Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: scaling from leaf to image

English Abstract:

Nitrogen (N) and chlorophyll are important constituents of photosynthesis in plants and at Mer Bleue there is a strong relationship among species between N concentration and CO₂ uptake of foliar tissues. The goal of this study was to model total chlorophyll and nitrogen at the landscape scale from remotely sensed data utilizing a model insensitive to plant functional type, species and season. The relationship between spectral reflectance and foliar chlorophyll and nitrogen was examined for 17 species over a six-month growing period at Mer Bleue, an ombrotrophic bog located near Ottawa, Ontario, Canada. To date the relationship between spectral reflectance and foliar properties are poorly understood in peatlands due to the spectral variability between mosses and vascular plants. A model comprised of a continuous wavelet transform coupled with a neural network was constructed to predict chlorophyll and nitrogen from selected wavelet features (coefficients) at both leaf and airborne image scales. The model was compared to thirteen common spectral indices used to determine vegetation properties in forest environments. The heterogeneity of the vascular plant/moss cover over small spatial scales and the spectral complexity of the vegetation cover precluded a regression model to be derived from the spectral indices for all species and across seasons; the best model for all species combined was R² = 0.3. The final continuous wavelet model resulted in a noticeable improvement with R² values ranging from 0.8-0.9. We scaled up our predictive model from the leaf/capitulum level data to 40 cm spatial resolution 72-band airborne imagery (CASI-2 429.6 to 968.8 nm) to create surfaces of foliar chlorophyll and nitrogen for the study area.