Spatial Functional Data Modeling of Plant Reflectances

TL;DR

This paper develops advanced spatial functional data models to analyze plant leaf reflectance spectra across regions, incorporating environmental covariates and interactions to predict spectra at unobserved locations and interpret ecological patterns.

Contribution

It introduces novel spatial models that integrate wavelength, environmental covariates, and their interactions for functional plant spectra analysis, enabling improved spatial prediction and ecological interpretation.

Findings

Wavelength-covariate interactions improve model fit.

Spatial dependence enhances prediction accuracy.

Model features are all informative for spectral data analysis.

Abstract

Plant reflectance spectra - the profile of light reflected by leaves across different wavelengths - supply the spectral signature for a species at a spatial location to enable estimation of functional and taxonomic diversity for plants. We consider leaf spectra as "responses" to be explained spatially. These spectra/reflectances are functions over a wavelength band that respond to the environment. Our motivating data are gathered for several families from the Cape Floristic Region (CFR) in South Africa and lead us to develop rich novel spatial models that can explain spectra for genera within families. Wavelength responses for an individual leaf are viewed as a function of wavelength, leading to functional data modeling. Local environmental features become covariates. We introduce wavelength - covariate interaction since the response to environmental regressors may vary with wavelength, so may variance. Formal spatial modeling enables prediction of reflectances for genera at unobserved locations with known environmental features. We incorporate spatial dependence, wavelength dependence, and space-wavelength interaction (in the spirit of space-time interaction). We implement out-of-sample validation to select a best model, discovering that the model features listed above are all informative for the functional data analysis. We then supply interpretation of the results under the selected model.