Work

Automated morphometric modelling of an endangered freshwater cetacean from unscaled UAV imagery using an internal scaling framework

Year: 2026

Type: article

Abstract: With the rapid advancement of drone technology, aerial photogrammetry has been increasingly applied to morphometric research, particularly for endangered species, owing to its fully non-invasive nature. Photogrammetry-derived morphometric models (e.g., three-dimensional reconstructions) provide critical insights into individual health conditions and population demographic characteristics. Conventional aerial photogrammetry relies on external scale calibration to convert image measurements into real-world dimensions, which remains a major challenge for free-ranging animals because accurate scaling requires prior knowledge of image acquisition parameters or external references that are often unavailable under field conditions. Using the endangered Yangtze finless porpoise ( Neophocaena asiaeorientalis asiaeorientalis ) as a model species, we demonstrated that the allometric development of blowhole length (distance from the tip of the rostrum to the posterior margin of the blowhole) results in significant age-specific differences in the blowhole-length-to-body-length ratio, providing an internal scaling metric that is independent of image scale. The long-term accumulation of age and body length measurements has generated a rich dataset that enables the establishment of accurate age–length relationships, while a recent rescue operation provided a unique opportunity to obtain direct and accurate morphometric measurements for validating the internal scaling framework and the novel Segmented Weighted Integration Model (SWIM). We further developed a computer program that automates pixel extraction, metric conversion, model implementation, parameter calculation, and data tabulation. Significant differences in the blowhole-length-to-body-length ratio were observed among calves (n = 11), juveniles (n = 36), and adults (n = 35) (ANOVA, P < 0.01), with all pairwise comparisons remaining significant (calves vs. juveniles, P < 0.001; calves vs. adults, P < 0.001; juveniles vs. adults, P < 0.01). The model demonstrated high accuracy in estimating body length from unscaled images without prior knowledge of flight altitude or camera focal length, achieving a mean absolute error of 6.69 cm and a mean absolute percentage error of 5.02% across 62 test images. Our study presents an automated and accurate approach for estimating body length from a single unscaled dorsal-view drone image using an internal scaling framework and the SWIM, providing a practical tool for rapid morphometric assessment of marine mammals.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2026.104070