Demonstration of LiDAR and Orthophotography for Wide Area Assessment at Pueblo Precision Bombing Range No. 2 Colorado and Borrego Military Wash Area, California

摘要

Munitions and explosives of concern (MEC) contamination is a high priority problem for the Department of Defense (DoD). Recent DoD estimates of MEC contamination across approximately 1,400 DoD sites indicate that 10 million acres are suspected of containing MEC. Because many sites are large in size greater than 10,000 acres, the investigation and remediation of these sites could cost billions of dollars. However, on many of these sites only a small percentage of the site may in fact contain MEC contamination. Therefore, determining applicable technologies to define the contaminated areas requiring further investigation and munitions response actions could provide significant cost savings. Therefore, the Defense Science Board (DSB) has recommended further investigation and use of wide area assessment (WAA) technologies to address the potential these technologies offer in terms of determining the actual extent of MEC contamination on DoD sites (DSB, 2003). In response to the DSB Task Force report and recent congressional interest, the Environmental Security Technology Certification Program (ESTCP) designed a WAA pilot program that consists of demonstrations at multiple sites to validate the application of a number of recently developed and validated technologies as a comprehensive approach to WAA. These demonstrations of WAA technologies include deployment of high airborne sensors, helicopter-borne magnetometry arrays and ground surveys. This report describes the cost and performance for the demonstration of the light detection and ranging (LiDAR) and orthophotography high airborne sensor technologies demonstrated at Pueblo Precision Bombing Range (PBR) No. 2 in Otero County, Colorado, and at Borrego Military Wash in southern California. LiDAR data are critical to the overall WAA process in the creation of an accurate high-resolution bare earth digital elevation model (DEM) for ortho-correction of all other remote-sensing datasets.