Month: April 2016

Assessing tree hazards through remote sensing

Jason Parent and Dr. John Volin

Stormwise will take many years to put into practice and will require a great deal of effort spent on educating and obtaining the cooperation of community officials and land owners. To get the most benefit in the short-term, Stormwise will focus on areas where the forest presents the greatest risk to power lines. Since Connecticut has nearly 17,000 miles of power lines, it doesn’t seem practical to evaluate the forest along the power lines from the ground, but fortunately, through remote sensing, we can learn much about the forest and the risk it poses to utility infrastructure from airborne sensors. Airborne light detection and ranging technology (LiDAR) allows us to measure tree heights and stand density as well as the slope and orientation of the terrain. Using Geographic Information Systems (GIS), a computer system that allows us to manage and analyze geographical data, we can use the LiDAR data to identify trees that are close enough and tall enough to be a potential risk to power lines. These data can then be incorporated into the University of Connecticut’s storm damage prediction model thereby making it more robust in its predictive ability.

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