Journal
REMOTE SENSING OF ENVIRONMENT
Volume 124, Issue -, Pages 217-223Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2012.05.002
Keywords
nitrogen; hyperspectral; continuum removal; derivative analysis; sagebrush
Funding
- Idaho Space Grant Consortium
- NOAA OAR ESRL Physical Sciences Division [NA06OAR4600124]
- NSF Idaho EPSCOR Program
- National Science Foundation [EPS-0814387]
- Office Of The Director
- EPSCoR [0814387] Funding Source: National Science Foundation
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This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands - a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R-2 value of 0.72 and an R-2 predicted value of 0.42 (n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R-2 to 0.95 (R-2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform. (c) 2012 Elsevier Inc. All rights reserved.
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