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Automated, high-throughput image calibration for parallel-laser photogrammetry

Citation

Levy, Emily; Richardson, Jack (2021), Automated, high-throughput image calibration for parallel-laser photogrammetry, Dryad, Dataset, https://doi.org/10.5061/dryad.51c59zw7d

Abstract

This contains the data required to recreate the analyses in this paper. The code for performing the machine learning and image processing methods presented in the paper are available as supplemental files to the manuscript, and are also available at https://github.com/ejlevy/Photogrammetry_Coding_InterLaser_Distance.

Paper abstract: Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject’s body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. 

Methods

Data represent measurements taken of images of baboons and gorillas collected in Amboseli National Park and Bwindi Impenetrable National Park, respectively. Images were collected by photographing study animals while parallel lasers were projecting onto the animal. These images were then processed to measure the distance, in pixels, between the laser spots in the image. These measurements were taken in one of three ways: manually, with the ImageJ method, and with the skimage method.

Usage Notes

Missing values are expected.