Lidar-based high resolution 3D imager and remote gas sensor: a new paradigm for terrestrial environmental monitoring

Predictive modeling and understanding of the response and resilience of the terrestrial environment to both climate change and human intervention is critical for sustainable management of natural resources. However, measurement and modeling of terrestrial environments is challenging due to the complexity and multitude of interactions occurring among plants, microbes, minerals, migrating fluids, and dissolved constituents within the bedrock-to-canopy zone. To improve predictive understanding of the coupled terrestrial ecosystem dynamics and to identify their interrelated controls, numerous coordinated measurements are needed. To this end, preliminary measurements at the Next Generation Ecosystem Experiment near Barrow, Ak (NGEE-Arctic) indicate that such co-analysis of different geophysical data streams holds promise for identifying fundamental relationships between different ecosystem parameters. However, current sensor technology makes data acquisition labor intensive, and yields low spatialtemporal resolution datasets. Improved scientific instrumentation that can acquire multi-parameter simultaneous measurements from UAV-mounted platforms are needed to enable high-spatiotemporal-resolution over wide geographic areas. Such instrumentation will facilitate a clear understanding of the interrelated processes that govern ecosystems to enable prediction of terrestrial and climate outcomes based on measurements. To address this measurement need, Bridger Photonics proposes collaborating with Susan Hubbard at Lawrence Berkeley National Laboratory to develop and test a combined 3D imager and remote gas sensor to advance terrestrial environmental monitoring at sites such as the NGEE-Arctic. The proposed sensor will combine Bridgers existing 3D imaging technology with simultaneous and co-aligned CO2 and water vapor concentration measurements. The LBNL team will provide data acquisition networks and data fusion algorithms to co-characterize lidar datasets with other subsurface and surface-based geophysical measurements to greatly improve understanding of ecosystem dynamics. The Phase II effort will adapt the proposed sensor for use from a UAV platform to greatly increase the spatiotemporal coverage for terrestrial environmental monitoring. The DOE will also have the option to develop additional sensor configurations including bathymetric imaging of inundated surfaces and measuring atmospheric CH4 concnetrations. In addition to the expected utility for scientific ecosystem monitoring, this technology is expected to be useful for monitoring processes relevant to industrial and commercial activities. Potential applications include: methane leak detection from pipelines, well platforms, and holding tanks for the petroleum industry, biomass density estimation in managed ecosystems for agriculture and timber industries, carbon dioxide emissions monitoring for increased accountability in the carbon economy, and monitoring and maintenance of critical infrastructure for transportation and geotechnical industries.