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Full-Text Articles in Social and Behavioral Sciences
Towards Systematic Selection Of Terrain- And Ground Cover-Specific Lidar Filtering Parameters, Vance Green
Towards Systematic Selection Of Terrain- And Ground Cover-Specific Lidar Filtering Parameters, Vance Green
Graduate Theses and Dissertations
Accurate automated classification of LiDAR point clouds is a well-known problem and proper parameterization of the classification algorithm is essential to creating useful bare-earth terrain models. Parameterization is particularly important in areas characterized by extremely low relief, such as the Little Red River Irrigation Project Area in central Arkansas. In this kind of landscape, analyses such as hydrological flow models are sensitive to small changes in the topography, and therefore prone to errors in the classification of the LiDAR point cloud and the digital elevation models (DEMs) derived from it. Developing effective project-specific parameters requires a high degree of knowledge …
Lidar And Machine Learning Estimation Of Hardwood Forest Biomass In Mountainous And Bottomland Environments, Bowei Xue
Graduate Theses and Dissertations
Light detection and ranging (lidar) has been applied in various forest applications, such as to retrieve forest structural information, to build statistical models for identification of tree species, and to monitor forest growth. However, despite significant progress in these areas, the choice of regression approach and parameter tuning remains an ongoing critical question. This study focused on choosing the right spatial generalization level to transform lidar point clouds to 2D images which can be further processed by mature image processing and pattern recognition approaches. It also compared the prediction ability of popular machine learning algorithms applied to aboveground forest biomass …