Human alteration of the terrestrial land surface through land use practices and land cover change alters natural systems at the local, regional, and global level.  While half of the human population lives in urban and urbanizing areas, at least a quarter live in areas that are densely populated but spatially disaggregated in an extent at least five times larger than dense urban environments.  These areas are a mosaic of residential, agricultural and other land uses that cannot be accurately resolved at coarse spatial resolutions.  Within these areas the management and spatial extent of vegetation cover often occurs at the scale of individual trees and fields.  To better understand the vegetation structure (e.g., biomass) within these landscapes, high spatial resolution remote sensing has proved an effective tool for acquiring inventory information.  However such methods, which include acquiring satellite imagery or scheduling an airplane flyover, can be too costly when it is necessary to inventory large numbers of relatively small sample sites at fine resolution across regional, continental, and global extents.  Here we present on recent work developing new tools for ecological analysis that can acquire inventory information at high resolution and is easily transportable to sample sites across a large area.
Results/Conclusions

Current research involves evaluating the accuracy of measurements of vegetation structure and the potential of this system to be deployed at small sampling sites across very large areas.  Preliminary results indicate that the methods used are able to recreate three-dimensional scenes in which it is possible to distinguish tree trunks, branches, and canopy.  Initial analysis of the data has found that this method of 3D scene reconstruction provides adequate information for estimating DBH and tree height. Work is currently underway to use field-based forest inventory measurements and high-accuracy GPS to further validate the reconstructed 3D scenes.  More data is being collected to develop more robust 3D scenes to further evaluate how well the recreated geometry compares to field-based measures and measurements collected from conventional remote sensing.  Methods commonly used for interpreting vegetation structure from LIDAR will be used on data collected through this method.