Next Article in Journal
Construction of a Wi-Fi System with a Tethered Balloon in a Mountainous Region for the Teleoperation of Vehicular Forestry Machines
Previous Article in Journal
Combining LiDAR, SAR, and DEM Data for Estimating Understory Terrain Using Machine Learning-Based Methods
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 11
10.3390/f15111993
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

Using Drones for Dendrometric Estimations in Forests: A Bibliometric Analysis

by
Bruna Rafaella Ferreira da Silva
1,2,
João Gilberto Meza Ucella-Filho
3,
Polyanna da Conceição Bispo
2,*,
Duberli Geomar Elera-Gonzales
1,4,
Emanuel Araújo Silva
1 and
Rinaldo Luiz Caraciolo Ferreira
1
1
Department of Forest Sciences, Federal Rural University of Pernambuco, Recife 52171-900, Brazil
2
Department of Geography, School of Environment Education and Development, University of Manchester, Manchester M13 9PL, UK
3
Department of Forestry Engineering, Federal University of Viçosa, Viçosa 36570-900, Brazil
4
Department of Forestry Sciences, Universidad Nacional Autonoma de Chota, Chota 06121, Peru
*
Author to whom correspondence should be addressed.
Forests 2024, 15(11), 1993; https://doi.org/10.3390/f15111993
Submission received: 4 September 2024 / Revised: 22 October 2024 / Accepted: 4 November 2024 / Published: 11 November 2024
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
Browse Figures
Versions Notes

Abstract

:
Traditional field inventories have been the standard method for collecting detailed forest attribute data. However, these methods are often time-consuming, labor-intensive, and costly, especially for large areas. In contrast, remote sensing technologies, such as unmanned aerial vehicles (UAVs), have become viable alternatives for collecting forest structure data, providing high-resolution images, precision, and the ability to use various sensors. To explore this trend, a bibliometric review was conducted using the Scopus database to examine the evolution of scientific publications and assess the current state of research on using UAVs to estimate dendrometric variables in forest ecosystems. A total of 454 studies were identified, with 199 meeting the established inclusion criteria for further analysis. The findings indicated that China and the United States are the leading contributors to this research domain, with a notable increase in journal publications over the past five years. The predominant focus has been on planted forests, particularly utilizing RGB sensors attached to UAVs for variable estimation. The primary variables assessed using UAV technology include total tree height, DBH, above-ground biomass, and canopy area. Consequently, this review has highlighted the most influential studies in the field, establishing a foundation for future research directions.

Graphical Abstract

1. Introduction

The concept of sustainable forest management is closely linked to the sustainable development of specific forest regions, aiming to balance the use of forest resources with the preservation of environmental integrity, ultimately promoting optimal environmental outcomes [1]. Comprehensive data collection regarding forest characteristics is conducted via forest inventory, aimed at tracking forest regeneration and quantitatively assessing the structure and dynamics of the forest at the individual tree level. This includes metrics such as tree count, crown base height, diameter at breast height (DBH), and tree height [2,3], in addition to the estimation of wood volume and biomass [4,5].
Traditional field research is known for its ability to collect comprehensive data regarding forest characteristics; however, the process of gathering data in forested areas and conducting structural measurements of individual trees can be economically unfeasible, labor-intensive, and physically demanding for field teams [4,6]. This challenge is particularly pronounced in extensive regions characterized by trees of diverse shapes and sizes [7].
To enhance both cost-effectiveness and precision, remote sensing technology has emerged as a viable method for acquiring information about the structure, dynamics, and distribution of forest resources [8]. Applications such as tree counting and forest condition reconstruction, integrating remote sensing and geographic information systems [9], as well as the potential to estimate tree-level forest biomass using LiDAR data [10], demonstrate the significant advancements in remote sensing techniques and technologies from the mid-20th century to the present. Among the various remote sensing technologies, satellites, manned aircraft, and unmanned aerial vehicles (UAVs) are the most widely utilized [11]. UAVs offer distinct advantages over traditional remote sensing platforms, including higher spatial resolution, better positioning accuracy, increased safety, and lower costs [12]. The deployment of UAVs can lead to substantial reductions in both capital and labor expenditures, as well as a decrease in the time required for data collection [11,13].
UAVs possess the significant advantage of accommodating a diverse array of sensors. The primary sensors used with UAVs include thermal, infrared, and multispectral cameras, as well as LiDAR technology [14,15], along with conventional digital cameras, such as RGB sensors. Cameras operating in the visible spectrum are widely used for vegetation monitoring [16]. The application of Structure from Motion (SfM) algorithms has become a promising and cost-effective approach in forestry research, enabling the collection of data on canopy structures [17], the identification of individual trees [18], and various other applications relevant to forest inventory data collection [2,19,20,21,22]. Additionally, LiDAR sensors integrated into mobile devices, such as the iPhone and iPad, are increasingly being employed to obtain forest inventory data [23,24].
Alternative sensor technologies can also be used to derive dendrometric variables related to forest structure in a more accurate and economic way. LiDAR and airborne laser scanning (ALS) are increasingly applied to estimate parameters, such as tree height, diameter at breast height (DBH), individual tree identification, canopy delineation, as well as volume and biomass assessments [4,7,25,26,27,28]. Nonetheless, many studies are still focused on temperate forest ecosystems or areas with open vegetation. As a result, using UAV imagery for the rapid estimation of dendrometric data is considered a more cost-effective alternative to traditional field data collection methods, improving the efficiency of forest inventory updates [7,27].
The growing use and potential of unmanned aerial vehicles (UAVs) in forest management require a thorough review of the existing scientific literature on this topic. Such an analysis seeks to identify the most significant contributions to the field and highlight knowledge gaps that could guide future research and practical applications. As the volume of academic output in a scientific discipline increases, it is essential for researchers to employ quantitative review methods to clarify the knowledge base of that domain [29].
Consequently, this bibliometric review seeks to delineate the developmental trends of scientific publications and evaluate the current state of UAV applications for estimating dendrometric variables in forest ecosystems. Specifically, the objectives are to (i) quantitatively analyze the annual growth of publications in this area, (ii) identify the leading authors and journals in this area, (iii) examine trends in international collaboration and the most prevalent keywords in the literature, (iv) ascertain the primary indexing categories relevant to the field, and (v) determine the types of vegetation, dendrometric variables, and sensor technologies most frequently employed in this research area.

2. Materials and Methods

A bibliometric analysis was performed to investigate the application of UAVs in the assessment of dendrometric variables within forest ecosystems. This type of analysis quantitatively evaluates research publications over a designated timeframe in a particular discipline, allowing for an examination of scientific progress over the years. It also facilitates the identification of key authors, influential journals, geographic study areas, and prominent academic institutions relevant to the field [30,31].
For this study, the database used was the Scopus platform, a widely recognized, multidisciplinary scientific database commonly used in bibliometric analyses and for assessing the current state of specific research areas [32,33].
To enhance the volume of documents pertaining to the application of UAVs in the assessment of dendrometric variables, a selection of pertinent keywords in both Portuguese and English was made to create a more extensive information repository on the subject. The search methodology employed was as follows: (TITLE-ABS-KEY (“vant” OR “veículo aéreo não tripulado” OR “drone” OR “uav” OR “unmanned aerial vehicle” OR “arp” OR “aeronave remotamente pilotada”) AND TITLE-ABS-KEY (“variáveis dendrométricas” OR “diâmetro à altura do peito” OR “dap” OR “altura total” OR “área de copa” OR “dendrometric variables” OR “total height” OR “diameter at breast height” OR “dbh” OR “crown area” OR “detection of individual trees” OR volume OR biomass OR “biomassa”) AND TITLE-ABS-KEY (“forest” OR “dry forests” OR “floresta” OR “floresta tropical seca” OR “dry tropical forest”)) AND PUBYEAR > 1986 AND PUBYEAR < 2023 AND (LIMIT-TO (DOCTYPE, “ar”)). This search was executed in August 2023, with an additional limitation on the publication timeframe, focusing on works published up to the conclusion of 2022.
Following the data collection phase, the identified documents were reviewed and subjected to an analysis based on inclusion criteria. This analysis focused on research articles, studies conducted solely within forest environments, those that examined at least one dendrometric variable, and investigations utilizing UAVs. Consequently, review articles, case studies, book chapters, and research not directly pertinent to forests and dendrometric variables were excluded from this study’s parameters.
Upon completing the article screening, data were extracted for a comprehensive analysis of the current state of research on the topic, specifically addressing (i) the types of vegetation investigated, (ii) the dendrometric variables measured, and (iii) the types of sensors employed. For the bibliometric analysis, information was gathered regarding the authors’ nationalities, their institutional affiliations, collaborative efforts among leading research nations, annual publication counts, highly cited works, the most prolific countries, and the frequently utilized keywords for indexing purposes.
The data collected that met the inclusion criteria were organized and subjected to descriptive statistical analysis to assess the current state of the art. Bibliometric analyses and world maps were generated using the biblioshiny function from the bibliometrix package [34] within the R software (version 4.1.1) environment [35]. The interaction network for the most frequently used keywords in the documents was constructed using the specified bibliometric analysis program, VOSviewer version 1.6.15 [36].

3. Results

A comprehensive analysis was conducted on a total of 454 articles, out of which 43.6% met the established selection criteria, resulting in the inclusion of 198 articles in this research. The remaining 56.4% comprised review articles, case studies, and research focused on agronomic crops, which did not align with the parameters set for this study. The findings revealed that Asia led in the number of publications within the field of study, totaling 98, followed closely by Europe with 95 publications. North America contributed 61 publications, while South America had 20, Oceania 16, and Africa 7. A total of 52 countries engaged in research related to this topic, with China leading the way with the highest number of studies (n = 60). This was followed by the United States (n = 41), Japan (n = 17), Brazil (n = 14), Australia (n = 14), Finland (n = 13), Canada (n = 13), Belgium (n = 12), Spain (n = 10), and Norway (n = 9), identifying them as the top ten contributors to research in this area.
The network of interactions among countries that have investigated the application of UAVs for the acquisition of dendrometric variables is illustrated in Figure 1. The thickness of the lines connecting these nations reflects the volume of collaborative publications produced. Notably, the most significant collaboration occurred between the United States and Brazil, which engaged in 10 joint research initiatives. This was closely followed by the partnership between China and the United States (n = 9), as well as collaborations between Australia and Belgium (n = 5) and between the United States and Spain (n = 5). These findings highlight a robust level of international cooperation, which is advantageous for enhancing the body of literature and understanding regarding the utilization of UAVs in forestry.
The institutions that produced the highest volume of research utilizing UAVs for the acquisition of dendrometric variables included Nanjing Forestry University, the Chinese Academy of Sciences, and the Ministry of Education of the People’s Republic of China, with respective publication counts of 13, 10, and 9. These universities are all situated in China, underscoring the nation’s prominent role in the research area explored in this study.
Collaboration between countries and institutions has significantly influenced advancements in UAV technology for forestry. The collaboration among European countries under the ICP Forest program has provided a standardized framework for forest assessment and monitoring, facilitating the exchange of methodologies and data, and enabling the development of more robust and versatile UAV applications in the forestry sector [37]. Despite this, the notable dominance of countries such as China and the United States in UAV technology for forestry can be attributed to substantial investments in research and development, advanced technological infrastructure, and strong academic–industry partnerships [38]. In China, for example, contributions to UAV technology are supported by extensive public funding and a rapidly growing tech industry [39]. Similarly, the United States benefits from its well-established research institutions and collaborations with private sector companies, driving innovation in UAV applications [40]. Over time, UAV applications in forestry initially focused on basic aerial photography and mapping, later migrating to forest inventories and health monitoring. Recent advancements have led to the integration of sophisticated sensors, such as LiDAR and multispectral cameras, improving the precision and efficiency of dendrometric estimations and the scope of data collection [37]. Recent studies continue to expand their potential applications, including forest fire management, biodiversity assessment, and carbon stock estimation [41,42]. This shift reflects the growing recognition of UAVs as versatile tools for addressing a wide range of forestry management issues.
In recent years, the application of remote sensing methodologies for the evaluation of forest resources has seen a significant increase, both in direct and indirect assessments [43,44]. This trend is supported by the data presented in Figure 2, which illustrate a notable rise in the number of scholarly articles addressing this subject, particularly following 2017. These studies primarily highlight the advantages of using drones to significantly reduce costs for data collection, analysis, and continuous monitoring of tree height, canopy openness, forest disturbances, and other forest parameters in both short-term and long-term monitoring [45]. Additionally, technological advancements have enhanced practical applications by increasing drone autonomy and obtaining high-resolution images that provide precise information about forest ecosystems, aiding in decision-making [46]. Furthermore, it is important to highlight that research concerning the utilization of UAVs for the acquisition of dendrometric variables commenced only in 2013, suggesting that this approach is relatively novel and continues to evolve within the scientific community.
Table 1 presents the ten most frequently cited articles related to this subject, along with their essential details. The research illustrates a variety of methodologies and dendrometric variables that can be acquired through the use of unmanned aerial vehicles (UAVs). Notably, a considerable proportion of the cited works employed RGB sensors, which are typically integrated cameras on UAVs. This preference is likely attributable to the advantages of the lower material and operational costs, as well as the flexibility and repeatability they offer in data collection [6,47]. However, excellent results have also been obtained with RGB sensors’ data, with studies showing that the digital aerial photogrammetry approach using UAVs is equivalent to or surpasses airborne LiDAR in measuring forest structure, not to mention cases in which the multi-angle approach of UAV digital aerial photogrammetry enhances the spatial accuracy of forest canopy height measurements [48]. In contrast, the studies conducted in [25,49] utilized UAV/LiDAR sensors, which can deliver more comprehensive three-dimensional insights into forest structures compared to conventional remote sensing methods. Furthermore, the digital terrain model (DTM) using LiDAR is much more accurate than the DTM generated from a point cloud by digital aerial photogrammetry [48], which brings comparative advantages, especially in dense forests or non-flat terrain. Nonetheless, the high costs associated with obtaining airborne LiDAR data pose a significant barrier to its widespread application [50,51,52], but it is also important to mention that the rapid pace of technological advancement could bridge these gaps, both by enhancing the technological capabilities of RGB sensors and by making LiDAR sensors more cost-effective. In relation to the dendrometric variable examined, height emerged as predominant across nearly all articles listed in the table, with the exception of [53], who investigated the volume of standing timber in forest growth by integrating data from UAVs, Sentinel-2 satellite imagery, and field measurements.
We should not fail to mention that one of the main challenges in this area is the high volume of data generated by UAVs, especially when using high-resolution sensors; in that sense, scalable data processing frameworks that can handle the influx of large datasets are required, so that it is possible to develop efficient data pipelines that can process and analyze large volumes of UAV data in a timely manner [54]. Efficient data storage solutions are also necessary and crucial. Options such as cloud computing facilitate collaborative research and provide access to large volumes of data and real-time analysis. This approach not only addresses storage issues but also enhances data accessibility and sharing among researchers [37]. Additionally, processing LiDAR and photogrammetric data involves complex algorithms and significant computational resources. The application of deep learning techniques, along with the use of open-source software and standardized workflows [55], will help mitigate complexity and ensure consistency in processing large volumes of data obtained with UAVs.
A total of sixty-two journals were recognized for their publication of articles related to the subject matter. Notably, Remote Sensing (n = 60), Forests (n = 23), Remote Sensing of Environment (n = 9), and the International Journal of Applied Earth Observation and Geoinformation (n = 9) emerged as the primary contributors to the advancement of research concerning the application of UAVs in the acquisition of dendrometric variables within forest ecosystems.
The analysis of the gathered data revealed that the published articles were organized and classified into 16 distinct thematic categories. Among these, 32.4% pertained to Earth and Planetary Sciences (n = 103), while Agricultural and Biological Sciences accounted for 25.5% of the articles (n = 81). Additionally, 14.8% of the articles were classified under Environmental Science (n = 47; Figure 3). It is noteworthy that multiple categories may have been assigned to individual articles, underscoring the interdisciplinary character of the research conducted across various academic domains [32].
The authors identified a total of 609 keywords in the articles published until the conclusion of 2022 (see Figure 4). These keywords play a crucial role in shaping the representation and dissemination of the articles within scientific communities, as they highlight the primary research themes [56,57]. The most frequently encountered terms in the studies included UAV (n = 38), forest inventory (n = 33), drone (n = 23), Structure from Motion (n = 19), and photogrammetry (n = 14). In terms of article indexing, a total of 1321 distinct terms were recorded, with notable examples being forestry (n = 133), unmanned aerial vehicles (UAVs; n = 124), antennas (n = 103), remote sensing (n = 63), and unmanned vehicle (n = 62).
The authors identified a variety of terminologies associated with unmanned aerial vehicles, which were among the most defined keywords. These terms included UAV (n = 38), drone (n = 23), UAS (n = 10), and unmanned aerial vehicle (n = 24).
Figure 5 presents an overview of the various vegetation types, dendrometric variables, and sensor types predominantly employed in studies utilizing UAV technology to gather dendrometric data in forested environments. The analysis revealed that planted forests were the most frequently examined vegetation type, appearing in 57 articles and accounting for 29% of the total vegetation types analyzed. Native forests followed, represented in 41 articles and comprising 21% of the vegetation types discussed. Although Pinus is classified as a coniferous species, the studies differentiated between Pinus forests (n = 24) and general coniferous forests (n = 13) due to the presence of other species within the latter category. Consequently, Pinus forests constituted 12% of the studies, while coniferous forests made up 7%. Additionally, mangroves were noted as a significant vegetation type, appearing in 9 articles and representing 5% of the total studies reviewed.
Considering that China has the largest area of planted forests in the world [58] and that a significant majority of research has been conducted within its borders (n = 60), it was anticipated that the literature on planted forests would be predominantly represented among the selected studies [59,60,61]. The authors of [49] conducted a study utilizing a UAV equipped with a LiDAR system to gather data on tree height, individual tree segmentation, and above-ground biomass. Their findings indicated that the variables could be effectively measured at the individual tree level rather than at the plot level, specifically in the context of planted forests.
Research conducted in planted forests has predominantly focused on the acquisition of dendrometric variables through the use of UAVs, particularly within plantations of various Pinus species [62,63,64,65] and Eucalyptus species [66,67,68,69]. Additionally, investigations have also been carried out on plantations of different Quercus species [17], Picea species [70,71,72], as well as Ginkgo plantations [49,73]. Furthermore, studies have explored planting areas that encompass a variety of species and genera [61,74].
Research on native forests is primarily conducted in countries with vegetation that differs significantly from that of Brazil. Among the fourteen studies identified within Brazil, only four were focused on native vegetation, specifically in the Amazon [75,76], Atlantic Forest [77], and Cerrado [42]. This underscores a notable gap in the literature regarding the application of UAVs for measuring dendrometric variables in other Brazilian vegetation types, such as the Dry Tropical Forest. This observation emphasizes the necessity for further investigations utilizing UAVs to assess dendrometric variables across various Brazilian ecosystems, thereby enhancing the scientific understanding in this field.
In the analysis of the most frequently examined dendrometric variables, tree height emerged as the most prominent, appearing in 104 studies, which accounts for approximately 28% of the total research. This was followed by diameter at breast height (DBH) with 60 studies, above-ground biomass with 59 studies, and canopy area with 56 studies, indicating that multiple variables were often investigated within individual articles. Most of these studies utilized point cloud data and the Canopy Height Model (CHM) to estimate tree height [78,79,80].
In research conducted in [81], the authors successfully acquired measurements of height, diameter at breast height (DBH), and canopy characteristics of a tropical forest in Australia through the utilization of point cloud data derived from UAV/Terrestrial Laser Scanning (TLS). Similarly, the authors of [5] employed UAV/LiDAR imagery to assess individual tree metrics, including DBH, height, canopy projection area, and above-ground biomass (AGB), within a planted forest in China. Additionally, the authors of [82] estimated the canopy volume of a pistachio plantation using UAV/RGB technology, providing an efficient and cost-effective solution.
We found that the RGB sensor integrated with UAVs was the most frequently utilized type of sensor in the reviewed studies (n = 118), followed by the LiDAR sensor (n = 62) and the Structure from Motion (SfM) technique (n = 18). This indicates a growing trend in research employing UAV-compatible sensors, while also revealing a notable gap in studies that utilize specific sensor types, such as multispectral sensors, for the acquisition of dendrometric variables.
The analysis of the studies incorporated in this review revealed several advantages and disadvantages associated with the most frequently utilized sensors. As illustrated in Figure 3, the RGB sensor emerged as the predominant choice among the studies, likely attributable to its affordability, high spatial resolution, ease of deployment, and adaptable flight planning [6,54,83]. Furthermore, it has demonstrated considerable efficacy in capturing forest dendrometric variables [51,82,84,85]. Nonetheless, UAV-based photography is constrained by its inability to provide terrain surface information in forested regions [54,86,87], and soil occlusion represents a significant limitation of the UAV/SfM technique in areas covered by forests [88,89].
Conversely, LiDAR sensors present the benefit of delivering superior resolution and rapidly providing highly accurate and spatially detailed data regarding forest characteristics across extensive landscapes [90]. These sensors possess the capability to penetrate the forest canopy, facilitating precise measurements of canopy structural parameters, such as tree height, canopy openness, and leaf density, thus enabling swift and reliable data acquisition concerning the forest structure [91,92]. However, the application of this method for extensive areas or large objects necessitates costly sensors, specialized team training, and advanced computational technology to ensure accuracy, resulting in substantial expenses [2,93]. Despite the financial implications, LiDAR technology is regarded as one of the most effective options for monitoring the forest structure, composition, and function, particularly in dense and irregular forest environments. In contrast, RGB or SfM sensors may be more suitable for uniform vegetation or when financial constraints are a consideration.
Regarding the algorithms available for these sensors, the most popular are based on detecting the maximum peaks in a Canopy Height Model (CHM), known as the local maximum, with variations in terms of window size, smoothing, and post-processing [7,37,94,95].
Finally, based on recent research and frequent innovations in unmanned aerial vehicle (UAV) technology, new trends for dendrometric estimates in forestry should undoubtedly emerge in the short to medium term. A key area for future research is the development of real-time data processing capabilities for UAVs, enabling immediate field analysis and decision-making, crucial for monitoring forest health and promptly detecting changes [96]. Additionally, integrating AI and machine learning with UAVs [97] and automating tree characteristic measurements can enhance the accuracy and efficiency in dendrometric estimations through advanced algorithms, such as convolutional neural networks (CNNs) [98]. Developing cloud-based platforms for UAV data, such as The Computree Platform (https://computree.onf.fr, accessed on 1 October 2024), or integrating data in Google Earth Engine will facilitate seamless integration with other remote sensing data and GIS, enabling comprehensive analyses and collaborative research [99]. In addition, advancements in sensor technology, including high-resolution cameras, multispectral and hyperspectral sensors, and improved LiDAR, will enhance UAV capabilities, providing detailed and accurate dendrometric data and capturing additional forest parameters. Also, future research should focus on the long-term monitoring of forest ecosystems [100]. This involves not only the continuous collection of data but also the integration of historical data to analyze trends and changes over time. Combining UAV data with other sources, such as satellite imagery and ground-based measurements, can provide a more holistic view of forest dynamics and support sustainable forest management practices.
Table 1. Articles with the highest citation counts as of the year 2022.
Table 1. Articles with the highest citation counts as of the year 2022.
AuthorsLocationType of VegetationSensor TypeDendrometric Variables ObtainedJournalNumber of Citations
Puliti et al. (2015) [85]NorwayBoreal forestUAV/RGBHeight, stem volume, basal areaRemote Sensing277
[2]United StatesPlanted forestUAV/SfMIndividual tree detectionForests271
[89]Costa RicaRestoration areaUAV/RGBHeight, crown sizeBiological Conservation211
[25]IndonesiaPlanted forestUAV/LiDAR, terrestrial laser scanner, airborne laser scanningDBH, heightSensors151
[51]ChinaNative vegetationUAV/RGBHeight, above-ground biomass, canopy coverInternational Journal of Remote Sensing124
[19]MalaysiaMangrove forest reserveUAV/RGBHeight, above-ground biomassForest Ecology and Management118
Brede et al. (2019) [101]NetherlandsNative forestUAV/RGBHeight, above-ground biomassRemote Sensing of Environment111
[53]NorwayPlanted forestUAV, Sentinel-2VolumeRemote Sensing of Environment111
Krause et al. (2019) [102]GermanyNative forestUAV/RGBHeightRemote Sensing108
[49]ChinaPlanted forestUAV/LiDARHeight, individual trees, above-ground biomassISPRS Journal of Photogrammetry and Remote Sensing105

4. Conclusions

This research included an analysis of the current literature concerning the application of UAVs for the measurement of dendrometric variables within forest ecosystems. Through a bibliometric assessment, the study identified and discussed prominent keywords in the existing body of work, the primary types of vegetation under investigation, and the scientific gaps that persist in this field. Furthermore, it emphasized the novelty of this research area and aimed to offer insights that will inform future inquiries related to UAV applications in forestry. It is essential to underscore several critical aspects and deficiencies to promote the scientific advancement of this subject:
(i)
The subject has experienced a notable rise in the volume of publications over time; nevertheless, the overwhelming majority of investigations aimed at acquiring dendrometric variables through the use of unmanned aerial vehicles predominantly took place in cultivated forests or in vegetation exhibiting comparable traits. Exploring research across diverse types of vegetation represents a valuable avenue to enhance the applicability of the methodology in various geographic contexts.
(ii)
In research pertaining to this subject, the sensors most frequently employed included RGB, LiDAR, and Structure from Motion (SfM), particularly for assessing variables such as total tree height, diameter at breast height (DBH), above-ground biomass, and canopy area. The LiDAR sensor appeared to be the most appropriate technology for acquiring dendrometric variables from forested areas, owing to its capability of utilizing a longer-range laser.
(iii)
Research efforts that utilize multispectral sensors to derive dendrometric variables should be enhanced, while also evaluating their potential contributions to vegetation mapping and detection.
(iv)
The identification of species through images of tree canopies has not yet been extensively explored in published studies, presenting a potential avenue for future research.
In conclusion, the bibliometric analysis offered fresh perspectives on the existing literature. The keywords employed in the research and their indexing highlighted the primary areas of focus regarding UAVs and the measurement of dendrometric variables. Furthermore, the compilation of the most frequently cited publications in the field aided in pinpointing the most significant studies conducted, thereby serving as a foundation for exploring potential avenues for future research.

Author Contributions

Writing—original draft preparation, B.R.F.d.S.; methodology, J.G.M.U.-F. and B.R.F.d.S.; funding acquisition and review, P.d.C.B. and E.A.S.; supervision, E.A.S. and P.d.C.B.; writing—review and editing, B.R.F.d.S., J.G.M.U.-F., P.d.C.B., D.G.E.-G., E.A.S. and R.L.C.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by University Open Access Fund (University of Manchester). The present work was carried out with support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Financial code 001.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable in this article.

Acknowledgments

The authors would also like to acknowledge Martin Evans and the School of Environment, Education, and Development (SEED) for supporting B.R.F.d.S. research visit to the University of Manchester. We also thank the University of Manchester Open Access Fund.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ribeiro, A.C.F.; Fonseca, L.C.D.; Pereira, C.M.P. O Plano de Manejo Florestal Como Instrumento de Desenvolvimento Sustentável na Amazônia. Direito E Desenvolv. 2020, 11, 264–276. [Google Scholar] [CrossRef]
  2. Mohan, M.; Silva, C.A.; Klauberg, C.; Jat, P.; Catts, G.; Cardil, A.; Hudak, A.T.; Dia, M. Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest. Forests 2017, 8, 340. [Google Scholar] [CrossRef]
  3. Strigul, N. Individual-Based Models and Scaling Methods for Ecological Forestry: Implications of Tree Phenotypic Plasticity; Stevens Institute of Technology: Hoboken, NJ, USA, 2012. [Google Scholar]
  4. Cosenza, D.N.; Vogel, J.; Broadbent, E.N.; Silva, C.A. Silvicultural Experiment Assessment Using Lidar Data Collected from an Unmanned Aerial Vehicle. For. Ecol. Manag. 2022, 522, 120489. [Google Scholar] [CrossRef]
  5. Wang, M.; Im, J.; Zhao, Y.; Zhen, Z. Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix Olgensis Henry) Using a Hierarchical Bayesian Approach. Remote Sens. 2022, 14, 4361. [Google Scholar] [CrossRef]
  6. Tang, L.; Shao, G. Drone Remote Sensing for Forestry Research and Practices. J. For. Res. 2015, 26, 791–797. [Google Scholar] [CrossRef]
  7. Goodbody, T.R.H.; Coops, N.C.; Marshall, P.L.; Tompalski, P.; Crawford, P. N O 1-The Forestry Chronicle; Canadian Institute of Forestry: Mattawa, ON, Canada, 2017; Volume 93, pp. 71–81. [Google Scholar]
  8. Li, X.; Xiao, J.; He, B. Chlorophyll Fluorescence Observed by OCO-2 Is Strongly Related to Gross Primary Productivity Estimated from Flux Towers in Temperate Forests. Remote Sens. Environ. 2018, 204, 659–671. [Google Scholar] [CrossRef]
  9. Valjarević, A.; Djekić, T.; Stevanović, V.; Ivanović, R.; Jandziković, B. GIS Numerical and Remote Sensing Analyses of Forest Changes in the Toplica Region for the Period of 1953–2013. Appl. Geogr. 2018, 92, 131–139. [Google Scholar] [CrossRef]
  10. Xu, D.; Wang, H.; Xu, W.; Luan, Z.; Xu, X. LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives. Forests 2021, 12, 550. [Google Scholar] [CrossRef]
  11. Tsouros, D.C.; Bibi, S.; Sarigiannidis, P.G. A Review on UAV-Based Applications for Precision Agriculture. Information 2019, 10, 349. [Google Scholar] [CrossRef]
  12. Xiang, T.Z.; Xia, G.S.; Zhang, L. Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects. IEEE Geosci. Remote Sens. Mag. 2019, 7, 29–63. [Google Scholar] [CrossRef]
  13. Koh, L.P.; Wich, S.A. Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation. Trop. Conserv. Sci. 2012, 5, 121–132. [Google Scholar] [CrossRef]
  14. Colomina, I.; Molina, P. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79–97. [Google Scholar] [CrossRef]
  15. Zarco-Tejada, P.J.; Diaz-Varela, R.; Angileri, V.; Loudjani, P. Tree Height Quantification Using Very High Resolution Imagery Acquired from an Unmanned Aerial Vehicle (UAV) and Automatic 3D Photo-Reconstruction Methods. Eur. J. Agron. 2014, 55, 89–99. [Google Scholar] [CrossRef]
  16. Paneque-Gálvez, J.; McCall, M.K.; Napoletano, B.M.; Wich, S.A.; Koh, L.P. Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas. Forests 2014, 5, 1481–1507. [Google Scholar] [CrossRef]
  17. Chandrasekaran, A.; Shao, G.; Fei, S.; Miller, Z.; Hupy, J. Automated Inventory of Broadleaf Tree Plantations with UAS Imagery. Remote Sens. 2022, 14, 1931. [Google Scholar] [CrossRef]
  18. Kükenbrink, D.; Marty, M.; Bösch, R.; Ginzler, C. Benchmarking Laser Scanning and Terrestrial Photogrammetry to Extract Forest Inventory Parameters in a Complex Temperate Forest. Int. J. Appl. Earth Obs. Geoinf. 2022, 113, 102999. [Google Scholar] [CrossRef]
  19. Otero, V.; Van De Kerchove, R.; Satyanarayana, B.; Martínez-Espinosa, C.; Fisol, M.A.B.; Ibrahim, M.R.B.; Sulong, I.; Mohd-Lokman, H.; Lucas, R.; Dahdouh-Guebas, F. Managing Mangrove Forests from the Sky: Forest Inventory Using Field Data and Unmanned Aerial Vehicle (UAV) Imagery in the Matang Mangrove Forest Reserve, Peninsular Malaysia. For. Ecol. Manag. 2018, 411, 35–45. [Google Scholar] [CrossRef]
  20. Puliti, S.; Dash, J.P.; Watt, M.S.; Breidenbach, J.; Pearse, G.D. A Comparison of UAV Laser Scanning, Photogrammetry and Airborne Laser Scanning for Precision Inventory of Small-Forest Properties. Forestry 2020, 93, 150–162. [Google Scholar] [CrossRef]
  21. Songsom, V.; Koedsin, W.; Ritchie, R.J.; Huete, A. Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand. Remote Sens. 2019, 11, 928. [Google Scholar] [CrossRef]
  22. Xu, Z.; Li, W.; Li, Y.; Shen, X.; Ruan, H. Estimation of Secondary Forest Parameters by Integrating Image and Point Cloud-Based Metrics Acquired from Unmanned Aerial Vehicle. J. Appl. Remote Sens. 2019, 14, 1. [Google Scholar] [CrossRef]
  23. Gollob, C.; Ritter, T.; Kraßnitzer, R.; Tockner, A.; Nothdurft, A. Measurement of Forest Inventory Parameters with Apple Ipad pro and Integrated Lidar Technology. Remote Sens. 2021, 13, 3129. [Google Scholar] [CrossRef]
  24. Tatsumi, S.; Yamaguchi, K.; Furuya, N. ForestScanner: A Mobile Application for Measuring and Mapping Trees with LiDAR-Equipped IPhone and IPad. Methods Ecol. Evol. 2023, 14, 1603–1609. [Google Scholar] [CrossRef]
  25. Brede, B.; Lau, A.; Bartholomeus, H.M.; Kooistra, L. Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 2017, 17, 2371. [Google Scholar] [CrossRef]
  26. Chen, S.; Nian, Y.; He, Z.; Che, M. Measuring the Tree Height of Picea Crassifolia in Alpine Mountain Forests in Northwest China Based on UAV-LiDAR. Forests 2022, 13, 1163. [Google Scholar] [CrossRef]
  27. Ghasemi, M.; Latifi, H.; Pourhashemi, M. A Novel Method for Detecting and Delineating Coppice Trees in UAV Images to Monitor Tree Decline. Remote Sens. 2022, 14, 5910. [Google Scholar] [CrossRef]
  28. de Oliveira, L.F.R.; Lassiter, H.A.; Wilkinson, B.; Whitley, T.; Ifju, P.; Logan, S.R.; Peter, G.F.; Vogel, J.G.; Martin, T.A. Moving to Automated Tree Inventory: Comparison of Uas-Derived Lidar and Photogrammetric Data with Manual Ground Estimates. Remote Sens. 2021, 13, 72. [Google Scholar] [CrossRef]
  29. Rivera, M.A.; Pizam, A. Advances in Hospitality Research: “From Rodney Dangerfield to Aretha Franklin”. Int. J. Contemp. Hosp. Manag. 2015, 27, 362–378. [Google Scholar] [CrossRef]
  30. Kaffash, S.; Nguyen, A.T.; Zhu, J. Big Data Algorithms and Applications in Intelligent Transportation System: A Review and Bibliometric Analysis. Int. J. Prod. Econ. 2021, 231, 107868. [Google Scholar] [CrossRef]
  31. Silva, T.C.; Araujo, E.C.G.; da Silva Lins, T.R.; Reis, C.A.; Sanquetta, C.R.; da Rocha, M.P. Non-Timber Forest Products in Brazil: A Bibliometric and a State of the Art Review. Sustainability 2020, 12, 7151. [Google Scholar] [CrossRef]
  32. Ucella-Filho, J.G.M.; Freire, A.d.S.M.; Carréra, J.C.; Lucas, F.M.F.; Zucolotto, S.M.; Dias Júnior, A.F.; Mori, F.A. Tannin-Rich Bark Extract of Plants as a Source of Antimicrobial Bioactive Compounds: A Bibliometric Analysis. S. Afr. J. Bot. 2022, 150, 1038–1050. [Google Scholar] [CrossRef]
  33. Vasconcelos, R.N.; Costa, D.P.; Duverger, S.G.; Lobão, J.S.B.; Cambuí, E.C.B.; Lentini, C.A.D.; Lima, A.T.C.; Schirmbeck, J.; Mendes, D.T.; Rocha, W.J.S.F.; et al. Bibliometric Analysis of Surface Water Detection and Mapping Using Remote Sensing in South America. Scientometrics 2023, 128, 1667–1688. [Google Scholar] [CrossRef]
  34. Aria, M.; Cuccurullo, C. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
  35. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org (accessed on 17 March 2024).
  36. van Eck, N.J.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed]
  37. Mohan, M.; Richardson, G.; Gopan, G.; Aghai, M.M.; Bajaj, S.; Galgamuwa, G.A.P.; Vastaranta, M.; Arachchige, P.S.P.; Amorós, L.; Corte, A.P.D.; et al. Uav-supported Forest Regeneration: Current Trends, Challenges and Implications. Remote Sens. 2021, 13, 2596. [Google Scholar] [CrossRef]
  38. Telli, K.; Kraa, O.; Himeur, Y.; Ouamane, A.; Boumehraz, M.; Atalla, S.; Mansoor, W. A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs). Systems 2023, 11, 400. [Google Scholar] [CrossRef]
  39. Atkinson, R.D.; Atkinson, R.D. China Is Rapidly Becoming A Leading Innovator in Advanced Industries; Information Technology and Innovation Foundation: Washington, DC, USA, 2024. [Google Scholar]
  40. Martinez, B.; Dehgan, A.; Zamft, B.; Baisch, D.; Mccormick, C.; Giordano, A.J.; Aicher, R.; Selbe, S.; Hoffman, C. Advancing Federal Capacities for the Early Detection of and Rapid Response to Invasive Species Through Technology Innovation; National Invasive Species Council: Washington, DC, USA, 2017. [Google Scholar]
  41. Leite, R.V.; Silva, C.A.; Broadbent, E.N.; Amaral, C.H.d.; Liesenberg, V.; Almeida, D.R.A.d.; Mohan, M.; Godinho, S.; Cardil, A.; Hamamura, C.; et al. Large Scale Multi-Layer Fuel Load Characterization in Tropical Savanna Using GEDI Spaceborne Lidar Data. Remote Sens. Environ. 2022, 268, 112764. [Google Scholar] [CrossRef]
  42. da Costa, M.B.T.; Silva, C.A.; Broadbent, E.N.; Leite, R.V.; Mohan, M.; Liesenberg, V.; Stoddart, J.; do Amaral, C.H.; de Almeida, D.R.A.; da Silva, A.L.; et al. Beyond Trees: Mapping Total Aboveground Biomass Density in the Brazilian Savanna Using High-Density UAV-Lidar Data. For. Ecol. Manag. 2021, 491, 119155. [Google Scholar] [CrossRef]
  43. Crowther, T.W.; Glick, H.B.; Covey, K.R.; Bettigole, C.; Maynard, D.S.; Thomas, S.M.; Smith, J.R.; Hintler, G.; Duguid, M.C.; Amatulli, G.; et al. Mapping Tree Density at a Global Scale. Nature 2015, 525, 201–205. [Google Scholar] [CrossRef]
  44. Khorram, S.; van der Wiele, C.F.; Koch, F.H.; Nelson, S.A.C.; Potts, M.D. Principles of Applied Remote Sensing; Springer International Publishing: Berlin/Heidelberg, Germany, 2016; ISBN 9783319225609. [Google Scholar]
  45. Zhang, J.; Hu, J.; Lian, J.; Fan, Z.; Ouyang, X.; Ye, W. Seeing the Forest from Drones: Testing the Potential of Lightweight Drones as a Tool for Long-Term Forest Monitoring. Biol. Conserv. 2016, 198, 60–69. [Google Scholar] [CrossRef]
  46. Almeida, D.R.A.d.; Broadbent, E.N.; Ferreira, M.P.; Meli, P.; Zambrano, A.M.A.; Gorgens, E.B.; Resende, A.F.; de Almeida, C.T.; do Amaral, C.H.; Corte, A.P.D.; et al. Monitoring Restored Tropical Forest Diversity and Structure through UAV-Borne Hyperspectral and Lidar Fusion. Remote Sens. Environ. 2021, 264, 112582. [Google Scholar] [CrossRef]
  47. Puliti, S.; Ene, L.T.; Gobakken, T.; Næsset, E. Use of Partial-Coverage UAV Data in Sampling for Large Scale Forest Inventories. Remote Sens. Environ. 2017, 194, 115–126. [Google Scholar] [CrossRef]
  48. Zhou, L.; Meng, R.; Tan, Y.; Lv, Z.; Zhao, Y.; Xu, B.; Zhao, F. Comparison of UAV-Based LiDAR and Digital Aerial Photogrammetry for Measuring Crown-Level Canopy Height in the Urban Environment. Urban. For. Urban. Green. 2022, 69, 127489. [Google Scholar] [CrossRef]
  49. Liu, K.; Shen, X.; Cao, L.; Wang, G.; Cao, F. Estimating Forest Structural Attributes Using UAV-LiDAR Data in Ginkgo Plantations. ISPRS J. Photogramm. Remote Sens. 2018, 146, 465–482. [Google Scholar] [CrossRef]
  50. Næsset, E.; Gobakken, T. Estimation of Above- and below-Ground Biomass across Regions of the Boreal Forest Zone Using Airborne Laser. Remote Sens. Environ. 2008, 112, 3079–3090. [Google Scholar] [CrossRef]
  51. Guo, Q.; Su, Y.; Hu, T.; Zhao, X.; Wu, F.; Li, Y.; Liu, J.; Chen, L.; Xu, G.; Lin, G.; et al. An Integrated UAV-Borne Lidar System for 3D Habitat Mapping in Three Forest Ecosystems across China. Int. J. Remote Sens. 2017, 38, 2954–2972. [Google Scholar] [CrossRef]
  52. Wallace, L.; Lucieer, A.; Malenovskỳ, Z.; Turner, D.; Vopěnka, P. Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests 2016, 7, 62. [Google Scholar] [CrossRef]
  53. Puliti, S.; Saarela, S.; Gobakken, T.; Ståhl, G.; Næsset, E. Combining UAV and Sentinel-2 Auxiliary Data for Forest Growing Stock Volume Estimation through Hierarchical Model-Based Inference. Remote Sens. Environ. 2018, 204, 485–497. [Google Scholar] [CrossRef]
  54. Guimarães, N.; Pádua, L.; Marques, P.; Silva, N.; Peres, E.; Sousa, J.J. Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens. 2020, 12, 1046. [Google Scholar] [CrossRef]
  55. Diez, Y.; Kentsch, S.; Fukuda, M.; Caceres, M.L.L.; Moritake, K.; Cabezas, M. Deep Learning in Forestry Using Uav-Acquired Rgb Data: A Practical Review. Remote Sens. 2021, 13, 2837. [Google Scholar] [CrossRef]
  56. Rejeb, A.; Abdollahi, A.; Rejeb, K.; Treiblmaier, H. Drones in Agriculture: A Review and Bibliometric Analysis. Comput. Electron. Agric. 2022, 198, 107017. [Google Scholar] [CrossRef]
  57. Uddin, S.; Khan, A. The Impact of Author-Selected Keywords on Citation Counts. J. Informetr. 2016, 10, 1166–1177. [Google Scholar] [CrossRef]
  58. SFA. China Forest Resources Report: The ninth National Forest Inventory; Chinese Academy of Forestry: Beijing, China, 2019. Available online: https://www.forestry.gov.cn/html/main/main_6153/20210729174001990465056/file/20210729174304614244351.pdf (accessed on 15 October 2024).
  59. Sun, Y.; Jin, X.; Pukkala, T.; Li, F. Predicting Individual Tree Diameter of Larch (Larix Olgensis) from UAV-LiDAR Data Using Six Different Algorithms. Remote Sens. 2022, 14, 1125. [Google Scholar] [CrossRef]
  60. Liang, Y.; Kou, W.; Lai, H.; Wang, J.; Wang, Q.; Xu, W.; Wang, H.; Lu, N. Improved Estimation of Aboveground Biomass in Rubber Plantations by Fusing Spectral and Textural Information from UAV-Based RGB Imagery. Ecol. Indic. 2022, 142, 109286. [Google Scholar] [CrossRef]
  61. Liu, H.; Cao, F.; She, G.; Cao, L. Extrapolation Assessment for Forest Structural Parameters in Planted Forests of Southern China by UAV-LiDAR Samples and Multispectral Satellite Imagery. Remote Sens. 2022, 14, 2677. [Google Scholar] [CrossRef]
  62. Gallardo-Salazar, J.L.; Pompa-García, M. Detecting Individual Tree Attributes and Multispectral Indices Using Unmanned Aerial Vehicles: Applications in a Pine Clonal Orchard. Remote Sens. 2020, 12, 4144. [Google Scholar] [CrossRef]
  63. Hartley, R.J.L.; Jayathunga, S.; Massam, P.D.; De Silva, D.; Estarija, H.J.; Davidson, S.J.; Wuraola, A.; Pearse, G.D. Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping. Remote Sens. 2022, 14, 3344. [Google Scholar] [CrossRef]
  64. Maesano, M.; Santopuoli, G.; Moresi, F.V.; Matteucci, G.; Lasserre, B.; Mugnozza, G.S. Above Ground Biomass Estimation from UAV High Resolution RGB Images and LiDAR Data in a Pine Forest in Southern Italy. IForest 2022, 15, 451–457. [Google Scholar] [CrossRef]
  65. Williams, T.; Williams, B.; Song, B.; O’halloran, T.L.; Forsythe, J. Mapping Natural Forest Stands with Low-Cost Drones. Math. Comput. For. Nat. Res. Sci. 2022, 14, 33–43. [Google Scholar]
  66. Almeida, A.; Gonçalves, F.; Silva, G.; Mendonça, A.; Gonzaga, M.; Silva, J.; Souza, R.; Leite, I.; Neves, K.; Boeno, M.; et al. Individual Tree Detection and Qualitative Inventory of a Eucalyptus Sp. Stand Using Uav Photogrammetry Data. Remote Sens. 2021, 13, 3655. [Google Scholar] [CrossRef]
  67. Borges, M.V.V.; de Oliveira Garcia, J.; Batista, T.S.; Silva, A.N.M.; Baio, F.H.R.; da Silva Junior, C.A.; de Azevedo, G.B.; de Oliveira Sousa Azevedo, G.T.; Teodoro, L.P.R.; Teodoro, P.E. High-Throughput Phenotyping of Two Plant-Size Traits of Eucalyptus Species Using Neural Networks. J. For. Res. 2022, 33, 591–599. [Google Scholar] [CrossRef]
  68. Corte, A.P.D.; Neto, E.M.d.C.; Rex, F.E.; Souza, D.; Behling, A.; Mohan, M.; Sanquetta, M.N.I.; Silva, C.A.; Klauberg, C.; Sanquetta, C.R.; et al. High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD). Drones 2022, 6, 48. [Google Scholar] [CrossRef]
  69. Liao, K.; Li, Y.; Zou, B.; Li, D.; Lu, D. Examining the Role of UAV Lidar Data in Improving Tree Volume Calculation Accuracy. Remote Sens. 2022, 14, 4410. [Google Scholar] [CrossRef]
  70. Solvin, T.M.; Puliti, S.; Steffenrem, A. Use of UAV Photogrammetric Data in Forest Genetic Trials: Measuring Tree Height, Growth, and Phenology in Norway Spruce (Picea Abies L. Karst.). Scand. J. For. Res. 2020, 35, 322–333. [Google Scholar] [CrossRef]
  71. Harikumar, A.; D’odorico, P.; Ensminger, I. Combining Spectral, Spatial-Contextual, and Structural Information in Multispectral UAV Data for Spruce Crown Delineation. Remote Sens. 2022, 14, 2044. [Google Scholar] [CrossRef]
  72. Kuželka, K.; Slavík, M.; Surový, P. Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement. Remote Sens. 2020, 12, 1236. [Google Scholar] [CrossRef]
  73. Shen, X.; Cao, L.; Yang, B.; Xu, Z.; Wang, G. Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries. Remote Sens. 2019, 11, 800. [Google Scholar] [CrossRef]
  74. Shi, Y.; Wang, Z.; Liu, L.; Li, C.; Peng, D.; Xiao, P. Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, Gaofen-2, and Uav Imagery. Remote Sens. 2021, 13, 4859. [Google Scholar] [CrossRef]
  75. Araujo, R.F.; Chambers, J.Q.; Celes, C.H.S.; Muller-Landau, H.C.; dos Santos, A.P.F.; Emmert, F.; Ribeiro, G.H.P.M.; Gimenez, B.O.; Lima, A.J.N.; Campos, M.A.A.; et al. Integrating High Resolution Drone Imagery and Forest Inventory to Distinguish Canopy and Understory Trees and Quantify Their Contributions to Forest Structure and Dynamics. PLoS ONE 2020, 15, e0243079. [Google Scholar] [CrossRef]
  76. d’Oliveira, M.V.N.; Broadbent, E.N.; Oliveira, L.C.; Almeida, D.R.A.; Papa, D.A.; Ferreira, M.E.; Zambrano, A.M.A.; Silva, C.A.; Avino, F.S.; Prata, G.A.; et al. Aboveground Biomass Estimation in Amazonian Tropical Forests: A Comparison of Aircraft-and Gatoreye UAV-Borne LIDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil. Remote Sens. 2020, 12, 1754. [Google Scholar] [CrossRef]
  77. Almeida, A.; Gonçalves, F.; Silva, G.; Souza, R.; Treuhaft, R.; Santos, W.; Loureiro, D.; Fernandes, M. Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds. Remote Sens. 2020, 12, 3560. [Google Scholar] [CrossRef]
  78. Giannetti, F.; Puletti, N.; Puliti, S.; Travaglini, D.; Chirici, G. Assessment of UAV Photogrammetric DTM-Independent Variables for Modelling and Mapping Forest Structural Indices in Mixed Temperate Forests. Ecol. Indic. 2020, 117, 106513. [Google Scholar] [CrossRef]
  79. Zhu, J.; Liu, Q.; Cui, X.; Zhang, W. Extraction of Individual Tree Parameters by Combining Terrestrial and UAV LiDAR. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng. 2022, 38, 51–58. [Google Scholar] [CrossRef]
  80. Qi, Y.; Coops, N.C.; Daniels, L.D.; Butson, C.R. Comparing Tree Attributes Derived from Quantitative Structure Models Based on Drone and Mobile Laser Scanning Point Clouds across Varying Canopy Cover Conditions. ISPRS J. Photogramm. Remote Sens. 2022, 192, 49–65. [Google Scholar] [CrossRef]
  81. Terryn, L.; Calders, K.; Bartholomeus, H.; Bartolo, R.E.; Brede, B.; D’hont, B.; Disney, M.; Herold, M.; Lau, A.; Shenkin, A.; et al. Quantifying Tropical Forest Structure through Terrestrial and UAV Laser Scanning Fusion in Australian Rainforests. Remote Sens. Environ. 2022, 271, 112912. [Google Scholar] [CrossRef]
  82. Vélez, S.; Vacas, R.; Martín, H.; Ruano-Rosa, D.; Álvarez, S. A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia Vera L.) Canopy Volume. Remote Sens. 2022, 14, 6006. [Google Scholar] [CrossRef]
  83. Wang, C.; Morgan, G.; Hodgson, M.E. Suas for 3d Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam. Forests 2021, 12, 659. [Google Scholar] [CrossRef]
  84. de Queiroz, R.F.P.; d’Oliveira, M.V.N.; Rezende, A.V.; de Alencar, P.A.L. Estimation of Aboveground Biomass Stock in Tropical Savannas Using Photogrammetric Imaging. Drones 2023, 7, 493. [Google Scholar] [CrossRef]
  85. Puliti, S.; Ørka, H.O.; Gobakken, T.; Næsset, E. Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sens. 2015, 7, 9632–9654. [Google Scholar] [CrossRef]
  86. Giannetti, F.; Puletti, N.; Quatrini, V.; Travaglini, D.; Bottalico, F.; Corona, P.; Chirici, G. Integrating Terrestrial and Airborne Laser Scanning for the Assessment of Single-Tree Attributes in Mediterranean Forest Stands. Eur. J. Remote Sens. 2018, 51, 795–807. [Google Scholar] [CrossRef]
  87. Ota, T.; Ogawa, M.; Shimizu, K.; Kajisa, T.; Mizoue, N.; Yoshida, S.; Takao, G.; Hirata, Y.; Furuya, N.; Sano, T.; et al. Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest. Forests 2015, 6, 3882–3898. [Google Scholar] [CrossRef]
  88. Tomaštík, J.; Mokroš, M.; Saloš, S.; Chudỳ, F.; Tunák, D. Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy. Forests 2017, 8, 151. [Google Scholar] [CrossRef]
  89. Zahawi, R.A.; Dandois, J.P.; Holl, K.D.; Nadwodny, D.; Reid, J.L.; Ellis, E.C. Using Lightweight Unmanned Aerial Vehicles to Monitor Tropical Forest Recovery. Biol. Conserv. 2015, 186, 287–295. [Google Scholar] [CrossRef]
  90. Silva, C.A.; Hudak, A.T.; Vierling, L.A.; Loudermilk, E.L.; O’Brien, J.J.; Hiers, J.K.; Jack, S.B.; Gonzalez-Benecke, C.; Lee, H.; Falkowski, M.J.; et al. Imputation of Individual Longleaf Pine (Pinus Palustris Mill.) Tree Attributes from Field and LiDAR Data. Can. J. Remote Sens. 2016, 42, 554–573. [Google Scholar] [CrossRef]
  91. Bergen, K.M.; Goetz, S.J.; Dubayah, R.O.; Henebry, G.M.; Hunsaker, C.T.; Imhoff, M.L.; Nelson, R.F.; Parker, G.G.; Radeloff, V.C. Remote Sensing of Vegetation 3-D Structure for Biodiversity and Habitat: Review and Implications for Lidar and Radar Spaceborne Missions. J. Geophys. Res. Biogeosci 2009, 114. [Google Scholar] [CrossRef]
  92. Pilarska, M.; Ostrowski, W.; Bakuła, K.; Górski, K.; Kurczyński, Z. The Potential of Light Laser Scanners Developed for Unmanned Aerial Vehicles—The Review and Accuracy. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives; International Society for Photogrammetry and Remote Sensing: Bethesda, MD, USA, 2016; Volume 42, pp. 87–95. [Google Scholar] [CrossRef]
  93. Lim, Y.S.; La, P.H.; Park, J.S.; Lee, M.H.; Pyeon, M.W.; Kim, J.I. Calculation of Tree Height and Canopy Crown from Drone Images Using Segmentation. J. Korean Soc. Surv. Geod. Photogramm. Cartogr. 2015, 33, 605–613. [Google Scholar] [CrossRef]
  94. Birdal, A.C.; Avdan, U.; Türk, T. Estimating Tree Heights with Images from an Unmanned Aerial Vehicle. Geomat. Nat. Hazards Risk 2017, 8, 1144–1156. [Google Scholar] [CrossRef]
  95. Bonnet, S.; Lisein, J.; Lejeune, P. Comparison of UAS Photogrammetric Products for Tree Detection and Characterization of Coniferous Stands. Int. J. Remote Sens. 2017, 38, 5310–5337. [Google Scholar] [CrossRef]
  96. Sharma, S.; Dhal, S.; Rout, T.; Acharya, B.S. Drones and Machine Learning for Estimating Forest Carbon Storage. Carbon Res. 2022, 1, 21. [Google Scholar] [CrossRef]
  97. Chen, L.; Liu, G.; Zhu, X.; Li, X. A Heuristic Routing Algorithm for Heterogeneous UAVs in Time-Constrained MEC Systems. Drones 2024, 8, 379. [Google Scholar] [CrossRef]
  98. Jensen, K.; Krogh, O.K.; Jorgensen, M.W.; Lehotsky, D.; Andersen, A.B.; Porqueras, E.; Sondergaard, J.A.S.; Gade, R. Determining Dendrometry Using Drone Scouting, Convolutional Neural Networks and Point Clouds. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Nashville, TN, USA, 20–25 June 2021; IEEE Computer Society: Washington, DC, USA, 2021; pp. 2906–2914. [Google Scholar] [CrossRef]
  99. Yang, L.; Driscol, J.; Sarigai, S.; Wu, Q.; Chen, H.; Lippitt, C.D. Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. Remote Sens. 2022, 14, 3253. [Google Scholar] [CrossRef]
  100. Ramdani, F.; Setiani, P.; Sianturi, R. Towards Understanding Climate Change Impacts: Monitoring the Vegetation Dynamics of Terrestrial National Parks in Indonesia. Sci. Rep. 2024, 14, 18257. [Google Scholar] [CrossRef] [PubMed]
  101. Brede, B.; Calders, K.; Lau, A.; Raumonen, P.; Bartholomeus, H.M.; Herold, M.; Kooistra, L. Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR. Remote Sens. Environ. 2019, 233, 111355. [Google Scholar] [CrossRef]
  102. Krause, S.; Sanders, T.G.M.; Mund, J.-P.; Greve, K. UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring. Remote Sens. 2019, 11, 758. [Google Scholar] [CrossRef]
Forests 15 01993 g001
Figure 1. Collaboration network between countries in publishing scientific articles on the topic. The color intensity represents the number of publications per country, with darker shades indicating higher publication outputs. The lines show the connections between countries, with thicker lines reflecting stronger collaboration.
Figure 1. Collaboration network between countries in publishing scientific articles on the topic. The color intensity represents the number of publications per country, with darker shades indicating higher publication outputs. The lines show the connections between countries, with thicker lines reflecting stronger collaboration.
Forests 15 01993 g001
Forests 15 01993 g002
Figure 2. Publication frequency per year of studies addressing the use of UAVs for obtaining dendrometric variables in forests.
Figure 2. Publication frequency per year of studies addressing the use of UAVs for obtaining dendrometric variables in forests.
Forests 15 01993 g002
Forests 15 01993 g003
Figure 3. Areas included in the indexing of articles in journals.
Figure 3. Areas included in the indexing of articles in journals.
Forests 15 01993 g003
Forests 15 01993 g004
Figure 4. Interaction network between keywords used by authors in articles and those used in indexing. The network is weighted by the frequency of each keyword, represented by the size of the circle areas. Terms positioned closer together indicate a higher frequency of co-occurrence. The links (lines) reflect the interaction between terms, with thicker links representing more frequent connections. Clusters, distinguished by different colors on the map, group items with higher affinity.
Figure 4. Interaction network between keywords used by authors in articles and those used in indexing. The network is weighted by the frequency of each keyword, represented by the size of the circle areas. Terms positioned closer together indicate a higher frequency of co-occurrence. The links (lines) reflect the interaction between terms, with thicker links representing more frequent connections. Clusters, distinguished by different colors on the map, group items with higher affinity.
Forests 15 01993 g004
Forests 15 01993 g005
Figure 5. Types of vegetation, dendrometric variables, and types of sensor most used in UAV research to obtain dendrometric variables.
Figure 5. Types of vegetation, dendrometric variables, and types of sensor most used in UAV research to obtain dendrometric variables.
Forests 15 01993 g005
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Silva, B.R.F.d.; Ucella-Filho, J.G.M.; Bispo, P.d.C.; Elera-Gonzales, D.G.; Silva, E.A.; Ferreira, R.L.C. Using Drones for Dendrometric Estimations in Forests: A Bibliometric Analysis. Forests 2024, 15, 1993. https://doi.org/10.3390/f15111993

AMA Style

Silva BRFd, Ucella-Filho JGM, Bispo PdC, Elera-Gonzales DG, Silva EA, Ferreira RLC. Using Drones for Dendrometric Estimations in Forests: A Bibliometric Analysis. Forests. 2024; 15(11):1993. https://doi.org/10.3390/f15111993

Chicago/Turabian Style

Silva, Bruna Rafaella Ferreira da, João Gilberto Meza Ucella-Filho, Polyanna da Conceição Bispo, Duberli Geomar Elera-Gonzales, Emanuel Araújo Silva, and Rinaldo Luiz Caraciolo Ferreira. 2024. "Using Drones for Dendrometric Estimations in Forests: A Bibliometric Analysis" Forests 15, no. 11: 1993. https://doi.org/10.3390/f15111993

APA Style

Silva, B. R. F. d., Ucella-Filho, J. G. M., Bispo, P. d. C., Elera-Gonzales, D. G., Silva, E. A., & Ferreira, R. L. C. (2024). Using Drones for Dendrometric Estimations in Forests: A Bibliometric Analysis. Forests, 15(11), 1993. https://doi.org/10.3390/f15111993

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