Introduction to Light Detection and Ranging (LiDAR) – Explore Point Clouds and Work with LiDAR Raster Data in R
The tutorials in this series introduces Light Detection and Ranging (LiDAR). Concepts covered include how LiDAR data is collected, LiDAR as gridded, raster data and an introduction to digital models derived from LiDAR data (Canopy Height Models (CHM), Digital Surface Models (DSM), and Digital Terrain Models (DTM)). The series introduces the concepts through videos, graphical examples, and text. The series continues with visualization of LiDAR-derived raster data using plas.io, plot.ly and R, three free, open-source tools.
Data used in this series are from the National Ecological Observatory Network (NEON) and are in .las, GeoTIFF and .csv formats.
After completing the series you will:
- Know what LiDAR data are
- Understand key attributes of LiDAR data
- Know what LiDAR-derived DTM, DSM, and CHM digital models are
- Be able to visualize LiDAR-derived data in .las format using plas.io
- Be able to create a Canopy Height Model in R
- Be able to create an interactive plot.ly map of LiDAR-derived data
Things You’ll Need To Complete This Series
To complete some of the tutorials in this series, you will need an updated
R and, preferably, RStudio installed on your computer.
is a programming language that specializes in statistical computing. It is a
powerful tool for exploratory data analysis. To interact with
R, we strongly
an interactive development environment (IDE).
Data is available for download in those tutorials that focus on teaching data skills.