| This dissertation deals with modeling the radiative regime in vegetation canopies and the possible remote sensing applications derived by solving the forward and inverse canopy transport equation. The aim of the research is to develop a methodology (called "end-to-end problem solution") that, starting from first principles describing the interaction between light and vegetation, constructs, as the final product, a tool that analyzes remote sensing data for precision agriculture (ripeness prediction). The procedure begins by defining the equations that describe the transport of photons inside the leaf and within the canopy. The resulting integro-differential equations are numerically integrated by adapting the conventional discrete-ordinate methods to compute the reflectance at the top of the canopy. The canopy transport equation is also analyzed to explore its spectral properties. The goal here is to apply Case's method to determine eigenvalues and eigenfunctions and to prove completeness.; A model inversion is attempted by using neural network algorithms. Using input-outputs generated by running the forward model, a neural network is trained to learn the inverse map. The model-based neural network represents the end product of the overall procedure.; During Oct 2002, an Unmanned Aerial Vehicles (UAVs) equipped with a camera system, flew over Kauai to take images of coffee field plantations. Our goal is to predict the amount of ripe coffee cherries for optimal harvesting. The Leaf-Canopy model was modified to include cherries as absorbing and scattering elements and two classes of neural networks were trained on the model to learn the relationship between reflectance and percentage of ripe, over-ripe and under-ripe cherries. The neural networks are interfaced with images coming from Kauai to predict ripeness percentage. Both ground and airborne images are considered. The latter were taken from the on-board Helios UAV camera system flying over the Kauai coffee field. The results are compared against hand counts and parchment data to evaluate the network performances on real applications. In ground images, the error is always less than 11%. In airborne image, the error bound is 20%.; The results are certainly adequate and show the tremendous potential of the methodology. |
