Abstract
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.
Original language | English (US) |
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Pages (from-to) | 217-223 |
Number of pages | 7 |
Journal | Remote Sensing of Environment |
Volume | 124 |
DOIs | |
State | Published - Sep 2012 |
Externally published | Yes |
Keywords
- Continuum removal
- Derivative analysis
- Hyperspectral
- Nitrogen
- Sagebrush
ASJC Scopus subject areas
- Soil Science
- Geology
- Computers in Earth Sciences