Relative Linkages of Canopy-Level CO2 Fluxes with the Climatic and Environmental Variables for US Deciduous Forests

Abstract

We used a simple, systematic data-analytics approach to determine the relative linkages of different climate and environmental variables with the canopy-level, half-hourly CO2 fluxes of US deciduous forests. Multivariate pattern recognition techniques of principal component and factor analyses were utilized to classify and group climatic, environmental, and ecological variables based on their similarity as drivers, examining their interrelation patterns at different sites. Explanatory partial least squares regression models were developed to estimate the relative linkages of CO2 fluxes with the climatic and environmental variables. Three biophysical process components adequately described the system-data variances. The 'radiation-energy' component had the strongest linkage with CO2 fluxes, whereas the 'aerodynamic' and 'temperature-hydrology' components were low to moderately linked with the carbon fluxes. On average, the 'radiation-energy' component showed 5 and 8 times stronger carbon flux linkages than that of the 'temperature-hydrology' and 'aerodynamic' components, respectively. The similarity of observed patterns among different study sites (representing gradients in climate, canopy heights and soil-formations) indicates that the findings are potentially transferable to other deciduous forests. The similarities also highlight the scope of developing parsimonious data-driven models to predict the potential sequestration of ecosystem carbon under a changing climate and environment. The presented data-analytics provides an objective, empirical foundation to obtain crucial mechanistic insights; complementing process-based model building with a warranted complexity. Model efficiency and accuracy (R(2) = 0.55-0.81; ratio of root-mean-square error to the observed standard deviations, RSR = 0.44-0.67) reiterate the usefulness of multivariate analytics models for gap-filling of instantaneous flux data.

Environmental Controls, Emergent Scaling, and Predictions of Greenhouse Gas (GHG) Fluxes in Coastal Salt Marshes

“…A systematic data analytics methodology (Ishtiaq & Abdul‐Aziz, ) was utilized to estimate the relative environmental controls and dominant drivers of NEE CO2,uptake and NEE CH4,emission in the coastal marshes. The analytics involved a sequential application and synthesis of Pearson correlation analysis, principal component analysis (PCA) and factor analysis (FA) (Jolliffe, ), and partial least squares regression (PLSR) modeling (Wold et al, ).…”

“…The research was conducted by collecting field data from four salt marshes on southern Cape Cod (Waquoit Bay and adjacent estuaries), MA, incorporating a range of nutrient loading conditions. The relative controls of various environmental drivers on the salt marsh GHG fluxes are first investigated and estimated by employing a systematic data analytics methodology (Ishtiaq & Abdul‐Aziz, ). Dominant drivers of the fluxes are identified by resolving their mutual correlations in the multivariate space alongside process understanding.…”

Relative linkages of stream water quality and environmental health with the land use and hydrologic drivers in the coastal‐urban watersheds of southeast Florida

“…The research employed a systematic data analytics framework (Figure ), which involves a sequential application of four complementary data‐mining techniques—Pearson correlation matrix, PCA, FA, and partial least squares regression (PLSR) [ Ishtiaq and Abdul‐Aziz , ]. The four‐step analytics was designed to corroborate findings from different steps and synthesize information toward the overall outcomes.…”

Estimation of the Maturity Date of Soybean Breeding Lines Using UAV-Based Multispectral Imagery

“…PLSR models create linear combinations (known as components) of the original predictor variables (image features) to explain the observed variability in the responses (measured maturity dates). Additionally, PLSR largely is able to reduce the variability and instability of estimated responses caused by multicollinearity among predictors [21]. The PLSR was conducted using 'plsregress' function with a 10-fold cross-validation.…”