Testing the robustness of predictive models for chlorophyll generated from spaceborne imaging spectroscopy data for a mixedwood boreal forest canopy

Gökkaya, Kemal; Thomas, Valerie A.; Noland, Thomas L.; McCaughey, Harry; Treitz, Paul

doi:10.1080/01431161.2013.866291

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…It is possible that the tendency of loblolly pine trees to increase leaf area index when fertilized, rather than foliar N, may confound model development (Gough, Seiler, and Maier 2004). Patterns across the 18 sites in the SE US were apparent because the nutrient ranges were much greater, much as other studies have shown success with interspecific models (Gökkaya et al 2014;Im et al 2009). Spectral differences exist between genotypes and hybrids of different species, as well as in the Bladen Lakes and Reynolds Homestead loblolly pine varieties (Espinoza, Hodge, and Dvorak 2012;Im et al 2009).…”

Section: Local Analysismentioning

confidence: 68%

“…Visible wavelengths, which are known to indicate K deficiency, had a significant effect on K predictions (Zhai et al 2013). Furthermore, wavelengths in the chlorophyll absorption features and red-edge are highly related to Mg, which is a chlorophyll component (Gökkaya et al 2014).…”

Section: Model Componentsmentioning

confidence: 99%

“…These indices were selected due to Table 1. Common vegetation reflectance indices and their formulas calculated for correlation with and prediction of foliar macronutrient concentrations (Gökkaya et al 2014;Schlemmer et al 2013;Stagakis et al 2010;Ustin et al 2009). "R" denotes reflectance and "D" the derivative for a given wavelength (nm).…”

Section: Statistical Approachmentioning

confidence: 99%

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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: Local Analysismentioning

confidence: 68%

Section: Model Componentsmentioning

confidence: 99%

Section: Statistical Approachmentioning

confidence: 99%

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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

View full text Add to dashboard Cite

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“…In recent years, hyperspectral estimation models have been established to retrieve chlorophyll content of wheat [2][3], boreal trees [4], soybean [5][6], corn [7][8][9], vegetable [10] by some scholars. The current research are using vegetation index and chlorophyll content to build regression function, these statistical function are simple and fast, but the function are relay to the sampling site and sampling environment, these statistical models can change with the time and space, which is lack of universality.…”

Section: Introductionmentioning

confidence: 99%

Estimating leaf chlorophyll concentration in soybean using random forests and field imaging spectroscopy

Yan

2014

2014 the Third International Conference on Agro-Geoinformatics

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An accurate quantitative estimation of crop chlorophyll content is of great importance for a wide range of monitoring crop grow health condition and estimating biomass, since radiative transfer model are complex caused by the nonlinear relationship between crop spectral and chlorophyll content and the uncertainties in the land surface systems, traditional inversion techniques can not satisfied with the demand of accurate estimation of chlorophyll content. Alternatively, random forests are able to cope with the strong nonlinearity of the functional dependence between the biophysical parameter and the observed reflected radiance, it may therefore be more suitable candidates for estimating crop biochemistry parameters from inversion of radiative transfer model. It is crucial to apply random forests for inversion of radiative transfer model, so as to construct hyperspectral estimation model for crop chlorophyll content. The aim of this paper is to explore the feasibility of using random forests and field imaging spectroscopy for the estimating leaf chlorophyll concentration in soybean.Field spectroscopy was carried out with an ASD FieldSpec3 in summer 2009, at the farmlands of city of Chang'chun, Jinlin province. The measured spectral range was between 350-2500 nm with a sampling interval of 1.4 nm in the 350-1000 nm range and 2 nm in the 1000-2500 nm range, and the spectral range between 350-1250 nm was used for the retrieval of leaf chlorophyll concentration. Leaf chlorophyll concentration in soybean was measured by SPAD-502. Each sample sites was recorded with a Global Position System (GPS).Firstly, a training data set through PROSPECT was established to link soybean spectrum and the corresponding chlorophyll content. Secondly, random forests were adopted to train the training data set, in order to establish leaf chlorophyll content estimation model. Thirdly, a validation data set was established based on proximal hyperspectral data, and the leaf estimation model of chlorophyll concentration was applied to the validation data set to estimate leaf chlorophyll content of soybean in the research area. The estimation model yielded results with a coefficient of determination of 0.9317 and a mean square error (MSE) of 74.2569. The results indicate that model based on random forests and field imaging spectroscopy can estimate leaf chlorophyll content of soybean accurately, and it can solve soybean chlorophyll content inversion problem even with inadequate samples. Random forests and field imaging spectroscopy would be used as a new quickly and nondestructive method to estimate the chlorophyll content of the soybean.Future study will concentrated on scaled up the field estimation model to satellite remote sensing level, which will monitor the soybean's health condition in a large scale.

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Winter respiratory C losses provide explanatory power for net ecosystem productivity

et al. 2017

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Accurate predictions of net ecosystem productivity (NEPc) of forest ecosystems are essential for climate change decisions and requirements in the context of national forest growth and greenhouse gas inventories. However, drivers and underlying mechanisms determining NEPc (e.g., climate and nutrients) are not entirely understood yet, particularly when considering the influence of past periods. Here we explored the explanatory power of the compensation day (cDOY)—defined as the day of year when winter net carbon losses are compensated by spring assimilation—for NEPc in 26 forests in Europe, North America, and Australia, using different NEPc integration methods. We found cDOY to be a particularly powerful predictor for NEPc of temperate evergreen needleleaf forests (R2 = 0.58) and deciduous broadleaf forests (R2 = 0.68). In general, the latest cDOY correlated with the lowest NEPc. The explanatory power of cDOY depended on the integration method for NEPc, forest type, and whether the site had a distinct winter net respiratory carbon loss or not. The integration methods starting in autumn led to better predictions of NEPc from cDOY then the classical calendar method starting 1 January. Limited explanatory power of cDOY for NEPc was found for warmer sites with no distinct winter respiratory loss period. Our findings highlight the importance of the influence of winter processes and the delayed responses of previous seasons' climatic conditions on current year's NEPc. Such carry‐over effects may contain information from climatic conditions, carbon storage levels, and hydraulic traits of several years back in time.

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Testing the robustness of predictive models for chlorophyll generated from spaceborne imaging spectroscopy data for a mixedwood boreal forest canopy

Cited by 8 publications

References 40 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

Estimating leaf chlorophyll concentration in soybean using random forests and field imaging spectroscopy

Winter respiratory C losses provide explanatory power for net ecosystem productivity

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