The endemic New Zealand kauri trees (Agathis australis
) are under threat by the deadly kauri dieback disease (Phytophthora agathidicida
(PA)). This study aimed to identify spectral index combinations for characterising visible stress symptoms in the kauri canopy. The analysis is based on an aerial AISA hyperspectral image mosaic and 1258 reference crowns in three study sites in the Waitakere Ranges west of Auckland. A field-based assessment scheme for canopy stress symptoms (classes 1–5) was further optimised for use with RGB aerial images. A combination of four indices with six bands in the spectral range 450–1205 nm resulted in a correlation of 0.93 (mean absolute error 0.27, RMSE 0.48) for all crown sizes. Comparable results were achieved with five indices in the 450–970 nm region. A Random Forest (RF) regression gave the most accurate predictions while a M5P regression tree performed nearly as well and a linear regression resulted in slightly lower correlations. Normalised Difference Vegetation Indices (NDVI) in the near-infrared / red spectral range were the most important index combinations, followed by indices with bands in the near-infrared spectral range from 800 to 1205 nm. A test on different crown sizes revealed that stress symptoms in smaller crowns with denser foliage are best described in combination with pigment-sensitive indices that include bands in the green and blue spectral range. A stratified approach with individual models for pre-segmented low and high forest stands improved the overall performance. The regression models were also tested in a pixel-based analysis. A manual interpretation of the resulting raster map with stress symptom patterns observed in aerial imagery indicated a good match. With bandwidths of 10 nm and a maximum number of six bands, the selected index combinations can be used for large-area monitoring on an airborne multispectral sensor. This study establishes the base for a cost-efficient, objective monitoring method for stress symptoms in kauri canopies, suitable to cover large forest areas with an airborne multispectral sensor.
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