Evaluating the efficacy of drone-based thermal images for measuring wildlife abundance and physiology

Monitoring the population dynamics and behaviors of wildlife is crucial for effective conservation. Although drones can provide a promising alternative to traditional monitoring methods, validation studies must be done to quantify the accuracy of drone-based abundance and distribution estimates in various biological systems. Here, we investigate the use of drones equipped with high-resolution Red-Green-Blue (RGB) and thermal cameras, along with machine learning techniques, for assessments of abundance and physiology in northern elephant seals (Mirounga angustirostris). Aerial images of N=3,415 northern elephant seals were collected at Año Nuevo Reserve during N=24 drone flights, along with ambient air temperatures, wind speed, and time-of-day data. The two-dimensional footprints and surface temperatures of seals were measured from the images. Machine learning algorithms were applied to detect seals in the imagery, and model performance was evaluated. Our findings indicate that seal dete..., , , # Evaluating the efficacy of drone-based thermal images for measuring wildlife abundance and physiology

## Code/Software

* Drone Dryad 2023_10_17.R can be used to reproduce the statistical analyses and figures in the paper.

## Description of the data and file structure

* AI Seal - Sheet1.csv headers: (number of seals counted for each drone flight)
* Date = drone flight date
* Type = method for image analysis
* Number = number of seals
* All Seal Temperature Data Combined.csv (seal temperature and weather to analyze relationships between the two)
* Date = drone flight date
* Time = drone flight time
* Location = beach location of drone flight
* Seal ID/Number = unique identifier
* Seal Temperature (C) = external skin temperature measurement, in degrees Celcius
* Ambient Temperature (C) = air temperature measurement, in degrees Celcius
* Wind Speed (MPH) = wind speed, in miles per hour
* JM_data.csv (seal size and t...