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

被引:13
作者
Bird, Clara N. [1 ]
Dawn, Allison H. [2 ]
Dale, Julian [3 ]
Johnston, David W. [3 ]
机构
[1] Oregon State Univ, Geospatial Ecol Marine Megafauna Lab, Mammal Inst, Dept Fisheries & Wildlife,Hatfield Marine Sci Ctr, Newport, OR 97365 USA
[2] Univ N Carolina, Environm Ecol & Energy Program, Chapel Hill, NC 27599 USA
[3] Duke Univ, Marine Lab, Nicholas Sch Environm, Div Marine Sci & Conservat, Beaufort, NC 28516 USA
关键词
penguins; guano; unoccupied aircraft systems; thermal imagery; multispectral imagery; image segmentation; ArcGIS; semi-automated workflow; remote sensing; UNMANNED AERIAL VEHICLES; COUNTS; PHOTOGRAPHY;
D O I
10.3390/rs12223692
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Monitoring Adelie 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 Adelie 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.
引用
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页码:1 / 14
页数:14
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