In this activity, you will work with real LiDAR data with an online viewing utility, plas.ioLinks to an external site. You will manipulate various options to assess the value of such adjustments in interpreting the scene.
You will look at one of the built-in LiDAR datasets within plas.io (preferably one of the landscape datasets, not the portrait-type datasets), and then you will find a unique dataset on OpenTopography.orgLinks to an external site., and will manipulate the viewing options on each and assess your findings. It is recommended that you register with OpenTopography using your .edu email address, as that will make more datasets available for download. You should select a dataset that offers a .LAS or .LAZ file for download. When selecting a unique dataset, try to choose a landmark in your hometown or somewhere you vacationed or something unique to you with which you have a connection in the real world to help comprehend the LiDAR visualization.
You can view built-in datasets within plas.io by choosing from the pull-down menu, as shown below, or by selecting “Browse” and selecting a locally saved .LAS or .LAZ file downloaded from OpenTopography.55
When looking at each dataset, you should manipulate each of the parameters below and assess if it helped your interpretation of the scene. It might take a few attempts to understand how the options in function. Note that not all datasets have all features, especially intensity and RGB colorization.
Particle Size – Sometimes displaying dense datasets with large particle size will obscure details, while displaying sparse datasets with small particle size will be difficult to recognize. Adjust this for your dataset to try to find a good visualization balance.
Z-Exaggeration – This can be useful when trying to find vertical features and exaggerate slight vertical variations. Try adjusting this and see if any features in your dataset become more noticeable.
Colorization – Change the colorization between RGB, classification, and heightmap (any that are available in your dataset).
Colormap – While the colorization is set to heightmap, select different colormaps and adjust their gradient with the slider. Do any colormaps appear to bring out the details of your dataset more or less than others?
Intensity – Set “Intensity Source” to “Intensity” and then adjust the “Intensity Blending” towards “All Intensity” to see the variation in the visualization (if your dataset features intensity). Do any features become more readily apparent when visualizing the intensity of the returns?
Inundation – This is a tool that helps visualize elevation and the potential for flooding. Once you enable it, a transparent blue plane appears, and you can move it up and down with a slider, essentially varying the height of the visualized flood. Does visualizing a floodplain highlight any elevation characteristics of your dataset?
http://plas.io/
https://portal.opentopography.org/datasets
