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Article

Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection

1
Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand
2
Scion, P.O. Box 29237, Fendalton, Christchurch 8041, New Zealand
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Author to whom correspondence should be addressed.
Academic Editors: Xiangguo Lin, Jixian Zhang, Clement Atzberger and Prasad S. Thenkabail
Received: 6 November 2016 / Revised: 7 February 2017 / Accepted: 9 February 2017 / Published: 15 February 2017
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
The spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne laser scanning (ALS) to develop methods to identify invasive conifers using remotely-sensed data. We examined the effect of ALS pulse density and the height threshold of the training dataset on classification accuracy. The results showed that adding spectral values to the ALS metrics/variables in the training dataset led to significant increases in classification accuracy. The most accurate models (kappa range of 0.773–0.837) had either four or five explanatory variables, including ALS elevation, the near-infrared band and different combinations of ALS intensity and red and green bands. The best models were found to be relatively invariant to changes in pulse density (1–21 pls/m2) or the height threshold (0–2 m) used for the inclusion of data in the training dataset. This research has extended and improved the methods for scattered single tree detection and offered valuable insight into campaign settings for the monitoring of invasive conifers (tree weeds) using remote sensing approaches. View Full-Text
Keywords: LiDAR; classification; near-infrared; random forest; weeds; ALS; simulation; data thinning; invasive conifer; invasion ecology; data fusion LiDAR; classification; near-infrared; random forest; weeds; ALS; simulation; data thinning; invasive conifer; invasion ecology; data fusion
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MDPI and ACS Style

Dash, J.P.; Pearse, G.D.; Watt, M.S.; Paul, T. Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection. Remote Sens. 2017, 9, 156. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9020156

AMA Style

Dash JP, Pearse GD, Watt MS, Paul T. Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection. Remote Sensing. 2017; 9(2):156. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9020156

Chicago/Turabian Style

Dash, Jonathan P., Grant D. Pearse, Michael S. Watt, and Thomas Paul. 2017. "Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection" Remote Sensing 9, no. 2: 156. https://0-doi-org.brum.beds.ac.uk/10.3390/rs9020156

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