Counting animals in aerial images with a density map estimation model

Qian, Yifei; Humphries, Grant R W; Trathan, Philip N.; Lowther, Andrew D.; Donovan, Carl

doi:10.1002/ece3.9903

Ecology and Evolution

2023

DOI: 10.1002/ece3.9903

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Counting animals in aerial images with a density map estimation model

Yifei Qian

Grant R W Humphries²,

Philip N. Trathan

et al.

Abstract: Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual postprocessing has been used extensively; however, volumes of such data are increasing, necessitating some level of automation, either for complete counting, or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as Pygoscelis penguins. We present here a customized CNN-based density map estimation method for counting of penguins from… Show more

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Cited by 9 publications

References 66 publications

(71 reference statements)

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Counting animals in aerial images with a density map estimation model

Qian

Humphries²,

Trathan

et al. 2023

Ecology and Evolution

Self Cite

View full text Add to dashboard Cite

Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual postprocessing has been used extensively; however, volumes of such data are increasing, necessitating some level of automation, either for complete counting, or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as Pygoscelis penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster-RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations and demonstrably improve monitoring efforts from aerial imagery.

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Counting animals in aerial images with a density map estimation model

Qian

Humphries²,

Trathan

et al. 2023

Ecology and Evolution

Self Cite

View full text Add to dashboard Cite

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Correcting for measurement errors in a long‐term aerial survey with auxiliary photographic data

Brusa,

Farr,

Evenson

et al. 2024

Ecosphere

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Long‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets.

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The use of orthoimagery and stereoscopic aerial imagery to identify muskrat (Ondatra zibethicus) houses

Greenhorn,

Sadowski,

Rodgers

et al. 2024

Wildlife Society Bulletin

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The muskrat (Ondatra zibethicus) is considered a ubiquitous inhabitant of wetlands across Canada and the United States, but recent studies indicate that muskrat populations in many parts of North America have experienced substantial declines over the last 40–60 years. Monitoring of muskrat abundance is therefore an important task for wildlife managers, but traditional methods such as house counts conducted during ground‐based surveys can be labor‐intensive and time‐consuming. Poor conditions or a lack of access may limit how much of a wetland can be surveyed. Aerial imagery has previously been used to census a diverse array of wildlife populations but is not yet a common tool for muskrat surveys. To investigate the accuracy of this alternative survey method, we collected aerial imagery from coastal wetlands along the north shore of Lake Ontario during the winter of 2014 for examination in both 2D orthorectified and 3D stereoscopic formats. We compared muskrat house counts obtained from imagery to counts recorded by ground survey crews in the same wetlands during the same winter. We found no significant difference between mean muskrat house counts obtained by ground survey crews and orthoimagery observers. In contrast, stereoscopic imagery observers overestimated mean house counts compared to ground survey crews, which we interpret was due to an increase in false positives. Our results indicate that orthoimagery is a promising tool for assessing muskrat occupancy, provides comparable broad‐scale results to traditional ground survey methods, and may be preferable to wildlife managers for a variety of reasons.

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Counting animals in aerial images with a density map estimation model

Cited by 9 publications

References 66 publications

Counting animals in aerial images with a density map estimation model

Counting animals in aerial images with a density map estimation model

Correcting for measurement errors in a long‐term aerial survey with auxiliary photographic data

The use of orthoimagery and stereoscopic aerial imagery to identify muskrat (Ondatra zibethicus) houses

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