Ecosystem monitoring programs support the Department of Defense (DoD) mission by facilitating training and mission readiness, and by providing data for decision support. However, traditional monitoring programs often suffer from issues with consistency, accuracy and efficiency. To ensure that DoD installations have the best data, most efficient monitoring, and most effective long-term ecosystem management, new approaches are needed. Focusing on pine-dominated ecosystems common to DoD installations, this project will demonstrate improvements that can be made in DoD natural resource monitoring programs through the adoption of two complementary technologies: IntELiMon, a system integrating Terrestrial Laser Scanning (TLS) data and automated processing, and CruzAssist, a tablet-based Light Detection and Ranging (LiDAR) tool for forestry data collection. The objectives are to assess the performance of IntELiMon and CruzAssist, by 1) quantifying accuracy, consistency, and efficiency of these tools, using key forest and fuel biometrics, 2) qualitatively assessing ease of use and training time, and 3) comparing these tools, and identifying their thresholds or capability limits. Finally, the project team will 4) develop a workshop for managers to present performance analysis results and operational guidance of the technologies.

TLS uses lasers or light pulses to rapidly map the three-dimensional structure of a typical forested monitoring plot area (~0.1 hectare) from a tripod. Although a growing research field, TLS has not yet been widely used in management as earlier instruments were too costly and cumbersome for field use. Specialized skills and software needed in processing also posed an additional barrier. Today, automated processing eliminates most technical expertise requirements, and the TLS used here, the Leica BLK360, is lightweight (1kg), affordable (<$25K), easy to use (push-button) and quick (< 5 minutes per scan). The IntELiMon TLS data collection and processing system facilitates automated and consistent calculations using the TLS data that outputs a large and downloadable suite of forest and fuel metrics representative of the scanned area.

The CruzAssist system accelerates forest inventory data collection by using browser-based web application to plan sampling layouts and load spatial data, and tablets with LiDAR capabilities used for measurements in the field. Using the tablet, users navigate to sampling plots, display plots in a map view superimposed on cached satellite imagery and contour lines, and rapidly measure tree diameters, heights and other attributes. Augmented Reality shows the plot center and plot boundary. Further capabilities arise between the tablet and browser application, such as data upload and custom report writing.

The performance assessment will include in-depth quantitative assessments documenting accuracy, consistency, and efficiency, which are estimated and statistically analyzed with intensively and repeatedly measured plot-level field and laser-based data, including tree structure, vegetation cover, and biomass. Qualitative assessments will capture ease of use and training time. Success will be quantified by demonstrating better accuracy and efficiency over traditional approaches. Although powerful individually, the synergies arising from using both systems will provide additional benefits addressed in this project.

The two laser-based technologies and automated processing pipelines presented in this project will enable DoD resource managers to collect considerably more data, faster than traditional methods, increasing accuracy and productivity. These gains enhance monitoring programs by enabling more extensive sampling for a similar effort, or reducing resources needed for an existing program. Automated analyses provide rapid data delivery, while push-button approaches enable higher quality data collection with lower technical skill required, resulting in more data collected and analyzed over a greater area at a reduced cost with more consistent results. Using these tools, DoD will be poised to rapidly upgrade its ecosystem monitoring capabilities, with long-term savings and benefits. Connections with other current or past SERDP & ESTCP projects ensure increasing benefits and synergies as technologies and decision tools advance for natural resource conservation.