eCite Digital Repository

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]


Preview
PDF
3Mb
  

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 (Ms Bethany Melville)
UTAS Author:Lucieer, A (Associate Professor Arko Lucieer)
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:131341
Year Published:2019
Deposited By:Geography and Environmental Studies
Deposited On:2019-03-13
Last Modified:2019-04-24
Downloads:12 View Download Statistics

Repository Staff Only: item control page