The Geographic Information Science for Environmental Management (GEM) Laboratory seeks to advance methods for detection, measurement, analysis, and prediction of environmental phenomena to inform and enable effective land and resource management. Seeking ultimately to improve societies’ ability to respond to and strategically alter its environment, GEM leverages the spatial sampling and analytical power of GIS and Remote Sensing to improve decision-making. This necessarily involves development of GIScience theory, methods, and technologies.
GEM houses a range of computing hardware (servers and workstations), GIS and remote sensing software (e.g., ENVI, Erdas, Idrisi, Menci APS, Agisoft Photoscan, ArcGIS), and field data collection equipment (e.g., unmanned imaging platforms, sensors, RTK GPS, Full range spectrometer) to enable students and faculty to both inform practical, immediate management decisions in the region and advance the state of GIScience for environmental management.
Introduction
The imagery collected from the Dear Plateau covers nearly 30 km2 of variable terrain, including cliffs, basins, and plateaus. The area is covered in piñon, juniper, and other herbaceous vegetation. The aerial acquisition of images from this site was conducted using an ultralight aircraft and two Canon 5D Mark II cameras, one with Blue, Green, and Red bands and the other with a filter to pass only near-infrared wavelengths.
Read more: Digital Terrain and 4-band Orthoimage: Deer Creek Plateau
This project is funded by the Research Allocation Committee at the University of New Mexico.
In the aftermath of a natural disaster, it is critical to evaluate the condition of infrastructure systems so that first responders can have access to the affected areas and officials can identify potential safety hazards. The need for maintenance of infrastructure similarly requires the routine monitoring of infrastructure status. Traditional assessment practices deploy engineers to perform on-site evaluations or airplane observations to evaluate areas that are inaccessible. These approaches have limitations in terms of lack of detail for the airplane-based observations and lack of accessibility for on-site observations, not to mention the cost of deploying observers to vast road networks. We therefor evaluate the use of unmanned remote sensing systems (URSS), which can fly lower to the ground than traditional airplanes, and thus, allow for detailed data to be collected without specially-designed, cost prohibitive sensors. Using roadway pavement assets as an example, this research explored the utility of hyper-spatial resolution (3-millimeter) multispectral digital aerial photography acquired from a low-altitude and low-cost URSS, in this case, a tethered helium weather balloon, to permit characterization of detailed pavement surface distress conditions.
Read more: Infrastructure Condition Assessment Using Low-cost Remote Sensing Techniques
This research, funded by United States Department of Transportation (USDOT) Office of the Assistant Secretary for Research & Technology (OST-R) Commercial Remote Sensing and Spatial Information Technologies Program (CRS&SI), is developing an operational prototype for the rapid detection of fine scale damage to transportation infrastructure following natural hazard events. Lead by Dr. Chris Lippitt of UNM Geography and Environmental Studies, the project team includes the University of New Mexico (Co-PI Dr. Susan Bogus-Halter of Civil Engineering), San Diego State University (institutional-PI Dr. Douglas Stow) and BAE Systems Inc, makers of the Socet GXP® line of image exploitation software. The project team is building on previous research and patent pending technology to design a complete remote sensing system catered to the needs of New Mexico DOT (NMDOT) and USDOT’s infrastructure assessment needs. Approaches developed through the project will be incorporated into the Socet GXP® line of desktop, server, and mobile applications for image exploitation and will be demonstrated in Albuquerque, NM. Ultimately, the project seeks to make available the latest in precision change detection and user optimized remote sensing systems to USDOT for operational assessment of damage to transportation infrastructure following hazard events.