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Open AccessArticle

Detection of New Zealand Kauri Trees with AISA Aerial Hyperspectral Data for Use in Multispectral Monitoring

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Environmental Remote Sensing and Geoinformatics, Trier University, D-54296 Trier, Germany
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Te Kura Ngahere | School of Forestry, University of Christchurch, Christchurch 8041, New Zealand
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Manaaki Whenua | Landcare Research, Palmerston North 4472, New Zealand
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2865; https://doi.org/10.3390/rs11232865
Received: 11 October 2019 / Revised: 18 November 2019 / Accepted: 28 November 2019 / Published: 2 December 2019
(This article belongs to the Section Forest Remote Sensing)
The endemic New Zealand kauri trees (Agathis australis) are of major importance for the forests in the northern part of New Zealand. The mapping of kauri locations is required for the monitoring of the deadly kauri dieback disease (Phytophthora agathidicida (PTA)). In this study, we developed a method to identify kauri trees by optical remote sensing that can be applied in an area-wide campaign. Dead and dying trees were separated in one class and the remaining trees with no to medium stress symptoms were defined in the two classes “kauri” and “other”. The reference dataset covers a representative selection of 3165 precisely located crowns of kauri and 21 other canopy species in the Waitakere Ranges west of Auckland. The analysis is based on an airborne hyperspectral AISA Fenix image (437–2337 nm, 1 m2 pixel resolution). The kauri spectra show characteristically steep reflectance and absorption features in the near-infrared (NIR) region with a distinct long descent at 1215 nm, which can be parameterised with a modified Normalised Water Index (mNDWI-Hyp). With a Jeffries–Matusita separability over 1.9, the kauri spectra can be well separated from 21 other canopy vegetation spectra. The Random Forest classifier performed slightly better than Support Vector Machine. A combination of the mNDWI-Hyp index with four additional spectral indices with three red to NIR bands resulted in an overall pixel-based accuracy (OA) of 91.7% for crowns larger 3 m diameter. While the user’s and producer’s accuracies for the class “kauri” with 94.6% and 94.8% are suitable for management purposes, the separation of “dead/dying trees” from “other” canopy vegetation poses the main challenge. The OA can be improved to 93.8% by combining “kauri” and “dead/dying” trees in one class, separate classifications for low and high forest stands and a binning to 10 nm bandwidths. Additional wavelengths and their respective indices only improved the OA up to 0.6%. The method developed in this study allows an accurate location of kauri trees for an area-wide mapping with a five-band multispectral sensor in a representative selection of forest ecosystems. View Full-Text
Keywords: hyperspectral; airborne; optical remote sensing; pixel-based; Random Forest; AISA Fenix; Waitakere Ranges; kauri dieback disease hyperspectral; airborne; optical remote sensing; pixel-based; Random Forest; AISA Fenix; Waitakere Ranges; kauri dieback disease
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Meiforth, J.J.; Buddenbaum, H.; Hill, J.; Shepherd, J.; Norton, D.A. Detection of New Zealand Kauri Trees with AISA Aerial Hyperspectral Data for Use in Multispectral Monitoring. Remote Sens. 2019, 11, 2865.

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