Evaluating UAV-Based Object Classification Workflows for Detecting the Endangered Tasmanian Grassland Paperdaisy Flower (Leucochrysum albicans subsp. tricolor)
- 1Threatened Species Section, Department of Natural Resources and Environment Tasmania, Hobart 7001, Australia
- 2School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart 7006, Australia
Abstract
Lowland temperate native grasslands are recognised as one of the most threatened vegetation communities in Australia, and even small remnant populations can hold high ecological value. One such site is Township Lagoon Nature Reserve in the Tasmanian Midlands, a key site for the endangered grassland paperdaisy (Leucochrysum albicans subsp. tricolor). Effective monitoring is required to understand population trends, but traditional field survey techniques are labour-intensive. This study evaluated Unpiloted Aerial Vehicle (UAV)-derived red, green and blue (RGB) and multispectral imagery for detecting, segmenting, classifying and estimating the abundance of grassland paperdaisy flowers using object-based image analysis and machine learning classifiers within ESRI ArcGIS Pro software. Imagery collected at 50 m above ground level enabled detection and classification of grassland paperdaisy flowers using Support Vector Machine and Random Tree (RT) algorithms with moderate to substantial levels of agreement. The multispectral RT output produced the highest accuracy (Kappa = 0.767), while workflows incorporating additional vegetation indices and texture measures generally produced lower classification accuracies. Comparison with an independent field counting survey conducted during the same flowering season as UAV capture showed variable agreement among plots, with differences potentially influenced by vegetation structure, plot characteristics, and temporal separation between field and UAV surveys. This study highlights the potential for UAV-based image analysis as a complementary approach for monitoring threatened plant species under operational conservation management conditions.
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BibTeX
@article{fagg2026evaluating,
title = {{Evaluating UAV-Based Object Classification Workflows for Detecting the Endangered Tasmanian Grassland Paperdaisy Flower (Leucochrysum albicans subsp. tricolor)}},
author = {Karen Fagg and Steve Harwin},
journal = {Geomatics},
year = {2026},
month = sep,
doi = {10.3390/geomatics6050106},
url = {https://doi.org/10.3390/geomatics6050106},
note = {CC BY},
}
RIS
TY - JOUR
TI - Evaluating UAV-Based Object Classification Workflows for Detecting the Endangered Tasmanian Grassland Paperdaisy Flower (Leucochrysum albicans subsp. tricolor)
AU - Karen Fagg
AU - Steve Harwin
PY - 2026
DA - 2026/09/01
JO - Geomatics
DO - 10.3390/geomatics6050106
UR - https://doi.org/10.3390/geomatics6050106
AB - Lowland temperate native grasslands are recognised as one of the most threatened vegetation communities in Australia, and even small remnant populations can hold high ecological value. One such site is Township Lagoon Nature Reserve in the Tasmanian Midlands, a key site for the endangered grassland paperdaisy (Leucochrysum albicans subsp. tricolor). Effective monitoring is required to understand population trends, but traditional field survey techniques are labour-intensive. This study evaluated Unpiloted Aerial Vehicle (UAV)-derived red, green and blue (RGB) and multispectral imagery for detecting, segmenting, classifying and estimating the abundance of grassland paperdaisy flowers using object-based image analysis and machine learning classifiers within ESRI ArcGIS Pro software. Imagery collected at 50 m above ground level enabled detection and classification of grassland paperdaisy flowers using Support Vector Machine and Random Tree (RT) algorithms with moderate to substantial levels of agreement. The multispectral RT output produced the highest accuracy (Kappa = 0.767), while workflows incorporating additional vegetation indices and texture measures generally produced lower classification accuracies. Comparison with an independent field counting survey conducted during the same flowering season as UAV capture showed variable agreement among plots, with differences potentially influenced by vegetation structure, plot characteristics, and temporal separation between field and UAV surveys. This study highlights the potential for UAV-based image analysis as a complementary approach for monitoring threatened plant species under operational conservation management conditions.
SN - 2673-7418
ER -