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

Stein, Beth R.; Thomas, Valerie A.; Lorentz, Laura J.; Strahm, Brian D.

doi:10.1080/15481603.2014.912875

Cited by 30 publications

(31 citation statements)

References 72 publications

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“…Moreover, the results obtained for ENF tree species are surprising as previous studies investigating the relationship between foliar N[%] and in situ measured spectra 305 reported higher r 2 values, r 2 = 0.59 and r 2 = 0.81 in spruce and pine forest, respectively (Stein et al, 2014;Schlerf et al, 2010). …”

Section: Canopy N Concentration Detection 285mentioning

confidence: 75%

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Loozen

Rebel

Karssenberg

et al. 2017

Preprint

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) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant relationships between MTCI and both canopy N concentration and content, r 2 = 0.32 and r 2 = 0.17, respectively.We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N 20 concentration was strongest for deciduous broadleaf and mixed plots (r 2 = 0.25 and r 2 = 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r 2 = 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots (r 2 = 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. capacity (Evans, 1989;Reich et al., 1995;Reich et al., 1997;Reich et al., 1999;Wright et al., 2004), specific leaf area, leaf life span (Reich et al., 1999;Wright et al., 2004) and light use efficiency (Kergoat et al., 2008). Leaf N concentration expressed on a leaf area basis, also called leaf N content (N g m -2 ) has also been linked with chlorophyll content, Rubisco content (Evans, 35 1989) and photosynthetic capacity (Evans, 1989;Reich et al., 1995). At stand scale, canopy nitrogen concentration, which represents the leaf N concentration averaged over the stand canopy, has also been found to correlate with above ground Net Primary Productivity (NPP) (Reich, 2012), while canopy N content has been linked with the canopy light use efficiency (Green et al., 2003).Given their links to many vegetation processes, leaf and canopy N variables could be used to constrain N cycle modules in 40 global vegetation models. At the global scale, ample data is available to constrain models for the C cycle; however, data to constrain the N cycle are limited. Currently, canopy N data is not widely available and canopy N sampling campaigns are timeconsuming and thus expensive tasks. Moreover, upscaling from local sampling campaign measurements represents an additional limitation. In this perspective, local, regional or even global remotely sensed canopy N estimates will be a valuable addition, enabling us to collect information in a less time intensive and expensive manner than traditional on-field sampling 45 campaigns. Such near global canopy N estimates will be beneficial as input in global vegetation models or to calibrate and validate these models.Currently, different remote sensing techniques have been applied to detect canopy N in terrestrial vegetation. Imaging spectrometry from either airborne or spaceborne sensors coupled with different analysis methods, including partial least squares regression (PLS), continuu...

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Section: Canopy N Concentration Detection 285mentioning

confidence: 75%

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Loozen

Rebel

Karssenberg

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…It is a nonparametric test (i.e., does not rely on data being part of a distribution) that ranks the data for each variable and then applies Pearson's correlation equation to determine the associations between the ranked data [23]. Other researchers have used Spearman correlations to assess relationships between spectral reflectance data and plant components [24] [25]. The R software (version 3.0.2 [26]) was used to complete the statistical analysis.…”

Section: Statistical Analysesmentioning

confidence: 99%

Relationships between Microsclerotia Content and Hyperspectral Reflectance Data in Soybean Tissue Infected by Macrophomina phaseolina

Fletcher¹,

Smith²,

Mengistu³

et al. 2014

AJPS

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Alternative methods are needed to assess the severity of charcoal rot disease [Macrophomina phaseolina (Tassi) Goid] in soybean [Glycine max (L.)] plant tissue. The objective of this study was to define the relationship between light reflectance properties and microsclerotia content of soybean stem and root tissue. Understanding that relationship could lead to using spectral reflectance data as a tool to assess the severity of charcoal rot disease in soybean plants, thus reducing human bias associated with qualitative analysis of soybean plant tissue and cost and time issues connected with quantitative analysis. Hyperspectral reflectance measurements (400 -2490 nm) were obtained with a non-imaging spectroradiometer of non-diseased and charcoal rot diseased ground stem and root tissue samples of six soybean genotypes ("Clark", "LD00-3309", "LG03-4561-14", "LG03-4561-19", "Saline", and "Y227-1"). Relationships between the reflectance measurements and tissue microsclerotia content were evaluated with Spearman correlation (rs) analysis (p < 0.05). Moderate (r s = ±0.40 to ±0.59), strong (r s = ±0.60 to ±0.79), and very strong (r s = ±0.80 to ±1.00) negative and positive statistically significant (p < 0.05) monotonic relationships were observed between tissue spectral reflectance values and tissue microsclerotia content. Nearinfrared and shortwave-infrared wavelengths had the best relationships with microsclerotia content in the ground tissue samples, with consistent results obtained with near-infrared wavelengths in that decreases in near-infrared spectral reflectance values were associated with increases in microsclerotia content in the stem and root tissue of the soybean plants. The findings of R. S. Fletcher et al. 3738 this study provided evidence that relationships exist between tissue spectral reflectance and tissue microsclerotia content of soybean plants, supporting spectral reflectance data as a means for assessing variation of microsclerotia content in soybean plants. Future research should focus on the modelling capabilities of the selected wavelengths and on the feasibility of using these wavelengths in machine learning algorithms to differentiate non-diseased from charcoal rot diseased tissue.

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“…However, technological advances in recent years have made it possible to analyze LNC using nondestructive methods (Pimstein et al, 2011;Mahajan et al, 2016;Oliveira et al, 2017). Spectral analysis techniques, using a variety of sensors, have enabled crops to be managed using non-destructive methods (Ustin et al, 2009;Ollinger, 2010;Schlemmer et al, 2013), and the technology could be extended to tree plantations (Stein et al, 2014;Oliveira et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Deficiencies in nutrients other than N can alter plant metabolism and consequently a leaf's reflectance (Mariotti et al, 1996;Mahajan et al, 2014). However, applications linking leaf reflectance to other macronutrients and micronutrients are poorly developed for trees (eg, Ponzoni and Gonçalves, 1999;Adams et al, 2000;Pimstein et al, 2011;Mahajan et al, 2014;Stein et al, 2014). Therefore, the study of relationships between N, phosphorous (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), boron (B), iron (Fe), zinc (Zn), manganese (Mn) and cooper (Cu) concentrations and leaf reflectance can be useful to procced nondestructive analysis in Eucalyptus stands.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Estimation of Leaf Nutrient Concentrations in Eucalyptus Plantations

Oliveira

Santana

Oliveira

2019

CERNE

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HIGHLIGHTSOptimal wavelengths related to nutrient concentrations were identified.Nutrient indices were developed to predict leaf nutrient concentrations. Nutrient concentration in Eucalyptus were predicted by spectral indices.Quantitative models between spectra and nutrient concentrations were established. ABSTRACTDetermination of leaf nutrient concentrations is traditionally performed by carrying out destructive procedures, requiring laboratory chemical analysis, specialized equipment, and skilled labor. However, technological advances in recent years have now made it possible to analyze leaf nutrient concentrations using non-destructive methods such as leaf reflectance. This study evaluates the relationship between leaf reflectance and nutrient concentration in order to develop and apply nutrient indices by proximal sensing data in plantations of Eucalyptus. Two experiments were carried out for the development, application and validation of nutrient indices in Eucalyptus stands in the municipalities of Lassance and Três Marias, Minas Gerais state, Brazil. Study I was undertaken in Eucalyptus stands with 25 months old and, three clones. Leaves from the lower crown were visually classified into five color patterns using the Munsell chart for plant tissues. Study II was carried out in commercial stands of a hybrid of Eucalyptus urophylla ST Blake, whith 9, 12, 15 and 25 months old. Analysis revealed strong relationships between leaf nutrients and leaf reflectance in the visible and near infrared regions (400 -900 nm) of the light spectrum. Correlation analysis between leaf reflectance and nutrients can be useful in an exploratory analysis of leaf nutrient concentration. Some nutrient indices developed (NI, PI, SI and CuI) provided satisfactory estimates of leaf nutrient concentration in Eucalyptus stands. The nutrient indices developed in this study may be a useful alternative to laboratory chemical analysis. v.25 n.2 2019 185 CERNE OLIVEIRA et al.

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

Cited by 30 publications

References 72 publications

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Relationships between Microsclerotia Content and Hyperspectral Reflectance Data in Soybean Tissue Infected by Macrophomina phaseolina

Nondestructive Estimation of Leaf Nutrient Concentrations in Eucalyptus Plantations

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