A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems
(2008)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.