Spectroscopic detection of nitrogen concentrations in sagebrush

Mitchell, Jessica J.; Glenn, Nancy F.; Sankey, Temuulen Tsagaan; Derryberry, DeWayne R.; Anderson, Matthew; Hruska, Ryan

doi:10.1080/01431161.2011.580017

Cited by 15 publications

(25 citation statements)

References 20 publications

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“…Our study assumed a single shrub stratum and calculated plot level N by combining sagebrush percent cover and leaf level N concentrations in each plot with a mean LMA measurement for big sagebrush (Artemisia tridentata spp.). A single mean LMA measurement was obtained by averaging LMA measurements collected from the leaves of 36 independent samples collected from the same study area in summer 2009 (Mitchell et al, 2012). Single-sided leaf area was measured using a Li-COR Biosciences area meter (model LI-3100; Lincoln, Nebraska).…”

Section: Field Data Collection and Analysismentioning

confidence: 99%

“…The authors reported late summer sagebrush samples had relatively low water and chlorophyll content and relatively strong absorption features related to constituents such as nitrogen, lignin and cellulose at 2100 and 2300 nm. A study of sagebrush N conducted at the field scale (Mitchell et al, 2012) reported encouraging results when estimating N concentrations using spectroradiometer data collected over individual shrub canopies and transformed using standard derivative analysis. The field spectroscopy study determined that leaf water and a known N absorption feature near 2180 nm accounted significantly for variations in N concentration in the live shrub dataset analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Remote sensing of sagebrush canopy nitrogen

Mitchell

Glenn

Sankey

et al. 2012

Remote Sensing of Environment

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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.

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Section: Field Data Collection and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote sensing of sagebrush canopy nitrogen

Mitchell

Glenn

Sankey

et al. 2012

Remote Sensing of Environment

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“…They are generally divided into three groups: gallic acid derivatives, gallotannins, and ellagitannins (Salminen and Karonen, 2011). Gallic acid derivatives and gallotannins are typified by galloyl groups esterified to glucose or quinic acid (Mueller-Harvey, 2001). More than 500 ellagitannin structures have been described and they occur more frequently in nature than gallotannins (Salminen and Karonen, 2011).…”

Section: Introductionmentioning

confidence: 99%

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

Kokaly

Skidmore

2015

International Journal of Applied Earth Observation and Geoinfor

119

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a b s t r a c tPast laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 m is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic C H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r 2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r 2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r 2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 m, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).Published by Elsevier B.V. This is an open access article under the CC BY license

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“…In situations without available hyperspectral or multispectral imagery, researchers have resampled the field spectra data to band settings of existing or planned sensors to test their capability for different remote sensing applications (Adjorlolo et al, 2012;Mansour et al, 2012;Mitchell et al, 2012a;Mutanga and Skidmore, 2005;Nocita et al, 2011;Oumar and Mutanga, 2010). A major challenge of directly linking resampled field spectral results with airborne or space borne images is that the signal to noise ratio is higher when using field spectrometers mainly due to the shorter signal path length than satellite or airborne images (Milton et al, 2009b).…”

Section: Introductionmentioning

confidence: 99%