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Semi-automated analysis of digital photographs for monitoring East Antarctic vegetation

Citation

KIng, DH and Wasley, J and Ashcroft, MB and Ryan-Colton, E and Lucieer, A and Chisholm, LA and Robinson, SA, Semi-automated analysis of digital photographs for monitoring East Antarctic vegetation, Frontiers in Plant Science, 11 Article 766. ISSN 1664-462X (2020) [Refereed Article]


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Copyright Statement

2021. King, Wasley, Ashcroft, Ryan-Colton, Lucieer, Chisholm and Robinson. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

DOI: doi:10.3389/fpls.2020.00766

Abstract

Climate change is affecting Antarctica and minimally destructive long-term monitoring of its unique ecosystems is vital to detect biodiversity trends, and to understand how change is affecting these communities. The use of automated or semi-automated methods is especially valuable in harsh polar environments, as access is limited and conditions extreme. We assessed moss health and cover at six time points between 2003 and 2014 at two East Antarctic sites. Semi-automatic object-based image analysis (OBIA) was used to classify digital photographs using a set of rules based on digital red, green, blue (RGB) and hue-saturation-intensity (HSI) value thresholds, assigning vegetation to categories of healthy, stressed or moribund moss and lichens. Comparison with traditional visual estimates showed that estimates of percent cover using semi-automated OBIA classification fell within the range of variation determined by visual methods. Overall moss health, as assessed using the mean percentages of healthy, stressed and moribund mosses within quadrats, changed over the 11 years at both sites. A marked increase in stress and decline in health was observed across both sites in 2008, followed by recovery to baseline levels of health by 2014 at one site, but with significantly more stressed or moribund moss remaining within the two communities at the other site. Our results confirm that vegetation cover can be reliably estimated using semi-automated OBIA, providing similar accuracy to visual estimation by experts. The resulting vegetation cover estimates provide a sensitive measure to assess change in vegetation health over time and have informed a conceptual framework for the changing condition of Antarctic mosses. In demonstrating that this method can be used to monitor ground cover vegetation at small scales, we suggest it may also be suitable for other extreme environments where repeat monitoring via images is required.

Item Details

Item Type:Refereed Article
Keywords:Antarctica, moss, vegetation cover, vegetation health, climate change, monitoring, OBIA, RGB
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Management
Objective Group:Management of Antarctic and Southern Ocean environments
Objective Field:Assessment and management of Antarctic and Southern Ocean ecosystems
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:148018
Year Published:2020
Funding Support:Australian Research Council (DP110101714)
Web of Science® Times Cited:3
Deposited By:Geography and Spatial Science
Deposited On:2021-11-30
Last Modified:2022-01-04
Downloads:4 View Download Statistics

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