By Topic

Addressing the Effects of Canopy Structure on the Remote Sensing of Foliar Chemistry of a 3-Dimensional, Radiometrically Porous Surface

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
K. Olaf Niemann ; Department of Geography, University of Victoria, Victoria, Canada ; G. Quinn ; David G. Goodenough ; F. Visintini
more authors

Airborne and spaceborne imaging spectroscopy applied to measuring foliar chemistry has received considerable attention in the literature. Typically, results are based on data measuring all the reflective components that make up a given pixel. This introduces confounding variables that cannot be easily modeled. Spectral unmixing methods yield estimates of the percentage endmember coverage in each pixel. This methodology fails to provide spectra representing variations in these specific components and thus is not as accurate for inferring chemistry. We report on the integration of airborne LiDAR data with high resolution imaging spectroscopy. We compared laboratory-based leaf-level pigment modeling with results from airborne data. In this comparison two airborne datasets were generated; one representing spectra composed of all reflective elements within a forested plot, and a second representing the top of the dominant/codominant canopy. Empirical modeling indicated that there is an influence on the spectral reflectance recorded over a defined area from the lower canopy levels. This influence did not, however, add to our understanding of forest biology and structure.

Published in:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  (Volume:5 ,  Issue: 2 )