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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 also 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.
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3D photogrammetry improves measurement of growth and biodiversity patterns in branching corals
Photogrammetry is an emerging tool that allows scientists to measure important habitat characteristics of coral reefs at multiple spatial scales. However, the ecological benefits of using photogrammetry to measure reef habitat have rarely been assessed through direct comparison to traditional methods, especially in settings where manual measurements are more feasible and affordable. Here, we applied multiple methods to measure coral colonies (Pocillopora spp.) and asked whether photogrammetric or manual observations better describe short-term colony growth and links between colony size and the biodiversity of coral-dwelling fishes and invertebrates. Using photogrammetry, we measured patterns in changes in coral volume that were otherwise obscured by high variation from manual measurements. Additionally, we found that photogrammetry-based estimates of colony skeletal volume best predicted the abundance and richness of animals living within the coral. This study highlights that photogrammetry can improve descriptions of coral colony size, growth, and associated biodiversity compared to manual measurements.
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- PAR ID:
- 10412960
- Date Published:
- Journal Name:
- Coral Reefs
- ISSN:
- 0722-4028
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00338-023-02367-7
