The DOI matches its Crossref record.
Are unmanned aircraft systems (UAS s) the future of wildlife monitoring? A review of accomplishments and challenges
Abstract
Regular monitoring of animal populations must be established to ensure wildlife protection, especially when pressure on animals is high. The recent development of drones or unmanned aircraft systems (UAS s) opens new opportunities. UAS s have several advantages, including providing data at high spatial and temporal resolution, providing systematic, permanent data, having low operational costs and being low‐risk for the operators. However, UASs have some constraints, such as short flight endurance. We reviewed studies in which wildlife populations were monitored by using drones, described accomplishments to date and evaluated the range of possibilities UAS s offer to provide new perspectives in future research. We focused on four main topics: 1) the available systems and sensors; 2) the types of survey plan and detection possibilities; 3) contributions towards anti‐poaching surveillance; and 4) legislation and ethics. We found that small fixed‐wing UAS s are most commonly used because these aircraft provide a viable compromise between price, logistics and flight endurance. The sensors are typically electro‐optic or infrared cameras, but there is the potential to develop and test new sensors. Despite various flight plan possibilities, mostly classical line transects have been employed, and it would be of great interest to test new methods to adapt to the limitations of UAS s. Detection of many species is possible, but statistical approaches are unavailable if valid inventories of large mammals are the purpose. Contributions of UAS s to anti‐poaching surveillance are not yet well documented in the scientific literature, but initial studies indicate that this approach could make important contributions to conservation in the next few years. Finally, we conclude that one of the main factors impeding the use of UAS s is legislation. Restrictions in the use of airspace prevent researchers from testing all possibilities, and adaptations to the relevant legislation will be necessary in future.
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
Cited by
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740
Gonzalez, L., Montes, G., Puig, E., Johnson, S., Mengersen, K., & Gaston, K. (2016) · Sensors · doi:10.3390/s16010097 · Cited by 470
Kellenberger, B., Marcos, D., & Tuia, D. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.06.028 · Cited by 335
Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery
Hong, S.-J., Han, Y., Kim, S.-Y., Lee, A.-Y., & Kim, G. (2019) · Sensors · doi:10.3390/s19071651 · Cited by 227
Detecting animals in African Savanna with UAVs and the crowds
Rey, N., Volpi, M., Joost, S., & Tuia, D. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.08.026 · Cited by 150
Metadata
- Authors
- Linchant, J., Lisein, J., Semeki, J., Lejeune, P., & Vermeulen, C.
- Venue
- Mammal Review
- Publisher
- Wiley
- Published
- 2015-08-24
- Volume
- 45
- Issue
- 4
- Pages
- 239-252
- ISSN
- 0305-1838 (ISSN-L)
- 1365-2907
- OpenAlex
- W2143897835
- Cited by
- 500
- References
- 0
- FWCI
- 26.81
- 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: Are unmanned aircraft systems (UAS s) the future of wildlife monitoring? A review of accomplishments and challenges. https://app.cochat.ai/papers/10.1111/mam.12046
- The paper
- Linchant, J., Lisein, J., Semeki, J., Lejeune, P., & Vermeulen, C. (2015). Are unmanned aircraft systems (UAS s) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review. https://doi.org/10.1111/mam.12046
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.