.D. Tree Disease Concepts; Prentice-Hall: Engelwood Cliffs, NJ, USA, 1991. [Google Scholar]
  50. Martins, L.M. As múltiplas causas para o declínio da Floresta Urbana. In Proceedings of the 7° Congresso Florestal Nacional, Vila Real/Bragança, Portugal, 5–8 June 2013; pp. 292–308. [Google Scholar]
  51. Martins, L.M. New challenges in urban forest. In Proceedings of the Conference in ERASMUS Program, Firenze, Italy, 23–30 May 2015; Università degli Studi di Firenze: Firenze, Italy, 2015. [Google Scholar]
  52. Rinn, F. Shell-wall thickness and breaking safety of mature trees. West. Arborist 2013, 39, 40–44. [Google Scholar]
  53. Gonçalves, R.; Linhares, C.; Yojo, T. Drag coefficient in urban trees. Trees 2020, 1–13. [Google Scholar] [CrossRef]
  54. Koeser, A.K.; Smiley, E.T. Impact of assessor on tree risk assessment ratings and prescribed mitigation measures. Urban For. Urban Green. 2017, 24, 109–115. [Google Scholar] [CrossRef]
  55. Zhang, B.; Brack, C.L. Urban Forest responses to climate change: A case study in Canberra. Urban For. Urban Green. 2021, 57, 126910. [Google Scholar] [CrossRef]
  56. Prebble, S.; McLean, J.; Houston, D. Smart urban forests: An overview of more-than-human and more-than-real urban forest management in Australian cities. Digit. Geogr. Soc. 2021, 2, 100013. [Google Scholar] [CrossRef]
  57. National Tree Safety Group (NTSG). Common Sense Risk Management of Trees; The Forestry Commission: Edinburgh, UK, 2011; p. 102. [Google Scholar]
  58. Dahle, G.; Grabosky, J.; Kane, B.; Miesbauer, J.; Peterson, W.; Telewski, F.W.; Koeser, A.K.; Watson, G.W. Tree biomechanics: A white paper from the 2012 international meeting and research summit at the Morton Arboretum (Lisle, Illinois, US). Arbor. Urban For. 2014, 40, 309–318. [Google Scholar] [CrossRef]
  59. Koeser, A.K.; Hauer, R.J.; Miesbauer, J.W.; Peterson, W. Municipal tree risk assessment in the United States: Findings from a comprehensive survey of urban forest management. Arbor. J. 2016, 38, 218–229. [Google Scholar] [CrossRef]
  60. Nowak, D.; Noble, M.; Sisinni, S.; Dwyer, J. People and trees: Assessing the US urban forest resource. J. For. 2001, 99, 37–42. [Google Scholar] [CrossRef]
  61. Myeong, S.; Nowak, D.; Duggin, M. A temporal analysis of urban forest carbon storage using remote sensing. Remote Sens. Environ. 2006, 101, 277–282. [Google Scholar] [CrossRef]
  62. Walton, J.; Nowak, D.; Greenfield, E. Assessing urban forest canopy cover using airborne or satellite imagery. Arbor. Urban For. 2008, 34, 334–340. [Google Scholar]
  63. McPherson, E.G.; Simpson, J.R.; Xiao, Q.; Wu, C. Million trees Los Angeles canopy cover and benefit assessment. Landsc. Urban Plan. 2011, 99, 40–50. [Google Scholar] [CrossRef]
  64. Sinn, G.; Wessoly, L. A contribution to the proper assessment of the strength and stability of trees. Arbor. J. 1989, 13, 45–64. [Google Scholar] [CrossRef]
  65. Detter, A.; Brudi, E.; Bischoff, F. Statics Integrated Methods: Results from pulling tests in the past decades. In La Visión del Árbol: Actas del 9º Congreso de Arboricultura; ISA Spain: Barcelona, Spain, 2005; pp. 103–112. [Google Scholar]
  66. Sani, L.; Lisci, R.; Moschi, M.; Sarri, D.; Rimediotti, M.; Vieri, M.; Tofanelli, S. Preliminary experiments and verification of controlled pulling tests for tree stability assessments in Mediterranean urban areas. Biosyst. Eng. 2012, 112, 218–226. [Google Scholar] [CrossRef]
  67. Koizumi, A.; Shimizu, M.; Sasaki, Y.; Hirai, T. In situ drag coefficient measurements for rooftop trees. J Wood Sci. 2016, 62, 363–369. [Google Scholar] [CrossRef] [Green Version]
  68. Beezleyet, K.E.; Dahle, G.A.; Miesbauer, J.; DeVallance, D. Strain Patterns Across the Root-Stem Transition Zone in Urban Trees. Arbor. Urban For. 2020, 46, 321–332. [Google Scholar] [CrossRef]
  69. Linhares, C.; Yojo, T.; Gonçalves, R. Proposta Metodológica para o Cálculo do Coeficiente de Arrasto para Folhosas utilizadas na Arborização Urbana. Encontro Brasileiro em Madeiras e em Estruturas de Madeira, Brasil. 2020. Available online: http://soac.eesc.usp.br/index.php/ebramem/xviiebramem/paper/view/2218 (accessed on 15 May 2021).
  70. Vojácková, B.; Tippner, J.; Horácek, P.; Sebera, V.; Praus, L.; Marík, R.; Brabec, M. The effect of stem and root-plate defects on the tree response during static loading—Numerical analysis. Urban For. Urban Green. 2021, 59, 127002. [Google Scholar] [CrossRef]
  71. Alani, A.M.; Lantini, L. Recent advances in tree root mapping and assessment using nondestructive testing methods: A focus on ground penetrating radar. Surv. Geophys. 2019, 41, 605–646. [Google Scholar] [CrossRef]
  72. Li, F.X.; Li, M.; Feng, X.G. High-Precision Method for Estimating the Three-Dimensional Green Quantity of an Urban Forest. J. Indian Soc. Remote 2021, 49, 1407–1417. [Google Scholar] [CrossRef]
  73. Salsabila, R.; Hariyadi, H.; Santoso, N. Tree Health Management Strategy in Cianjur Urban Forest. J. Sylva Lestari 2021, 9, 86–103. [Google Scholar] [CrossRef]
  74. Vidal, D.; Pitarma, R. Infrared thermography applied to tree health assessment: A review. Agriculture 2019, 9, 156. [Google Scholar] [CrossRef] [Green Version]
  75. Matheny, N.; Clark, J. A Photographic Guide to the Evaluation of Hazard Trees in Urban Areas; International Society of Arboriculture: Champaign, IL, USA, 1994; p. 85. [Google Scholar]
  76. Mattheck, C.; Breloer, H. Field guide for visual tree assessment (VTA). Arbor. J. 1994, 18, 1–23. [Google Scholar] [CrossRef]
  77. Ostrovský, R.; Kobza, M.; Gažo, J. Extensively damaged trees tested with acoustic tomography considering tree stability in urban greenery. Trees 2017, 31, 1015–1023. [Google Scholar] [CrossRef]
  78. Dunster, J.A.; Smiley, E.T.; Matheny, N.; Lilly, S. Tree risk Assessment Manual; International Society of Arboriculture: Champaign, IL, USA, 2013; p. 194. [Google Scholar]
  79. Pokorny, J.D. Urban Tree Risk Management, a Community Guide to Program Design and Implementation; USDA Forest Service North-eastern Area State and Private Forest: Washington, DC, USA, 2003. [Google Scholar]
  80. Ellison, M.J. Quantified tree risk assessment used in the management of amenity trees. J. Arbor. 2005, 31, 57–65. [Google Scholar]
  81. Smiley, E.T.; Matheny, N.; Lilly, S. Best Management Practices: Tree Risk Assessment; International Society of Arboriculture: Champaign, IL, USA, 2011; p. 86. [Google Scholar]
  82. Lin, C.J.; Huang, Y.H.; Huang, G.S.; Wu, M.L.; Yang, T.H. Detection of termite damage in Hoop pine (Araucaria cunninghamii) trees using nondestructive evaluation techniques. J. Trop. For. Sci. 2016, 28, 79–87. [Google Scholar]
  83. Wessolly, L.; Erb, M. Manual of Tree Statics and Tree Inspection; Patzer Verlag: Berlin/Hannover, Germany, 2016; p. 288. ISBN 978-87617-143-2. [Google Scholar]
  84. Habermehl, A.; Ridder, H.W. Methodik der Computer-Tomographie zur zerstörungsfreien Untersuchung des Holzkörpers von stehenden Bäumen. Holz Als Roh-Und Werkst. 1992, 50, 465–474. [Google Scholar] [CrossRef]
  85. Swanson, J.S.; Hailey, J.R.T. Scanning and imaging thechniques for assessing decay and wood quality in logs and standing trees. In Proceedings of the Nondestructive Testing and Evaluation of Wood Symposium, Pullman, WA, USA, 28–30 August 1978. [Google Scholar]
  86. Habermehl, A.; Ridder, H.W.; Naumann, A. Nondestructive Testing of tapped Pines by computerized tomography. In Proceedings of the International Symposium on Nondestructive Testing of Wood, Lausanne, Switzerland, 26–28 August 1996. [Google Scholar]
  87. Kölbel, M. Application of computerized tomography to old Beeches in forest reserves of Bavaria. In Proceedings of the International Symposium on Nondestructive Testing of Wood, Lausanne, Switzerland, 26–28 August 1996. [Google Scholar]
  88. Henderson, R.P.; Webster, J.G. An impedance camera for spatially specific measurements of the thorax. IEEE Trans. Biomed. Eng. 1978, 25, 250–254. [Google Scholar] [CrossRef] [PubMed]
  89. Qian, J.; Zhou, J.; Ruijuan, G.; Yang, L.; Gang, Z. Freezing resistance evaluation of rose stems during frost dehardening using electrical impedance tomography. BMC Plant Biol. 2021, 21, 199. [Google Scholar] [CrossRef]
  90. Weigand, M.; Kemna, A. Multi-frequency electrical impedance tomography as a non- invasive tool to characterize and monitor crop root systems. Biogeosciences 2017, 14, 921–939. [Google Scholar] [CrossRef] [Green Version]
  91. Corona-Lopez, D.D.; Sommer, J.; Rolfe, S.A.; Podd, F.; Grieve, B.D. Electrical impedance tomography as a tool for phenotyping plant roots. Plant. Methods 2019, 15, 49. [Google Scholar] [CrossRef] [Green Version]
  92. Li, X.; Cui, M.; Yang, J.; Han, W. Tomographic image reconstruction of plant single root by electrical impedance tomography. Transact. Chin. Soc. Agric. Eng. 2014, 30, 173–180. [Google Scholar]
  93. Dolwin, J.A.; Lawday, G.; Lonsdale, D.; Barnett, J.R.; Hodges, P. Development and Use of Stress Wave Meter, to Detect the Presence of Decay in Wood Blocks. In Proceedings of the International Symposium on Nondestructive Testing of Wood, Sopron, Hungary, 13–15 September 2000. [Google Scholar]
  94. Bucur, V. High Resolution Imaging of Wood. In Proceedings of the International Symposium on Nondestructive Testing of Wood, Berkeley, CA, USA, 19–21 August 2002. [Google Scholar]
  95. McCracken, F. Using sound to detect decay in standing hardwood trees. In Proceedings, 5th Symposium on Nondestuctive Testing of Wood; Pullman, Washington State University: Washington, DC, USA, 1985. [Google Scholar]
  96. Divos, F.; Meszaros, K. Root decay detection by stress wave technique. In Proceedings of the 1st European Symposium on Nondestructive Testing, Sopron, Hungary, 21–23 September 1994; p. 524. [Google Scholar]
  97. Mattheck, C.G.; Bethge, K.A. Detection of decay in trees with the Metriguard Stresswave timer. J. Arbor. 1993, 16, 374–378. [Google Scholar]
  98. Stefan, H.; Gottfried, S. Detection of Butt Rot. In Proceedings of the International Symposium on Nondestructive Testing of Wood, Sopron, Hungary, 13–15 September 2000. [Google Scholar]
  99. Divos, F.; Szalai, L. Tree Evaluation by Acoustic Tomography. In Proceedings of the International Symposium on Nondestructive Testing of Wood, Berkeley, CA, USA, 19–21 August 2002. [Google Scholar]
  100. Schubert, S. Acousto-Ultrasound Assessment of Inner Wood-Decay in Standing Trees: Possibilities and Limitations. Ph.D. Thesis, Swiss Federal Institute of Technology Zurich, Züric, Switzerland, 2007. [Google Scholar]
  101. Maurer, H.; Schubert, S.I.; Bachle, F.; Clauss, S.; Gsell, D.; Dual, J.; Niemz, P. A simple anisotropy correction procedure for acoustic wood tomography. Holzforschung 2006, 60, 567–573. [Google Scholar] [CrossRef] [Green Version]
  102. Du, X.; Li, S.; Li, G.; Feng, H.; Chen, S. Stress wave tomography of wood internal defects using ellipse-based spatial interpolation and velocity compensation. BioResources 2015, 10, 3948–3962. [Google Scholar] [CrossRef]
  103. Bucur, V. Ultrasonic techniques for nondestructive testing of standing trees. Ultrasonics 2005, 43, 237–239. [Google Scholar] [CrossRef] [PubMed]
  104. Bucur, V. Acoustics of Wood (Second Edition). J. Acoust. Soc. Am. 2006, 119, 3506. [Google Scholar] [CrossRef]
  105. Wang, X.; Divos, F.; Pilon, C.; Brashaw, B.K.; Ross, R.J.; Pellerin, R.F. Assessment of Decay in Standing Timber Using Stress Wave Timing Non-Destructive Evaluation Tools: A Guide for Use and Interpretation; General Technical Report (GTR), FPL-GTR-147; Department of Agriculture, Forest Service, Forest Products Laboratory: Madison, WI, USA, 2004; 12p. [Google Scholar]
  106. Zhu, J.; Tadooka, N.; Takata, K.; Koizumi, A. Growth and wood quality of sugi (Cryptomeria japonica) planted in Akita prefecture (II). Juvenile/mature wood determination of aged trees. J. Wood Sci. 2005, 51, 95–101. [Google Scholar] [CrossRef]
  107. Kijidani, Y.; Kitahara, R. Variation of wood properties with height position in the stems of Obi-sugi [Cryptomeria japonica] cultivars. J. Jpn. Wood Res. Soc. 2009, 55, 198–206. [Google Scholar] [CrossRef]
  108. Booker, R.E.; Harrington, J.J.; Shiokura, T. Variation of Young´s modulus with microfibril angle, density and spiral grain. In Microfibril Angle in Wood; Butterfield, B.G., Ed.; University of Canterbury: Christchurch, New Zealand, 1998; pp. 296–311. [Google Scholar]
  109. Trinca, A.J.; Gonçalves, R. Effect of the transversal section dimensions and transducer frequency on ultrasound wave propagation velocity in wood. Rev. Árvore 2009, 33, 177–184. (In Portuguese) [Google Scholar] [CrossRef] [Green Version]
  110. Bond, L.; Saffari, M. Mode-conversion ultrasonic testing. In Nondestructive Testing; Sharpe, R.S., Ed.; Academic Press: London, UK, 1984; Volume 7, pp. 145–189. [Google Scholar]
  111. Wedgwood, F. Data processing in ultrasonic ndt. Proc. Ultrason. Int. 1987, 87, 381–386. [Google Scholar]
  112. Secco, C.B. Detention of Hollow in Logs Using Methods of Propagation of Ultrasonic Waves. Master’s Thesis, University of Campinas, Campinas, Brazil, 2011. [Google Scholar]
  113. Palma, S.S.A.; Gonçalves, R.; Trinca, A.J.; Costa, C.P.; Guerra, M.N.R.; Martins, G.A. Interference from knots, wave propagation direction and effect of juvenile and reaction wood on velocities in ultrasound tomography. BioResources 2018, 13, 2834–2845. [Google Scholar] [CrossRef]
  114. Brancheriau, L.; Ghodrati, A.; Gallet, P.; Thaunay, P.; Lasaygues, P. Application of ultrasonic tomography to characterize the mechanical state of standing trees (Picea abies). J. Phys. Conf. Ser. 2012, 353, 012007. [Google Scholar] [CrossRef] [Green Version]
  115. Feng, H.; Li, G.; Fu, S.; Wang, X. Tomographic image reconstruction using an interpolation method for tree decay detection. BioResources 2014, 9, 3248–3263. [Google Scholar] [CrossRef]
  116. Zeng, L.; Lin, J.; Hua, J.; Shi, W. Interference resisting design for guided wave tomography. Smart Mater. Struct. 2013, 22, 055017. [Google Scholar] [CrossRef]
  117. Lin, C.J.; Kao, Y.C.; Lin, T.T.; Tsai, M.J.; Wang, S.Y.; Lin, L.D.; Chan, M.H. Application of an ultrasonic tomographic technique for detecting defects in standing trees. Int. Biodeterior. Biodegrad. 2008, 62, 434–441. [Google Scholar] [CrossRef]
  118. Strobel, J.R.A.; de Carvalho, M.A.G.; Gonçalves, R.; Pedroso, C.B.; dos Reis, M.N.; Martins, P.S. Quantitative image analysis of acoustic tomography in woods. Eur. J. Wood Wood Prod. 2018, 76, 1379–1389. [Google Scholar] [CrossRef]
  119. Pádua, F.A. Amostragem Para Avaliação da Densidade Básica da Madeira de um Híbrido de Eucalyptus grandis W. Hill ex Maiden x Eucalyptus urophylla S.T. Blake. Ph.D. Thesis, Universidade Federal de Lavras, Lavras, Brasil, 2009. (In Portuguese). [Google Scholar]
  120. Sprague, J.R.; Talbert, J.T.; Jett, J.B.; Bryant, R.L. Utility of the Pilodyn in selection for mature wood specific gravity in loblolly pine. For. Sci. 1983, 29, 696–701. [Google Scholar] [CrossRef]
  121. Gao, S.; Wang, X.; Wiemann, M.C.; Brashaw, B.K.; Ross, R.J.; Wang, L. A critical analysis of methods for rapid and non-destructive determination of wood density in standing trees. Ann. Sci. 2017, 74, 27. [Google Scholar] [CrossRef] [Green Version]
  122. Martínez, D.L. Métodos no Destructivos de Estimación de la Densidad de la Madera. Ph.D. Thesis, Universidade de Santiago de Compostela, Madrid, Spain, 2018. (In Spanish). [Google Scholar]
  123. Downes, G.M.; Hudson, I.L.; Raymond, C.A.; Dean, G.H.; Michell, A.J.; Schimleck, L.R.; Evans, R.; Muneri, A. Sampling Plantation Eucalypts for Wood and Fibre Properties; CSIRO Publishing: Clayton, Australia, 1997; 126p. [Google Scholar]
  124. Oliveira, J.T.; Wang, X.; Vidaurre, G.B. Assessing specific gravity of young eucalyptus plantation trees using a resistance drilling technique. Holzforschung 2017, 71, 137–145. [Google Scholar] [CrossRef]
  125. Dos Reis, M.N.; Gonçalves, R.; Lopes Garcia, G.H.; Manes, L. Profiles of a Non-Calibrated Resistance Drill Compared with Deteriorated Stem Cross Sections. Arbor. Urban For. 2019, 45, 1–9. [Google Scholar] [CrossRef]
  126. Tannert, T.; Anthony, R.W.; Kasal, B.; Kloiber, M.; Piazza, M.; Riggio, M.; Rinn, F.; Widman, N.R.; Yamaguchi, N. In situ assessment of structural timber using semi-destructive techniques. Mater Struct 2014, 47, 767–785. [Google Scholar] [CrossRef]
  127. Rinn, F. A new method for direct measuring of wood density of broadleaf trees and conifers. Dendrochronologia 1989, 7, 159–168. [Google Scholar]
  128. Brashaw, B.K.; Vatalaro, R.; Ross, R.J.; Wang, X.; Schmieding, S.; Okstad, W. Historic Log Cabin structural condition assessment and rehabilitation—A case study. In Proceedings of the 17th International Non-destructive Testing and Evaluation of Wood Symposium, Sopron, Hungary, 14–16 September 2011; pp. 505–512. [Google Scholar]
  129. Rinn, F. Basics of micro Resistance-Drilling for Timber Inspection. Holztechnologie 2012, 53, 24–29. [Google Scholar]
  130. Kubus, M. The Evaluation of Using Resistograph when Specifying the Health Condition of a Monumental Tree. Not. Bot. Horti Agrobot. Cluj-Napoca 2009, 37, 157–164. [Google Scholar] [CrossRef]
  131. Wang, X.; Wiedenbeck, J.; Liang, S. Acoustic tomography for decay detection in black cherry trees. Wood Fiber Sci. 2009, 41, 127–137. [Google Scholar]
  132. Isik, F.; Li, B. Rapid assessment of wood density of live trees using the Resistograph for selection in tree improvement programs. Can. J. For. Res. 2003, 33, 2427–2435. [Google Scholar] [CrossRef]
  133. Gwaze, D.; Stevenson, A. Genetic variation of wood density and its relationship with drill resistance in shortleaf pine. South. J. Appl. For. 2008, 32, 130–133. [Google Scholar] [CrossRef] [Green Version]
  134. Eckard, T.J.; Isik, F.; Bullock, B.; Li, B.; Gumpertz, M. Selection efficiency for solid wood traits in Pinus taeda using time-of-flight acoustic and micro-drill resistance methods. For. Sci. 2010, 56, 233–241. [Google Scholar] [CrossRef]
  135. Grassé, P.P. Termitologia; Masson: Paris, France, 1982; Volume 1. [Google Scholar]
  136. Oliveira, A.M.F.; Lelis, A.T.; Lepage, E.S. Agentes Destruidores da Madeira. In Manual de preservação de madeiras; Lepage, E.S., Ed.; IPT: São Paulo, Brazil, 1986; Volume 1, pp. 99–278. (In Portuguese) [Google Scholar]
  137. Brazolin, S. Biodeterioração, Anatomia do Lenho e Análise de Risco de Queda de Árvores de Tipuana, Tipuana Tipu (Benth.) O. Kuntze, Nos Passeios Públicos da Cidade de São Paulo, SP. Ph.D. Thesis, Recursos Florestais, ESALQ—Escola Superior de Agricultura Luiz de Queiroz, USP—Universidade de São Paulo, Piracicaba, São Paulo, Brazil, 2009. (In Portuguese). [Google Scholar]
  138. Duarte, S.; Nunes, L.; Borges, P.A.; Fossdal, C.G.; Nobre, T. Living inside termites: An overview of symbiotic interactions, with emphasis on flagellate protists. Arquipel. Life Mar. Sci. 2017, 34, 21–43. [Google Scholar]
  139. Passos, E.M.; Albuquerque, A.C.; Marques, E.J.; Teixeira, V.W.; Silva, C.C.M.; Oliveira, M.A.P. Efeitos de isolados do fungo Isaria (Persoon) sobre o cupim subterrâneo Coptotermes gestoi. Arq. Inst. Biol. 2014, 81, 232–237. [Google Scholar] [CrossRef] [Green Version]
  140. Edwards, R.; Mill, A.E. Termites in Buildings: Their Biology and Control; Rentokil: Felcourt, UK, 1986; p. 231, ISBN-10: 0906564069. [Google Scholar]
  141. Waller, D.A.; La Fage, J.P. Nutritional ecology of termites. In Nutritional Ecology of Insects, Mites, Spiders and Related Invertebrate; Slansky, F., Jr., Rodriguez, J.C., Eds.; John Wiley & Sons: New York, NY, USA, 1986; pp. 487–532. Available online: http://www.dowagro.com/br/sentricon/cupins/subter.htm (accessed on 15 May 2021).
  142. Brazolin, S. Propriedades físico-mecânicas do lenho deteriorado por fungos apodrecedores de árvores de Tipuana tipu. Cerne 2014, 20, 183–190. [Google Scholar] [CrossRef] [Green Version]
  143. Lin, C.J.; Chang, T.T.; Juan, M.Y.; Lin, T.T. Detecting deterioration in royal palm (Roystonea regia) using ultrasonic tomographic and resistance microdrilling techniques. J. Trop. For. Sci. 2011, 23, 260–270. [Google Scholar]
  144. Linhares, C.; Trinca, A.; Gonçalves, R. Avaliação de Imagens Ultrassônicas de Madeira inoculadas com Xilófagos. In Proceedings of the XXII Congresso de Iniciação Científica da Unicamp, Campinas, Brazil, 22–24 October 2014. [Google Scholar]
  145. Rayner, A.D.M.; Boddy, L. (Eds.) Fungal Decomposition of Wood: Its Biology and Ecology; John Willey: Chichester, UK, 1988; 587p. [Google Scholar]
  146. Palma, S.S.A. Reconhecimento de Padrões em Imagens Geradas por Ultrassom: Recognition of Patterns in Images Gerated by Ultrasond. 2017. 1 Recurso Online (210p.) Dissertação (mestrado)—Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola, Campinas, SP. Available online: http://acervus.unicamp.br/index.asp?codigo_sophia=989570 (accessed on 10 December 2021). (In Portuguese).
  147. Aghajani, H.; Bari, E.; Bahmani, M.; Humar, M.; Ghanbary, M.A.T.; Nicholas, D.D.; Zahedian, E. Influence of relative humidity and temperature on cultivation of Pleurotus species. Maderas Cienc. Tecnol. 2018, 20, 571–578. [Google Scholar] [CrossRef] [Green Version]
  148. Bari, E.; Aghajani, H.; Ohno, K.M.; Shahi, R.; Hale, M.D.; Bahmani, M. Ecology of wood-inhabiting fungi in northern forests of Iran. For. Pathol. 2019, 49, e12501. [Google Scholar] [CrossRef]
  149. Nicholas, D. Wood Deterioration and Its Prevention by Preservative Treatments: Degradation and Protection of Wood; Syracuse University Press: Syracuse, NY, USA, 1982. [Google Scholar]
  150. Schmidt, O. Wood and Tree Fungi: Biology, Damage, Protection, and Use; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2006; p. 336. ISBN 978-3-540-32139-2. [Google Scholar]
  151. Azimi, Y.; Bahmani, M.; Jafari, A.; Riyahi Bakhtyari, H.R. Anatomical, Chemical and Mechanical Characteristics of Beech Wood Degraded by Two Pleurotus Species. Drv. Ind. Znan. Časopis Za Pitanja Drv. Tehnol. 2020, 71, 47–53. [Google Scholar] [CrossRef]
  152. Rowell, R.M. Wood Chemistry and Wood Composites; CRC Press: Boca Raton, FL, USA, 2005; 473p. [Google Scholar]
  153. Wong, D.W. Structure and action mechanism of ligninolytic enzymes. Appl. Biochem. Biotechnol. 2009, 157, 174–209. [Google Scholar] [CrossRef]
  154. Schmidt, O.; Bahmani, M.; Koch, G.; Potsch, T.; Brandt, K. Study of the fungal decay of oil palm wood using TEM and UV techniques. Int. Biodeterior. Biodegrad. 2016, 111, 37–44. [Google Scholar] [CrossRef]
  155. Deflorio, G.; Fink, S.; Schwarze, F.W. Detection of incipient decay in tree stems with sonic tomography after wounding and fungal inoculation. Wood Sci. Technol. 2008, 42, 117–132. [Google Scholar] [CrossRef]
  156. Kim, K.; Pard, J.; Lee, S.; Yeo, H.; Lee, J. Development of a portable ultrasonic computed tomography system for detecting decay in wood. In Proceedings of the International Non-Destructive Testing and Evaluation of Wood Symposium, Duluth, MN, USA, 10–12 September 2007; pp. 191–195. [Google Scholar]
  157. Allison, R.B.; Wang, X.; Ross, R.J. Visual e nondestructive evaluation of red pine supporting a ropes course in the USFS Nesbit Lake Camp, Sidnaw, Michigan. In Proceedings of the 15th International Non-Destructive Testing and Evaluation of Wood Symposium, Duluth, MN, USA, 10–12 September 2007; pp. 43–48. [Google Scholar]
  158. Wang, X.; Carter, O.; Ross, R.J.; Brashaw, B.K. Acoustics assessment of wood quality of ray forest materials. For. Prod. J. 2007, 57, 6–14. [Google Scholar]
  159. Bari, E.; Daryaei, M.G.; Karim, M.; Bahmani, M.; Schmidt, O.; Woodward, S.; Sistani, A. Decay of Carpinus betulus wood by Trametes versicolor-An anatomical and chemical study. Int. Biodeterior. Biodegrad. 2019, 137, 68–77. [Google Scholar] [CrossRef]
  160. Gonçalves, R.; Trinca, A.J.; Pellis, B.P. Elastic constants of wood determined by ultrasound using three geometries of specimens. Wood Sci. Technol. 2014, 48, 269–287. [Google Scholar] [CrossRef]
  161. Cavalcanti, N.M.O.; Goncalves, R.; Brazolin, S.; Bertoldo, C.; Ruy, M. Ultrasound test for root wood elastomechanical characterization. BioResources 2018, 13, 5818–5835. [Google Scholar]
  162. Gonçalves, R.; Garcia, G.H.L.; Brazolin, S.; Bertoldo Pedroso, C.; Ruy, M. Methodology for characterization of elastic constants of wood tree branches. BioResources 2019, 14, 8439–8454. [Google Scholar]
  163. Bucur, V. An ultrasonic method for measuring the elastic constants of wood increment cores bored from living trees. Ultrasonics 1983, 21, 116–126. [Google Scholar] [CrossRef]
  164. Keunecke, D.; Sonderegger, W.; Pereteanu, K.; Lüthi, T.; Niemz, P. Determination of Young’s and shear moduli of common yew and Norway spruce by means of ultrasonic waves. Wood Sci. Technol. 2007, 41, 309–327. [Google Scholar] [CrossRef] [Green Version]
  165. Longo, R.; Delaunay, T.; Laux, D.; El Mouridi, M.; Arnould, O.; Le Clezio, E. Wood elastic characterization from a single sample by resonant ultrasound spectroscopy. Ultrasonics 2012, 52, 971–974. [Google Scholar] [CrossRef] [Green Version]
  166. Ozyhar, T.; Hering, S.; Sanabria, S.J.; Niemz, P. Determining moisture-dependent elastic characteristics of beech wood by means of ultrasonic waves. Wood Sci. Technol. 2013, 47, 329–341. [Google Scholar] [CrossRef] [Green Version]
  167. Gonçalves, R.; Trinca, A.J.; Dos Ferreira, G.C.S. Effect of coupling media on velocity and attenuation of ultrasonic waves in Brazilian wood. J. Wood Sci. 2011, 57, 282–287. [Google Scholar] [CrossRef]
  168. Gonçalves, R.; Trinca, A.J.; Cerri, D.G.P. Comparison of Elastic Constants of Wood Determined by Ultrasonic Wave Propagation and Static Compression Testing. Wood Fiber Sci. 2011, 43, 64–75. [Google Scholar]
  169. Vázquez, C.; Gonçalves, R.; Bertoldo, C.; Baño, V.; Vega, A.; Crespo, J.; Guaita, M. Determination of the mechanical properties of Castanea sativa Mill. Using ultrasonic wave propagation and comparison with static compression and bending methods. Wood Sci. Technol. 2015, 49, 607–622. [Google Scholar] [CrossRef]
  170. Francois, M. Identification des Symétries Matérielles de Matériaux Anisotropies. Ph.D. Thesis, Université Pierre et Marie Curie-Paris VI, Paris, France, 1995. (In French). [Google Scholar]
Forests 12 01752 g001 550
Figure 1. Absorption of gases by plants. Adapted from [22], with permission from IntechOpen, 2013.
Figure 1. Absorption of gases by plants. Adapted from [22], with permission from IntechOpen, 2013.
Forests 12 01752 g001
Forests 12 01752 g002 550
Figure 2. Diffraction mesh with eight measurement points produced by software (ImageWood). Source: Reproduced with permission from Raquel Gonçalves (2021), Nondestructive Testing Research Group Coordinator, School of Agricultural Engineering, University of Campinas, Brazil.
Figure 2. Diffraction mesh with eight measurement points produced by software (ImageWood). Source: Reproduced with permission from Raquel Gonçalves (2021), Nondestructive Testing Research Group Coordinator, School of Agricultural Engineering, University of Campinas, Brazil.
Forests 12 01752 g002
Forests 12 01752 g003 550
Figure 3. (A) Pequia species (Aspidosperma desmanthum) with an artificial circular hole. (B) Resulting image using a diffraction grid. (C) Resulting image using a reticulated grid. Source: Reproduced from [112], whose is deposited in the Scientific and Intellectual Production Repository of University of Campinas [DOI: https://doi.org/10.47749/T/UNICAMP.2011.793973, accessed on 10 November 2021].
Figure 3. (A) Pequia species (Aspidosperma desmanthum) with an artificial circular hole. (B) Resulting image using a diffraction grid. (C) Resulting image using a reticulated grid. Source: Reproduced from [112], whose is deposited in the Scientific and Intellectual Production Repository of University of Campinas [DOI: https://doi.org/10.47749/T/UNICAMP.2011.793973, accessed on 10 November 2021].
Forests 12 01752 g003
Forests 12 01752 g004 550
Figure 4. Example of ultrasound tomography image. Source: Reproduced with permission from Raquel Gonçalves (2021), Nondestructive Testing Research Group Coordinator, School of Agricultural Engineering, University of Campinas, Brazil. Legend: Yellow = velocities from 80% to 100% of maximum velocity; Brown = velocities from 50% to 80% of maximum velocity; Red = velocities from 36% to 50% of maximum velocity; Yellow = velocities from 0% to 36% of maximum velocity.
Figure 4. Example of ultrasound tomography image. Source: Reproduced with permission from Raquel Gonçalves (2021), Nondestructive Testing Research Group Coordinator, School of Agricultural Engineering, University of Campinas, Brazil. Legend: Yellow = velocities from 80% to 100% of maximum velocity; Brown = velocities from 50% to 80% of maximum velocity; Red = velocities from 36% to 50% of maximum velocity; Yellow = velocities from 0% to 36% of maximum velocity.
Forests 12 01752 g004
Forests 12 01752 g005 550
Figure 5. Evaluation of xylophagous attack through ultrasound tomography. Source: Reproduced from [146], whose is deposited in the open Scientific and Intellectual Production Repository of University of Campinas [DOI: ttps://doi.org/10.47749/T/UNICAMP.2017.989570, accessed on 10 November 2021]. Legend: (A) = initial condition of the disc; (B) = Condition of the disc 135 days after inoculation with termites; (C) = Condition of the disc 195 days after inoculation with termites.
Figure 5. Evaluation of xylophagous attack through ultrasound tomography. Source: Reproduced from [146], whose is deposited in the open Scientific and Intellectual Production Repository of University of Campinas [DOI: ttps://doi.org/10.47749/T/UNICAMP.2017.989570, accessed on 10 November 2021]. Legend: (A) = initial condition of the disc; (B) = Condition of the disc 135 days after inoculation with termites; (C) = Condition of the disc 195 days after inoculation with termites.
Forests 12 01752 g005
Forests 12 01752 g006 550
Figure 6. Tomographic images showing the evolution of the biodeterioration caused by fungi. Source: Reproduced from [146], whose is deposited in the open Scientific and Intellectual Production Repository of University of Campinas [DOI: https://doi.org/10.47749/T/UNICAMP.2017.989570, accessed on 10 November 2021]. Legend: (A) = initial condition of the disc; (B) = Condition of the disc 3 months after fungi inoculation ; (C) = Condition of the disc 5 months after fungi inoculation; (D) = Condition of the disc 7 months after fungi inoculation; (E) = Condition of the disc 9 months after fungi inoculation; (F) = Condition of the disc 11 months after fungi inoculation.
Figure 6. Tomographic images showing the evolution of the biodeterioration caused by fungi. Source: Reproduced from [146], whose is deposited in the open Scientific and Intellectual Production Repository of University of Campinas [DOI: https://doi.org/10.47749/T/UNICAMP.2017.989570, accessed on 10 November 2021]. Legend: (A) = initial condition of the disc; (B) = Condition of the disc 3 months after fungi inoculation ; (C) = Condition of the disc 5 months after fungi inoculation; (D) = Condition of the disc 7 months after fungi inoculation; (E) = Condition of the disc 9 months after fungi inoculation; (F) = Condition of the disc 11 months after fungi inoculation.
Forests 12 01752 g006
Table
Table 1. Predisposing, inciting, and contributing factors.
Table 1. Predisposing, inciting, and contributing factors.
Predisposing FactorsInciting FactorsContributing FactorDendrological Parameters
Urban environmentDroughtBiotic agentsDiameter of breast height—DBH
Soil compactionFloods Height of the tree
Genetic potentialSevere pruning Height of the canopy
AgeExcavation Height of the stem
Lack of lightAir pollution Diameter of canopy
Sea proximityOverwatering
Root cuting
Table
Table 2. Indicative signs or symptoms related to visual analysis.
Table 2. Indicative signs or symptoms related to visual analysis.
RootCavity; Injury; Surface
StemCavity; Injury; Codominance; Inclined; Adventitious; Spheroplast; Included bark; Protuberance
BranchCavity; Wound; Codominance; Dense; Dry; Large; Slender
TwigAdventitious; Dry; Low; Dense
LeafDry; Necrosis; Chlorosis; Small; Large; Perforated; Epinasty
CrownDead; Dieback; Transparent; Inclined; High; Low; Unbalanced; Dense
Biotic AgentsWhite rot; Brown rot; Soft rot; Fumagine; Aphids; Anthracnose; Xanthogaleruca luteola; Termite; Other phytopathogenes
LesionHeight; Width; Depth; Height above the ground; Distance to the back wall; Thickness of the front wall; Tree diameter at lesion entrance
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Linhares, C.S.F.; Gonçalves, R.; Martins, L.M.; Knapic, S. Structural Stability of Urban Trees Using Visual and Instrumental Techniques: A Review. Forests 2021, 12, 1752. https://doi.org/10.3390/f12121752

AMA Style

Linhares CSF, Gonçalves R, Martins LM, Knapic S. Structural Stability of Urban Trees Using Visual and Instrumental Techniques: A Review. Forests. 2021; 12(12):1752. https://doi.org/10.3390/f12121752

Chicago/Turabian Style

Linhares, Camila S. F., Raquel Gonçalves, Luis M. Martins, and Sofia Knapic. 2021. "Structural Stability of Urban Trees Using Visual and Instrumental Techniques: A Review" Forests 12, no. 12: 1752. https://doi.org/10.3390/f12121752

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop