MorphoMetriX is a flexible photogrammetry graphical user interface (GUI) developed in PyQt5 for making efficient manual morphometric measurements of wild animals via aerial imagery. It was designed as a simple to use and accurate program for robust morphometric analysis that does not require knowledge of any scripting language for customization. The user can import an image and create custom length, area, and angle measurements, as well as measure perpendicular widths based off a length measurement (i.e., used to calculate body condition). MorphoMetriX allows the user to input flight and sensor parameters (such as altitude, focal length, pixel dimensions) so that all measurements in pixels are automatically scaled to real world values (i.e., meters). All measurements and their labels are exported into a .csv, along with an image (.png) of all the measurements that were made on the animal.
Developed by Walter Torres and KC Bierlich
Torres, W.I., and Bierlich, K.C (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna.. Journal of Open Source Software, 4(44), 1825.
CollatriX is a graphical user interface (GUI) developed using PyQt5 to collate outputs from MorphoMetriX (Torres & Bierlich, 2020). CollatriX was designed as a user-friendly GUI that collates the measurement outputs into a single data sheet (.csv) based on the animal's individual ID. CollatriX includes a 'safety' function to correct user input errors by allowing the user to provide the correct altitude, focal length, and pixel dimension per image through a csv. Furthermore, CollatriX has two add-on functions, one to correct for altitude error from Unoccupied Aerial Systems (UAS or drone) flights and another for calculating different animal body condition metrics, such as body volume and body area index (BAI). The framework of CollatriX was also designed to have the flexibility to accommodate and encourage other future add-on functions.
Developed by Clara Bird and KC Bierlich
Bird, C.N., and Bierlich, K.C. (2020). CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), 2328.
Predicting Photogrammetric Uncertainty
This Bayesian statistical model provides a framework for predicting uncertainty associated with drone-based morphological measurements. This statistical framework jointly estimates errors from altitude and length measurements from multiple observations and accounts for altitudes measured with both barometers and laser altimeters while incorporating errors specific to each. This Bayesian model outputs a posterior predictive distribution of measurement uncertainty around length measurements and allows for the construction of highest posterior density (HPD) intervals to define measurement uncertainty, which allows one to make probabilistic statements and stronger inferences pertaining to morphometric features critical for understanding life history patterns and potential impacts from anthropogenically altered habitats.
Bierlich, K. C., Schick, R. S., Hewitt, J., Dale, J., Goldbogen, J. A., Friedlaender, A. S., & Johnston, D. W. (2020). Data and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones. Duke Research Data Repository. V2 https://doi.org/10.7924/r4sj1jj6s