Lab 5: LiDAR

Goals and Background:

The main goal of this lab exercise is for students to gain basic knowledge on LiDAR data structure and processing. Specific objectives encompass 1) processing and retrieval of various surface and terrain models, and 2) processing and creation of intensity image and other derivative products from point cloud. LiDAR is one of the recently expanding areas of remote sensing with significant growth in new job creation. This lab will include working with LiDAR point clouds in LAS file format.

LiDAR is an active remote sensing system which utilizes a suborbital sensor. These sensors can be attached to manned (airplanes) or unmanned (drones) aerial systems. The lasers on the sensors send pulses to the ground; as the come into contact with object such as trees, a 'return' will be sent back to the sensor. As shown in figure one, there are

Figure 1. Diagram of how LiDAR works using a suborbital sensor, in this case a plane. The laser sends out a pulse and returns back to the sensor many times before it hits the bare earth.

Methods:

Point cloud visualization in Erdas Imagine

Without having seen a LiDAR point cloud, it can be very hard to visualize its output. Our first step in this lab was to gain a raw understanding of what a point cloud consists of. The image below displays 'returns'. The red points indicate the first return, whereas the blue points indicate the last return or no return at all (figure 2).

Figure 2. LiDAR point cloud of Eau Claire, Wisconsin.

Generate a LAS dataset and explore LiDAR point clouds with ArcGIS

The rest of this lab was conducted in ArcMap. In order to do this, however, the LAS dataset (the LiDAR point cloud) needed to be projected. When an LAS dataset is not projected, one must have a tile index and metadata to ensure a correct projection. To begin this process, a LAS dataset was created in ArcMap. Under the LAS Dataset Properties window, all of the LAS tiles of Eau Claire county were added. The dataset statistics were run in order to determine basic information such as x, y, and z max/min/range. Examining this numbers verifies that the data is correct (i.g. the z values match the elevation of the study area).

If no coordinate system is established for the LAS files, projecting the LAS dataset can be done within the LAS Dataset Properties window, under the XY Coordinate System and Z Coordinate System tabs. It is important to consult the metadata when projecting a coordinate system. NAD 83 HARM Wisconsin CRS Eau Claire (US Feet) was used for the XY coordinate system and NAVD 1988 US feet was used for the Z coordinate system for this lab (figure 3). In order to ensure that the tiles were projected correctly, a county shapefile was used to compare locations.

Figure 3. LAS dataset points in ArcMap. Notice the point's elevation variation on the upper left corner. Also, the white areas indicate areas of 'no data'; this is because these areas are comprised of water and therefore do not provide a 'returnable' surface for lasers.

When displaying the points as a TIN in ArcMap, there appears to be some anomalies that have drastically higher first returns than the surrounding landscape. This is likely due to some form of interference, likely a bird or something of the sort (figure 4).

Figure 4. First Return anomalies. Note the 'red' dots in the water body; these are likely birds or some other interference.

Another useful tool in ArcMap is the 3D viewer, which allows the viewer to see their LiDAR point clouds in...you guessed it...3D! (figure 5). The images can also be viewed as a profile. The profile below is of a cross section of the Chippewa River (figure 6).

Figure 5. 3D viewer in ArcMap.

Figure 6. 3D profile in ArcMap.

Various models were used for the rest of the lab. These provide the framework in which LiDAR is used most often. Before the models could be used, however, parameters needed to be set. These were established under the Layer Properties for the LAS database. The parameters included displaying the points only as elevation via first returns. In order to be able to run the models, the LAS dataset needed to be converted to a raster. This was done using the LAS Dataset to Raster tool. Parameters within this tool included setting the Value Field = Elevation, Cell Type = Maximum, Void Filling = Natural Neighbor, and Cell Size = 6.56168 (or 2 meters).

Multiple images were created once converting the image to a raster. This included creating digital surface models (DSM) (output image in figure 7) and digital terrain models (DTM) (output image in figure 8). A DSM looks like a special version of LiDAR, whereas the DTM only shows the bare earth without any building features on it.

Once the two main rasters were created, more rasters were developed, this time using hillshade (output images in figure 9 and 10).

The final tool aspect of this lab was highlighting the 'Intensity' of the image. This was done in the same way as the other images in this lab, except the Value Field was changed to 'Intensity' (output image in figure 11).

Results:

The digital surface model (DSM) illustrates the LiDAR points collected from the first return. This imagery includes buildings, trees, etc that are 'picked up' by the LiDAR sensor (figure 7).

Figure 7. Digital Surface Model (DSM). 

The digital terrain model (DTM), on the other hand, gives a very generalized picture of the landscape. This makes sense because the DTM takes the last return, thus giving the 'closest to the actual ground' elevation (figure 8).

Figure 8. Digital Terrain Model (DTM).

The hillshade tool was on the existing DSM imagery seen above in figure 7. Rather than looking like a photo, like a DSM, the hillshade 'normalizes' the black and white gradient and really draws attention to the individual buildings and landscape features by creating shading by all of the features (figure 9).

Figure 9. Hillshade of DSM.

Similarly to the hillshade of the DSM, the hillshade of the DTM draws more attention to the landscape. However, the DTM highlights the overall natural landscape, such as the paleochannels, much more than buildings and the like. This is due to the fact that this imagery is produced from the last return of the LiDAR sensor, thus replicating the bare earth (figure 10).

Figure 10. Hillshade of  DTM.

An intensity image was created to illustrate the strongest voltage returned. Having this imagery aids in interpreting and classifying LiDAR masspoints, which is used in Lidargrammetry (figure 11).

Figure 11. Intensity image of Eau Claire LiDAR raster.

Conclusion:

LiDAR technology is quite new compared to satellite remote sensing. Unlike images from space such as Landsat, LiDAR allows the user to truly customize the extent of their imagery, density of points collected, temporal resolution amongst many other factors. I am looking forward to doing further LiDAR analysis in the future, as just the few things that I have done in this lab have been exciting. I have used DTMs in the past, but never created them for myself. LiDAR is another useful tool to add to my geospatial toolbox.

Sources:

LiDAR point cloud and Tile Index are from Eau Claire County, 2013.

Eau Claire County Shapefile is from Mastering ArcGIS 6th Edition data by Margaret Price, 2014.