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Prediction of senescent rangeland canopy structural attributes with airborne hyperspectral imagery

Permanent URL:
http://handle.nal.usda.gov/10113/58400
File:
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Abstract:
Canopy structural and chemical data are needed for senescent, mixed-grass prairie landscapes in autumn; yet data-driven models are lacking for rangelands dominated by non-photosynthetically active vegetation (NPV). We report how field data and aerial hyperspectral imagery were modeled to predict canopy attributes post growing-season using two approaches: (1) application of narrow spectral regions with Vegetation Indices (VIs) and (2) application of the full spectrum with Partial Least Squares Regression (PLSR). Analyses of the full spectrum using PLSR resulted in slightly lower root-mean-square error of prediction, as compared to VIs, which represent reflectance ratios for specific spectral bands.
Author(s):
Rebecca Phillips , Mark West , Nicanor Saliendra , Brad Rundquist , Duane Pool
Note:
USDA Scientist Submission
Source:
GIScience & Remote Sensing 2013 4 1 April 2013 v.50 no.2
Language:
English
Publisher:
Taylor & Francis
Year:
2013
Collection:
Journal Articles, USDA Authors, Peer-Reviewed