| Autor: |
Petras V; Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA., Petrasova A; Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA., McCarter JB; Department of Forestry and Environmental Resources, North Carolina State University, 2820 Faucette Dr., Campus Box 8001, Raleigh, NC 27695, USA., Mitasova H; Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA.; Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, 2800 Faucette Drive, Campus Box 8208, Raleigh, NC 27695, USA., Meentemeyer RK; Center for Geospatial Analytics, North Carolina State University, 2800 Faucette Dr., Campus Box 7106, Raleigh, NC 27695, USA.; Department of Forestry and Environmental Resources, North Carolina State University, 2820 Faucette Dr., Campus Box 8001, Raleigh, NC 27695, USA. |
| Abstrakt: |
In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability. |
