This proposal contributes to a national effort to develop and calibrate methods for quantifying vegetation characteristics and snow cover from aerial LiDAR data. The study will involve all the Critical Zone Observatories and will therefore encompass diverse physiographic, climatic, and geologic settings from California to Delaware to Puerto Rico. The new methods will allow accurate, spatially explicit estimates to be obtained; current approaches are either too labor-intensive or lack precision when applied to large areas. Broader Impacts The methods developed during this study will provide useful data for a wide variety of applications, including watershed scale hydrological modeling, natural resource assessments, mesoscale climate models, and other studies that require accurate, spatially explicit assessment of vegetation and snow cover.
Aerial LiDAR (Light Detection And Ranging) surveys were conducted at each Critical Zone Observatory (CZO) site in order to provide researchers with unprecedented high-resolution data of the topographic relief and forest canopy structure of the CZO sites. Such data is critical in order to develop digital elevation models to better understand erosion and sediment transport processes as well as the complicated nature of forest canopy structure that partly affects biosphere-atmosphere interactions. Thus, the intellectual merit of the project is the ground truthing of airborne imagery that will yield a high resolution product with the potential to answer cutting-edge questions and hypotheses that advance scientific understanding of the critical zone. The project supplied funds to conduct a field campaign to ground truth the aerial LiDAR imagery. Supervised by the project principal investigators, the funded graduate students conducted a field campaign in accord with standard CZO protocol that provided the requisite data to ground truth the LiDAR imagery. The ground truthing data were forwarded to the University of California, Merced for data processing in relation to the aerial LiDAR imagery. The data are currently being processed for the Christina River Basin CZO at the University of California, Merced. The validated LiDAR imagery will serve as a valuable community resource for researchers wishing to conduct studies that require high-resolution LiDAR imagery. An example of such research includes ecological research that seeks to better understand the effects of forest canopy structure on precipitation partitioning. As such, the broader impact of this research is the correction and validation of the LiDAR imagery that will be able to be used with confidence by a large cross-section of the scientific community to expand research opportunities with our unique data set that will lead to a better understanding of the critical zone.
