The DOI matches its Crossref record; 145 of the 145 references it cites were found in a registry or an index.
UAV remote sensing applications in marine monitoring: Knowledge visualization and review
Abstract
With the booming development of information technology and the growing demand for remote sensing data, unmanned aerial vehicle (UAV) remote sensing technology has emerged. In recent years, UAV remote sensing technology has developed rapidly and has been widely used in the fields of military defense, agricultural monitoring, surveying and mapping management, and disaster and emergency response and management. Currently, increasingly serious marine biological and environmental problems are raising the need for effective and timely monitoring. Compared with traditional marine monitoring technologies, UAV remote sensing is becoming an important means for marine monitoring thanks to its flexibility, efficiency and low cost, while still producing systematic data with high spatial and temporal resolutions. This study visualizes the knowledge domain of the application and research advances of UAV remote sensing in marine monitoring by analyzing 1130 articles (from 1993 to early 2022) using a bibliometric approach and provides a review of the application of UAVs in marine management mapping, marine disaster and environmental monitoring, and marine wildlife monitoring. It aims to promote the extensive application of UAV remote sensing in the field of marine research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
IOP Conference Series: Earth and Environmental Science
(2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/issn.1755-1315 · Cited by 12,764
Chen, C. (2006) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20317 · Cited by 6,433
Confronting the coral reef crisis
Bellwood, D. R., Hughes, T. P., Folke, C., & Nyström, M. (2004) · Nature · doi:10.1038/nature02691 · Cited by 3,415
Harmful algal blooms and eutrophication: nutrient sources, composition, and consequences
Anderson, D. M., Glibert, P. M., & Burkholder, J. M. (2002) · Estuaries · doi:10.1007/bf02804901 · Cited by 2,965
The structure and dynamics of cocitation clusters: A multiple‐perspective cocitation analysis
Chen, C., Ibekwe‐SanJuan, F., & Hou, J. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21309 · Cited by 1,585
Classifications, applications, and design challenges of drones: A review
Hassanalian, M., & Abdelkefi, A. (2017) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.04.003 · Cited by 1,454
Lightweight unmanned aerial vehicles will revolutionize spatial ecology
Anderson, K., & Gaston, K. J. (2013) · Frontiers in Ecology and the Environment · doi:10.1890/120150 · Cited by 1,397
Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace
Chen, C., Hu, Z., Liu, S., & Tseng, H. (2012) · Expert Opinion on Biological Therapy · doi:10.1517/14712598.2012.674507 · Cited by 1,364
Watts, A. C., Ambrosia, V. G., & Hinkley, E. A. (2012) · Remote Sensing · doi:10.3390/rs4061671 · Cited by 927
On the Use of Unmanned Aerial Systems for Environmental Monitoring
Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Madrigal, V. P., Mallinis, G., Dor, E. B., Helman, D., Estes, L. D., Ciraolo, G., et al. (2018) · Remote Sensing · doi:10.3390/rs10040641 · Cited by 853
Cited by
A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)
Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S., & Mansoor, W. (2023) · Systems · doi:10.3390/systems11080400 · Cited by 331
Heidari, A., Jafari Navimipour, N., Unal, M., & Zhang, G. (2023) · ACM Computing Surveys · doi:10.1145/3571728 · Cited by 195
Self-citation
Metadata
- Authors
- Yang, Z., Yu, X., Dedman, S., Rosso, M., Zhu, J., Yang, J., Xia, Y., Tian, Y., Zhang, G., & Wang, J.
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 838
- Issue
- Pt 1
- Pages
- 155939
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W4280496735
- Cited by
- 283
- References
- 145
- FWCI
- 29.52
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: UAV remote sensing applications in marine monitoring: Knowledge visualization and review. https://app.cochat.ai/papers/10.1016/j.scitotenv.2022.155939
- The paper
- Yang, Z., Yu, X., Dedman, S., Rosso, M., Zhu, J., Yang, J., Xia, Y., Tian, Y., Zhang, G., & Wang, J. (2022). UAV remote sensing applications in marine monitoring: Knowledge visualization and review. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2022.155939
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
