UC Merced

Light Detection and Ranging (lidar) has been used widely for the remote sensing of multiple parameters from earth’s surface. Lidar systems are used to measure light scattered to find and or range a specific target using laser pulses and radio waves by measuring the time delay between transmission of a pulse and detection of reflected signal. Lidar has proven to be a promising technology for estimating forest biophysical parameters, but due to high-cost of flights, computer processing times, hard drive storage limitations, lidar flights are not numerous and difficult to process at high-resolutions. Discreet return lidar (three dimensional point cloud data) is used for a variety of applications including: urban planning, forest management, wildlife habitat analysis, and forest biomass estimations. This study aims to provide a framework in generating lidar-derived product such as Digital Elevation Models (DEMs), Digital Surface Models (DSMs) and lidar-derived biomass estimates for a study area in the Sierra Nevada. This study also provides an open-source framework for storing and sharing spatial data using an online web-content management system. Results include USGS and lidar-derived DEM error, generating DSMs across a variety of platforms including point-density reduction, interpolation methods and resolutions, as well as a comparison of estimating biomass using individual tree extraction from lidar and a multivariate point cloud regression approach using ground-truthed plot data. The web-based software in this study is used to store and share data amongst a variety of teams and persons including the public, the Sierra Nevada Adaptive Management Project, National Critical Zone Observatories and other research teams associated with UC Merced.