Exercise 1. Data Preprocessing
Point cloud data usually requires preprocessing before any forest metrics could be calculated. Preprocessing can include outlier removal, ground point classification, and data normalization. Normalization can eliminate impacts of the terrain on point elevation, and serves as the basis of forest metrics calculation.
Outliers are common noises in LiDAR datasets. High-level outliers are usually caused by the returns of high-flying objects (such as birds or aircraft) during the process of data collection; low-level outliers are returns with extremely low attitudes caused by the multipath effect of a laser pulse. LiDAR360 provides automatic tool to remove these noises.
1 Launch LiDAR360 and add ALSData.LiData to the project.
2 Go to Data Management > Point Cloud Tools > Remove Outliers, accept the default parameters, and then click OK.
Classify Ground Points
3 Go to Classify > Classify Ground Points. Use ALSData_Remove Outliers.LiData as input, accept the default parameters and click OK.
4 Go to Terrain > DEM, accept the default parameters and click OK.
In this tutorial, we will use the default settings of tools in most cases. However, in your own project, please refer to our User Guide to understand the parameter settings of the tools and adjust accordingly to meet your project requirements. User Guide for DEM tool: DEM.
Upon loading in DEM, the display will switch to a 2D view. To go back to 3D display, start a new display window or remove DEM from current display. You can also convert DEM to 3D by going to Data Management > Conversion > Convert TIFF to LiModel.
5 Go to Data Management > Point Cloud Tools > Normalize by DEM. If you already have DEM data loaded in, you can find it in the Input DEM File drop down menu. Otherwise, you can add your DEM input by clicking on . Click OK to run.