Use of near-infrared reflectance spectroscopy in predicting nitrogen, phosphorus and calcium contents in heterogeneous woody plant species

Petisco, Cristina; García-Criado, B.; Aldana, Beatriz R. Vázquez de; Zabalgogeazcoa, Íñigo; Mediavilla, Sonia; García-Ciudad, A.

doi:10.1007/s00216-004-3046-7

Cited by 101 publications

(70 citation statements)

References 26 publications

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“…Derivative models improved predictions and parsimony across all nutrients. This result agrees with a similar study by Petisco et al (2005) that obtained better results using first and second derivative transformations for N, P, and Ca than the logarithm transformation. Furthermore, Karnieli, Karnieli, and Bonfil (2007) found greater success with derivatives and wavelength selection than the full spectrum of reflectance measurements for N predictions.…”

Section: Regional Analysissupporting

confidence: 93%

“…Asner et al (2011) used PLSR on imaging spectroscopy data to predict macronutrients in tropical forest canopies consisting of several thousand plant species; they obtained R 2 values for N, P, K, Ca, and Mg of 0.77, 0.63, 0.51, 0.65, and 0.57, respectively. Similarly, Petisco et al (2005) found much higher R 2 values for N, P, and Ca (R 2 = 0.99, 0.94, and 0.95) across 18 different woody plant species in the Iberian Peninsula. The absorbance transformations, major indices, and subsets of selected wavelengths did not considerably improve the explanatory power of the models.…”

Section: Regional Analysismentioning

confidence: 82%

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Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Stein

Thomas

Lorentz

et al. 2014

GIScience & Remote Sensing

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Given the economic importance of loblolly pine (Pinus taeda) in the southeastern US, there is a need to establish efficient methods of detecting potential nutrient deficiencies that may limit productivity. This study evaluated the use of remote sensing for macronutrient assessment in loblolly pine. Reflectance-based models were developed at two spatial scales: (1) a natural nutrient gradient across the species' range, and (2) localized fertilization and genotype treatments in North Carolina and Virginia. Fascicles were collected regionally from 237 samples of 3 flushes at 18 sites, and locally from 72 trees with 2 fertilization treatments and 6 genotypes. Sample spectral reflectance was calculated using a spectroradiometer, and nutrient concentrations were measured with dry combustion and wet chemical digestion. Results were analyzed statistically using nutrient correlations with reflectance and common vegetation indices, and partial least squares regression (PLSR). PLSR performed well at the regional scale, with R 2 values for nitrogen, phosphorus, potassium, calcium, and magnesium of 0. 81, 0.70, 0.68, 0.42, and 0.51, respectively. No model successfully predicted nutrients at local sites for any treatment or canopy stratum. This discrepancy implies that a large nutrient range and/or spatial scale may be necessary to model loblolly pine nutrients with spectral reflectance.

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Section: Regional Analysissupporting

confidence: 93%

Section: Regional Analysismentioning

confidence: 82%

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Stein

Thomas

Lorentz

et al. 2014

GIScience & Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The number of PLS components deemed optimal in the final variable selection with respect to RMSE CV was one, resulting in a model of low complexity. This identified wavelength range for determining phosphorus agrees with wavelength regions which have previously been reported in studies of phosphorus content in dried woody plant species 20 and in soil. 21 The latter study in soil phosphorus concentration also found large regression coefficient values at other wavelengths, including the visible region.…”

Section: Wavelength Selectionsupporting

confidence: 89%

Estimation of phosphorus-based flame retardant in wood by hyperspectral imaging—a new method

Stefansson

Burud

Thiis

et al. 2018

J. Spectral Imaging

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It is recognised that flame retardant chemicals degrade and leach out of flame-protected wood claddings when exposed to natural weathering. However, the ability to survey the current state of a flame retardant treatment applied to a wood cladding, an arbitrary length of time after the initial application, is limited today. In this study, hyperspectral imaging in the near infrared to short-wavelength infrared region is used to quantify the amount of flame retardant present on wooden surfaces. Several sets of samples were treated with various concentrations of a flame retardant chemical and scanned with a push broom hyperspectral camera. An inductively coupled plasma (ICP) spectroscopy analysis of the outermost layer of the treated samples was then carried out in order to determine each sample’s phosphorus content, the active ingredient in the flame retardant. Spectra from the hyperspectral images were pre-processed with extended multiplicative scatter correction, and the phosphorus content was modelled using a partial least squares (PLS) regression model. The PLS regression yielded robust predictions of surface phosphorus content with a coefficient of determination, R2, between 0.8 and 0.9 on validation data regardless of whether the flame retardant chemical had been applied to the surface of the wood or pressure-impregnated into it. The result from the study indicates that spectral imaging around the 2400–2531 nm wavelength region is favourable for quantifying the amount of phosphorus-based flame retardant contained in the outermost layer of non-coated wooden claddings. The results also reveal that the uptake of phosphorus-based flame retardant does not occur uniformly throughout the wood surface, but is to a larger extent concentrated in the earlywood regions than in the latewood.

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“…It is well known that total nitrogen (N), inorganic ash (ash), crude fibre (CF), ether extract (EE) and nitrogen free extracts (NFE) can be determined with NIRS (e.g. [32,45] as well as mineral compositions [38,33,16]. However, most studies concentrate on pure sample sets of wheat [25], maize silage [11,23], herbages, grass silages [30,2], legumes [19,9], rice [48] sunflower [26] or hemp [44].…”

Section: Focus Of This Studymentioning

confidence: 98%

Assessment of Mass Flows and Fuel Quality During Mechanical Dehydration of Silages Using Near Infrared Reflectance Spectroscopy

et al. 2009

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This study was implemented to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) technology to estimate chemical composition of dried press cake samples characterised by a wide range of parent materials. A total of 210 samples, derived from two studies on production of solid fuels from agricultural crops by application of the IFBB technology (Integrated production of solid fuels and biogas from biomass), were analysed to determine their chemical composition. A Foss XDSspectrometer was used to obtain near-infrared spectra (400-2,500 nm). Prediction equations, developed for chemical components, showed that NIRS technology could predict N, inorganic ash (ash), crude fiber (CF), ether extract (EE) and nitrogen free extracts (NFE) accurately (RSQ cal and SECV of 0.93 and 0.04 % DM, 0.89 and 0.48 % DM, 0.93 and 1.67 % DM, 0.87 and 0.28 % DM and 0.93 and 1.72 % DM, respectively). Mineral components could also be predicted with a moderate degree of accuracy using NIRS technology (RSQ cal and SECV of 0.85 and 0.10 % DM (K), 0.77 and 0.01 % DM (P) and 0.84 and 0.02 % DM (Cl), respectively), whereas calibration of gross energy (GE) did not succeed. Subsequent, external validation confirmed these results. Regression of mass flows with measured and NIRS-predicted values showed accurate results (RSQ 0.72-0.99) and promise an accelerated quality management in working biogas plants.

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Use of near-infrared reflectance spectroscopy in predicting nitrogen, phosphorus and calcium contents in heterogeneous woody plant species

Cited by 101 publications

References 26 publications

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales

Estimation of phosphorus-based flame retardant in wood by hyperspectral imaging—a new method

Assessment of Mass Flows and Fuel Quality During Mechanical Dehydration of Silages Using Near Infrared Reflectance Spectroscopy

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