A Simulation Based Approach to Estimating the Three Dimensional Structure of the Harvard Forest with Multi-Modal Remote Sensing

Tracking carbon as it enters and exists each stage of the carbon cycle is necessary to help build understanding of the cycle's mechanics and its effect on climate. Satellite and airplane based remote sensing technologies have shown promising results in aiding in human understanding of our planet, including vegetative areas. The Harvard Forest has been studied in various ways over the course of the last century. In particular, synthetic aperture radar, LiDAR, and passive optical sensors have each been used to study the Harvard Forest. Employing a form of data fusion, we present an approach to estimate a forest stand's mean canopy height and biomass for each component tree species while employing minimal ground measurements. We present an approach where a database of simulated forest stands is generated containing both homogeneous stands and heterogeneous stands with up to four tree species present in a given stand. Each simulated stand is compared to an input stand on a number of criteria and a figure of similarity is calculated. In the case that a simulated stand isn't found with a figure of similarity below a set threshold, an iterative process is employed to modify the most similar stand to improve the factor of similarity by modifying the stand's species composition, tree densities, heights, and biomasses. A simulated stand, either pre-existing or developed dynamically will be considered a reasonable representation of the physical forest stand and the 3-D structure of the simulated stand will be reported as an estimate for that of the physical forest stand. This method relies heavily on our sensor simulators, including our fractal-based tree geometry generator, as well as SAR, IfSAR, LiDAR, and Optical simulators. We have previously investigated the ability of our method to differentiate between coniferous and deciduous trees in the same forest stand. We propose to extend this to a maximum of four different tree species, and to validate our approach in the Harvard Forest, a heavily studied region in central Massachusetts.