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Article

Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles

by
Chad Ajamian
1,
Hsing-Chung Chang
1,*,
Kerrie Tomkins
1,
William Farebrother
1,
Rene Heim
2 and
Shahriar Rahman
1
1
Department of Earth and Environmental Sciences, Macquarie University, Sydney, NSW 2109, Australia
2
Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Geomatics 2021, 1(2), 177-191; https://doi.org/10.3390/geomatics1020011
Submission received: 17 December 2020 / Revised: 15 March 2021 / Accepted: 18 March 2021 / Published: 1 April 2021
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Abstract

This study examined the use of hyperspectral profiles for identifying three selected weed species in the alpine region of New South Wales, Australia. The targeted weeds included Orange Hawkweed, Mouse-ear Hawkweed and Ox-eye daisy, which have caused a great concern to regional biodiversity and health of the environment in Kosciuszko National Park. Field surveys using a spectroradiometer were undertaken to measure the hyperspectral profiles of leaves and flowers of the selected weeds and companion native plants. Random Forest (RF) classification was then applied to distinguish which spectral bands would differentiate the weeds from the native plants. Our results showed that an accuracy of 95% was achieved if the spectral profiles of the distinct flowers of the weeds were considered, and an accuracy of 80% was achieved if only the profiles of the leaves were considered. Emulation of the spectral profiles of two multispectral sensors (Sentinel-2 and Parrot Sequoia) was then conducted to investigate whether classification accuracy could potentially be achieved using wider spectral bands.
Keywords: weeds management; ox-eye daisy; orange hawkweed; mouse-ear hawkweed; hyperspectral remote sensing; multispectral; random forest; Kosciuszko National Park weeds management; ox-eye daisy; orange hawkweed; mouse-ear hawkweed; hyperspectral remote sensing; multispectral; random forest; Kosciuszko National Park
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MDPI and ACS Style

Ajamian, C.; Chang, H.-C.; Tomkins, K.; Farebrother, W.; Heim, R.; Rahman, S. Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles. Geomatics 2021, 1, 177-191. https://doi.org/10.3390/geomatics1020011

AMA Style

Ajamian C, Chang H-C, Tomkins K, Farebrother W, Heim R, Rahman S. Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles. Geomatics. 2021; 1(2):177-191. https://doi.org/10.3390/geomatics1020011

Chicago/Turabian Style

Ajamian, Chad, Hsing-Chung Chang, Kerrie Tomkins, William Farebrother, Rene Heim, and Shahriar Rahman. 2021. "Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles" Geomatics 1, no. 2: 177-191. https://doi.org/10.3390/geomatics1020011

APA Style

Ajamian, C., Chang, H.-C., Tomkins, K., Farebrother, W., Heim, R., & Rahman, S. (2021). Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles. Geomatics, 1(2), 177-191. https://doi.org/10.3390/geomatics1020011

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