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Article

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

by
Marek Hrdina
* and
Peter Surový
Faculty of Forestry and Wood Science, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(24), 5712; https://doi.org/10.3390/rs15245712
Submission received: 8 November 2023 / Revised: 10 December 2023 / Accepted: 11 December 2023 / Published: 13 December 2023
(This article belongs to the Topic AI Enhanced Civil Infrastructure Safety)
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Abstract

:
The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models.

1. Introduction

Detection of decayed trees is a necessary task for urban greenery managers, park managers and road maintenance personnel. This activity is essential for the early detection of potentially hazardous trees and for preventing injuries, fatalities, or material damages. However, the risk assessment requires experience and time-consuming examination of a tree stem using, e.g., penetrometry, acoustic tomography or electrical impedance tomography [1,2,3,4,5]. Methods of tomography may consume around thirty minutes per single tree and are therefore inappropriate for large-scale investigation and for all the potentially hazardous trees. It is also essential to apply the tomography correctly, especially on unevenly shaped trees [1], because an incorrect measurement may lead to a wrong conclusion and unnecessary removal of a highly valued tree in a park, causing a loss of both esthetical value and shelters for wildlife in an urban area. Even though the tomographic methods are accurate, the final risk assessment still depends on subjective opinion, producing inconsistency in the estimation [6]. For these reasons, a faster, easy-to-use, yet reliable and unbiased method would find utilisation in the aforementioned forest management services. The other quality that arborists and forest managers seek is non-destructiveness [5,7]. This feature could be offered by the proposed method in this study. Even though tomography is considered non-destructive [8], nails are put into the stem during an examination, causing minor wounds to the bark and wood. The method also consumes much time and requires manual labour. That should be avoided by the proposed method, allowing even people without experience in tree risk assessment to estimate tree stem states.
The impact of holes caused by sensors of an acoustic tomograph is not indisputably considered unharmful [5,8,9]. Therefore, the task at hand is to develop a technique of internal tree stem decay detection that definitely is not harmful and produces reliable, unbiased results.
Over the past years, the development of remote sensing has created a new opportunity to make accurate representations of 3D objects and to take advantage of having such a representation for various purposes. In forestry, the most usual utilisation of this data is tree diameter at breast height (DBH), height, volume, or species estimation by both aerial and terrestrial approaches [10,11]. While the accuracy of tree DBH estimation has a very low error (3 to 13 mm) [12,13,14], the general presumption for this study is that Close Range Photogrammetry (CRP) can accurately reconstruct tree stem shape with all its details and may, therefore, be able to provide a good resource for deep learning and internal tree stem decay detection.
Some studies have examined the possible ways to reconstruct the basal part of a tree using photogrammetry. A practical and intuitive way of collecting imagery is sequential imaging while following imaginary circles of 2 and 3 m in diameter, taking images of a tree stem every few steps [1,15,16]. The number of collected images may vary when using such an approach, but the best results may be expected when each point of the reconstructed object is visible in eight pictures [14]. Also, while using CRP, it is necessary to implement any scale bar to scale the 3D reconstruction correctly [17].
An alternative way of 3D reconstruction is the use of LiDAR. Nowadays, LiDAR sensors are installed in some consumer-grade phones or tablets, which makes the technology more accessible than it used to be. These small sensors use the Time-of-Flight method combined with an RGB-D camera and can reconstruct the scene nearly in real time. On the other hand, its drawbacks are a small reach of 5 to 6 m and a tendency to misalign repeatedly scanned objects [18]. This method was already successfully used in forestry research with feasible accuracy for forestry inventory purposes [19,20]. A detailed model of each tree stem was not the goal of these studies. Therefore, the accuracy of DBH estimation is not as high as in cited studies using CRP to reconstruct individual trees.
The decay caused by fungi or bacteria is the main reason a tree or its parts fail [4,6,21,22,23,24]. There are two main types of decay in tree stems. Heart rot occurs in the centre of a stem, whereas sap rot starts close to the surface, in the sapwood of a stem [22]; therefore, the latter may often be identified visually by examining mushroom fruiting bodies on the bark or underneath it. Regardless of the location in the stem where the mushroom grows, the types of wood rotting can be further classified by the kind of wood tissue degraded by the mushroom. It can be either cellulose (Brown rot), lignin (Soft rot), or both (White rot) [25]. The tree’s reaction to this kind of damage can then be specific to the infecting mushroom species or individual adaptations of a tree to the ongoing wood degradation in the stem shape and morphology. The shape changes caused by heart rot can be reliably shown in the example of Picea abies trees infected by Armillaria mushrooms, creating typical shape changes of the stem base caused by progressive growth of the mushroom from the stem centre to its sides [26]. Other stem shape changes are associated with the tree’s effort to support weaker parts of the infected stem by building more wood in these locations and forming bulges or cancers [27,28,29]. Since heart rot is often hard to detect visually, several methods allow the detection of decay based on physical properties, mainly acoustic response or electrical impedance [4,5,6]. In this study, only acoustic tomography was used to detect the actual health state of trees. This method is based on measuring the time of flight of sound waves [5]. Generally, the stress wave propagates faster in dense and healthy wood, whereas the decayed wood results in a much slower stress wave propagation, as the stress wave needs to bypass holes and gaps created by the decay [7]. The time is measured using a timer and a series of accelerometers placed on a stem at the same height level. These sensors require metal nails to be knocked into the wood. As soon as a stress wave is emitted from one accelerometer by a hammer tap, the time of flight is measured for each of the receiving accelerometers. Afterwards, the same process is done for all the other accelerometers [5,30]. The alternative methods for tree stem decay detection are electrical impedance tomography, which measures electrical conductivity in the wood, where healthy wood has a higher resistivity than decayed wood due to its wetness [2,5], and resistance drills, which measure the power needed to drill through a piece of timber, thus indicating its state [3,5].
In practical use, the stem shape is often simplified and replaced by a circle or cylinder shape, leading to an incorrect estimation of sensor-to-sensor distances and distortion of the outcome tomogram. Previous studies verified that sensor locations may be detected on 3D photogrammetric models with very high precision, and calculation of the exact distances of sensors from each other for tomography is possible [1,17,31]. A similar procedure of sensor location estimation will be described in the proposed study as well. Some of these studies also attempted to overcome a standard limitation of tomography. The method can only show the status of wood in a 2D cross-section. Multiple sections must be examined to obtain a more precise representation of the whole stem base, which requires a significant amount of time. The mentioned studies attempted to reconstruct the state of decay in the entire stem base using multiple cross-sections of tomography and interpolation between them, resulting in a complete model of the inner stem state [1,17]. However, the result is only an estimate based on several 2D cross-sections and might not improve the ability to perform a correct risk assessment.
A new trend in data evaluation is the use of deep learning. Usually, images or 3D point clouds are often evaluated by neural networks, and exciting results are reported in object detection or classification—specifically, in the detection of individual trees or tree species classification [32,33,34,35], but it is appropriate for the detection of any feature with distinctive patterns [36]. As mentioned before, it is known that some fungi species cause specific changes to the external shape of a tree stem base, and stem decay is commonly related to the presence of bulges, cancer, and swelling. These changes are caused by the tree’s response to the weakening structures in its wood. Therefore, the proposed study presumes that an AI deep learning model could be utilised for tree stem internal decay detection, using just the 3D properties of the stem base, potentially detecting the presence of the decay based on the morphological responses of the tree stem to its infection. Nevertheless, more data are needed to support this claim, but we believe our results will stimulate other researchers to follow this direction. For 3D data classification, the PointNet deep learning algorithm is often used [32,35,37,38], and due to its satisfactory performance, it was also used in this study to evaluate 3D meshes or point clouds respectively, and classify them into one of the desired classes, where the input data contain 3D models of standing tree stems, scanned in urban greenery by both close-range photogrammetry and iPhone LiDAR. The algorithm used for point cloud classification in the proposed study is PointNet. The algorithm works directly with unordered 3D point clouds and is invariant to geometric transformations of the point cloud. It is suitable for object classification, segmentation or semantic parsing [39].
In some studies, the need to process a point cloud directly was bypassed by using other interpretations of point clouds, such as images or snapshots from varying views, which leads to processing 2D data with satisfying results and accuracy higher than that from processing 3D data [37,40].
By combining terrestrial remote sensing and deep learning, the proposed work aims to reconstruct basal parts of standing tree stems and classify them based on the decay state inside the tree stem, which was estimated using acoustic tree stem tomography. This study should represent a low-cost and well-accessible approach to close-range remote sensing and object classification problems in arboriculture or forestry. The proposed method introduces a novel way of tree health evaluation. Therefore, in the future, the methods used should be accessible to most enterprises potentially interested in this method.

2. Materials and Methods

2.1. Study Site

The data collection was conducted at two locations, shown in Figure 1. The first location, lying at an altitude around 510 MSL, is the property of the enterprise Lázeňské lesy a parky Karlovy Vary PBC and lies in its rope-climbing park Svatý Linhart, on the southern edge of the city of Karlovy Vary. This area is a mature, managed, even-aged European Spruce forest with a moderate European Beech understory and stocking density of 0.9 [41]. The soil type is Dystric Cambisol on primary granite rock [42,43]. The mean yearly temperature is between 8 and 9 °C, and the mean annual precipitation ranges from 600 to 700 mm [44].
The second study site is related to the town of Mašťov, 30 km east of Karlovy Vary. The object of interest was an old and unkept castle park associated with the local palace. The altitude of the area is around 400 MSL, and the climatological data are the same as in the first study site [44]. Regarding geology, this site is composed of vulcanite and sedimentary layers. The soil types present are eutrophic cambisol and gleyic fluvisol [42,43]. Present tree species are mainly Acer pseudoplatanus, Acer platanoides, Quercus robur, Alnus glutinosa and Fraxinus excelsior in the main level and understory. The forested area of the park is fully stocked with a rich understory.

2.2. Data Collection

Fieldwork took place from April 2023 until October 2023. During this time, trees of varying species were selected, primarily based on large DBH, as thick trees are potentially the most dangerous and were closely examined. A workflow was conducted on each tree, providing RGB imagery for further photogrammetric processing, iPhone LiDAR point cloud (shown in Figure 2) and up to four tomograms from various stem heights obtained by acoustic tomography. Both CRP and LiDAR data were collected for the trees to make sure the usage of the trained classifier is possible for both kinds of data. Also, in some cases, the iPhone LiDAR cannot reliably reconstruct stems in grown-up parts (low branches with leaves, neighbouring thickets, etc.) and the photogrammetric data performed better in these cases.
Firstly, images for CRP were collected using an iPhone 12 Pro RGB camera with the Lens Buddy app [45], allowing users to collect sequential images of predefined quality. In the case of the proposed study, the interval was set to one second, and the photo output format was DNG. Other parameters of the app settings are described in Table 1. These images aimed at getting a clear view of the basal part of a tree stem up to the height of approximately 3 m and, simultaneously, depiction of printed paper scale bars, exported from Agisoft Metashape [46], which were placed on certain spots around the stem base. Photographs were taken on a circular trajectory, which consisted of two circles. The first circle was approximately 3 m in diameter and aimed at capturing the stem and all paper scale bars. The second circle was smaller and aimed mainly at capturing the stem itself. In total, there were about 70 pictures taken for each tree, which is given by the fact that saving images in the RAW format is slow, and the phone cannot keep up with the one-second interval per each image taken.
Secondly, iPhone LiDAR scanning was conducted using the LiDAR sensor of the iPhone 12 Pro. For this purpose, the 3d Scanner App [47] was used. It was set to the LiDAR Advanced mode; the further app settings are shown in Table 1. This scanning was made on a single circular trajectory, very close to the stem. For a good result, the iPhone had to be held parallel to the ground, display facing up, and follow a wavy trajectory to reconstruct both the higher and lower parts of the stem. This way, the stem up to the height of 2.5 m could have been scanned. The created point cloud was exported from the app in LAS format and at a high density of points.
As the third step, acoustic tomography took place. The device and software ArborSonic 3D [48], produced by Fakopp Enterprise Bt., was used. Each time, ten sensors were put equally around the tree stem. The first one was placed on the northern side of the stem, and the following sensors were placed in the counterclockwise direction. Further, the manufacturer’s instructions were followed. The tomography was conducted at up to four height levels, from 0.5 m to 2 m, if possible. After the entire procedure, spots where the sensors were nailed in were marked with white chalk. For simplicity, the stem shape was considered a circle, but this drawback was resolved in later processing, obtaining an accurate representation of tree stem shape and sensor distances with the help of marks made by the chalk and accurate photogrammetric models. An output of the tomography can be seen in Figure 3.
The last step was practically the same as the first step, where images for CRP were collected. This second imaging aimed to obtain a 3D representation of a stem with the locations of tomograph sensors, now marked by white chalk, which was used to highlight these locations and, unlike paper markers, does not occlude the actual shape of the stem.

2.3. Data Processing

The data processing followed several main steps, described in Figure 4. The task was to create models of scanned tree stems as precisely as possible, without any other objects in the scan, label the trees according to the state of decay in the stem and use these models as learning and validation data for PointNet training.
The images for CRP were processed using Agisoft Metashape software and were turned into 3D Point Clouds using basic workflow. Scale bars had to be manually added in the created dense point clouds using the automatically detected markers. Distances among the detected paper markers were known and could have been defined in the software as well. As soon as the point clouds were scaled, it was necessary to manually orientate them correctly to make the Z-axis follow the axis of each stem. This is necessary for further automated processing of point clouds.
In the point clouds made of images taken after tomography and containing marked tomograph sensor locations, white chalk points have been manually detected and marked by markers in the Agisoft Metashape software. As soon as this procedure was done, the local coordinates of sensors were exported for each tree and used to calculate accurate distances among sensors. There were ten sensors in each examined cross-section on the tree stem. If the circumstances did not allow conducting tomography in four levels, a lower number of levels was measured. Sensor distances were calculated using a Python script that implemented the formula for calculating distances in 3D space:
|1;2| = ((x2 − x1)2 + (y2 − y1)2 + (z2 − z1)2)1/2
The distances were later entered manually into the ArborSonic 3D projects, replacing the distorted values collected in the field when the stem shapes were only considered a circle, as shown in Figure 5.
Once the tomograms representing decay were more accurate, a file containing information about each tree, mainly Tree ID, Genus, Species, Decay Severity, and other less essential parameters, was created. The Decay Severity was determined based on tomograms subjectively and expresses the amount of decay in the tree stem on a scale from 0 to 3, where 0 corresponds to no decay, 1 to very small decay, 2 to substantial decay and 3 to massive decay. These values served later as labels for the neural network training.
The following steps are done automatically based on a Python script, which processes all point clouds of desired tree species into a 3D mesh, filtering out the terrain and other objects, keeping only the stem of interest.
For this purpose, the Verticality feature is calculated based on the normals of the point cloud, allowing for accurate distinction between terrain and stem, as horizontal surfaces have the Verticality value equal to zero and a vertical equal to 1. This feature is shown in Figure 2. In case normals were absent, which was the case with iPhone LiDAR data, these had to be estimated using the Octree Normals function in CloudCompare Command Line mode. Trees usually can be separated from terrain by keeping points with Verticality between approximately 0.6 and 1.0. Another step is necessary, as the filtering based on Verticality does not remove all unwanted points from the original point cloud. This procedure makes use of the CloudCompare function named Label Connected Components, which detects clusters of points at a defined level of rigour. Usually, small features are parts of terrain or noise, whereas the most extensive feature is mostly the woody part of a stem and is separated from all the other points, resulting in a separated stem and terrain.
The filtered stem point cloud is then turned into a 3D mesh using Poisson Surface Reconstruction, which solves the reconstruction as a spatial Poisson problem, considering all present points simultaneously, unlike other methods. This surface reconstruction method allows for robust reconstruction of real-world objects from a point cloud into a 3D mesh, even if the input point cloud is highly noisy. However, the input point cloud must contain normals; otherwise, the algorithm would not work. The normals are used for the so-called Indicator Function and its Gradient, which detects points belonging to the object, its surface and noise [49]. A brief overview of the point cloud processing is shown in Figure 6.
The meshes serve as input data for the PointNet neural network, which firstly plots a certain amount of points onto the mesh and creates a new point cloud this way. The original point clouds from iPhone LiDAR and close-range photogrammetry cannot be used, as all input point clouds must have the exact same number of points.
PointNet trains a classifier based on labelled 3D meshes and predefined hyperparameters: the number of points sampled on the 3D mesh, batch size, number of epochs and learning rate. The selection of proper hyperparameters is a crucial part of deep learning classifier training and is described further in the text.
Filtering a tree stem was done using the Command line mode of the CloudCompare software [50]. Its functions, namely the Verticality feature and the Label Connected Components function, were embedded in the mentioned Python code.
The other parts of the Python code were done using freely accessible libraries such as open3D, tensorflow, keras, numpy, os, shutil and others. Poisson surface reconstruction was conducted using the open3d library, providing quick and satisfactory data processing.
The final step is PointNet processing, which requires the determination of well-performing hyperparameters, which is done iteratively by the Grid Search method [36], described in the following section.

2.4. Data Analysis

Analysis of tree stem models was done by a slightly updated version of the PointNet algorithm [39], trying to classify trees into either the Healthy class or Decayed class, substituted by the dummy values 0 or 1. This was done due to insufficient samples for training a four-class classifier. Due to the algorithm’s invariance to geometric transformations, it is not possible to synthetically extend the dataset by simply rotating, resizing or moving the input point clouds, and for that reason, the dataset could not have been extended in this manner. Therefore, Point clouds were slightly modified during the PointNet algorithm processing, moving each point slightly by a randomly selected value equal to up to 5 mm in any direction.
There were three kinds of classifiers created. One is for the Coniferous trees decay classification, which consists only of European spruce models. The second one is for the Deciduous tree species decay classification, containing Fagus sylvatica, Fraxinus excelsior, Acer platanoides, Acer pseudoplatanus and Quercus robur, and the third one is for a mix of all the aforementioned tree species with other tree species which were marginally examined during the fieldwork.
For proper training, optimal hyperparameters had to be chosen. The hyperparameter values change based on the dataset size and the complexity of the task an AI classifier has to conduct [36]. It is impossible to train a classifier well without properly examining and adjusting the hyperparameter. Therefore, the hyperparameters in this study were examined by an iterative approach of Grid Search. This approach requires a user-defined set of values that shall be examined. As all possible preset hyperparameter values are processed, this approach is only suitable for examining cases with approximately three or fewer hyperparameters. The input values have to be set based on prior experience and respecting results of previous stages of the Grid Search to save computational resources, as these are already heavily exploited with the iterative approach and may grow exponentially by adding new examined hyperparameters to the search [36]. Therefore, a matrix of potential hyperparameters, which are closely described further, was made, and the training was run with every possible combination of these. As the variation of validation accuracy varied significantly, even if the same input parameters were used, each combination was run one hundred times to confidently state a mean performance of the validation. The modified hyperparameters were Batch Size, Point Number, Epochs and Learning Rate, and most of the time was spent tweaking the number of epochs, which defines how many times the data will be seen by the PointNet algorithm before the training finishes. The batch size, on the other hand, defines how many point clouds from the overall dataset will be used by the algorithm for training at one time. This number cannot be too high, as the computational resources of a PC may not suffice, but it also needs to be as high as possible, as it improves the final classifier’s accuracy. It is a common practice to use batch size values with the power of 2 for better performance on GPUs. In this study, the difference between batch size values 32 and 64 was not proven, and the value 32 was used in further processing, allowing the algorithm to work at a relatively higher speed and with better performance than if a smaller value was selected. The batch size 128 was not used in this study, as it exceeded the computer’s computational capacity. In certain situations, a minimal batch size equal to 1 can be reasonable for the models’ better generalisation [36], but this was not the case in the proposed study.
The number of points defines how many points will be plotted on a 3D mesh, creating a point cloud. PointNet does not seem to perform differently when exceeding 2048 points, so this value was used even in this study [34,39].
The number of epochs has to follow specific rules as well when the datasets are of smaller extent, as in the proposed study. The number of epochs should be relatively higher to achieve better model accuracy. However, at the same time, the risk of overfitting should be avoided in order to get a more generalised model. The examination of training accuracy and validation accuracy can do this. If the validation accuracy becomes significantly lower than the training accuracy at a particular epoch, overfitting may have happened, and the number of epochs should be reduced appropriately [36].
Lastly, the learning rate, which determines the size of the steps taken during the optimisation process, requires a fair amount of attention. It is a critical parameter because it influences how quickly or slowly a neural network learns. A very high learning rate causes faster training but with a risk of missing the best solution to its large modifications in the model’s parameters. In contrast, a very low learning rate causes slow training, and optimisation may get stuck at the local minimum, preventing it from reaching the total minimum, i.e., the optimal solution [36,51].
In order to verify that the PointNet training program itself works well, the training and validation were also conducted on the ModelNet10 benchmark dataset with appropriate hyperparameters and a validation accuracy of 90%.

3. Results

The performance of the PointNet classifier for decayed tree detection was demonstrated on a validation set extracted from the original dataset. However, it has been demonstrated that the accuracy of the best classifiers in mean reaches approximately 65% for Coniferous, 58% for Deciduous, and 58% for Mixed data, as shown in Figure 7. Therefore, it was verified that the internal state of a tree stem could be, to some extent, deduced by deep learning methods, and this approach may be helpful later in practical use. However, as this application is in the initiation phase, more similar experiments are necessary. As of the proposed study, the classification can, at this point, only help with investigating whether the tree is healthy or has a probability of internal decay presence, but without specifying any of the decay properties or its shape, since the input data include only the 3D shape of the stem.
Table 2 represents varying results of Coniferous stem classifiers, which were trained and tested on a dataset consisting of 180 3D meshes, out of which 69 represented healthy trees with no decay and 111 with varying states of decay from slight to massive rot. Out of the total, 35 meshes were used for validation. The best performance was shown using the following hyperparameters for the PointNet function: number of points = 2048, batch size = 32, learning rate = 0.001 and number of epochs = 30 or 70. For each combination of hyperparameters, 100 iterations were made to eliminate speculation that the positive results were obtained by chance. The course of validation accuracy and its dependence on the number of epochs is shown in Figure 8.
Table 3 describes the results of Deciduous tree stem classifiers, which worked with a dataset of 355 3D meshes. Of them, 205 represented healthy trees with no decay and 150 with varying decay from slight to massive rot. For validation, 71 meshes were used. The best performance was shown while using the following hyperparameters for the PointNet function: number of points = 2048, batch size = 32, learning rate = 0.001 and number of epochs = 40, which is also indicated by Figure 9.
Table 4 presents the accuracies of Mixed stem classifiers, consisting of merged datasets of Coniferous, Deciduous and other tree species that were marginally examined during the fieldwork, consisting of 15 tree species and 649 3D meshes. One hundred twenty-nine meshes were used for the validation. As shown in Table 4 and Figure 10, the best-performing training hyperparameters for this case were the number of points = 2048, batch size = 32, learning rate = 0.001 and number of epochs = 70.
The results are of promise that further work may reveal the higher potential of this method and make the predictions more reliable.

4. Discussion

Deep learning algorithms are increasingly used in many fields of human activities, such as medicine, the automotive industry, and even forestry [52,53]. In the latter mentioned field, the method is used mainly to identify tree species or tree damage in aerial images [38,54,55]. In some studies from the field of forest inventory, even terrestrial photography is used for tree species determination or pest detection, but no studies were found examining the relationship between internal tree stem decay and its shape in 3D space [56,57]. Nevertheless, the task of object classification is common. It is applied to many more or less complicated objects in various fields of research, including forestry [38,39,51,52,53], providing 92.5% accuracy in the task of tree segmentation from a complex scene [35] or tree species classification from 3D point clouds with more than 90% accuracy [34]. For this reason, the proposed study correctly assumed that deep learning classification of decayed or healthy tree stems is possible.
In some studies, it was recommended to use images of point clouds instead of actual point clouds for deep learning, as it seems to be a robust approach with lesser computing capacity required [37,40]. On the other hand, it does not work with the entire continuous shape of the object or an entire point cloud, but with only discrete views of it, potentially omitting some features of the original shape, missing small objects or rarely present classes [40,58]. This study did not work with the described approach, as nowadays, acquiring and processing a 3D point cloud has become more accessible, thanks to the implementation of LiDAR scanners on consumer-grade cell phones and the increasing performance of computers.
In the results of this study, a significant standard deviation in the validation accuracy of identically adjusted training attempts was observed. In this case, it is caused by the small size of the processed dataset, which is supported by the fact that the observed deviation decreased in larger datasets, such as Mixed or Deciduous. It is usually better to collect more data to increase performance, not only in the matter of standard deviation but also in the question of overall model accuracies [36]. In the case of this study, collecting more data was not affordable. Therefore, much time was given to tuning the hyperparameters by the Grid Search approach [36]. This step had a significant impact on the accuracy of the validation that was achieved. As Figure 7 shows, the dependence of Data Type on Validation Accuracy was significant on the confidence level α = 0. Assuming that the dataset Coniferous only contained European Spruce models, it seems that single tree species classifiers may be performing better than general classifiers. The relationship between the number of Epochs and Validation Accuracy was not very strong, yet it was significant. A linear regression model with α = 0.05 proved to be a positive effect of this feature. A graphical representation of the Epochs impact can be seen in Figure 8, Figure 9 and Figure 10, whereas the effect of examined tree species can be seen in Figure 7.
The combined impact of epochs and tree species explained 16.63% of the variability in the validation accuracy. It could be further improved by a more precise examination of the learning rate and its relationship to the number of epochs, as the learning rate is considered a very important hyperparameter that may prevent training error from decreasing and affects how many epochs need to be carried out for training, therefore influencing the training duration. All these steps must be made carefully, observing the risk of overfitting. This phenomenon can be further controlled by modifying the number of layers and hidden features in the neural network [36].
From the extent of the learning rate examination done during the study, a conclusion can be made, confirming a significant impact of this hyperparameter on the validation accuracy, as shown in Figure 11.
Expectedly, during the training and validation process, it was observed that the standard deviation correlated negatively with the number of training epochs. However, this standard deviation was not the smallest in the most accurate hyperparameters, as described in Table 2, Table 3 and Table 4 and Figure 8, Figure 9 and Figure 10. This effect is caused by the gradual generalisation of the trained models, leading to more stable performance and converging to a possible solution to the classification problem. However, the risk of using a larger number of training epochs is still related to the potential overfitting. It should, therefore, be adjusted temperately, considering the size of the training dataset. A larger dataset allows for more epochs and a potentially improved validation or testing accuracy, as the model can learn from a more extensive variety of objects [36]. Nevertheless, studies confirm that the training and classification work well even when using small datasets [59,60]. Mentioning the dataset size, it is necessary to mention that in this study, no test set was created, as it would be too small and imply statistical uncertainty, but it should be considered in future works, allowing for independent testing of trained classifiers.
As the classification of point clouds by the PointNet algorithm is not dependent on the orientation of the data or its resizing [39], the dataset cannot be extended synthetically in a simple manner. Still, a slight randomisation or augmentation of the Point Cloud data was conducted even in the case of this study, allowing points to be slightly moved, by up to 5 mm, around their original position in the space. This may have caused changes in the accuracy of classifiers but may have also helped to generalise the model.
Some tree species may have similar properties of shape change after decaying, and combining such data into a single category might be helpful, as it could provide more training data for each classifier and potentially improve classification accuracy. Unfortunately, in many cases, this is not true. The proposed study used three datasets containing various 3D meshes and tree species. The larger datasets in this study resulted in smaller validation accuracy, as they were made only by sorting the data by tree type: broadleaved or coniferous, respectively. The results confirm the presumption that creating a single classifier for each tree species is justifiable and results in more satisfying classification validation accuracy. Regarding the dataset and its size, it is necessary to note that the trained classifiers may only be successfully used for the tree species used in the training phase. As shown in the proposed study, the best accuracy may be obtained using a species-specific classifier instead of a general one. The proposed study only used 3D models of Central European tree species, and its outcome may be only successfully used in this location.
Overall, the idea of allowing the use of the proposed method in the future seems to be possible with a further improved version of the proposed method. As cell phones allow the direct creation of a 3D mesh, developing a mobile app for pre-processing the mesh and evaluating the created point cloud using one of the pre-trained classifiers may be possible for any personnel that need to quickly evaluate the potential risk of tree failure at frequented communication routes, such as parks, parkways, roads, and others. This proposed approach requires no special knowledge associated with tree failure risk assessment and could be used by less educated staff, pointing out trees that require more attention and closer examination.

5. Conclusions

This study tested the potential use of deep learning to classify decayed or healthy stems of standing urban trees based on CRP, iPhone LiDAR and tomography data. The method’s ability for the task was indicated by the validation results performed on classifiers made for three data types: Coniferous stems, Deciduous tree stems and Mixed data. The validation was performed with an accuracy of 65.5% for Coniferous, 58.4% for Deciduous and 57.7% for Mixed data and should motivate future research into this method in the field of tree failure risk assessment, enabling non-professional staff to evaluate the internal state of a standing tree stem quickly and without manual labour. As the proposed research project was not able to obtain testing datasets for testing the classifiers’ applicability in different conditions, the future research should aim at having this possibility. Future research should aim to create deep learning classifiers for individual tree species, as the general models seem to perform worse, and the type of used data was proven statistically significant (p = 2 × 10−16) for the final validation accuracy. Classification into more than two classes based on the severity of the tree stem decay would also be beneficial in the potential practical use of the method and should be considered in future research. During the classifier training, attention should be paid to the correct hyperparameters of the training algorithm, mainly the learning rate.

Author Contributions

Conceptualisation, M.H. and P.S.; Funding acquisition, M.H. and P.S.; Investigation, M.H.; Methodology, M.H.; Project administration, P.S.; Resources, M.H. and P.S.; Software, M.H.; Supervision, P.S.; Validation, M.H.; Visualization, M.H. and P.S.; Writing—original draft, M.H.; Writing—review and editing, M.H. and P.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the project A_07_22 of the Internal Grant Agency at the Czech University of Life Sciences, Faculty of Forestry and Wood Sciences and also by project TH74010001 funded by the Technological Agency of Czech Republic, through the program CHIST-ERA and Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague through grant FORESTin3D.

Data Availability Statement

The data are not publicly available due to its extensive storing requirements. Data are available upon request by the authors.

Acknowledgments

Many thanks to the company Lázeňské lesy a parky Karlovy Vary for providing an acoustic tomograph and allowing the execution of the experiment in its park. A large thank also belongs to the local administration of Mašťov for allowing the execution of the investigation on its property.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. Locations of study plots south of the city of Karlovy Vary and north of the town of Mašťov.
Figure 1. Locations of study plots south of the city of Karlovy Vary and north of the town of Mašťov.
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Figure 2. iPhone Lidar point cloud. Colours depict Verticality: 0 (Blue); 0.3 (Green); 0.6 (Yellow) 1 (Red).
Figure 2. iPhone Lidar point cloud. Colours depict Verticality: 0 (Blue); 0.3 (Green); 0.6 (Yellow) 1 (Red).
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Figure 3. (a) Acoustic tomograph Arbor Sonic 3D; (b) Tomograms of a healthy and decayed stem. Numerical values with colour–scale correspond to the speed of sound wave propagation.
Figure 3. (a) Acoustic tomograph Arbor Sonic 3D; (b) Tomograms of a healthy and decayed stem. Numerical values with colour–scale correspond to the speed of sound wave propagation.
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Figure 4. Basic workflow of the data processing. The asterisk indicates steps that had to be done manually.
Figure 4. Basic workflow of the data processing. The asterisk indicates steps that had to be done manually.
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Figure 5. (a) Tomogram before adjustment; (b) Tomogram after adjustment by calculating more accurate sensor distances. Sensor locations are represented by the black dots with numerical values. Colours are explained in the Figure 3b.
Figure 5. (a) Tomogram before adjustment; (b) Tomogram after adjustment by calculating more accurate sensor distances. Sensor locations are represented by the black dots with numerical values. Colours are explained in the Figure 3b.
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Figure 6. (a) Original CRP Point Cloud (b) Automatically separated stem (c) 3D Mesh of the stem.
Figure 6. (a) Original CRP Point Cloud (b) Automatically separated stem (c) 3D Mesh of the stem.
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Figure 7. The representation of validation accuracy changes with the type of data used. Other parameters are kept as described in the text.
Figure 7. The representation of validation accuracy changes with the type of data used. Other parameters are kept as described in the text.
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Figure 8. The representation of validation accuracy changes with the number of epochs on the Coniferous dataset. Other parameters are kept as described in the text.
Figure 8. The representation of validation accuracy changes with the number of epochs on the Coniferous dataset. Other parameters are kept as described in the text.
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Figure 9. Representation of the validation accuracy change with changing number of epochs on the Deciduous dataset. Other parameters are kept as described in the text.
Figure 9. Representation of the validation accuracy change with changing number of epochs on the Deciduous dataset. Other parameters are kept as described in the text.
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Figure 10. Representation of the validation accuracy change with changing number of epochs on the Mixed dataset. Other parameters are kept as described in the text.
Figure 10. Representation of the validation accuracy change with changing number of epochs on the Mixed dataset. Other parameters are kept as described in the text.
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Figure 11. Effect of varying Learning Rate on the Validation Accuracy of Mixed Data classifiers.
Figure 11. Effect of varying Learning Rate on the Validation Accuracy of Mixed Data classifiers.
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Table 1. Parameters used for scanning with iPhone apps.
Table 1. Parameters used for scanning with iPhone apps.
Lens Buddy3D Scanner App
Interval: 1 sResolution: 5 mm
Capture Optimisation: BalancedRange: 5 m
Format: RAW (DNG)Confidence: High
Masking: None
Format: LAS
Table 2. Properties of validation accuracy of Coniferous model with batch size equal to 32, obtained based on 100 iterations of the classifier training. The best results are highlighted in bold.
Table 2. Properties of validation accuracy of Coniferous model with batch size equal to 32, obtained based on 100 iterations of the classifier training. The best results are highlighted in bold.
EpochsMeanSt. Dev.ModeMinMax
155.27.4543471
1059.172634074
2063.57.5604077
3065.56.2664980
4060.36.8604680
5063.56.5634677
6059.16.4604374
7065.47.1665183
8062.26.0604677
9059.36.9604071
10058.65.7574974
Table 3. Properties of validation accuracy of Deciduous model with batch size equal to 32, obtained based on 100 iterations of the classifier training. The best results are highlighted in bold.
Table 3. Properties of validation accuracy of Deciduous model with batch size equal to 32, obtained based on 100 iterations of the classifier training. The best results are highlighted in bold.
EpochsMeanSt. Dev.ModeMinMax
156.15.1594468
1058.44.4614669
2055.15.2554468
3056.84.5584168
4058.43.6585168
5058.43.8564968
6054.93.6564462
7057.34.1584269
8058.54.0564666
9054.34.4544266
10057.53.3554566
Table 4. Properties of validation accuracy of Mixed model with batch size equal to 32, obtained based on 100 iterations of the classifier training. The best results are highlighted in bold.
Table 4. Properties of validation accuracy of Mixed model with batch size equal to 32, obtained based on 100 iterations of the classifier training. The best results are highlighted in bold.
EpochsMeanSt. Dev.ModeMinMax
152.64.0534060
1054.13.5534465
2055.33.1534964
3055.23.7574362
4055.52.9575064
5054.83.2534762
6056.93.0574963
7057.73.4575064
8056.23.0574964
9057.82.9605167
10056.12.9574663
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Hrdina, M.; Surový, P. Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method. Remote Sens. 2023, 15, 5712. https://doi.org/10.3390/rs15245712

AMA Style

Hrdina M, Surový P. Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method. Remote Sensing. 2023; 15(24):5712. https://doi.org/10.3390/rs15245712

Chicago/Turabian Style

Hrdina, Marek, and Peter Surový. 2023. "Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method" Remote Sensing 15, no. 24: 5712. https://doi.org/10.3390/rs15245712

APA Style

Hrdina, M., & Surový, P. (2023). Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method. Remote Sensing, 15(24), 5712. https://doi.org/10.3390/rs15245712

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