LiDAR; Exploration of Point Cloud

LiDAR Remote Sensing

Of the Environment

 

 

Goals and Learning objectives

This lab exercise allows students to get involved with LiDAR data structuring and processing, which most commonly is provided in .LAS format. This format is often used to provide point clouds which can be processed to produce digital surface (DSM) , and digital terrain (DTM) models. Point cloud data can be very informative and suitable for the analysis in a large variety of different projects. Skills with LiDAR data will become more important in the coming future with both technology and career jobs opportunities increasing every year.

 

            Methods

The Lab begins with basic exploration of point cloud data, facilitated by Esri ArcMaps. Point cloud data is formatted into separate tiles, to help keep the size of the files low. Small data packages are useful because LiDAR data along with other forms of remotely sensed data is disseminated via online internet portals for customers/consumers to download and use. If file packages get too large, download times become immensely long. Once the all the tiles are fully downloaded, and imported into ArcMaps several important things need to be addressed. Information like coordinate system, units, and to address any inconsistencies in the data are examined and manipulated for the particular project. Looking at metadata and coordinates should be a beginning step to any project, and can help minimize mistakes later. While visualizing the point cloud information, which shows the elevation for all returns as an approximate of the terrain. LiDAR point cloud data included many variations of information, like the first return, ground, and non-ground point data.  This is where we can begin to derive several distinct products from the data. First, a DSM is created using the first return. This will display forestry and buildings which are structures above ground. This DSM is visually very rough, which makes it more difficult to see any trends or specific objects to view. To compensate, the Hillshade tool is used smooth the DSM and give a better perspective of any elevation differences.

The Next product which can be extracted from the point cloud data using a .LAS o Raster tool is the Digital Terrain model. This model is produced to show only true ground level. Ground truth is very important because it represents the real elevation of the terrain, since does not account for any vegetation, or impending structures that are above ground. The DTM raw product appears very rough, so like the DSM a hill shade tool is used to smooth the output and give another perspective on the elevation patterns.

Figure 2 &3: Subset of the City of Eau Claire, and the Chippewa River. Pictured left is DTM with Hillshade tool, Right is the DTM without the hillshade tool.

The Final Product which is introduced is the Intensity image, which utilizes the First return data to echo the differences in spectral characteristics for the image subset. Intensity images are particularly useful for break line detection and extraction.

Figure 4: Point cloud Intensity map of Eau Claire, and parts of Chippewa and Eau Claire Rivers.

 

            Conclusion

LiDAR point cloud data is a fluid, form of data which leads to a GIS ready data source. Often, point cloud data is highly suitable for capturing and classifying features. This lab provided students with a brief introduction to point cloud tools, manipulating metadata and making concise decisions biased on the project parameters. Along with the experience working with raw data, post-products of the LiDAR data like DSM, DTM, and intensity models were made. Although the lab was only an introduction, many important things were learned throughout the lab. For me specifically, the importance of the specifics of the tools like whether to use “nearest neighbor” or “Bilinear interpolation”, or the differences between each of the options for building the post product (first return, ground, non ground) are extremely important. A good foundation is a pivotal key to successful analysis, and project outcomes.