Honors College Thesis
 

Impact of Classification Algorithms on Modeling Forest Ground from Point Clouds

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https://ir.library.oregonstate.edu/concern/honors_college_theses/qv33s480w

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  • Projects in forestry and civil engineering depend on accurate information, which is often acquired through remote sensing techniques. One of the remote sensing technologies that has gained importance in the last decade is light detection and ranging (lidar), which describes the surveyed area with 3D points, presented as point clouds. Lidar point clouds are currently the preferred data to identify the ground, as they are the most accurate and precise among the existing technologies, such as radar or photogrammetry. From point clouds, surface models describing the ground terrain without any objects are created, called digital terrain models (DTMs). The creation of DTMs is often done through the use of classification algorithms, which use a combination of parameters to classify points as ground or non-ground. There are a wide variety of classification algorithms and associated parameters that can be used for this process. A 1.37km² point cloud near Panther Creek, Oregon was surveyed and used as the dataset for the analysis. The objective of this study is to assess the impact of algorithm parameters on the creation of the DTM. The impact will be primarily measured in computed volume differences between compared DTMs.
  • Keywords: Digital Terrain Models, Remote Sensing, Forestry, Engineering, LIDAR
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