CoChat
Sign Up
Cited by 135Wildlife Ecology and ConservationAdvanced Neural Network ApplicationsSmart Agriculture and AI

Improving the precision and accuracy of animal population estimates with aerial image object detection

Open access
Eikelboom, Jasper A. J., Wind, Johan, van de Ven, Eline, Kenana, Lekishon M., Schroder, Bradley, de Knegt, Henrik J., van Langevelde, Frank, Prins, Herbert H. T.·2019·Methods in Ecology and Evolution
DOI 10.1111/2041-210x.13277
Open this paper in CoChat — free accountOpen PDF

Abstract

Animal population sizes are often estimated using aerial sample counts by human observers, both for wildlife and livestock. The associated methods of counting remained more or less the same since the 1970s, but suffer from low precision and low accuracy of population estimates. Aerial counts using cost‐efficient Unmanned Aerial Vehicles or microlight aircrafts with cameras and an automated animal detection algorithm can potentially improve this precision and accuracy. Therefore, we evaluated the performance of the multi‐class convolutional neural network RetinaNet in detecting elephants, giraffes and zebras in aerial images from two Kenyan animal counts. The algorithm detected 95% of the number of elephants, 91% of giraffes and 90% of zebras that were found by four layers of human annotation, of which it correctly detected an extra 2.8% of elephants, 3.8% giraffes and 4.0% zebras that were missed by all humans, while detecting only 1.6 to 5.0 false positives per true positive. Furthermore, the animal detections by the algorithm were less sensitive to the sighting distance than humans were. With such a high recall and precision, we posit it is feasible to replace manual aerial animal count methods (from images and/or directly) by only the manual identification of image bounding boxes selected by the algorithm and then use a correction factor equal to the inverse of the undercounting bias in the calculation of the population estimates. This correction factor causes the standard error of the population estimate to increase slightly compared to a manual method, but this increase can be compensated for when the sampling effort would increase by 23%. However, an increase in sampling effort of 160% to 1,050% can be attained with the same expenses for equipment and personnel using our proposed semi‐automatic method compared to a manual method. Therefore, we conclude that our proposed aerial count method will improve the accuracy of population estimates and will decrease the standard error of population estimates by 31% to 67%. Most importantly, this animal detection algorithm has the potential to outperform humans in detecting animals from the air when supplied with images taken at a fixed rate.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/2041-210x.13277

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Use of machine learning massively reduces analysis costs, with reduction factors estimated between 2 and 10.

    “use of ML also massively reduces analysis costs, with reduction factors estimated between 2 and 1042.”

    From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [42]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 7

  1. Chamoso, P., Raveane, W., Parra, V., & González, A. (2014). UAVs ap- plied to the counting and monitoring of animals. In Springer (Ed.), Ambient intelligence -Software and applications (pp. 71-80). Cham, Switzerland. https ://doi.org/10.1007/978-3-319-07596-9 · DOI
  2. Davis, D. E., & Winstead, R. L. (1980). Estimating the numbers of wild- life populations. In S. D. Schemnitz (Eds.), Wildlife management tech- niques manual (4th ed., pp. 221-245).
  3. Dunn, W. C., Donnelly, J. P., & Krausmann, W. J. (2002). Using thermal infrared sensing to count Elk in the Southwestern United States. Wildlife Society Bulletin, 30(3), 963-967.
  4. WR07081 · DOI
  5. 2041210x, 2019, 11, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13277, Wiley Online Library on [01/09/2026]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 1887 Methods in Ecology and Evoluঞon EIKELBOOM Et aL.
  6. Norton-Griffiths, M. (1978). Counting animals: Revised second edition. Handbook No. 1. Serengeti Ecological Monitoring Programme. Nairobi, Kenya: African Wildlife Leadership Foundation.
  7. Rabe, M. J., Rosenstock, S. S., & DeVos, J. C. Jr (2002). Review of big- game survey methods used by wildlife agencies of the Western United States. Wildlife Society Bulletin, 30(1), 46-52. https ://doi. org/10.2307/3784634 · DOI

Cited by

Sorted by citations

Related

Authors
Eikelboom, J. A. J., Wind, J., van de Ven, E., Kenana, L. M., Schroder, B., de Knegt, H. J., van Langevelde, F., & Prins, H. H. T.
Affiliations
  • Wageningen University & Research (NL)
  • Resource Ecology Group Wageningen University and Research Wageningen The Netherlands
  • Kenya Wildlife Service (KE)
Corresponding author
Eikelboom, Jasper A. J.
ORCID
Venue
Methods in Ecology and Evolution
Publisher
Wiley
Published
2019-08-29
Volume
10
Issue
11
Pages
1875-1887
ISSN
2041-210X (ISSN-L)
DOI
10.1111/2041-210x.13277
OpenAlex
W2945020140
Cited by
135
References
36
Citations by year
2026: 10 · 2025: 29 · 2024: 31 · 2023: 18 · 2022: 17 · 2021: 21 · 2020: 9
FWCI
7.62
Type
journal article
Language
en
Open access
Licence
Locations
  • Methods in Ecology and Evolution (publishedVersion, cc-by)
  • Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Advanced Instrumentation for Wildlife Protection)
Topics
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords
RetinaNet, convolutional neural networks, elephants, giraffe, zebra, unmanned aerial vehicles, human observer comparison, sampling effort, wildlife census

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