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Not seeing the forest for the points: Novel LiDAR metrics elucidate forest structure and increase LiDAR usability by managers

  • Author(s): Kramer, Heather Anuhea
  • Advisor(s): Stephens, Scott L
  • Kelly, Nina M
  • et al.
Abstract

Forest and fire ecology have long utilized remote sensing datasets to learn more about landscapes. Advances in gps spatial accuracy, GIS software capabilities, computing power, and remote sensing technology and software, as well as increases in the spatial and temporal resolution of remote sensing products, have made remote sensing a critical component of forest and fire ecology. Aerial light detection and ranging (LiDAR) is a fast-growing active remote-sensing technology that can be mined for detailed structural information about forests. These data are utilized in the fields of hydrology, forest ecology, silviculture, wildland fire ecology, wildlife ecology, and habitat modeling. LiDAR coverage has also become increasingly common, yet still contains much untapped potential.

Despite widespread research that derived copious valuable metrics from aerial LiDAR, few of these metrics are available to managers due to a significant knowledge and software barrier for LiDAR processing. Even when LiDAR is utilized to derive more complex metrics by scientists and LiDAR experts, metrics are often predictions of plot-based data across the landscape. While these metrics are useful, LiDAR can offer so much more. Because it holds information about forest structure in 3 dimensions, new metrics can be derived that capture the full complexity of forest structure.

I explore ways in which managers can use the plot network and data layers already available to them to derive large tree density, a metric that is critical for habitat modeling for many species, including the California spotted owl. I also explore the utility of LiDAR for estimating ladder fuels that carry fire from the ground into the canopy. Because there was no reliable method for quantifying these fuels, I also developed a plot-based methodology to collect these data. My dissertation work aims to increase LiDAR accessibility to managers and to develop new ways to use LiDAR to solve old problems. While there is much more work to be done, I am excited to share my work with LiDAR experts and forest managers, and hope that my findings improve the way we use LiDAR, the way we manage forests, and the way that we model and manage for wildland fire.

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