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Remote Sensing of Foliar N across a Range of Scales Using Data from Multiple Platforms
Scott
V.
Ollinger, University of New Hampshire, scott.ollinger@unh.edu
Lucie
C.
Lepine, University of New Hamphire, lucie.lepine@unh.edu
(Presenter)
Mary
E.
Martin, University of New Hamsphire, mary.martin@unh.edu
The concentration of nitrogen in foliage is central to numerous biogeochemical processes and can serve as an indicator of carbon assimilation, species composition and linkages between terrestrial and aquatic ecosystems. Efforts to detect foliar N via remote sensing began decades ago and have been continually improved using a variety of methods and sensors. Despite this, the use of foliar N in regional- to global-scale analyses has lagged, in part because we lack instruments that provide applicable data at broad scales and because our understanding of mechanisms that underlie foliar N detection is incomplete. Although the basic properties of leaf-level spectra have been known for decades, interpreting canopy level spectra is more challenging because leaf-level effects are complicated by stem- and canopy-level traits.
There are at least two potential solutions to these challenges. One is development of a space-based imaging spectrometer capable of providing regional to global coverage. Although planning for such instruments is underway (e.g. HyspIRI), it will be years before data become routinely available. A second possibility is through further investigation of the complex linkages between leaf, canopy, tree, and ecosystem properties that lead to repeatable correlations between mean %N and canopy reflectance in the NIR region. Here, we report progress on these potential solutions. First, we present results from refining generalized methods for estimating foliar N by expanding the range of vegetation conditions and plant traits in iterative regression equations with whole canopy spectral reflectance from imaging spectrometer data. Next, we examine whether these methods can be translated from imaging spectrometers to broad-band spectral features that are sensitive to plant traits. Our work is based on an integration of a large data set consisting of intensive field measurements, imaging spectrometry scenes from AVIRIS, and broad-band sensor data from Landsat and MODIS.
Presentation:
2013_Poster_Ollinger_70_89.ppt (8258k)
Presentation Type: Poster
Session: Poster Session 2-B
(Wed 4:30 PM)
Associated Project(s):
Poster Location ID: 70
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