Introduction

As technologies in unmanned aerial vehicles (UAVs) have attracted attention as the growth engine of the fourth industrial era, related technologies are being rapidly developed and social interest is increasing. With the recent expansion of UAV applications, research papers on each topic have been published through various international journals. It is not easy, however, to identify UAV application research trends in specific areas.

In recent years, the applicability of informetric analysis methods to research the knowledge and attributes recorded in media, such as papers, by applying mathematical and statistical methods to the bibliographic information of literature has increased. The analysis of the research contents and topics of such academic papers is useful in revealing the interdisciplinarity of academic areas (Lee & Kwak 2011). It is also significant as basic research that can objectively identify the performance of each detailed research area and present the future research direction.

As such, an attempt was made to objectively evaluate the status of domestic research by identifying active research topics in the ecological environment research area through the analysis of domestic and international UAV research trends. This study was conducted for two purposes.

First, UAV-related research areas were subdivided, and the research status of the environment/ecology area was quantitatively identified among them. Second, research topics that have recently attracted attention were presented by analyzing UAV research trends for 20 years. Third, UAV technologies that are applicable to the environment/ecology research area in the future were identified.

Materials|Methods

Theoretical considerations

Among big data analysis methods, the text mining technique derives formalized information from a large amount of non-formalized text data and constructs it in the form of new information, such as visualization and digitization. It can effectively extract and utilize meaningful information desired by the research when applied to text-based data, such as research literature. Therefore, this study attempted to quantitatively identify research trends using text mining and co-word analysis as research literature analysis methods. Visualization of Similarities (VOSviewer; Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands) is an analysis tool frequently used by many researchers for the analysis of bibliometric data, such as recent collaborative relationships between publications, journals, or researchers, and citation relationships between scientific terms (Heersmink et al., 2011; van Eck & Waltman, 2010; Waltman et al., 2010).

VOSviewer can create a two-dimensional (2D) map from network data, such as co-word and proximity matrices, and perform data clustering using its own clustering algorithm (Choi et al., 2011). It also includes an algorithm that identifies only the noun phrases from a text document after classifying and tagging the phrases in each sentence into verbs, adjectives, and nouns, thereby enabling analysis using the relevance score specified by the researcher. These functions extract words related to a specific research topic from the collected paper data and classify them into similar topics, and then analyze research trends by identifying the appearance frequency and association strength of each word.

The analysis principle of VOSviewer is to create an intuitive network map using nodes, appearance frequency, and association strength based on the number of edges, which can be explained in a conceptual analysis diagram (Fig. 1). For example, when A1 to A4 are assumed as authors or words and D1 to D3 as documents, A1 and A3 have a connection strength of 2 because both of them were used in two documents (D1 and D2).

This relationship analysis is mainly conducted in three steps. In the first step, a co-occurrence matrix is created for words that constitute the literature, and similarities between categories are calculated based on the matrix. The similarities are calculated using the association strength method in equation 1, one of the similarity calculation methods.

(1)
Association strength = m C ij K i K j

where Cij is the number of simultaneous occurrences between items (words or papers) i and j. Kj and Ki are the total number of occurrences of i and j, respectively, and m is the population parameter. In the second step, a 2D map is created based on the similarities calculated in the first step. Items with high similarities are close, but those with low similarities are distant. In the third step, each parameter is clustered. Depending on the occurrence frequency, the graph is enlarged if the density of the parameters is high and reduced if it is low. In this study, keywords for each main research area were extracted to examine their association strengths and changes from the past to the present were investigated for the analysis of UAV-related research trends.

Data collection

In this study, the Web of Science Database (DB) by Thomson Reuters was utilized to identify domestic and international UAV-related research trends. DBs for collecting paper information are also available for Scopus and ScienceDirect. The Web of Science not only has a separate DB to search for journals registered in South Korea but it also makes it possible to collect a large amount of data on quality papers, such as the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI). In addition, data processing is easy because domestic and international data can be provided in the same format during the collection of bibliographic information.

Papers from 1997 to 2017 that used “UAV” in the title or as a keyword were targeted. Citation indices were limited to SCI, SCIE, SSCI, and KCI, excluding articles, reports, and theses. The collected papers were classified by title, year, abstract, and journal name. Data that could cause errors in analysis, such as papers without an English abstract or unrelated to the topic, were removed. Consequently, 745 domestic papers published since 2002 and 3,858 international papers published since 1997 were collected.

Keyword extraction

The process of extracting keywords and phrases through VOSviewer was performed using the following criteria. First, words with similar meanings were excluded or replaced with identical words for analysis (e.g., UAV, UAS, Smart UAV, and Unmanned aerial vehicle). Second, words or phrases that appeared in at least three papers were selected. Third, for general words and postpositions that cannot be determined as keywords among the words with high appearance frequency, only the words that corresponded to the top 60% of the keyword relevance scores were extracted using the basic values (van Eck & Waltman, 2014) of the function provided by VOSviewer (Fig. 2).

Results|Discussion

Preprocessing results

To identify UAV-related research topics, the co-occurrence frequencies of the words and phrases used in abstracts were analyzed. There were 13,359 occurrences extracted for domestic papers and 73,628 for international papers. Among them, words with similar meanings (e.g., unmanned, unmanned aircraft, aviation, and UAV) were excluded. Considering the size of the collected literature, the words that appeared in at least 10 domestic papers or 20 international papers were extracted in the first step. In the second step, a network map was created by setting Resolution to 1.5 and Min Cluster size to 5 in the clustering option for the visualization of the words that corresponded to the top 60% (VOSviewer setting), which acquired the relevance score as a keyword. A time-series network map to identify the time-series flow for research areas was created using the time information for the occurrence of the main keywords. The data before 2013 were excluded from the map because they did not meet the minimum criterion of the co-occurrence frequency.

UAV-related research trends

A total of 3,858 international papers from 1997 to 2017 were collected, and 745 domestic papers from 2002 to 2017 were collected (Table 1). In international countries, the number of papers increased by 7 per year from 1997 to 2008 and sharply increased by 89.8 per year from 2009 to 2017. In South Korea, the number of papers increased by 4 per year until 2013 and by 19.9 per year from 2014 to 2017 (Fig. 3). This appears to be because of the rapid growth of the private drone industry market, and the application range of drones expanded due to the improvement of hardware performance, miniaturization of related imaging devices, and the development of sensor technologies for drones. Considering the time at which the number of papers began to rapidly increase, there was a time difference of approximately five years between South Korea and international countries.

Research area classification results

The analysis results revealed that there were 173 main keywords for domestic papers and 477 for international papers. When clustering was performed based on this, domestic UAV-related studies could be classified into seven key areas compared to eight areas for international studies. In addition, a time-series network map was created based on the keyword appearance time to identify changes in the time series for each research area.

As for international studies, 99 co-words were derived in Cluster1, a research area related to “remote sensing/data analysis”. The cluster represented the largest proportion in the entire research area. The word that appeared most frequently was “monitoring”, and the cluster was highly associated with “tree”, “plant”, “vegetation”, and “species” (Fig. 4).

Research on UAV remote sensing is similar to research that uses satellites and aerial imaging in terms of methodologies, but this study was conducted with a focus on the characteristics of UAV images that can acquire more precise spatial information than the existing data. The publication of related research literature has shown an increase since 2014, and research has been conducted most actively along with the Cluster4 area (Fig. 5).

In Cluster2, a research area related to “mission performance”, 77 co-words were derived and the word that most frequently appeared was “simulation”. This cluster was highly associated with “path”, “constraint”, “obstacle”, and “collision”. As UAV is a research area on flight paths, control, obstacles, and collisions for performing UAV missions, it was dominated by experimental studies using software and hardware. Considering the publication time and number of papers, research was most active before 2014 and has been decreasing recently. The stabilization of the corresponding technologies appears to be the main cause (Table 2).

Cluster3 is a research area related to “flight control”. A total of 76 co-words were derived and the word that most frequently appeared was “control”. This cluster was highly associated with “trajectory”, “dynamic”, “stability”, and “scheme”. Studies on trajectory, dynamics, stability, and design, which are related to the UAV control performance, corresponded to this cluster. This cluster is in the center of Cluster2 and Cluster5, which are highly associated research areas where research has been continuously conducted.

In Cluster4, a research area related to “data collection/processing”, 76 co-words were derived. This cluster is close to Cluster1, indicating a high association in terms of contents. While Cluster1 represents applied research for various areas, Cluster4 is a research area for improving the basic functionality of UAV images. The word that most frequently appeared was “image”, which was used with “quality”, “accuracy”, “site”, “map”, and “point cloud”. This cluster was dominated by studies on software processing, such as UAV image quality, accuracy, maps, destinations, mapping, and point cloud data. Active research has been conducted in this area.

Cluster5 is a research area related to “airframe performance”. Sixty-nine co-words derived and the word that most frequently appeared was “design”. This cluster was highly associated with “aircraft”, “angle”, “degree”, “wing”, and “landing”. As UAV is a research area related to the flight performance according to the airframe structure, research was most active around 2010 but has been decreasing.

Cluster6 is a research area related to the “UAV technology/industry trend”. Thirty-five co-words derived and the word that most frequently appeared was “tool”. This cluster was highly associated with “year”, “precision”, “variety”, and “opportunity”. It was dominated by studies with a focus on the precision and various applications of the data acquired by UAVs.

Cluster7 is a research area related to “navigation/location-based technology”. Thirty-one co-words were derived and the word that most frequently appeared was “position”. This cluster was highly associated with “velocity”, “navigation”, “GPS”, and “localization”. Research on the application, accuracy verification, and improvement of location-based technologies, such as velocity, navigation, and GPS, among the functions required for UAV operation, corresponds to this cluster.

Cluster8 is a research area related to “radio wave/radar”. Thirteen co-words were derived and the word that most frequently appeared was “pattern”. This cluster was highly associated with “band”, “antenna”, “validity”, and “processing”. It is a research area on the acquisition, processing, and analysis of radar images that can only be obtained by large UAVs.

As for domestic studies, 40 co-words were derived in Cluster1, a research area related to the “UAV technology/industry trend”. The cluster represented the largest proportion in the entire research area. The word that most frequently appeared was “aircraft”, which was used to indicate UAVs. The words that exhibited a high association were “Korea”, “industry”, “regulation”, “law”, and “trend”. In South Korea, research on regulations and laws for UAVs began in 2013, and studies on the UAV industry and technology trends have been increasing recently (Figs. 6, 7).

Cluster2 is a research area related to “data collection/processing”. Thirty-five co-words were derived and the word that most frequently appeared was “image”. The words that exhibited a high association were “accuracy”, “map”, “field”, “survey”, and “error”, which were similar to the co-words in Cluster4 of the international studies. This cluster was dominated by studies with a focus on the utilization of UAV images, such as location accuracy, height, and mapping. Such studies began to increase in 2015 and have been actively conducted (Table 3).

Cluster3 is a research area related to “flight control”. Twenty-eight co-words were derived and the word that most frequently appeared was “performance”. The words that exhibited a high association were “design”, “control”, “simulation”, and “stability”. Simulation and experimental studies related to the UAV control performance correspond to this cluster. Such studies were active around 2014 and related studies have decreased recently.

Cluster4 is a research area related to “airframe performance”. Twenty-three co-words were derived and the word that most frequently appeared was “test”. The words that showed a high association were “power”, “configuration”, “wing”, “hale”, and “shape”. This cluster was dominated by test studies on wings, batteries, and airframe geometry, which are related to endurance.

In Cluster5, a research area related to “remote sensing/data analysis”, 21 co-words were derived. The word that most frequently appeared was “monitoring” as in Cluster1 of the international studies where the corresponding studies were similar. The words that exhibited a high association included “estimation”, “coefficient”, “imagery”, “height”, “classification”, and “NDVI”. This cluster was dominated by papers on vegetation. Such papers have increased since 2015.

Cluster6 is a research area related to “reconnaissance technology”. Fifteen co-words were derived and the word that most frequently appeared was “algorithm”. The words that exhibited a high association were “mission”, “surveillance”, and “reconnaissance”. Studies on reconnaissance and surveillance using UAVs correspond to this cluster. In this research area, which is distinguished from the international analysis results, the contents of the research papers were mainly published for military purposes, but most of the research results focused on technology trends and utilization. Therefore, this cluster is determined to be similar to Cluster1.

Cluster7 is a research area related to “navigation/location-based technology”. Eleven co-words were derived and the word that most frequently appeared was “signal”. The words that showed a high association were “navigation”, “flight path”, and “ground control system”. Studies related to location-based technology and the accuracy of the UAV route correspond to this cluster. This cluster exhibited a low association with other research areas and was observed in limited time periods.

When the domestic and international research trend analysis results were summarized, it was found that they were generally clustered into areas with similar meanings, but the year the keywords appeared differed, which indicated research topics, association strength between words, and diversity. For example, basic studies on UAV technology trends and utilization were abundant in South Korea, but studies on data collection and analysis through UAV remote sensing technology were most abundant in international countries.

Fig. 8 compares changes in main domestic and international research areas for 17 y between the two periods. The main international research areas between 2000 and 2013 were found to be “mission performance”, “airframe performance”, and “location-based technology”. After 2014, research areas related to “remote sensing/data analysis”, “data collection/processing”, and “UAV technology/industry trend” sharply increased. This change appears to be the result of the stabilization of UAV-related basic technologies and the gradual increase in demand for application areas. On the other hand, the main domestic research area between 2000 and 2013 was found to be “airframe performance”, and other studies were not significant. Since 2014, research has been actively conducted in all areas and a tendency to follow the international research areas was observed. Research is expected to be expanded in various areas in the future even though the research area related to “remote sensing/data analysis” shows a time difference of approximately three to four years.

Ecological environment research area derivation using UAVs

The research area related to the ecological environment is included in “remote sensing/data analysis”, which corresponds to Cluster1 for international studies and Cluster5 for domestic studies. The words that appeared were “monitoring”, “plant”, “crop”, “classification”, “forest”, “temperature”, “tree”, “species”, “Normalized Difference Vegetation Index (NDVI)”, and “Leaf Area Index (LAI)”, which were mostly associated with plant ecology (Table 4). These words became the keywords of research topics that attracted attention recently because the advancement in UAV optical sensor technology and the development of image processing technology made it possible to acquire data that could not be obtained from the existing spatial information. The fact that related domestic papers have recently increased in a similar manner to international research trends indicates the recognition of the importance for future research in this area.

In conclusion, UAVs have attracted attention as the key industry of the fourth industrial revolution era because they integrate innovative technologies, such as aviation, information communication, sensors, and software, and can be combined with various areas. Despite this potential, research that utilizes the data acquired through UAV systems is in the preliminary stage. In particular, studies on spatial information acquisition and analysis methods in the ecological environment area are extremely insufficient. This study examined domestic and international research trends using the text mining analysis method and found that active attempts have been made to apply various research methodologies to UAVs. For example, the shape of the canopy gap was identified through ultra-high-resolution UAV images to analyze species diversity for forest vegetation, and such images were used to collect time-series vegetation index data for research on phenology. Moreover, the ground surface temperature images of vegetation distribution areas were acquired for their wide utilization in ecosystem research, such as habitat environment analysis.

The keywords that appeared in such domestic and international research literature were “monitoring”, “classification”, “height”, “temperature”, and “NDVI”. Among them, “monitoring” exhibited the highest appearance frequency. This is because it was used as a gerund along with subwords. For example, it was used as keywords that represent research topics, such as “plant monitoring” and “tree monitoring”. “Classification” is a word that appeared in studies related to the extraction of ground surface information, such as land cover and vegetation classification, using matching images. “Height” was found in studies that utilized tree height information to identify the growth status in relation to farmlands or crop cultivation. “Temperature” is a keyword in studies that analyzed the habitat environments of animals and plants or identified individuals through precise ground surface temperature image data. NDVI is the vegetation information utilized in various research types, such as forests and farmlands, and it most frequently appeared in studies that used near-infrared cameras.

While research has been conducted on various topics internationally, UAV-related domestic research is in the early stage. Although preliminary studies were conducted at different target sites on different topics, studies that were examined in terms of accuracy verification and utilization based on the field data are insufficient.

Therefore, to increase UAV utilization in the ecological environment area, it is necessary to increase the accuracy of testing and calibration methods considering various factors that affect homogeneous data acquisition, such as weather, airframe performance, and image processing methods, in processes from data acquisition to the derivation of results. Follow-up research on this topic will increase UAV utilization in the ecological environment area.

Acknowledgement

Conflict of Interest

The authors declare that they have no competing interests.

References

1 

ChoiS.H., KimH.J., LeeL.J. (2011). Use of Open Software for Quantitative Bibliographic Analysis. Daejeon: Korea Institute of Science and Technology Information.

2 

HeersminkR., van den HovenJ., van EckN.J., van den BergJ. (2011). Bibliometric mapping of computer and information ethics. Ethics and Information Technology, 13, 241.

3 

LeeH.Y., KwakS.J. (2011). Relation analysis among academic research areas using subject terms of domestic journal papers. Journal of the Korean BIBLIA Society for Library and Information Science, 22, 353-371.

4 

van EckN.J., WaltmanL. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523-538, https://doi.org/10.1007/s11192-009-0146-3, pubmed id:20585380, PMC2883932.

5 

van EckN.J., WaltmanL., DingY., RousseauR., WolframD. (Eds.). (2014). Measuring Scholarly Impact. Cham: Springer. Visualizing bibliometric networks, pp. 285-320.

6 

van EckN.J., WaltmanL. (2018). VOSviewer Manual: Manual for VOSviewer Version 1.6.7. Leiden: Universiteit Leiden.

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WaltmanL., van EckN.J., NoyonsE.C. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4, 629-635.

Figures and Tables

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Fig. 1

Authorship links between four authors and three documents (left), co-authorship network constructed using full counting (right) (Source: van Eck, N.J., and Waltman, L., 2018).

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Fig. 2

Text mining analysis process. DB, database; KCI, Korea Citation Index; SCI, Science Citation Index; SCIE, Science Citation Index Expanded; UAV, unmanned aerial vehicle.

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Fig. 3

Increase in the number of articles per year.

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Fig. 4

Unmanned aerial vehicle research field keyword network visualization result of international.

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Fig. 5

Unmanned aerial vehicle research field keyword network visualization result for time series emergence of oversea.

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Fig. 6

Unmanned aerial vehicle research field keyword network visualization result of domestic.

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Fig. 7

Unmanned aerial vehicle research field keyword network visualization result for time series emergence of domestic.

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Fig. 8

Percentage increase and decrease by research area for 2000–2017 years. UAV, unmanned aerial vehicle.

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Table 1

Yearly number of domestic and international papers

97 98 99 20 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 Total
Domestic - - - - - 3 3 12 25 16 32 33 29 38 39 49 49 72 88 134 123 745
International 8 14 21 31 27 29 37 38 51 95 72 84 127 141 182 229 300 362 440 605 965 3,858
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Table 2

Clustering classification filed with keyword of international papers

Cluster Classification filed Top 20% frequent words
1 Remote sensing/data analysis(99 words) Monitoring
Imagery
Tree
Classification
Coefficient
Correlation temperature satellite
High
Value
Remote sensing plant
Crop
Vegetation
Forest
Vegetation index NDVI
Assessment Observation
Index
Object
High resolution species
2 Perform mission(77 words) Simulation constraint
Path
Target
Function
Scenario
Optimization
Team
Search
Surveillance operator
Obstacle
Behavior
Formation
Node
Robot
Guidance
Communication
Path planning formulation
3 Flight control(76 words) Rotorcraft autopilot
Flight control system
Helicopter
Fault detection hardware
Actuator
Loop simulation controller design control system simulation study subsystem control law
Proof, control respect stabilization control input control algorithm
4 Collecting data/process(76 words) Image
Accuracy
Map, mapping surface
Quality
Survey
Site photogrammetry point cloud
Low cost
Case study dataset
Uav image
Sfm
Acquisition volume
Density
Lidar
Aerial image
5 Airframe performance(69 words) Design
Aircraft
Angle
Degree
Concept
Wing
Landing
Flight test unmanned Air vehicle
Equation
Power
Force
Modeling
Energy
Load
Endurance
6 UAV Technology/industry trend(35 words) Tool
Year
Precision
Drone
Variety
Uav platform, uas opportunity unmanned aerial system decade overview
7 Navigation/location-based technology(31 words) Position experimental Result vision
Velocity
Navigation
Gps
Localization
Noise
Global positioning System
8 Radio wave/radar (13 words) Pattern
Processing
Imaging band antenna
[i]

NDVI, Normalized Difference Vegetation Index.

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Table 3

Clustering classification filed with keywords of domestic studies

Cluster No. Classification filed Top 20% frequent words
1 UAV technology/industry trend(40 words) Aircraft
Requirement
Industry
Safety
Korea
Law
Trend
Regulation
2 Utilization/data acquisition(35 words) Image
Accuracy
Field
Error
Survey
Map
Possibility
3 Airframe performance(28 words) Performance
Design
Control
Simulation
Controller
Stability
4 Flight performance (23 words) Test
Power
Configuration
Wing
Hale
Shape
5 Remote sensing/data analysis (21 words) Monitoring
Estimation
Coefficient
Growth
Imagery
6 Reconnaissance technology/navigation(15 words) Algorithm
Mission
Surveillance
Path
Reconnaissance
7 Navigation/location-based technology(11 words) Signal
Navigation
Flight path
[i]

UAV, unmanned aerial vehicle.

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Table 4

Key words of environment and ecology

Country No. Label Cluster Total link strength Occurrences
International 1 Monitoring 1 3,447 292
2 Height 2,460 187
3 Classification 1,715 134
4 Temperature 1,210 109
5 Tree 1,444 109
6 Plant 1,307 93
7 Crop 1,416 92
8 Species 1,175 89
9 Vegetation 1,223 83
10 Forest 1,065 79
11 NDVI 699 45
Domestic 1 Monitoring 5 246 33
2 Classification 138 20
3 Height 148 16
4 Temperature 93 16
5 NDVI 131 13
[i]

NDVI, Normalized Difference Vegetation Index.