Improving Wildlife Population Inference Using Aerial Imagery and Entity Resolution

Xinyi Lu (), Mevin B. Hooten, Andee Kaplan, Jamie N. Womble and Michael R. Bower
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Xinyi Lu: Colorado State University
Mevin B. Hooten: The University of Texas at Austin
Andee Kaplan: Colorado State University
Jamie N. Womble: National Park Service
Michael R. Bower: The University of Texas at Austin

Journal of Agricultural, Biological and Environmental Statistics, 2022, vol. 27, issue 2, No 9, 364-381

Abstract: Abstract Recent technological advancements have seen a rapid growth in the use of imagery data to estimate the abundance and spatial distribution of animal populations. However, the value of imagery data may not be fully exploited under traditional analytical frameworks. We developed a method that leverages aerial imagery data for population modeling through entity resolution, a technique that stochastically links the same individual across multiple images. Resolving duplicate individuals in overlapping images that are distorted requires realigning observed point patterns optimally; however, popular machine learning algorithms for image stitching do not often account for alignment uncertainty. Moreover, duplicated individuals can provide insight about detection probability when overlaps are viewed as replicate surveys. Our model resolves individual identities by linking observed locations to latent activity centers and estimates total population as informed by the linkage structure. We developed a hierarchical framework to achieve entity resolution and abundance estimation cohesively, thereby avoiding single-direction error propagation that is common in two-stage models. We illustrate our method through simulation and a case study using aerial images of sea otters in Glacier Bay, Alaska. Supplementary materials accompanying this paper appear on-line

Keywords: Bayesian; Data augmentation; Hierarchical model; Spatial capture-recapture (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13253-021-00484-w

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