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Open AccessArticle

Assessing the Impact of Spectral Resolution on Classification of Lowland Native Grassland Communities Based on Field Spectroscopy in Tasmania, Australia

1
Discipline of Geography and Spatial Sciences, University of Tasmania, Private Bag 78, Hobart 7001, Australia
2
Earth Observation Lab, Faculty of Communication and Environment, Hochschule Rhein-Waal, 25 Friedrich-Heinrich-Allee, Kamp-Lintfort 47475, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 308; https://doi.org/10.3390/rs10020308
Received: 12 December 2017 / Revised: 24 January 2018 / Accepted: 9 February 2018 / Published: 16 February 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
This paper presents a case study for the analysis of endangered lowland native grassland communities in the Tasmanian Midlands region using field spectroscopy and spectral convolution techniques. The aim of the study was to determine whether there was significant improvement in classification accuracy for lowland native grasslands and other vegetation communities based on hyperspectral resolution datasets over multispectral equivalents. A spectral dataset was collected using an ASD Handheld-2 spectroradiometer at Tunbridge Township Lagoon. The study then employed a k-fold cross-validation approach for repeated classification of a full hyperspectral dataset, a reduced hyperspectral dataset, and two convoluted multispectral datasets. Classification was performed on each of the four datasets a total of 30 times, based on two different class configurations. The classes analysed were Themeda triandra grassland, Danthonia/Poa grassland, Wilsonia rotundifolia/Selliera radicans, saltpan, and a simplified C3 vegetation class. The results of the classifications were then tested for statistically significant differences using ANOVA and Tukey’s post-hoc comparisons. The results of the study indicated that hyperspectral resolution provides small but statistically significant increases in classification accuracy for Themeda and Danthonia grasslands. For other classes, differences in classification accuracy for all datasets were not statistically significant. The results obtained here indicate that there is some potential for enhanced detection of major lowland native grassland community types using hyperspectral resolution datasets, and that future analysis should prioritise good performance in these classes over others. This study presents a method for identification of optimal spectral resolution across multiple datasets, and constitutes an important case study for lowland native grassland mapping in Tasmania. View Full-Text
Keywords: hyperspectral; multispectral; random forest; grassland hyperspectral; multispectral; random forest; grassland
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MDPI and ACS Style

Melville, B.; Lucieer, A.; Aryal, J. Assessing the Impact of Spectral Resolution on Classification of Lowland Native Grassland Communities Based on Field Spectroscopy in Tasmania, Australia. Remote Sens. 2018, 10, 308.

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