LiDAR Data and LiDAR tools you need to process it
What is LiDAR
LiDAR is a remote sensing technology that uses lasers to make measurements of a surface, enabling us to use that information to create 3D models and maps. LiDAR is an acronym that means Light Detection and Ranging. Since we know the speed of light, by bouncing light off a surface, sensors can measure the distance to a surface by counting the time it takes a beam of light to return to the sensor.
How is LiDAR used?
LiDAR sensors can be mounted onto almost any mobile vehicle, typically aircraft or satellites but also found-wheel motorbikes, agricultural machinery, drones, and even long poles LiDAR technology is used in a wide variety of industries, from agriculture, archaeology, environmental management and even law enforcement.
Listen to the podcast with Lukas Fraser from NV5 Geospatial to find out more about combining drones and LiDAR.
Listen to this podcast about how Bathmetic LiDAR is being used to monitor the carbon stored in seagrass!
How the Data Comes Together
As far as applications of LiDAR data, you’re probably most familiar with Digital Elevation Models (DEMs). Before these become the smooth DEMs you know and love, they start as raw 3D point clouds. The 3D point cloud is each point measurement from a scan, and there can be hundreds of thousands to millions to billions of them. The point cloud needs to be converted into raster format, or a 3D mesh to create a better, and more useful visual.
Where Can You Get LiDAR Data?
There are many commercial operators that will conduct a LiDAR survey for a price, but it’s not cheap. These surveys and services are targeted towards building construction, agricultural assessors, or manufacturers. The majority of people don’t need a specialized LiDAR survey of their field, but rather large-scale data covering a region or country. Luckily, a large amount of the high-resolution LiDAR data is available free online, including ever improving coverage of the entire United States, and New Zealand.
Did you know? LiDAR technology has also been built into the latest iPhone 12, as a way of improving depth measurement for better photography.
The Trouble with LiDAR Data
Broadly, the issue with LiDAR data has always been the size of it. Particularly with point cloud data, these files are enormous, with each point having its own attribute information. There can be millions of points in even the smallest point cloud dataset. As such the data is often quite ‘noisy’, requiring cleaning and sometimes thinning to remove excess information before it will display smoothly.
While point cloud data has some similarity to vector data, the algorithms used to manipulate the data are also significantly more complex. As a result, processing LiDAR data has been challenging and often has required compression (i.e the .laz) or heavy cleaning and the use of specialized software.
LiDAR Tools Available to You
Got LiDAR data to process or display? Below, we recommended a range of software that can handle LiDAR data well, or is especially designed for LiDAR data manipulation and processing.
Naturally, the world’s leading GIS software, ArcGIS, has LiDAR processing functionality. ArcGIS accepts LAS or ASCII file types and has both 2D and 3D visualization options. Of course, with ArcGIS you’re not going to face too many limitations, if any at all. That is if you’re lucky enough to have an ESRI license, and appropriate computing resources. If not you’ll be using the open-source software like the rest of us, and may want to consider shelling out some money for a virtual machine from Microsoft or Amazon to do the heavy lifting.
As of early 2021, QGIS was updated to enable the manipulation of LiDAR and point cloud data. Point cloud data can be viewed in 2D, and 3D. It won’t be able to handle any serious manipulation or analysis of LiDAR data, however, you’ll need a more specialized program for that.
QGIS does still have some limitations with LiDAR, as it doesn’t actually read the LAS files. The newer version of QGIS is able to convert LAS files internally to EPT format (Entwine point tile). You might hit problems if the file size is too large, which is highly likely. QGIS will build an index the first time you load a point cloud, so it will load faster in subsequent loads. It’s often recommended to use the LiDAR Plugin or another tool like LAStools or PDAL to convert the data or manipulate it.
While the functionality of Whitebox Tools is certainly not limited to processing, analyzing, and manipulating LiDAR data, you will have the ability to carry out all the standard LiDAR processing most GIS professionals would require. Point cloud data can be uploaded, analyzed, manipulated and interpolated to create high quality terrain visualizations. The toolbox offers flexibility, allowing you to work in a variety of programming languages. It also includes a handy progress display, so you can see how much time remains to complete an operation. The LiDAR processing functionality with Whitebox Tools is really advanced, even before paying for the LiDAR extension feature.
Whitebox Tools is an invaluable addition to your usual mapping software and can be easily connected to both QGIS and ArcGIS. While QGIS does also have the ability to process LiDAR data, you’ll find Whitebox Tools to be faster and more powerful.
Find out more from the creator of Whitebox Tools itself in our recent podcast episode with its creator, John Lindsay.
While not offering quite the range of specialized LiDAR tools as Whitebox, Saga GIS offers a great deal of options for processing and viewing data in raster format. There is also 3D functionality. Saga GIS was specifically built for terrain and hydrological analysis, so there are some really awesome functions for creating a watershed catchments layer, a wetness index, or modeling water flow. These functionalities won’t work with raw LAS data, it will have to be converted into Saga’s point cloud format first.
Since Grass GIS was originally built for use in environmental management work, it has a lot of terrain analysis functionality. If you’ve managed to master the interface and workflows, you’ll find Grass GIS can read and write LAS files and other common LiDAR file types. It will provide you with all the standard processing and analysis functions you need, and then some. There is an ample supply of documentation and tutorials available for all the 3D raster rendering and LiDAR analysis you require.
CloudCompare is an open-source software solely designed for 3D point data cleaning and processing. In a slightly different shift to a mapping only focus, CloudCompare can be used for all kinds of point cloud data manipulation. It an be used for all kinds of applications, including working with data from laser scanners used in mechanical engineering or 3D printing technology. CloudCompare allows you to read and write a variety of file types from LAS, ASCII, CSV, SHP, OBJ and PTX.
LAStools offers itself as a fast, reliable software for manipulating LiDAR data. It’s very popular and can handle large file sizes with low memory requirements. It can also be added to QGIS or ArcGIS as an extension toolbox, and is also offered as a command line interface. Not all of the tools available are free, some do require a paid license for use, however the more frequently used tools are free to use.
PDAL – Point Data Abstraction Library is a C++ library specifically for translating and manipulating point cloud data. It’s not limited to LiDAR data specifically, but this is what it’s most commonly used for. It uses the Python programming language and allows you to write a script and batch the execution for multiple datasets. You will need to ensure you have enough RAM on your computer, as PDAL is very memory intensive and will seem to want to use all of it. It does present a sharp learning curve, but the good news is PDAL is entirely free and open-source.
Click here to listen to our podcast episode on PDAL – Point Data Abstraction Library
Plas.io is a point cloud rendering tool for your browser, no download or installation required. It will work with LAS or LAZ files, but only from a Google Chrome browser. Using Plas.io you can customize the attribute display and change the perspective view. It’s a very simple tool, yet effective and the only tool of its kind available from a web browser.
Entwine is to point clouds what GeoServer is to geospatial data. Entwine is a light weight server that indexes point clouds and creates lossless tiles ( Entwine Point Tile – EPT ) in a json format. This is the point cloud equivalent of tiled imagery caches and effectively makes it possible to stream large point cloud datasets. Entwine allows range requests, this is the same deep magic that has allowed Cloud Optimised Geotiffs to take over the world.
side note – A range request basically asks the server to send a portion of the file back to the client. This is the part of the technology that makes streaming media work. The client only requests small chucks of the file on an as needed basis. This means that media can be requested in linear fashion start to finish or a client can request randon chucks of media. If we think about his in terms of geospatial data, range requests mean that we can request information directly from the file based on geographic extent, so only the geographic area of the file we are interested in and get the data back to the client. its important to note that we get data back, not an image of data
Also if you are interested in streaming point cloud data it would be worth checking out Cloud Optimized Point Cloud which aims at becoming the point cloud version of Cloud Optimised GeoTiff ( COG )