A comparison of established and digital surface model (DSM)-based methods to determine population estimates and densities for king penguin colonies, using fixed-wing drone and satellite imagery

Coleman, J. ORCID: https://orcid.org/0000-0002-0162-8356; Fenney, N. ORCID: https://orcid.org/0000-0001-5835-7975; Trathan, P.N. ORCID: https://orcid.org/0000-0001-6673-9930; Fox, A. ORCID: https://orcid.org/0000-0001-7477-0671; Fox, E.; Bennison, A. ORCID: https://orcid.org/0000-0001-9713-8310; Ireland, L. ORCID: https://orcid.org/0000-0003-0960-0486; Collins, M.A. ORCID: https://orcid.org/0000-0001-7132-8650; Hollyman, P.R. ORCID: https://orcid.org/0000-0003-2665-5075. 2024 A comparison of established and digital surface model (DSM)-based methods to determine population estimates and densities for king penguin colonies, using fixed-wing drone and satellite imagery. Remote Sensing in Ecology and Conservation. 18, pp. 10.1002/rse2.424

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Text (Open Access) 2024 The Author(s). Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Remote Sens Ecol Conserv - 2024 - Coleman - A comparison of established and digital surface model DSM ‐based methods to.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (20MB) | Preview

Abstract/Summary

Drones are being increasingly used to monitor wildlife populations; their large spatial coverage and minimal disturbance make them ideal for use in remote environments where access and time are limited. The methods used to count resulting imagery need consideration as they can be time-consuming and costly. In this study, we used a fixed-wing drone and Beyond Visual Line of Sight flying to create high-resolution imagery and digital surface models (DSMs) of six large king penguin colonies (colony population sizes ranging from 10,671 to 132,577 pairs) in South Georgia. We used a novel DSM-based method to facilitate automated and semi-automated counts of each colony to estimate population size. We assessed these DSM-derived counts against other popular counting and post-processing methodologies, including those from satellite imagery, and compared these to the results from four colonies counted manually to evaluate accuracy and effort. We randomly subsampled four colonies to test the most efficient and accurate methods for density-based counts, including at the colony edge, where population density is lower. Sub-sampling quadrats (each 25 m2) together with DSM-based counts offered the best compromise between accuracy and effort. Where high-resolution drone imagery was available, accuracy was within 3.5% of manual reference counts. DSM methods were more accurate than other established methods including estimation from satellite imagery and are applicable for population studies across other taxa worldwide. Results and methods will be used to inform and develop a long-term king penguin monitoring programme.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1002/rse2.424
ISSN:	20563485
Additional Keywords:	Counting methods, Digital surface model, DSM, Penguins, Satellite, UAV survey
Date made live:	29 Nov 2024 12:00 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536463

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