Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

Abstract

Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95-98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts' 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.

Figure 1. Locations of grey seal Halichoerus grypus breeding colonies on Saddle and Hay Island, Nova Scotia, Canada surveyed with unmanned aircraft systems (UAS) during January 29–February 2 2015.

This map was created with ArcMap GIS software (version 10.4.1, Esri Inc.) using ArcMap’s World Imagery service layer. Service Layer Credits: Source: Esri, Digital Globe, GeoEye, Earthstar, Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo and the GIS user community.

Figure 2. An overview of the seal detection model’s logical processes.

Yellow icons are inputs, blue icons are intermediate processes and outputs, and green icons are final outputs.

Figure 3

(A) Thermal imagery with overlaid human-identified seal points (red = YOY, green = adult). (B) Red seal polygons outlined by blue convex hulls. (C) Tier 1 model classification of seals. Blue polygons are adult aggregations, orange polygons are YOY aggregations, green polygons are individual adults and red polygons are individual YOYs. (D) Aggregation polygons after high pass filtering, broken up into individual adults and YOYs. This map was created with ArcMap GIS software (version 10.4.1, Esri Inc.).

Figure 4

An RGB orthomosaic (A) and a representative individual RGB image (B) of the grey seal colony at Hay Island, NS Canada. This footrprint of the individual image is projected onto the orthomosaic, providing a detailed view of adult and YOY grey seals and the habitats surveyed (rock, beach and frozen ground). This map was created with ArcMap GIS software (version 10.4.1, Esri Inc.).

Figure 5

A thermal infrared spatial index map (A) and a representative individual thermal infrared image (B) of the grey seal colony at Hay Island, NS Canada. This footprint of the individual image is projected onto the spatial index, providing a detailed view of adult and YOY grey seals and the habitats surveyed (rock, beach and frozen ground). This map was created with ArcMap GIS software (version 10.4.1, Esri Inc.).