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Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian midlands
Citation
Melville, B and Lucieer, A and Aryal, J, Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian midlands, Drones, 3, (1) Article 5. ISSN 2504-446X (2019) [Refereed Article]
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Copyright Statement
Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
DOI: doi:10.3390/drones3010005
Abstract
his paper presents the results of a study undertaken to classify lowland native grassland communities in the Tasmanian Midlands region. Data was collected using the 20 band hyperspectral snapshot PhotonFocus sensor mounted on an unmanned aerial vehicle. The spectral range of the sensor is 600 to 875 nm. Four vegetation classes were identified for analysis including Themeda triandra grassland, Wilsonia rotundifolia, Danthonia/Poa grassland, and Acacia dealbata. In addition to the hyperspectral UAS dataset, a Digital Surface Model (DSM) was derived using a structure-from-motion (SfM). Classification was undertaken using an object-based Random Forest (RF) classification model. Variable importance measures from the training model indicated that the DSM was the most significant variable. Key spectral variables included bands two (620.9 nm), four (651.1 nm), and 11 (763.2 nm) from the hyperspectral UAS imagery. Classification validation was performed using both the reference segments and the two transects. For the reference object validation, mean accuracies were between 70% and 72%. Classification accuracies based on the validation transects achieved a maximum overall classification accuracy of 93.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | hyperspectral, UAS, native grassland, random forest |
| Research Division: | Engineering |
| Research Group: | Geomatic engineering |
| Research Field: | Photogrammetry and remote sensing |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding knowledge |
| Objective Field: | Expanding knowledge in the environmental sciences |
| UTAS Author: | Melville, B (Dr Bethany Cox) |
| UTAS Author: | Lucieer, A (Professor Arko Lucieer) |
| UTAS Author: | Aryal, J (Dr Jagannath Aryal) |
| ID Code: | 131341 |
| Year Published: | 2019 |
| Web of Science® Times Cited: | 23 |
| Deposited By: | Geography and Spatial Science |
| Deposited On: | 2019-03-13 |
| Last Modified: | 2020-05-19 |
| Downloads: | 45 View Download Statistics |
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