A Semi-Automated Method for Estimating Adélie Penguin Colony Abundance from a Fusion of Multispectral and Thermal Imagery Collected with Unoccupied Aircraft Systems

• Published in: Remote Sensing
• Main Authors: Clara N. Bird, Allison H. Dawn, Julian Dale, David W. Johnston
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: penguins; guano; unoccupied aircraft systems; thermal imagery; multispectral imagery; image segmentation; Science; Q; Antarctic; Antarctic Peninsula; Guano; Torgersen; Torgersen Island; Avian Island; Antarc*; Pygoscelis adeliae
• Online Access: https://doi.org/10.3390/rs12223692; https://doaj.org/article/7f6ecdc6f9ff4ecb87a2816579feeaa5

Monitoring Adélie penguin ( Pygoscelis adeliae) populations on the Western Antarctic Peninsula (WAP) provides information about the health of the species and the WAP marine ecosystem itself. In January 2017, surveys of Adélie penguin colonies at Avian Island and Torgersen Island off the WAP were conducted via unoccupied aircraft systems (UAS) collecting optical Red Green Blue (RGB), thermal, and multispectral imagery. A semi-automated workflow to count individual penguins using a fusion of multispectral and thermal imagery was developed and combined into an ArcGIS workflow. This workflow isolates colonies using multispectral imagery and detects and counts individuals by thermal signatures. Two analysts conducted manual counts from synoptic RGB UAS imagery. The automated system deviated from analyst counts by −3.96% on Avian Island and by 17.83% on Torgersen Island. However, colony-by-colony comparisons revealed that the greatest deviations occurred at larger colonies. Matched pairs analysis revealed no significant differences between automated and manual counts at both locations ( p > 0.31) and linear regressions of colony sizes from both methods revealed significant positive relationships approaching unity ( p < 0.0002. R 2 = 0.91). These results indicate that combining UAS surveys with sensor fusion techniques and semi-automated workflows provide efficient and accurate methods for monitoring seabird colonies in remote environments.