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The Use of LiDAR Remote Sensing in Measuring Forest Carbon Stocks

Salopek, Michael
Thesis/Dissertation; Online
Salopek, Michael
Laushway, Francis
Epstein, Howard
Shugart, Hank
Atmospheric carbon levels have increased dramatically since the industrial revolution, creating an increasing concern in forming a better understanding of the global carbon budget. A key part of this budget involves forest biomass and its ability to act as a source of carbon sink or carbon gain. It is understood that terrestrial areas serve as a large source of carbon sink in terms of the global carbon budget, however the degree of spatial variation, particularly with respect to densely vegetated areas, is less certain. Advancements in LiDAR technology, an active remote sensing instrument, have been key to researchers’ abilities to accurately measure these types of forest parameters. Lidar instrumentation can be used at three different scales including terrestrial, airborne, and space-born. Researchers have developed strategies involving the use of airborne lidar coupled with satellite data to develop cost-effective, high-resolution maps of carbon stocks and emissions in these densely vegetated areas. Airborne lidar has helped these researchers to observe to effects of forest degradation and secondary growth at large scales, such as the Columbian Amazon, which saw an increase in regional carbon emission of 47% from 1999 to 2009 dude to selective logging with an offset of only 18% provided by secondary growth. Great improvements have also been made with respect to space-born lidar data, particularly with the launch of ESA’s Biomass satellite, which is set for launch in 2020. Biomass will assay the entire range of global vegetation on a 6-month rotation, providing useful information to create an accurate representation of the global carbon cycle. This paper will highlight the importance of lidar remote sensing in estimating forest biomass, provide information on the instrument and data processing techniques, and discuss recent work involving the application of lidar data in highly productive regions.
University of Virginia, Department of Environmental Sciences, MA, 2013
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