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Abstract: Mountain pine beetle (Dendroctonus ponderosae Hopkins) is the most destructive insect infesting mature pine forests in North America and has devastated millions of hectares of forest in western Canada. Past studies have demonstrated the use of multispectral imagery for remote identification and mapping of visible or red attack damage in forests. This study aims to detect pre-visual or green attack damage in lodgepole pine needles by means of hyperspectral measurements, particularly via continuous wavelet analysis. Field measurements of lodgepole pine stands were conducted at two sites located northwest of Edmonton, Alberta, Canada. In June and August of 2007, reflectance spectra (350–2500nm) were collected for 16 pairs of trees. Each of the 16 tree pairs included one control tree (healthy), and one stressed tree (girdled to simulate the effects of beetle infestation). In addition, during the period of June through October 2008, spectra were collected from 15 pairs of control- and beetle-infested trees. Spectra derived from these 31 tree pairs were subjected to a continuous wavelet transform, generating a scalogram that compiles the wavelet power as a function of wavelength location and scale of decomposition. Linear relationships were then explored between the wavelet scalograms and chemical properties or class labels (control and non-control) of the sample populations in order to isolate the most useful distinguishing spectral features that related to infested or girdled trees vs. control trees. A deficit in water content is observed in infested trees while an additional deficit in chlorophyll content is seen for girdled trees. The measurable water deficit of infested and girdled tree samples was detectable from the wavelet analysis of the reflectance spectra providing a novel method for the detection of green attack. The spectral features distinguishing control and infested trees are predominantly located between 950 and 1390nm from scales 1 to 8. Of those, five features between 1318 to 1322nm at scale 7 are consistently found in the July and August 2008 datasets. These features are located at longer wavelengths than those investigated in previous studies (below 1100nm) and provide new insights into the potential remote detection of green attack. Spectral features that distinguish control and girdled trees were mostly observed between 1550 and 2370nm from scales 1 to 5. The differing response of girdled and infested trees appears to indicate that the girdling process does not provide a perfect simulation of the effects caused by beetle infestation. It remains to be determined if the location of the 1318–1322nm features, near the edge of a strong atmospheric water absorption band, will be sufficiently separable for use in airborne detection of green attack. A plot comparing needle water content and wavelet power at 1320nm reveals considerable overlap between data derived from both infested and control samples, though the groups are statistically separable. This obstacle may preclude a high accuracy separation of healthy and infected single individuals, but establishing threshold identification levels may provide an economical, efficient and expeditious method for discriminating between healthy and infested tree populations. [Copyright &y& Elsevier]

Abstract: A growing number of investigations have shown that remote sensing of foliar nitrogen (N) concentration in plant canopies can be achieved with imaging spectroscopy, or hyperspectral remote sensing, from satellite or airborne sensors. Development of this approach has been fueled by recognition that foliar N is related to a variety of ecological and biogeochemical processes, ranging from the spread of invasive species to the ecosystem effects of insect defoliation events to patterns of N cycling in forest soils. To date, most studies have focused on building site-specific foliar N detection algorithms applied to individual scenes or small landscapes that have been intensively characterized with local field measurements. However, the growing number of well-measured sites, combined with improvements in image data quality and processing methods provide an opportunity to begin seeking more general N detection methods that can be applied to a broader range of sites or to locations that lack intensive field measurements. Here, we combine data from several independent efforts in North America, Central America and Australia, to examine whether development of calibration methods to determine canopy nitrogen concentration across a wide range of forest ecosystems is possible. The analysis included data from 137 individual field plots within eight study sites for which imagery has been acquired from NASA''s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and/or Hyperion instruments. The combined dataset was used to evaluate site-specific calibration results as well as results obtained with data pooled across all sites. We evaluated the accuracy of results using plot- and site-level cross-validation wherein individual plots or entire sites were withheld and used as an independent validation of the resulting algorithms. In instances where all sites were represented in the calibration, canopy-level foliar N concentration was predicted to within 7–15% of the mean field-measured values indicating a strong potential for broadly applied foliar N detection. When whole sites were iteratively dropped from the calibration and predicted by remaining data, predictions were still significant, but less accurate (7–47% of mean canopy-level N concentration). This suggests that further development to include a wider range of ecosystems will be necessary before cross-site prediction accuracy approaches that seen in site-specific calibrations. Nevertheless, we view these results as promising, particularly given the potential value of foliar N estimates, even at a reduced level of confidence, at sites for which there is no possibility of conducting field data collections. [Copyright &y& Elsevier]