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Title: Canopy Fuels Inventory and Mapping Using Large-Footprint Lidar
Department Of Geography
Abstract Of Thesis: This dissertation examines the use of large5 footprint, waveform-digitizing lidar data to predict and create maps of CBD and CBH as well as the use of lidar-derived products to run a fire behavior model. Lidar metrics are compared to field-based estimates of CBD and CBH and, based on the regression models resulting from these comparisons, maps of CBD and CBH are generated that are then tested as inputs into a fire behavior model. The remainder of this introductory chapter is organized as follows.
The next sections provide
(1) a background to fire behavior models, focusing specifically on FARSITE (Fire Area Simulator-Model); (2) a review of field- and remote-sensingbased collection of wildland fuels data;
(3) a synopsis of the previous use of remotesensing- derived fuels data as input into fire behavior models;
(4) a summary of the current state of the utilization of lidar to predict fuels and
(5) an overview of the research objectives addressed in the remaining chapters.
Keywords: Geography, Remote Sensing, Environmental Sciences, lidar, canopy bulk, density, canopy base height, canopy fuels estimation, FARSITE.
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