Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease

Abstract

Insect and pathogen outbreaks have a major impact on northern forest ecosystems. Even for pathogens that have been present in a region for decades, such as beech bark disease (BBD), new waves of tree mortality are expected. Hence, there is a need for innovative approaches to monitor disease advancement in real time. Here, we test whether airborne hyperspectral imaging – involving data from 344 wavelengths in the visible, near infrared (NIR) and short‐wave infrared (SWIR) – can be used to assess beech bark disease severity in southern Quebec, Canada. Field data on disease severity were linked to airborne hyperspectral data for individual beech crowns. Partial least‐squares regression (PLSR) models using airborne imaging spectroscopy data predicted a small proportion of the variance in beech bark disease severity: the best model had an R2 of only 0.09. Wavelengths with the strongest contributions were from the red‐edge region (~715 nm) and the SWIR (~1287 nm), which may suggest mediation by canopy greenness, water content, and canopy architecture. Similar models using hyperspectral data taken directly on individual leaves had no explanatory power (R2 = 0). In addition, airborne and leaf‐level hyperspectral datasets were uncorrelated. The failure of leaf‐level models suggests that canopy structure was likely responsible for the limited predictive ability of the airborne model. Somewhat better performance in predicting disease severity was found using common band ratios for canopy greenness assessment (e.g., the Green Normalized Difference Vegetation Index, gNDVI, and the Normalized Phaeophytinization Index, NPQI); these variables explained up to 19% of the variation in disease severity. Overall, we argue that the complexity of hyperspectral data is not necessary for assessing BBD spread and that spectral data in general may not provide an efficient means of improving BBD monitoring on a larger scale.