Abstract
New Zealand kauri trees are threatened by the kauri dieback disease (Phytophthora agathidicida (PA)). In this study, we investigate the use of pan-sharpened WorldView-2 (WV2) satellite and Light Detection and Ranging (LiDAR) data for detecting stress symptoms in the canopy of kauri trees. A total of 1089 reference crowns were located in the Waitakere Ranges west of Auckland and assessed by fieldwork and the interpretation of aerial images. Canopy stress symptoms were graded based on five basic stress levels and further refined for the first symptom stages. The crown polygons were manually edited on a LiDAR crown height model. Crowns with a mean diameter smaller than 4 m caused most outliers with the 1.8 m pixel size of the WV2 multispectral bands, especially at the more advanced stress levels of dying and dead trees. The exclusion of crowns with a diameter smaller than 4 m increased the correlation in an object-based random forest regression from 0.85 to 0.89 with only WV2 attributes (root mean squared error (RMSE) of 0.48, mean absolute error (MAE) of 0.34). Additional LiDAR attributes increased the correlation to 0.92 (RMSE of 0.43, MAE of 0.31). A red/near-infrared (NIR) normalised difference vegetation index (NDVI) and a ratio of the red and green bands were the most important indices for an assessment of the full range of stress symptoms. For detection of the first stress symptoms, an NDVI on the red-edge and green bands increased the performance. This study is the first to analyse the use of spaceborne images for monitoring canopy stress symptoms in native New Zealand kauri forest. The method presented shows promising results for a cost-efficient stress monitoring of kauri crowns over large areas. It will be tested in a full processing chain with automatic kauri identification and crown segmentation.
Highlights
The deadly kauri dieback disease (Phytophthora agathidicida (PA)) was first officially confirmed as a new pathogen by Beever in 2008 [1] in the Waitakere Ranges west of Auckland and later taxonomically described by Weir et al [2]
A selection of eight WV2 attributes and the maximum crown height resulted in a correlation of 0.85 and an root mean square error (RMSE) of 0.59 for all 1089 crowns with a minimum diameter of 3 m (Table 3, Table A3 in Appendix D)
The high overall performance of WV2 attributes with a correlation of 0.89 (RMSE of 0.48, mean absolute error (MAE) of 0.34) for the seven stress symptom levels and crowns with a diameter larger than 4 m diameter showed that WV2 data is well-suited to describe stress symptoms in kauri canopies
Summary
The deadly kauri dieback disease (Phytophthora agathidicida (PA)) was first officially confirmed as a new pathogen by Beever in 2008 [1] in the Waitakere Ranges west of Auckland and later taxonomically described by Weir et al [2]. It was detected over most of the natural distribution range of New Zealand kauri [3]. Airborne Light Detection and Ranging (LiDAR) data recently became available for the northern kauri forests, so that the main part of the distribution range of kauri trees in New Zealand is covered. We analyse the use of WorldView-2 (WV2) satellite data in combination with LiDAR data to detect stress symptoms in kauri trees in an object-based approach on manually segmented kauri crowns
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