DOI: 10.5061/dryad.g4f4qrfwp
title: Evaluating the efficacy of drone-based thermal images for measuring wildlife abundance and physiology

description: 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 detection was more accurate using RGB images compared to Thermal
 images, but that Thermal images could be used to determine that time of
 day and ambient temperature (but not wind speed or body size) strongly
 influenced seal external skin temperature. In other words, RGB and Thermal
 cameras have different strengths and weaknesses that should be carefully
 considered when designing research studies. Our study highlights the
 promising integration of drones, thermal imaging, and machine learning for
 wildlife research, contributing to faster, safer, cheaper, less
 disruptive, and more accurate wildlife monitoring and conservation
 efforts.
description: # 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
 temperature data to analyze relationships between the two) * filename =
 name of image * type = image type (RGB or Thermal) * date = drone flight
 date * poly_id = unique identifier for each polygon * class = seal * area
 tempc_mean = seal area, meters squared, in Celcius * tempc_median = median
 temperature of polygon, in Celcius * tempc_max = maximum temperature of
 polygon, in Celcius * tempc_min = minimum temperature of polygon, in
 Celcius * tempc_sd = sd temperature of polygon, in Celcius * tempc_n =
 number for polygon, in Celcius * tempc_var = variance of polygon
 temperature, in Celcius * temp_cov = covariance of polygon temperature, in
 Celcius ## The zipped eSeal\_imagery\_for\_manuscript folder contains many
 imagery and annotation files, as described below: # aligned\_imagery ##
 combined * Includes four 4-channel tiff files that have with red, green,
 blue, thermal channels.  * "20200224
 FINAL_transparent_reflectance_grayscale_warped.tif" * "20200403
 Thermal FINAL_transparent_reflectance_grayscale_warped.tif" *
 "20200610_MBBL_Thermal2_transparent_reflectance_grayscale_wraped.tif" * "20200618 MBBL Thermal Final_transparent_reflectance_grayscale_warped.tif" ## manual\_annotations * Include shape files with manually drawn polygons for each seal corresponding to each image.  These shape files were converted from cvat XML files and originally annotated on the RGB imagery.  These shape files should align with the combined, RGB, and thermal imagery in the aligned_imagery folder. ## predicted\_polygons * Includes shape files from model predictions from the RGB model trained as part of this project. There is one file for each of the combined images ## rgb * These are the original RGB images ## thermal * These are the thermal images after the warping process so that the results align with the RGB imagery and the shape files in the aligned imagery folders. # original\_thermal ## manual\_annotations * Includes shape files for the original thermal images. These shape files were converted from cvat XML files and originally annotated on the thermal imagery.   These shape files will **not **align correctly with the warped imagery in the  aligned imagery folders. ## thermal * 4 thermal images # manual\_annotation\_polygons.csv * This is a CSV with 1 row per polygon vertex with the following columns * Filename - the name of the image file that these polygons were originally  annotated on        * Class - seal or NULL for areas with no seals * x - the x coordinate in pixels from right * y - the y coordinate in pixels from top * Height - the hight in pixels of the image * Width -  the width in pixels of the image * Order -  the plotting order of the vertices * Poly_id - an id for each polygon In all datasets, NA means that no measurements were made.