Open Access Repository

Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian midlands

Downloads

Downloads per month over past year

Melville, B ORCID: 0000-0003-3040-6071, Lucieer, A ORCID: 0000-0002-9468-4516 and Aryal, J ORCID: 0000-0002-4875-2127 2019 , 'Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian midlands' , Drones, vol. 3, no. 1 , pp. 1-12 , doi: 10.3390/drones3010005.

[img]
Preview
PDF
131341 - Classi...pdf | Download (3MB)

| Preview

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 Type: Article
Authors/Creators:Melville, B and Lucieer, A and Aryal, J
Keywords: hyperspectral, UAS, native grassland, random forest
Journal or Publication Title: Drones
Publisher: MDPIAG
ISSN: 2504-446X
DOI / ID Number: 10.3390/drones3010005
Copyright Information:

Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP