A biochemical-spectral leaf model and method for characterizing ozone-damaged pine trees

This dissertation provides biochemical and spectral bases for remotely monitoring ozone-damaged forest, and improves theoretical understanding of radiative properties of vegetation.;I assembled 504 samples of Jeffrey and Ponderosa pine needles representing dominant needle conditions---green, winter fleck, sucking insect, scale insect, ozone damage---and a random mixture of non-ozone-damaged conditions. I measured visible reflectance and transmittance of these samples, and concentrations of total carotenoids and chlorophylls a and b.;Ozone-damaged needles have significantly lower pigment concentrations and chlorophyll to carotenoid ratios than other needles. A biochemical marker comprising Chl a, Chl a/Car, and Chl a/Chl b distinguishes ozone-damaged Jeffrey pine needles from non-ozone-damaged Ponderosa and Jeffrey pine needles. This marker is manifest in the spectral properties of needles.;Visible reflectance of ozone-damaged needles is significantly different from that of other needles. The spectral marker for ozone-damaged needles consists of increased visible reflectance, greater magnitude of reflectance slope from 516-527 nm and 637-669 nm, and a shift of the red edge (694-720 nm) to shorter wavelengths. This marker can be used with imaging spectroscopy and available aerial data to identify ozone-damaged forest.;I incorporated three pigments in a leaf radiative transfer model to better understand the relationship between leaf biochemical and spectral properties. The model successfully estimates reflectance from known pigment concentrations, but transmittance estimates are poor, and pigment concentration estimates from known reflectance and transmittance range from good to poor. Estimates of in vivo SACs are qualitatively correct, but values are distorted by inconsistencies in model physics.;This novel integration of leaf biochemistry and spectroscopy through application and theory is a significant step toward remotely measuring biophysical parameters. Biochemical and spectral properties can be used to identify specific leaf responses to stress, and models incorporating multiple pigments and accurate leaf structures can explain relationships among these properties. Understanding these relationships is crucial for measuring biophysical leaf responses with remote sensing.