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Article

The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years

by
Alonso Pizarro
1,
Desirée Valera-Gran
2,3,
Eva-María Navarrete-Muñoz
2,3 and
Silvano Fortunato Dal Sasso
4,*
1
Escuela de Ingeniería en Obras Civiles, Universidad Diego Portales, Santiago 8370109, Chile
2
Grupo de Investigación en Terapia Ocupacional (InTeO), Miguel Hernández University, 03550 Alicante, Spain
3
Alicante Institute for Health and Biomedical Research (ISABIAL-FISABIO Foundation), 03550 Alicante, Spain
4
Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DICEM), University of Basilicata, 75100 Matera, Italy
*
Author to whom correspondence should be addressed.
Hydrology 2024, 11(6), 80; https://doi.org/10.3390/hydrology11060080
Submission received: 12 April 2024 / Revised: 30 May 2024 / Accepted: 5 June 2024 / Published: 7 June 2024
(This article belongs to the Special Issue UAS and Satellite-Based Remote Sensing for Hydrological Observations and Applications)
Browse Figures
Versions Notes

Abstract

:
Cutting-edge technology for fluvial monitoring has revolutionised the field, enabling more comprehensive data collection, analysis, and interpretation. Traditional monitoring methods were limited in their spatial and temporal resolutions, but advancements in remote sensing, unmanned aerial systems (UASs), and other innovative technologies have significantly enhanced the fluvial monitoring capabilities. UASs equipped with advanced sensors enable detailed and precise fluvial monitoring by capturing high-resolution topographic data, generate accurate digital elevation models, and provide imagery of river channels, banks, and riparian zones. These data enable the identification of erosion and deposition patterns, the quantification of sediment transport, the evaluation of habitat quality, and the monitoring of river flows. The latter allows us to understand the dynamics of rivers during various hydrological events, including floods, droughts, and seasonal variations. This manuscript aims to provide an update on the main research themes and topics in the literature on the use of UASs for river monitoring. The latter is achieved through a bibliometric analysis of the publication trends and identifies the field’s key themes and collaborative networks. The bibliometric analysis shows trends in the number of publications, number of citations, top contributing countries, top publishing journals, top contributing institutions, and top authors. A total of 1085 publications on UAS monitoring in rivers are identified, published between 1999 and 2023, showing a steady annual growth rate of 24.44%. Bibliographic records are exported from the Web of Science (WoS) database using a comprehensive set of keywords. The bibliometric analysis of the raw data obtained from the WoS database is performed using the R software. The results highlight important trends and valuable insights related to the use of UASs in river monitoring, particularly in the last decade. The most frequently used author keywords outline the core themes of UASs monitoring research and highlight the interdisciplinary nature and collaborative efforts within the field. “River”, “topography”, “photogrammetry”, and “Structure-from-Motion” are the core themes of UASs monitoring research. These findings can guide future research and promote new interdisciplinary collaborations.

1. Introduction

Fluvial systems, characterised by flowing water bodies such as rivers and streams, are vital components of the Earth’s dynamic landscape, serving as conduits for water, sediment, and nutrients. These systems are fundamental in shaping the Earth’s surface, influencing natural ecosystems, providing essential resources, and supporting human civilisations. The spatio-temporal monitoring of fluvial rivers is an essential task to build adaptation strategies for climate variability and quantify anthropisation’s impact on hydromorphological processes. However, the complex and dynamic nature of fluvial systems presents numerous challenges in terms of monitoring, understanding, and managing their behaviour and impacts. In recent years, the integration of innovative and cutting-edge technologies, particularly unmanned aerial systems (UASs), has been conducted in a new era in the study of fluvial systems. UASs, often called drones or unmanned aerial vehicles (UAVs), have emerged as powerful tools for remote sensing and data collection, offering researchers a transformative means of capturing high-resolution spatial and temporal information over riverine landscapes.
Fluvial systems are dynamic, and their behaviour is influenced by many natural and anthropogenic factors. Understanding these systems is paramount in addressing various challenges, such as flood prediction and related risk assessments and management, water resource quantification and allocation, water quality status, ecological conservation, and sustainability. Historically, the monitoring of these systems has relied on conventional methods that often entail significant resource allocation, time constraints, and limited spatial coverage. These issues have contributed to the decreased spatial coverage of monitoring observations in rivers. In developing countries and small hydrological catchments, hydrometric observations are typically lacking or available for limited flow conditions, and water quality measurements are non-continuous over time. The latter leads to significant uncertainty in the assessment of water resources’ quantity and quality. With their versatility, adaptability, and cost-effectiveness, UASs have emerged as a game-changer in this domain, allowing for an improved monitoring of fluvial systems in a wide spatial and temporal domain. In terms of monitoring variables, the following paragraphs cover some recent advancements regarding river flow velocity, river morphology and bathymetry, water levels, river discharge, flood monitoring, and river water quality and pollution (not intended to be a formal review, but giving a holistic view of the topic in question).

1.1. River Flow Velocity

Mobile platforms coupled with optical techniques (based on detecting and matching physical, buoyant objects and features) allow the non-invasive monitoring of river flows and the development of surface velocity field maps in extended river environments [1,2,3,4,5,6,7,8,9]. The core idea is to detect or define points or areas of interest, which are tracked through consecutive images or frames based on similarity measures. The main steps of this procedure can be performed with established image processing techniques, which include classical correlation-based algorithms as well as new optical flow approaches based on image intensity pattern detection. Examples of these techniques are large-scale particle image velocimetry (LSPIV), particle tracking velocimetry (PTV), and space–time image velocimetry (STIV).

1.2. River Morphology and Bathymetry

The introduction of user-friendly photogrammetric techniques, such as Structure-from-Motion (SfM) and multi-view stereo (MVS) algorithms, has revolutionised high-resolution topographic reconstructions [10]. High-resolution data allow the 3D reconstruction of a river scene (3D point cloud, digital elevation model—DEM) using computer vision techniques (SfM). This methodology has been applied to extrapolate river bathymetry in clear and shallow waters, correcting the underwater areas for the refraction effect [11], or for the more detailed characterisation of bank erosion processes [12]. Similarly, artificial neural networks (ANNs) have been demonstrated to be a high-precision tool for the automated recognition of hydromorphological features at a reach scale, such as bars, deep and shallow waters, and submerged and emerging vegetation [13].

1.3. Water Levels

Water levels can be retrieved from images acquired by optical cameras as the demarcation line between water and land. The general approach consists of two steps: (i) the pixel-wise segmentation of the current image to generate a binary mask separating water and non-water regions [14] and (ii) the analysis of the mask to infer the waterline position. Using consecutive image sequences, waterlines are detectable assuming that moving water results in significant changes in the spatio-temporal pixel texture [15]. The distinctiveness of the spatio-temporal texture for waterline segmentation is estimated using histogram analyses.

1.4. River Discharge

River discharge is usually derived from depth-integrated water velocity profiles and cross-sectional areas. These variables may be obtained through (i) bathymetric measurements of the cross-section or (ii) depth-integrated water velocities obtained by reconstructing the flow velocity profile using a model like the entropy one [16], using known conditions such as the flow velocity at the surface, flow velocity at the bottom, and water depth in each vertical water column.

1.5. Flood Monitoring

Recently, different image-based approaches have been successfully applied to identify water regions during flood events, supporting early warning systems [14,17,18]. These methods are mainly based on deep learning techniques (e.g., convolutional neural network) for image classification and automatic waterline detection using radiometric pixel values or the temporal texture of the changing water surface [19].

1.6. River Water Quality and Pollution

Moreover, image-based techniques are particularly suitable for the monitoring of surface water quality processes that are generally related to low dissolved oxygen levels (hypoxia), microbiological pollution, eutrophication, and toxic pollution. In this regard, a mobile camera mounted on a drone has been successfully used to detect plastic waste floating in rivers, applying image processing techniques based on the colour difference of floating macro-debris [20,21] or machine learning methods for the automation of plastic detection based on colour intensity feature descriptors [22]. Other studies (see, for example, [23,24]) have demonstrated that UASs with multispectral sensors combined with machine learning approaches can quantify water quality parameters such as turbidity, algal blooms, chlorophyll-a concentrations, and the presence of metals [23,24].
The research products derived from these applications are often made available in open data repositories. Some examples of datasets and codes related to river monitoring and environmental research have been discussed in recently published works [25,26,27] (see also the Supplementary Materials). These repositories serve as valuable resources for the scientific community, allowing researchers to access and utilise the data and codes for further analysis, validation, and the development of new methodologies in the field of river monitoring and environmental research.
The integration of cutting-edge technology into fluvial research has the potential to provide unparalleled insights into the dynamics of these systems. In the rapidly evolving technological landscape, where the UAS capabilities continue to expand, it is imperative to assess the impact and reach of research in UAS-based monitoring. This does not only acknowledge the importance of UASs as a transformative tool in fluvial research but also serves as a compass guiding future investigations and decision-making processes related to river systems. As we progress further into the era of technological innovation, UAS-based monitoring is poised to contribute significantly to our understanding and management of fluvial systems, providing valuable insights that will inform sustainable practices and policies for years to come.
Considering the current trends in innovative technology for fluvial monitoring globally, this bibliometric analysis aims to uncover the multifaceted aspects of the UAS-based monitoring research in rivers, shedding light on its historical development, current state, and future trajectories. It is not intended to be a general review but offers a panoramic view of the research status by examining key metrics such as publication growth, authorship patterns, international collaboration, journal impacts, and the frequency of used keywords. Furthermore, it seeks to identify productive authors, influential publications, and emerging trends within this field.

2. Materials and Methods

2.1. Search Strategy and Data Extraction

A comprehensive search for publications on UAS in rivers was conducted in the Web of Science (WoS) database on 1 April 2024. We utilised the WoS Core Collection, known for its rigorous selection and evaluation of academic information, to ensure the reliability and accuracy of our findings. It provides content coverage and detailed citation analysis information, making it an ideal resource for this study. The search strategy involved the use of a carefully designed equation that incorporated relevant terms such as “Unmanned Aerial System”, “Unmanned Aerial Vehicle” or “Uncrewed Aircraft System” (and synonyms), and “River”. These terms were searched within the topic field, encompassing titles, abstracts, author keywords, and keyword plus terms. The exact search equation was “((TS = (Unmanned Aerial Vehicle) OR TS = (Unmanned Aircraft System) OR TS = (Unoccupied Aircraft System) OR TS = (Uncrewed Aircraft System) OR TS = (Remotely Piloted Aircraft System) OR TS = (uav) OR TS = (RPAS) OR TS = (uas)) AND (TS = (river)))”. All references indexed and published from 1 January 1999 to 31 December 2023 were included to provide a comprehensive analysis of the complete period (i.e., 25 full years; a filter was applied to consider full years, and, therefore, documents published outside the period of analysis were not considered). The WoS extraction tool was employed to extract the raw data from the WoS database, generating data in BibTeX format available to download. The following information was extracted from each document: title, journal, article type, author names, affiliations, keywords, publication date, research area, abstract, cited references, language, and open access information. These data formed the foundation for the bibliometric analysis.

2.2. Data Analyses

The bibliometric analysis of the raw data obtained from the WoS database was performed using the R software version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). Specifically, the analysis was performed using the Bibliometrix R package [28] (https://www.bibliometrix.org/home/index.php, accessed on 4 June 2024), which encompasses a comprehensive range of bibliometric methods. These methods allow the quantification of time trends, the identification of highly cited papers, and the detection of highly productive authors, journals, institutions, and countries, as well as the calculation and ranking of scientific production and collaboration. The descriptive bibliometric analysis of the main data was based on the metrics of sources (journals), authors, and documents. We also analysed two structures of knowledge (conceptual and social ones) to complete the science mapping of the UAS monitoring research in rivers. The conceptual structure was measured by co-word analysis, while a collaboration network analysis measured the social structure. Additionally, to enhance the bibliometric results by measuring the influence and quality of scientific production, the impact factors (IFs) of the journals in question were extracted from the latest Journal Citation Reports (JCR, 2022) by Clarivate Analytics.

3. Results

3.1. Publication Analysis Based on Numbers

The search strategy produced 1085 publications on UAS monitoring in rivers, published between 1999 and 2023 (25 full years). Figure 1 shows the annual evolution of the scientific production of UAS fluvial research. From 1999, the annual growth was 24.44%. However, the annual publication volume has grown exponentially in the last decade, achieving its peak in 2022, with 215 documents.
The 1085 documents recovered from the WoS search were based on 396 sources, in which 3748 authors were involved. Single-authored publications constituted 24 (2.21% of all publications), leading to active collaboration within the field (international co-authorship rose to 27.00%). According to the type of document, the most relevant source of scientific production was derived from research articles (n = 915, 84.33%). The remaining documents were proceeding papers (n = 140, 12.90%) and reviews (n = 23, 2.12%). Editorial material (n = 2, 0.18%), book chapters (n = 2, 0.18%), and data papers (n = 3, 0.28%) represented less than 1% (all together) of the documents.

3.2. Publication Analysis Based on Journals

Table 1 shows the general characteristics of the 15 most productive journals that published articles on UAS monitoring in rivers during the last 25 years. These journals published 423 articles, which accounted for 38.99% of all recovered publications. The most significant contributions were published by highly rated journals that primarily fell within the categories of “Water Resources”, “Geosciences, Multidisciplinary”, and “Environmental Sciences” in WoS. The most productive journals (>25 articles arbitrarily imposed) were Remote Sensing (n = 146; IF: 5.0), Landslides (n = 28; IF: 6.7), Water (n = 28; IF: 3.4), Earth Surface Processes and Landforms (n = 26; IF: 3.3), and Geomorphology (n = 26; IF: 3.9), which published approximately 20% (254 articles) of the total production.

3.3. Publication Analysis Based on Countries/Regions and Institutions

Countries’ scientific production was measured using the number of author appearances by country affiliation. The results showed that the scientific production in UAS monitoring in rivers was geographically located in 80 countries worldwide. Table 2 shows the main features of the top 15 most productive corresponding authors’ countries publishing research on UAS monitoring in rivers. China, the USA, Italy, and the UK were the most productive countries, accumulating more than half of the total production (n = 625, 57.60%). The intra-country collaboration analysis showed that these countries also had the highest number of articles published by authors from their own countries (China = 292; USA = 113; Italy = 49; UK = 24). China was at the forefront of inter-country collaboration, registering 79 documents that included authors from other countries. The USA (n = 25) and Italy (n = 22) were the second and third most significant contributors. However, in relative terms, Brazil (multiple-country publication (MCP) ratio = 0.57, total documents = 14), Germany (MCP ratio = 0.48, total documents = 21), and the UK (MCP ratio = 0.47, total documents = 45) were the countries collaborating more actively.
Figure 2 illustrates the network mapping of international collaboration among countries. A general description of the top 20 most productive research institutions (and/or universities) publishing research on UAS monitoring in rivers is displayed in Table 3. These institutions produced 569 documents (52.44%) out of the 1085 recovered publications. It is worth mentioning that several countries in Africa, as well as some in Latin America and Asia, presented zero publications on this topic. On the other hand, China was mentioned several times, leading the article production.

3.4. Publication Analysis Based on Citations

The 1085 documents included in the present bibliometric analysis had 43,011 references, with an average of 16.19 citations per document (considering all years). There were 161 (14.84%) documents with no citations, 506 (46.64%) were cited between one and ten times, 395 (36.41%) received between 11 and 100 citations, and 23 (2.12%) were cited over 100 times. The most cited article received 783 citations. The top ten most cited articles are presented in Table 4 and Table 5 (in terms of local and global citations, respectively). The main research topics of the articles are also introduced in these tables. River channel bathymetry and morphology, as well as image velocimetry and reviews, were the most mentioned topics. These articles were published in only nine scientific journals.

3.5. Publication Analysis Based on Term Frequency

Figure 3 displays the network analysis of the most frequently used keyword plus terms of the WoS database. Based on the 50 most frequently used author keywords, the analysis of the keyword plus co-occurrences, considering both their frequency and connections, showed a network structure with ten different clusters of interconnected keywords (Figure 3; each cluster is represented with a different colour). However, it is possible to see two main clusters, one green and one blue. The most frequently used keywords were “river”, “topography”, “photogrammetry”, and “Structure-from-Motion”.

4. Discussion

This bibliometric analysis summarised the evolution and trends in UAS monitoring in fluvial systems from 1999 to 2023. The results showed that such innovative technologies for river monitoring had broad diffusion, especially in the last decade. The annual scientific production shows a generally increasing trend in the number of published documents from 2011 to 2023, with a highly productive stage in 2022. This exponential progress is strictly linked to technological advancements (e.g., the miniaturisation of sensors, strong computing power, the simplification of procedures) and is reflected by the productive activity of the commercial sector. It is worth noting that 2023 presented a lower number of published articles than 2022.
The increasing number of published papers demonstrates the strong interest of the academic community in this topic, with the main goal of identifying new strategies for river monitoring that are able to support water budget and quality assessment. In this regard, this study evidences a noteworthy level of collaboration among researchers from diverse academic backgrounds, highlighting the interdisciplinary nature of this field. Different branches of environmental science are involved, including water science, hydrology, fluvial hydraulics, ecohydrology, hydroecology, ecology, computer science, geography, geomorphology, and image processing, among others.
In the last few years, several UAS-based approaches and methods have been successfully applied for river monitoring, highlighting the great potential for further development, but also some limitations. For instance, most of them show sensitivity to changing environmental conditions (lighting effects, shadows, changes in the surroundings), which requires more efforts to be overcome [25]. Moreover, it is worth noting that these different approaches have been developed and tested for specific river applications (in terms of the morphology, flow regime, and environmental conditions), not allowing a complete overview of the river system in its current state. Considering these issues and recent findings, nowadays, there is a need to (i) identify the strengths and limitations of methods in different contexts and transfer them to other fluvial conditions and (ii) compare and harmonise the results obtained from the different automatic image-based algorithms with respect to the specific morphological context. Establishing standardised UAS-based monitoring approaches represents the critical goal for future developments and collaborative networks [26].
Moreover, future research must be directed towards searching for a robust approach with the aim of integrating methods and algorithms to obtain a comprehensive description of the river system’s key parameters. This can be very useful to (i) achieve a complete overview of the river status through the estimation of key morphological, hydrological, and ecological variables and (ii) capture the spatio-temporal variability of these variables within river reaches. This task is even more crucial considering that water management will face critical challenges in the coming years due to the simultaneous impacts of climate variability, population growth, and pollution. The spread of these monitoring systems and their interconnection with other remote sensing methodologies can facilitate the production of high-density (near-)real-time data on the river status that are critically relevant for environmental protection and the definition of early warning measures.
In this regard, the results of this study contribute to a better understanding of the state of research on UAS-based fluvial monitoring and provide insights not only for academics but also for practitioners, policymakers, and companies operating in the environmental monitoring market. Table 4 and Table 5 provide information for individuals who are interested in this topic but do not know where to start reading.

5. Conclusions

This bibliometric analysis offers a comprehensive overview of the past, present, and future research trends in UAS-based monitoring in rivers. The study reveals several key findings.
  • Rapid Growth: The field has experienced exponential growth, with a significant increase in publications, particularly in the last decade.
  • Global Collaboration: International collaboration is a prominent feature, with researchers from different countries actively contributing to this interdisciplinary field.
  • Journal Information: High-impact journals in water resources, physical geography, remote sensing, and environmental sciences are the primary outlets for UAS monitoring research.
  • Country Contributions: China, the USA, and Italy are leading in both publications and intra-country collaborations. Productive authors from various countries have contributed significantly to this research area, often through multi-authored publications.
  • Citation Impact: Certain papers have garnered substantial attention and citations, emphasising the field’s relevance and impact. Refs. [29,39] were the most cited papers, in terms of local and global citations, respectively.
  • Keyword Themes: Author keywords such as “river”, “topography”, “photogrammetry”, and “Structure-from-Motion” outline the core themes of UAS monitoring research.
In summary, this analysis highlights the dynamic and interdisciplinary nature of UAS-based monitoring research in rivers. It emphasises the global collaboration, institutional contributions, and the evolving landscape of this field. As technology advances, UAS-based monitoring is playing an increasingly vital role in understanding and addressing global environmental and geomorphological challenges in river systems. Possible additional research is mentioned as follows: (a) expanding the search equation with other additional synonyms (such as “drones”, “riverine”, “fluvial”, and “riparian”) with the intention of conducting a sensitivity analysis of the search equation and (b) addressing the historical developments of the research topic, not only in terms of a bibliometric analysis but also in terms of a formal systematic review.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/hydrology11060080/s1, Table S1: Examples of software packages for UASs data analysis in image-based applications; Table S2: Examples of datasets for UASs data analysis in image-based applications.

Author Contributions

Conceptualization, A.P., D.V.-G., E.-M.N.-M. and S.F.D.S.; methodology, A.P., D.V.-G., E.-M.N.-M. and S.F.D.S.; formal analysis, A.P. and S.F.D.S.; investigation, A.P., D.V.-G., E.-M.N.-M. and S.F.D.S.; writing—original draft preparation, A.P.; writing—review and editing, A.P., D.V.-G., E.-M.N.-M. and S.F.D.S. All authors have read and agreed to the published version of the manuscript.

Funding

AP was partially supported by The National Research and Development Agency of the Chilean Ministry of Science, Technology, Knowledge and Innovation (ANID), grant no. FONDECYT Iniciación 11240171.

Data Availability Statement

Raw data can be downloaded from the following link: https://www.doi.org/10.17605/OSF.IO/YPNA6.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Pearce, S.; Ljubičić, R.; Peña-Haro, S.; Perks, M.; Tauro, F.; Pizarro, A.; Dal Sasso, S.F.; Strelnikova, D.; Grimaldi, S.; Maddock, I.; et al. An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems. Remote Sens. 2020, 12, 232. [Google Scholar] [CrossRef]
  2. Pizarro, A.; Sasso, S.F.D.; Manfreda, S. Refining Image-Velocimetry Performances for Streamflow Monitoring: Seeding Metrics to Errors Minimization. Hydrol. Process. 2020, 34, 5167–5175. [Google Scholar] [CrossRef]
  3. Pizarro, A.; Dal Sasso, S.F.; Perks, M.T.; Manfreda, S. Identifying the Optimal Spatial Distribution of Tracers for Optical Sensing of Stream Surface Flow. Hydrol. Earth Syst. Sci. 2020, 24, 5173–5185. [Google Scholar] [CrossRef]
  4. Dal Sasso, S.F.; Pizarro, A.; Pearce, S.; Maddock, I.; Manfreda, S. Increasing LSPIV Performances by Exploiting the Seeding Distribution Index at Different Spatial Scales. J. Hydrol. 2021, 598, 126438. [Google Scholar] [CrossRef]
  5. Manfreda, S.; Sasso, S.F.D.; Pizarro, A.; Tauro, F. New Insights Offered by UAS for River Monitoring. In Applications of Small Unmanned Aircraft Systems; CRC Press: Boca Raton, FL, USA, 2019; ISBN 978-0-429-24411-7. [Google Scholar]
  6. Ljubičić, R.; Strelnikova, D.; Perks, M.T.; Eltner, A.; Peña-Haro, S.; Pizarro, A.; Dal Sasso, S.F.; Scherling, U.; Vuono, P.; Manfreda, S. A Comparison of Tools and Techniques for Stabilising UAS Imagery for Surface Flow Observations. Hydrol. Earth Syst. Sci. Discuss. 2021, 25, 5105–5132. [Google Scholar] [CrossRef]
  7. Perks, M.T.; Dal Sasso, S.F.; Hauet, A.; Jamieson, E.; Le Coz, J.; Pearce, S.; Peña-Haro, S.; Pizarro, A.; Strelnikova, D.; Tauro, F.; et al. Towards Harmonisation of Image Velocimetry Techniques for River Surface Velocity Observations. Earth Syst. Sci. Data 2020, 12, 1545–1559. [Google Scholar] [CrossRef]
  8. Tauro, F.; Grimaldi, S. Ice Dices for Monitoring Stream Surface Velocity. J. Hydro-Environ. Res. 2017, 14, 143–149. [Google Scholar] [CrossRef]
  9. Tauro, F.; Tosi, F.; Mattoccia, S.; Toth, E.; Piscopia, R.; Grimaldi, S. Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations. Remote Sens. 2018, 10, 2010. [Google Scholar] [CrossRef]
  10. Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. ‘Structure-from-Motion’Photogrammetry: A Low-Cost, Effective Tool for Geoscience Applications. Geomorphology 2012, 179, 300–314. [Google Scholar] [CrossRef]
  11. Dietrich, J.T. Bathymetric Structure-from-Motion: Extracting Shallow Stream Bathymetry from Multi-View Stereo Photogrammetry. Earth Surf. Process. Landf. 2017, 42, 355–364. [Google Scholar] [CrossRef]
  12. Hemmelder, S.; Marra, W.; Markies, H.; De Jong, S.M. Monitoring River Morphology & Bank Erosion Using UAV Imagery—A Case Study of the River Buëch, Hautes-Alpes, France. Int. J. Appl. Earth Obs. Geoinf. 2018, 73, 428–437. [Google Scholar] [CrossRef]
  13. Casado, M.R.; Gonzalez, R.B.; Kriechbaumer, T.; Veal, A. Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery. Sensors 2015, 15, 27969–27989. [Google Scholar] [CrossRef]
  14. García, M.; Alcayaga, H.; Pizarro, A. Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach. Remote Sens. 2023, 15, 1170. [Google Scholar] [CrossRef]
  15. Kröhnert, M.; Meichsner, R. Segmentation of Environmental Time Lapse Image Sequences for the Determination of Shore Lines Captured by Hand-Held Smartphone Cameras. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, 4, 1–8. [Google Scholar] [CrossRef]
  16. Moramarco, T.; Corato, G.; Melone, F.; Singh, V.P. An Entropy-Based Method for Determining the Flow Depth Distribution in Natural Channels. J. Hydrol. 2013, 497, 176–188. [Google Scholar] [CrossRef]
  17. Arshad, B.; Ogie, R.; Barthelemy, J.; Pradhan, B.; Verstaevel, N.; Perez, P. Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review. Sensors 2019, 19, 5012. [Google Scholar] [CrossRef]
  18. Albertini, C.; Gioia, A.; Iacobellis, V.; Manfreda, S. Detection of Surface Water and Floods with Multispectral Satellites. Remote Sens. 2022, 14, 6005. [Google Scholar] [CrossRef]
  19. Eltner, A.; Kaiser, A.; Castillo, C.; Rock, G.; Neugirg, F.; Abellán, A. Image-Based Surface Reconstruction in Geomorphometry – Merits, Limits and Developments. Earth Surf. Dyn. 2016, 4, 359–389. [Google Scholar] [CrossRef]
  20. Geraeds, M.; van Emmerik, T.; de Vries, R.; bin Ab Razak, M.S. Riverine Plastic Litter Monitoring Using Unmanned Aerial Vehicles (UAVs). Remote Sens. 2019, 11, 2045. [Google Scholar] [CrossRef]
  21. van Lieshout, C.; van Oeveren, K.; van Emmerik, T.; Postma, E. Automated River Plastic Monitoring Using Deep Learning and Cameras. Earth Space Sci. 2020, 7, e2019EA000960. [Google Scholar] [CrossRef]
  22. Gonçalves, G.; Andriolo, U.; Pinto, L.; Bessa, F. Mapping Marine Litter Using UAS on a Beach-Dune System: A Multidisciplinary Approach. Sci. Total Environ. 2020, 706, 135742. [Google Scholar] [CrossRef]
  23. Zeng, C.; Richardson, M.; King, D.J. The Impacts of Environmental Variables on Water Reflectance Measured Using a Lightweight Unmanned Aerial Vehicle (UAV)-Based Spectrometer System. ISPRS J. Photogramm. Remote Sens. 2017, 130, 217–230. [Google Scholar] [CrossRef]
  24. Flores, H.; Lorenz, S.; Jackisch, R.; Tusa, L.; Contreras, I.C.; Zimmermann, R.; Gloaguen, R. UAS-Based Hyperspectral Environmental Monitoring of Acid Mine Drainage Affected Waters. Minerals 2021, 11, 182. [Google Scholar] [CrossRef]
  25. Dal Sasso, S.F.; Pizarro, A.; Manfreda, S. Recent Advancements and Perspectives in UAS-Based Image Velocimetry. Drones 2021, 5, 81. [Google Scholar] [CrossRef]
  26. Manfreda, S.; Eyal, B.D. Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. 2023. Available online: https://www.sciencedirect.com/book/9780323852838/unmanned-aerial-systems-for-monitoring-soil-vegetation-and-riverine-environments#book-info (accessed on 11 April 2024).
  27. Manfreda, S.; Miglino, D.; Saddi, K.C.; Jomaa, S.; Eltner, A.; Perks, M.; Peña-Haro, S.; Bogaard, T.; van Emmerik, T.H.; Mariani, S. Advancing River Monitoring Using Image-Based Techniques: Challenges and Opportunities. Hydrol. Sci. J. 2024, 69, 657–677. [Google Scholar] [CrossRef]
  28. Aria, M.; Cuccurullo, C. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
  29. Tamminga, A.; Hugenholtz, C.; Eaton, B.; Lapointe, M. Hyperspatial Remote Sensing of Channel Reach Morphology and Hydraulic Fish Habitat Using an Unmanned Aerial Vehicle (UAV): A First Assessment in the Context of River Research and Management. River Res. Appl. 2015, 31, 379–391. [Google Scholar] [CrossRef]
  30. Cook, K.L. An Evaluation of the Effectiveness of Low-Cost UAVs and Structure from Motion for Geomorphic Change Detection. Geomorphology 2017, 278, 195–208. [Google Scholar] [CrossRef]
  31. Dietrich, J.T. Riverscape Mapping with Helicopter-Based Structure-from-Motion Photogrammetry. Geomorphology 2016, 252, 144–157. [Google Scholar] [CrossRef]
  32. Dunford, R.; Michel, K.; Gagnage, M.; Piégay, H.; Trémelo, M.-L. Potential and Constraints of Unmanned Aerial Vehicle Technology for the Characterization of Mediterranean Riparian Forest. Int. J. Remote Sens. 2009, 30, 4915–4935. [Google Scholar] [CrossRef]
  33. Tauro, F.; Petroselli, A.; Arcangeletti, E. Assessment of Drone-based Surface Flow Observations. Hydrol. Process. 2016, 30, 1114–1130. [Google Scholar] [CrossRef]
  34. Coveney, S.; Roberts, K. Lightweight UAV Digital Elevation Models and Orthoimagery for Environmental Applications: Data Accuracy Evaluation and Potential for River Flood Risk Modelling. Int. J. Remote Sens. 2017, 38, 3159–3180. [Google Scholar] [CrossRef]
  35. Tomsett, C.; Leyland, J. Remote Sensing of River Corridors: A Review of Current Trends and Future Directions. River Res. Appl. 2019, 35, 779–803. [Google Scholar] [CrossRef]
  36. Koutalakis, P.; Tzoraki, O.; Zaimes, G. UAVs for Hydrologic Scopes: Application of a Low-Cost UAV to Estimate Surface Water Velocity by Using Three Different Image-Based Methods. Drones 2019, 3, 14. [Google Scholar] [CrossRef]
  37. Zhao, C.; Zhang, C.; Yang, S.; Liu, C.; Xiang, H.; Sun, Y.; Yang, Z.; Zhang, Y.; Yu, X.; Shao, N. Calculating E-Flow Using UAV and Ground Monitoring. J. Hydrol. 2017, 552, 351–365. [Google Scholar] [CrossRef]
  38. Michez, A.; Piégay, H.; Lisein, J.; Claessens, H.; Lejeune, P. Classification of Riparian Forest Species and Health Condition Using Multi-Temporal and Hyperspatial Imagery from Unmanned Aerial System. Environ. Monit. Assess. 2016, 188, 146. [Google Scholar] [CrossRef] [PubMed]
  39. Fonstad, M.A.; Dietrich, J.T.; Courville, B.C.; Jensen, J.L.; Carbonneau, P.E. Topographic Structure from Motion: A New Development in Photogrammetric Measurement. Earth Surf. Process. Landf. 2013, 38, 421–430. [Google Scholar] [CrossRef]
  40. Manfreda, S.; McCabe, M.F.; Miller, P.E.; Lucas, R.; Pajuelo Madrigal, V.; Mallinis, G.; Ben Dor, E.; Helman, D.; Estes, L.; Ciraolo, G.; et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sens. 2018, 10, 641. [Google Scholar] [CrossRef]
  41. Woodget, A.S.; Carbonneau, P.E.; Visser, F.; Maddock, I.P. Quantifying Submerged Fluvial Topography Using Hyperspatial Resolution UAS Imagery and Structure from Motion Photogrammetry. Earth Surf. Process. Landf. 2015, 40, 47–64. [Google Scholar] [CrossRef]
  42. Smith, M.W.; Vericat, D. From Experimental Plots to Experimental Landscapes: Topography, Erosion and Deposition in Sub-Humid Badlands from Structure-from-Motion Photogrammetry. Earth Surf. Process. Landf. 2015, 40, 1656–1671. [Google Scholar] [CrossRef]
  43. Lejot, J.; Delacourt, C.; Piégay, H.; Fournier, T.; Trémélo, M.-L.; Allemand, P. Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surf. Process. Landf. 2007, 32, 1705–1725. [Google Scholar] [CrossRef]
  44. Flener, C.; Vaaja, M.; Jaakkola, A.; Krooks, A.; Kaartinen, H.; Kukko, A.; Kasvi, E.; Hyyppä, H.; Hyyppä, J.; Alho, P. Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography. Remote Sens. 2013, 5, 6382–6407. [Google Scholar] [CrossRef]
Hydrology 11 00080 g001
Figure 1. Annual scientific production of UAS monitoring in rivers during the last 25 years.
Figure 1. Annual scientific production of UAS monitoring in rivers during the last 25 years.
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Hydrology 11 00080 g002
Figure 2. Geographical distribution map of countries’ scientific production in publishing papers on UAS monitoring in rivers, and country collaboration network map (brown lines) from 1999 to 2023. Darker blue colour means more scientific production.
Figure 2. Geographical distribution map of countries’ scientific production in publishing papers on UAS monitoring in rivers, and country collaboration network map (brown lines) from 1999 to 2023. Darker blue colour means more scientific production.
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Hydrology 11 00080 g003
Figure 3. Network map of the 50 most frequently used author keywords in documents on UAS monitoring in rivers from 1999 to 2023. Different colours represent different clusters. The size of the box (and the keyword in question) represents the number of times that the keyword appeared within the database.
Figure 3. Network map of the 50 most frequently used author keywords in documents on UAS monitoring in rivers from 1999 to 2023. Different colours represent different clusters. The size of the box (and the keyword in question) represents the number of times that the keyword appeared within the database.
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Table 1. Top 15 most productive journals publishing papers on UAS monitoring in rivers from 1999 to 2023.
Table 1. Top 15 most productive journals publishing papers on UAS monitoring in rivers from 1999 to 2023.
RankSourceN° of Articles (% b)Category (JIF Quartile and Rank)IF (JCR) a
1stRemote Sensing146 (13.46%)Geosciences, Multidisciplinary; Environmental Sciences (Q1: 31/202; Q2: 78/275)5.0
2ndLandslides28 (2.58%)Engineering, Geological; Geosciences, Multidisciplinary
(Q1: 4/41; Q1: 14/202)		6.7
3rdWater28 (2.58%)Environmental Sciences; Water Resources (Q2: 135/275; Q2: 38/103)3.4
4th Earth Surface Processes
and Landforms		26 (2.40%)Geography, Physical; Geosciences, Multidisciplinary (Q2: 21/49; Q2: 75/201)3.3
5th Geomorphology26 (2.40%)Geography, Physical; Geosciences, Multidisciplinary (Q2: 13/49; Q2: 53/201)3.9
6thSensors25 (2.30%)Chemistry, Analytical; Instruments & Instrumentation (Q2: 27/86; Q2: 19/63)3.9
7thScience of the Total
Environment		24 (2.21%)Environmental Sciences (Q1: 26/275)9.8
8thDrones21 (1.94%)Remote Sensing (Q2: 14/34)4.8
9thSustainability20 (1.84%)Environmental Sciences; Environmental Studies (Q2: 114/275; Q2: 48/128)3.9
10thInternational Journal of
Remote Sensing		18 (1.66%)Imaging Science & Photographic Technology; Remote Sensing (Q2: 13/28; Q3: 21/34)3.4
11thRiver Research and
Applications		14 (1.29%)Environmental Sciences; Water Resources (Q3: 202/275; Q3: 71/103)2.2
12thJournal of Hydrology13 (1.20%)Engineering, Civil; Geosciences, Multidisciplinary (Q1: 13/139; Q1: 15/201)6.4
13thEcological Indicators12 (1.11%)Environmental Sciences (Q1: 48/275)6.9
14thLand12 (1.11%)Environmental Studies (Q2: 48/128)3.9
15thInternational Journal of Applied Earth Observation and Geoinformation10 (0.92%)Remote Sensing (Q1: 5/34)7.5
a IF (JCR), impact factor (Journal Citation Reports); impact factor obtained from the Journal Citation Reports (2022). b Percentage calculated out of the retrieved 1085 documents.
Table 2. Top 15 productive corresponding authors’ countries publishing papers on UAS monitoring in rivers (1999–2023).
Table 2. Top 15 productive corresponding authors’ countries publishing papers on UAS monitoring in rivers (1999–2023).
CountriesN of Documents% aSCPMCPMCP Ratio b
China37134.19292790.21
USA13812.72113250.18
Italy716.5449220.31
United Kingdom454.1524210.47
Japan403.693190.23
Canada322.952570.22
Korea262.402060.23
Poland262.402240.15
Germany211.9411100.48
Netherlands191.751180.42
Greece181.661350.28
Russia171.571430.18
France161.471150.31
India151.38960.40
Brazil141.29680.57
Abbreviations: SCP, single-country publications; MCP, multiple-country publications. a Percentage calculated out of the retrieved 1085 documents. b We calculated the multiple-country publication ratio as the MCP divided by the total number of published documents per country.
Table 3. Top 20 most productive research institutes and/or universities publishing papers on UAS monitoring in rivers (during the last 25 years), sorted by the total number of articles.
Table 3. Top 20 most productive research institutes and/or universities publishing papers on UAS monitoring in rivers (during the last 25 years), sorted by the total number of articles.
Research InstituteCountryN of Articles% a
Beijing Normal UniversityChina726.64
University of Chinese Academy of SciencesChina595.44
Institute of Geographic Sciences and Natural Resources ResearchChina322.95
Wuhan UniversityChina312.86
Peking UniversityChina302.76
Chengdu University of TechnologyChina292.67
China University of GeosciencesChina282.58
Hohai UniversityChina272.49
Shandong Agricultural UniversityChina272.49
Guilin University of TechnologyChina262.40
Southwest UniversityUSA222.03
Sun Yat-sen UniversityChina222.03
Dartmouth CollegeUSA211.94
Institute of Mountain Hazards and EnvironmentChina211.94
Lanzhou UniversityChina211.94
Technical University of DenmarkDenmark211.94
Universitas Gadjah MadaIndonesia211.94
Xinjiang UniversityChina211.94
Universidad Austral de ChileChile191.75
University of FloridaUSA191.75
a Percentage calculated out of the retrieved 1085 documents.
Table 4. Top ten most cited research papers from 1999 to 2023, sorted by the number of local citations.
Table 4. Top ten most cited research papers from 1999 to 2023, sorted by the number of local citations.
RankReferenceYearJournalDOILCGCLC/GC Ratio (%)Topic
1st[29]2015River Research and Applicationshttps://doi.org/10.1002/rra.27435413240.91Channel morphology and hydraulic habitats
2nd[30]2017Geomorphologyhttps://doi.org/10.1016/j.geomorph.2016.11.0094225616.41Geomorphic change detection
3rd[31]2016Geomorphologyhttps://doi.org/10.1016/j.geomorph.2015.05.0082810426.92Fluvial Geomorphology
4th[32]2009International Journal of Remote Sensinghttps://doi.org/10.1080/014311609030230252615916.35Classification of riparian forest
5th[33]2016Hydrological Processeshttps://doi.org/10.1002/hyp.10698245147.06Image velocimetry in rivers
6th[34]2017International Journal of Remote Sensinghttps://doi.org/10.1080/01431161.2017.1292074206928.99Photogrammetric DEMs for flood prediction assessment
7th[35]2019River Research and Applicationshttps://doi.org/10.1002/rra.3479197525.33Review of river corridor remote sensing
8th[36]2019Droneshttps://doi.org/10.3390/drones3010014194740.43Image velocimetry in rivers
9th[37]2017Journal of Hydrologyhttps://doi.org/10.1016/j.jhydrol.2017.06.047173548.57Environmental flows and supply rates for dominant fish species
10th[38]2016Environmental Monitoring and Assessmenthttps://doi.org/10.1007/s10661-015-4996-2151629.26Classification of riparian forest species and health condition
Abbreviations: DOI, Digital Object Identifier; LC, local citations; GC, global citations.
Table 5. Top ten most cited research papers from 1999 to 2023, sorted by the number of global citations.
Table 5. Top ten most cited research papers from 1999 to 2023, sorted by the number of global citations.
RankReferenceYearJournalDOILCGCLC/GC
Ratio (%)		Topic
1st[39]2013Earth Surface Processes and Landformshttps://doi.org/10.1002/esp.336607830.00Topographic modelling by SfM, LiDAR, and GPS techniques
2nd[40]2018Remote Sensinghttps://doi.org/10.3390/rs1004064104240.00Review on the use of UAS for environmental monitoring
3rd[41]2015Earth Surface Processes and Landformshttps://doi.org/10.1002/esp.361302730.00Submerged fluvial topography
4th[30]2017Geomorphologyhttps://doi.org/10.1016/j.geomorph.2016.11.0094225616.41Geomorphic change detection
5th[42]2015Earth Surface Processes and Landformshttps://doi.org/10.1002/esp.374702170.00Geomorphic change detection
6th[11]2017Earth Surface Processes and Landformshttps://doi.org/10.1002/esp.406001670.00Bathymetric Structure-from-Motion photogrammetry
7th[38]2016Environmental Monitoring and Assessmenthttps://doi.org/10.1007/s10661-015-4996-2151629.26Classification of riparian forest species and health condition
8th[32]2009International Journal of Remote Sensinghttps://doi.org/10.1080/014311609030230252615916.35Classification of riparian forest
9th[43]2007Earth Surface Processes and Landformshttps://doi.org/10.1002/esp.159501570.00River channel
bathymetry and topography
10th[44]2013Remote Sensinghttps://doi.org/10.3390/rs512638201510.00River channel mapping by LIDAR and UAV photography
Abbreviations: DOI, Digital Object Identifier; LC, local citations; GC, global citations.
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Pizarro, A.; Valera-Gran, D.; Navarrete-Muñoz, E.-M.; Dal Sasso, S.F. The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years. Hydrology 2024, 11, 80. https://doi.org/10.3390/hydrology11060080

AMA Style

Pizarro A, Valera-Gran D, Navarrete-Muñoz E-M, Dal Sasso SF. The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years. Hydrology. 2024; 11(6):80. https://doi.org/10.3390/hydrology11060080

Chicago/Turabian Style

Pizarro, Alonso, Desirée Valera-Gran, Eva-María Navarrete-Muñoz, and Silvano Fortunato Dal Sasso. 2024. "The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years" Hydrology 11, no. 6: 80. https://doi.org/10.3390/hydrology11060080

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