Remote sensing of sagebrush canopy nitrogen

Abstract

This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublandsa 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.

Distinguishing Early Successional Plant Communities Using Ground-Level Hyperspectral Data

“…An example of this calculation is illustrated using an average buckthorn spectral profile (Figure 4). The continuum hull is comprised of several lines connecting local maxima and has been used by many researchers to minimize noise, increase discriminability, and increase accuracy in estimating physiological characteristics [17,36,37]. For more information on continuum removal, refer to Schmidt and Skidmore [38] and Clark and Roush [39].…”

Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance

“…For calculating the first derivatives of the averaged reflectance values, the smallest derivative gap (3) in ViewSpec Pro™ was selected to avoid masking narrow absorption features (Mitchell et al, 2012). Then the first derivatives were employed to obtain red edge slope (maximum) and area (integral) between 680 and 750 nm (Zhu et al, 2013).…”

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

“…Ignoring these factors, the established relationships may turn out to be wrong [26][27][28]. Another empirical approach is multiple regression models among which SMLR and PLSR are the most commonly used [20,21,[29][30][31][32][33][34]. Influences of canopy structure and background are always neglected when nitrogen content is estimated with multiple regression models at the canopy scale.…”

“…The absorption features of nitrogen, which mainly result from the vibration of chemical bonds, are shadowed by water or are affected by the signal/noise ratio (SNR) [38]. Therefore, many studies at the canopy scale removed spectral regions with strong water absorptions after atmospheric correction, e.g., [17,29,30,32,39,40]. However, this practice may lead to the loss of useful spectral information.…”