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.