Abstract
Tree Risk Assessment (TRA) is a detailed tree risk management protocol evaluating both structural soundness and potential failure probabilities, resulting in a comprehensive hazard classification. The accuracy of TRA heavily depends on the proficiency of individual assessors, whose educational backgrounds, personal inclinations, and risk perspectives vary. This variability affects the perceived level of tree hazard and its implications. Hence, there is a need for improved assessment methodologies, with Unmanned Aerial Vehicle (UAV) technology offering significant potential in this context. A rigorous quantitative literature review of 27 peer-reviewed articles was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to determine the current status of UAV integration in TRA. This review documented temporal publication patterns and geographical and geological research details, presenting them alongside specific research focuses. The review concluded that research in this field is still nascent, with most studies emerging from China, the United States, and Iran. Research was predominantly conducted in mildly temperate climates, often in traditional research article formats. The primary focus areas included plant stress and plant pest/disease content, with some attention to plant threats, 3D tree modeling, ecosystem degradation, plant characteristics, and inaccessible zones. Thermal, multispectral, and LiDAR-equipped UAVs can identify issues fast, allowing for quicker resolution and lower costs. Identified knowledge gaps necessitating future research include (1) unclear terminology and a narrow understanding of UAV applications in TRA; and (2) a scarcity of research in warmer climate regions like South Africa and South America, which are equally valuable for study.
Introduction
Tree Risk Assessment (TRA) has gained attention for its ability to mitigate risks, thereby prolonging a tree’s health, safety, and useful life. A standardized procedure for assessing tree risk has been developed, encompassing a systematic process for identifying, analyzing, and determining tree risks. The most comprehensive and authoritative reference on this topic can be found in the “Tree Risk Assessment Manual,” published by the International Society for Arboriculture (Dunster et al. 2017). The evolution of TRA involves a systematic analysis that includes the development of probing tools and detection methods to overcome the limitations of visual inspections (Li et al. 2022). However, there is a noticeable lack of comprehensive data regarding the application of Unmanned Aerial Vehicle (UAV) technology in the field of TRA. The extent to which UAV technology can enhance TRA remains a subject of ongoing exploration and research. The main concern is the feasibility of using UAVs for TRA. A number of variables, such as postprocessing complexity, diverse landscapes, and sensor capabilities, can affect their applicability. These variables vary greatly between nations, areas, users, and particular applications. Undertaking a systematic quantitative literature review holds substantial merit as it serves to concisely delineate the existing body of knowledge on UAV applications in TRA, thereby providing valuable guidance for future research endeavors.
To gain insight into the current state of UAV integration within TRA knowledge, this paper conducted a comprehensive review of the literature published on Scopus (Elsevier, Amsterdam, Netherlands). The PRISMA reporting framework, which highlights key steps such as data identification, evaluation, exclusion, and inclusion, is presented. This is a suggested reporting element for reviews and meta-analyses. Derived from these reviews, the focus centers predominantly on pertinent UAV attributes for TRA, with the aim of delivering findings that are both practically valuable and scientifically intriguing, ultimately enhancing tree risk management practices. The paper initiates by providing brief definitions of TRA practice and UAV technology. Following this, a thorough explanation of the research methods employed is presented. The paper then proceeds to expound upon the findings derived from the systematic review, offering an in-depth discussion. Finally, the paper concludes with valuable recommendations for guiding future research endeavors.
Tree Risk Assessment (TRA) Practice and Unmanned Aerial Vehicle (UAV) Technology
Tree Risk Assessment (TRA) practice is an essential aspect of arboriculture and urban forestry management, serving to ensure the safety of people and property in environments where trees are present. TRA involves a systematic evaluation of individual trees or groups of trees to determine their potential risk factors and hazards (Li et al. 2022). Arborists and tree care professionals utilize TRA to identify trees with structural defects, diseases, or environmental stressors that could lead to failure and pose a threat to safety. This assessment process often includes visual inspections, diagnostic tests, and consideration of environmental conditions and tree health indicators (Koeser and Smiley 2017). Pradipta et al. (2018) explained effective TRA practice not only aids in preventing accidents and property damage but also supports informed decisions regarding tree maintenance, preservation, or removal. It plays a crucial role in maintaining the delicate balance between conserving valuable trees and mitigating potential risks, contributing to the overall health and safety of communities and the sustainable management of urban and natural tree populations. Traditional manual TRA procedures are laborious, costly, and prone to human error. UAVs present a promising substitute.
Unmanned Aerial Vehicle (UAV) technology, also commonly known as drones, has revolutionized various industries by offering a versatile platform for aerial data collection, surveillance, and remote sensing. Stewart and Martin (2021) described UAVs as aircraft operated without a human pilot on board, and their applications span a wide range of fields, including agriculture, environmental monitoring, infrastructure inspection, search and rescue, and filmmaking, among others. Equipped with advanced sensors and cameras, UAVs can capture high-resolution imagery, thermal data, and even LiDAR scans, enabling precise data acquisition from vantage points that were previously inaccessible or cost-prohibitive (Feng and Li 2019). This technology has not only increased operational efficiency but also reduced human risk in hazardous environments (Kas and Johnson 2019). Moreover, Zhang et al. (2018) explained UAVs are invaluable in disaster response and management, as they provide real-time situational awareness and aid in rapid assessment. As UAV technology continues to evolve and become more affordable, its potential for transformative applications across various sectors is only expanding, promising a future where aerial data collection is more accessible and effective than ever before.
Materials and Methods
This systematic quantitative literature review adheres to a methodology endorsed by several notable authors, including Mariani et al. (2023), Parker and de Baro (2019), and Song et al. (2018), while also following the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines by Page et al. (2021). For additional details regarding our approach, supplementary information is available at https://prisma-statement.org. By systematically searching and classifying pertinent data, these evaluations provide reliable and credible assessments of the current status of a research issue. Furthermore, the process for identifying, selecting, and classifying articles is described in full, which lowers the risk of bias in some narrative style evaluations.
In October 2023, a comprehensive literature search was conducted, utilizing a vast repository of over 2,000 Scopus databases. This search aimed to identify articles that featured the precise and exclusive term ‘Unmanned Aerial Vehicle in tree risk assessment’ within their content, without imposing any restrictions based on the publication year. UAVs are a useful tool in forestry and agriculture for plant monitoring, which includes identifying nutritional deficits, infections, and crown defoliation. The study risk of bias evaluation in TRA review of UAV studies is analyzing each study’s design and methods to find any potential biases that can compromise the validity of the results. To make sure that the conclusions reached are credible, the risk of bias in studies is then examined to ascertain how these biases can affect the overall validity and reliability of the findings. Lastly, the evaluation method looks at reporting biases, searching for any selective reporting or findings omission that can distort how the evidence on UAV applications in TRA is interpreted. This all-encompassing strategy guarantees a careful and objective assessment of the available research. Articles covering this context were therefore chosen for review even though they don’t look at tree risk assessment. In accordance with the PRISMA methodology, a total of 27 articles were deemed eligible for inclusion in this review. The PRISMA flowchart, represented as Figure 1, provides additional insight into the meticulous inclusion and exclusion criteria applied during the selection process.
The systematic literature review follows the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, ensuring a standardized and comprehensive approach to reporting and evaluating the included studies.
Metadata
The metadata, meticulously gathered from the analysis of 27 articles and meticulously transcribed into Microsoft Excel (Microsoft, Redmond, WA, USA), is presented below. The collected metadata was organized into 4 distinct dataset categories: article descriptors (Table 1), geographical data (Table 2), article types (Table 3), and focus areas (Table 4). Abstracts were individually entered and assessed, with a comprehensive list of data markers, data collection techniques, and an explanation of their purposes provided in Tables 1 to 4.
Descriptors for the articles.
Geographical data for the articles. NA (not applicable); NS (not specified).
Article type.
Focus areas for the articles.
The descriptors for the articles in Table 1 are relatively uncomplicated; we obtained information from the individual articles and promptly entered it into the Microsoft Excel workbook. The geographical data in Table 2 relied on information provided by the article authors. In cases where the information was unavailable or unspecified, we denoted this with the codes NA (Not Applicable) and NS (Not Specified). Concerning geographic data, countries and cities were entered as text when the information was available, facilitating clustering analysis. Additionally, climate classification was conducted using the Köppen climate classification model (Kottek et al. 2006). Similarly, in cases where information was lacking to confirm a climate band, we denoted this with the codes NA (Not Applicable) and NS (Not Specified). Article types in Table 3 were classified into 3 primary categories: review articles, research articles, and/or case studies. Articles that fell into multiple categories were appropriately documented. The focus areas outlined in Table 4 were identified within the articles and categorized into 8 possible groups: Plant Pest and Diseases, Plant Stress, Plant Threats, 3D Tree Modeling, Ecosystem Degradation, Plant Characteristics, Unreachable Zone, and Other. Articles covering multiple focus areas were appropriately documented.
Data Analyses
This review utilized a variety of techniques for analyzing the collected data from the articles, including visual, graphical, and numerical methods. Figures presenting data proportions were accompanied by 95% confidence error bars to aid in assessing result variations. Given that all sample sizes exceeded the minimum threshold of 10, nonoverlapping error bars or overlapping by less than half of the total error bar length were considered as evidence of a statistically significant difference between data points. Additionally, a Chi-Squared test (χ2) was employed to assess the goodness of fit for all proportion data sets as a secondary measure. Lastly, Pearson’s Coefficient of Correlation was applied to analyze binary data, and the significance of all correlations was confirmed using the t-Test.
Results
Research Effort
As detailed in the methods section, an electronic literature search was conducted in October 2023. Following the screening and eligibility assessment phases, a total of 27 articles were found to meet the criteria and were consequently included in this review. The analysis of these articles revealed the emergence of several trends, prompting further investigation.
Concerning ‘UAV and TRA’ the study reveals a notable evolution in research efforts and changes in publication rates. The majority of articles focus on the identification of deterioration and deficiencies in trees, which is in line with TRA’s objectives, even though they do not explicitly address the scope of TRA, which includes the supervision of forests and agricultural crops. The search methodology employed did not impose any restrictions on publication years, with the earliest article dating back to 2016. During the period from 2016 to 2021, the annual publication rates remained relatively modest, with no year surpassing 3 publications. However, in 2022, there was a noticeable increase in annual publication rates, and this trend continued with 14 publications in 2023. For a visual representation of these findings, please refer to Figure 2 below, which illustrates both the annual publication rates and the cumulative total of publications.
The annual publication rates of articles incorporating ‘Unmanned Aerial Vehicles in tree risk assessment’ without any restrictions on publication years (n = 27).
The review process, after screening and eligibility checks, encompassed 27 articles sourced from a total of 16 distinct journals. Out of the 27 articles, the top 3 frequently appearing journals, namely Remote Sensing (6 articles), Forests (4 articles), and Drones (3 articles), collectively accounted for 48.1% of the total (n = 13).
Geographical Research Distribution
Research efforts were focused in China, which accounted for 30% of the publications, followed by the United States and Iran, each with 10% of the publications, according to data from 20 peer-reviewed studies. There were 7 articles lacking a location, while 10 featured co-authors who were researchers from different nations. The publication rates across climatic categories showed a significant difference (χ2 = 48.13 ; P = 8.89E–10) when classified by climate zones using the Köppen system (Kottek et al. 2006). Less than 10% of the research originated from other categories, with the majority (75%) coming from mild temperate zones.
Figure 3 displays the data regarding publication rates within climate categories, illustrating the relationships between these categories and including error bars that represent the ± 95% confidence intervals for the analyzed articles (n = 27).
Reporting the publication rates of climate categories, including the ± 95% confidence intervals for the proportions.
Categorization of Articles
Articles were categorized into one or more of the following types: review article, research article, and case study. There is a statistically significant difference (χ2 = 2.33; P < 0.31; df = 2) in the reporting approaches among these 3 categories (Figure 4). The majority of articles (n = 12; 44.4%) were classified as research articles. Review articles (n = 7; 26.0%) and case studies (n = 8; 29.6%) were also prevalent.
Reporting the publication rates, along with ± 95% confidence intervals, for the proportions of article types within categories.
Article Focus Areas
In a manner similar to the categorization of articles, the focus area(s) were noted for each article. The nominated categories were Plant Pest and Diseases, Plant Stress, Plant Threats, 3D Tree Modeling, Ecosystem Degradation, Plant Characteristics, Unreachable Zone, and Other as illustrated in Figure 5.
Reporting the rates of focus area distribution among publications, including ± 95% confidence intervals for the proportions.
To simplify reporting, each focus area was given a numerical designation:
a) Plant Pest and Diseases = 1
b) Plant Stress = 2
c) Plant Threats = 3
d) 3D Tree Modeling = 4
e) Ecosystem Degradation = 5
f) Plant Characteristics = 6
g) Unreachable Zone = 7
h) Other = 8
The articles included in the review exhibited a wide range of focus areas, and it was determined that there was a significant statistical difference (χ2 = 22.98; P < 0.002; df = 7) in the recorded focus areas. The analyzed papers (totaling 27) predominantly emphasized 2 main focus areas: Plant Stress (with 10 papers, constituting 37.0%) and Plant Pest and Diseases aspects (with 6 papers, comprising 22.2%). The prevalence of 5 key focus areas exhibits a balanced distribution in the literature. Specifically, Plant Characteristics takes the lead with 3 papers, constituting 11.1% of the research. Following closely are considerations related to Plant Threats, with 2 papers making up 7.4%, and 3D Tree Modeling, also represented by 2 papers and accounting for 7.4%. Ecosystem Degradation and the Unreachable Zone each contribute significantly, with one paper each, making up 3.7% of the scholarly discourse in both cases. The category labeled as ‘Other’ had a prevalence of 2 (7.4%).
Analysis of Trends in Focus Areas
Apart from the specified focus areas discussed in the section Categorization of Articles above, we conducted an analysis to uncover underlying trends within these focus area records (Table 5). We delved into the possibility of elevated correlations arising from double negatives being treated similarly to double positives in the Discussion section. We identified 6 correlations between focus areas that were statistically significant (with a P-value of < 0.005) and confirmed through t-test analysis: Plant Threats and Plant Pest/Diseases (P = 0.943); Plant Threats and Plant Stress (P = 0.980); 3D Tree Modeling and Plant Pest/Diseases (P = 0.943); 3D Tree Modeling and Plant Stress (P = 0.980); Plant Characteristics and Plant Pest/Diseases (P = 0.943); and Plant Characteristics and Plant Stress (P = 0.980).
Correlation coefficients used to assess trends among the article focus areas within the testing group.
Discussion
Research Effort
The first article containing ‘UAV in TRA’ in the electronic database search, with no restrictions on publication year, was found in 2016. Since then, the rate of publications related to ‘UAV in TRA’ has been relatively modest. However, there are indications that publication rates for ‘UAV in TRA’ have been steadily increasing, especially in the past 3 years. If this upward trend in research interest and publication continues, it is assumed to contribute to a better understanding and stronger foundation for UAV in TRA research and implementation. This, in turn, may enhance the perceived value and adoption of UAV in TRA. Monitoring future temporal trends will be necessary to make definitive claims about the sustained progress in this field.
Yazicioglu and Borat (2020) have proposed the importance of taking into account the various interchangeable terminologies associated with UAV in TRA in different contexts, as well as the deliberate nuances in how the term is used. This caution is necessary to ensure that genuine research efforts are not obscured. This notion could help explain the relatively low number of UAV in TRA related publications. Additionally, our search methodology required that the term ‘Unmanned Aerial Vehicle and Tree Risk Assessment’ be explicitly mentioned in the article’s title for inclusion in the review. This criterion might imply that there could be a larger body of research, along with potentially different publication rates and trends, related to UAV in TRA that did not explicitly feature the term ‘Unmanned Aerial Vehicle and Tree Risk Assessment’ in their titles.
As detailed in the Results section, the 27 articles included in this review were sourced from a diverse range of journals (totaling 16), with slightly over half originating from just 3 specific journals. These top 3 journals were Remote Sensing (with 6 articles and an impact factor of 4.2 [2023]), Forests (with 4 articles and an impact factor of 2.4 [2023]), and Drones (with 3 articles and an impact factor of 4.4 [2023]). The PRISMA-based search approach showed that there were very few studies on UAVs in TRA. Despite the small number of reviewed studies, their thoroughness, breadth, and quality offer a solid basis for producing insightful findings. The analysis might prioritize quality over quantity by concentrating on a small number of studies, guaranteeing a thorough investigation of the techniques used. In certain situations, such as UAV technology, where the available research may still be in its infancy, this method works very well. Additionally, a more nuanced knowledge of how UAV technology contributes to improved evaluation methodologies is made possible by a smaller dataset, which offers insights that are directly related to the goals of the study. This strongly underscores the prevalence of technology and the environmental perspective within these publications. Furthermore, it implies that although UAV in TRA research intersects with various disciplines, judging by publication discipline and journal selection, it is most prominently associated with the environmental field. The extensive variety of journals that have published articles featuring ‘UAV and TRA’ in their titles confirms that UAV in TRA research has a broad scope and likely carries far-reaching research implications.
Distribution of Research
Excluding publications that lacked information on their country of origin and those with authors from multiple countries, we conducted an analysis of the location (city and country) of publication and its associated climate information. The primary countries with significant publication activity included China, the United States, and Iran. It came as no surprise that China led in terms of the number of publications (6). However, it was somewhat unexpected that Iran publication rates were tied for second place with the United States (2 each). Situated in the ‘Mild Temperate’ zone, these nations share distinct climatic conditions, including extremes in temperature, heat waves, droughts, and aridity. Roussos (2024) claims that when compared to trees in other climates, these areas present more substantial growth challenges for trees. This situation emphasizes how urgently innovative approaches and instruments are needed to manage and lessen the harmful effects of these extreme environmental conditions on tree health. As a result, UAV technology emerges as a particularly viable option, offering creative ways to identify and attend to the unique requirements of trees under these harsh circumstances. Lin et al. (2023) showed that in less than half a working day, a UAV fitted with an integrated camera with 6 imaging sensors (1600 × 1300 pixels) mounted on a Phantom 4 Multispectral (P4M)(DJI, Shenzhen, China) could gather data on the height of 796 trees over an area of about 6.5 hectares. Conventional methods cannot achieve this level of efficiency and coverage.
Moreover, the technology found in UAVs can solve the problem of restricted access that conventional tree assessments face, which arises from extreme climates like extremely high or low temperatures and risky to high wind speed. Weather-resistant UAVs, like the DJI M200 series, offer better flight performance than regular UAVs, as Gao et al. (2021) and Kumar et al. (2021) point out. In order to prevent compass calibration errors, these cutting-edge UAVs have built-in Wi-Fi chips that guarantee continuous connectivity in all weather conditions, including rain, snow, and strong winds. They also have self-heating batteries that allow them to operate in cold weather. Kulhavy et al. (2016) provided evidence to support this claim, showing that the weather-resistant Parrot AR.Drone 2.0 (Parrot, Paris, France) functions well in TRA context. In order to match the limits of human capacity, UAVs are therefore widely researched and used in mildly temperate regions.
Article Type
Research is conducted and published for various reasons and serves multiple purposes. In this review, 3 primary types of articles that are considered highly relevant due to their capacity to provide insight into research trends are identified and explained as follows:
A review article is an article created with the purpose of thoroughly evaluating the existing body of literature within a particular field or according to predetermined search criteria. Such articles are typically presented in the format of literature reviews, systematic reviews, or meta-analyses.
A case study is an article that centers on a particular location or subject. Typically, these articles offer newly discovered data regarding the specific case study topic.
A research article is sometimes called an original research article and typically entails the investigation of a research question, hypothesis, or objective.
Various academic fields may exhibit varying patterns in the proportions of article types they publish. These patterns can be influenced by factors like the research field’s focus, the prevailing demand for specific article types, and the stage of development of the research area.
In this review, as indicated by the results, a substantial majority of articles were categorized as Research Articles. This could be considered an appropriate reflection of a developing field. In the context of a relatively new and emerging area of research, a notable proportion of articles primarily focused on, or at least incorporated, some form of review. This can be attributed in part to the comprehensive nature of the concept, as well as the ongoing efforts to define it and its various facets in the broader context of the UAV in TRA concept.
Article Focus Areas
The articles under review mainly focused on plant stress and plant pests/diseases as the 2 main areas of UAV applications in precision TRA. In addition to promoting new methods to improve plant health management and lower risks, authors addressing plant stress concentrated on enhancing current UAV guidelines or frameworks. Monitoring, analysis, and early disease detection are critical functions of a number of sensor technologies, such as RGB cameras, LiDAR sensors, and multispectral and hyperspectral sensors (Dash et al. 2018; Zhang et al. 2023). Notably, digital elevation model data was provided by LiDAR and hyperspectral sensors during the preprocessing of hyperspectral data (Kouadio et al. 2023). The information obtained from both RGB and hyperspectral sensors was then used to identify the symptoms of plant diseases.
Furthermore, UAV Low-Altitude Remote Sensing (UAV-LARS) is advised since TRA entails thorough evaluations at the level of individual trees (Zhao et al. 2023). When it comes to giving precise and accurate data for tree risk management, these technologies are essential. Their capacity to identify and evaluate pest and disease conditions as well as plant stress from low altitudes improves decision-making and intervention tactics, leading to more successful management results.
Ultimately, the emphasis of the reviewed articles highlights how crucial it is to continue developing UAV guidelines and investigating novel technologies in order to achieve efficient plant management in TRA settings. The focus on UAV applications in TRA, in summary, is in line with anticipated priorities and emphasizes the need for cutting edge methods in risk management and plant health monitoring.
Correlations Among Article Focus Areas
Plant threats, plant stress, plant characteristics, and plant pests and diseases are the focus areas that are correlated. These relationships highlight important areas for future research and practical applications in urban tree management, especially when paired with the capabilities of UAVs. The potential for improved monitoring, detection, and data collection becomes clear when UAV technology is incorporated into these focus areas. UAVs can offer vital information about the health, structural integrity, and environmental conditions of trees by taking high-resolution photos and data, which can be utilized in the context of TRA.
Pests and diseases are examples of plant threats that frequently occur in tandem with increased plant stress, which shows up as observable alterations in plant characteristics. With their sophisticated sensors, UAVs are able to keep a close eye on these alterations and spot stress markers such as shifted leaf color, decreased growth, and modifications to the canopy’s structure. Plant stress and threats have a linear relationship, which means that cities can use UAVs to quickly assess large regions, gather real-time data, and help mitigate threats through early detection to prioritize areas of concern.
Understanding plant dynamics and managing stresses, pests, and diseases require the use of 3D tree modeling. UAVs can help with the monitoring and analysis of these factors by producing intricate 3D models of plant structures. Researchers and urban areas can identify minute alterations in the structure and health of plants by employing UAVs to create accurate models, which could stop the spread of pests and diseases.
Particularly when plants are subjected to stress or pest invasions, traits like growth habits and leaf structure are important markers of a plant’s health. UAVs equipped with multispectral imaging sensors and high-resolution cameras can effectively monitor these properties over an extended period of time. The ability to detect changes brought on by illness or stress early on thanks to this real-time data collection presents chances for focused interventions.
Using UAV technology, researchers and tree managers can gain a better understanding of plant characteristics, stress, threats, and pests or diseases. More proactive tree care results from this, enhancing plant health and ecosystem resilience. UAVs are useful because they can cover large areas quickly, take detailed images, and reach places that are difficult to reach without disturbing the surrounding area. UAVs are an essential tool in TRA, helping to examine the stability of trees and find problems that are difficult to see from the ground, such as disease, dead branches, and root damage. UAVs enable faster action to prevent tree failure, improve public safety, and extend tree life, helping managers use resources more effectively. This is done by improving the speed and accuracy of risk assessments.
Conclusions
UAVs are a valuable tool for identifying practical values and facilitating efficient implementation in the field of dynamic urban tree monitoring, especially for TRA efforts. This systematic review is thought to be the inaugural effort dedicated exclusively to UAV in TRA and has offered an overview of the existing knowledge, areas where knowledge about UAV in TRA is lacking, and emerging research directions in this relatively new field.
Uncertainty and lack of clarity regarding terms and concepts in UAV in TRA research can have a substantial impact on the advancement and effectiveness of this specific research field. It is evident that the vagueness and ambiguity surrounding the term ‘UAV in TRA’ have played a role in its relatively modest beginnings and may continue to affect its limited research influence, prominence, and implementation rates. It is essential for tree risk monitoring researchers to make deliberate efforts to maintain clarity and consistency in their publications to enhance the development of this research discipline.
The publication rates of research are experiencing rapid exponential growth, and this trend is anticipated to persist. The advantage of this trend is that it will result in a more extensive and profound body of knowledge and information becoming accessible to decision-makers and planners. This, in turn, is expected to lead to a greater utilization of UAV and TRA assets as a response to the diverse environmental challenges both currently and in the future.
In this review, the majority of UAV in TRA research originated from China, the United States, and Iran, accounting for approximately 50% of the research output. Additionally, most of this research was conducted in cities falling within the ‘Mild Temperate’ and ‘Dry’ climate classification band. This situation introduces a bias that restricts the generalizability of assumptions and insights on a global scale. This geographical bias is likely one of the most significant research gaps in the field, given that climate significantly influences or exacerbates environmental challenges. It is advisable that researchers from South African and South American regions explore the applicability, relevance, and feasibility of UAV for TRA in their local contexts. Such research efforts could help bridge the gap in uneven geographical distribution and alleviate associated knowledge limitations.
Plant stress and disease problems are the main areas of interest for UAV research in TRA. Economically, plant stress and diseases are especially crucial since they can result in large losses because of the higher expenses associated with removing or mitigating dangerous trees. UAVs using cutting-edge sensors, such as thermal, multispectral, and LiDAR imaging, can assist in managing and identifying these issues, which lowers costs by allowing for prompt actions. The tree managers could all benefit from TRA research’s increased use of UAV technology, which could provide a more all-encompassing approach to tree risk management. Given that UAV in TRA represents an extensive network of assets, it is believed that a significant portion of its value has yet to be fully explored. It is hypothesized that in the collective effort to address growing environmental challenges, UAV in TRA could serve as a cost-effective solution across a wide array of applications.
Acknowledgements
This study was funded by the MyRA Research Grant from the Research Management Centre (RMC), UiTM [PY/2022/00912].
- © 2025 International Society of Arboriculture
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