LiDAR Data Processing in R: A Beginner's Guide
LiDAR (Light Detection and Ranging) technology has revolutionized how we understand and interact with the world, supplying high-resolution 3D representations of natural and built environments. From creating digital elevation models (DEMs) to analyzing forest canopies, LiDAR data has applications in fields such as urban planning, forestry, hydrology, and disaster management.
When diving into LiDAR data processing, R offers a powerful and flexible ecosystem for analyzing and visualizing LiDAR datasets. In this guide, we will talk about the applications LiDAR data using RStudio
What is LiDAR Data?
LiDAR is a remote sensing method that utilizes laser pulses to measure distances between the sensor and the Earth’s surface. The data is collected as a point cloud, where each point has coordinates (X, Y, Z) and additional attributes such as intensity or classification.
LiDAR datasets are typically stored in formats such as .las or .laz (compressed). These files can be large and complex, requiring specialized tools for processing and analysis.
Why Use R for LiDAR Processing?
R provides several advantages for working with LiDAR data:
lidR Package: A comprehensive library for reading, processing, and analyzing LiDAR data.
Reproducibility: R scripts ensure reproducibility, making it easier to share and document workflows.
Visualization: Tools like rgl and ggplot2 offer both 2D and 3D visualizations of point cloud data.
The following image is the visualization of the LiDAR data in z elevation
The following image is a classified, and extract ground data from the .las file using RStudio
Then, we can create a DEM using the .las file
Once, we have created a DEM, we can create a canopy height model
We can then plot trees on the Canopy Height Model. The trees are plotted in red
The Model Height can visualized using the Z Metrics
The ground, and vegetation has been classified using the point cloud as shown below
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