Lab 5: LiDAR and Remote Sensing

Introduction:

For this lab, students experiment with LiDAR in remote sensing, learning to process and generate a number of surface models, terrain models, and intensity images through the use of point cloud data. 

Methods:

Part 1: Point Cloud Visualization in Erdas Imagine-

In Part 1 of the lab, students upload the LAS dataset into ArcMap and examine its properties. Special notes are taken regarding information found in the Metadata and the Tile Index of the dataset. Since there is no particular projection for the original dataset, one will have to be prescribed.

Part 2: Generating a LAS Dataset and Exploring LiDAR Point Clouds with ArcGIS-

Using ArcCatalog, students generate a new LAS dataset in their Lab 5 folder. All LAS files used in Part 1 are selected for the new dataset, and statistics are calculated for input into the new LAS Dataset. The Metadata, examined in Part 1, revealed the intended projection to be used for the files collected in the dataset: NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet). Once all these items were addressed and changes were saved to the New LAS Dataset Properties in ArcCatalog, the file could be directly transferred and generated in ArcMap to further explore the point cloud data.

To explore the point cloud data, students simply need zoom in to the tiles until reaching a scale where the point cloud data becomes visible. In this case, point scale data could be seen beginning at the 1:5,000 feet scale. With the LAS Dataset toolbar activated, students could opt to view elevation, slope, contour, and aspect data evaluated using any number of classes at any range of returns.

Part 3: Generating LiDAR Derivative Products (DSMs, DTMs, and Intensity Images)-

In the last section of the lab, students learned to generate Digital Surface and Terrain Models using the point cloud data and the LAS Dataset to Raster tool. Students later created Hillshades of the resulting DSM and DTM models for a smoother visual of the landscapes featured in the models. Using a similar procedure, students later generate an intensity image of the City of Eau Claire from the LAS Dataset by simply changing the value field in the tool window to 'Intensity.' The resulting image was then brought back into Erdas for a more clear visual of the image produced.

Results:

Figure 1: Phoenix Park bridge, Aerial View

In exploring capabilities of data manipulation in Part 2, students learned how to examine 3D interactive view scenes using the LAS Dataset Profile View tool. The tool was used   to highlight Phoenix Park bridge, and view a profile of the feature as it  would be seen from the ground rather than how it appears in the aerial photograph. The results of this tool use can be seen pictured in Figure One to the right and  Figure Two below.

Figure 2: Phoenix Park Bridge, Profile View

In Part 3 of the lab, students generated a series of three LiDAR Derivative Products, including a Digital Surface Model, a Digital Terrain Model, and an Intensity Image of the City of Eau Claire using point cloud data from the LAS Dataset.

The Digital Surface Model shows a greyscale Raster Image of UW- Eau Claire campus as seen in Figure 3. The DSM was derived Elevation as the value. This model is good for distinguishing classes above the ground surface, such as buildings from vegetation. Figure 4 shows the hillshade of the result, which makes the texture of the observed objects much more distinctive.

Figure 3: UWEC Digital Surface Model

Figure 4: Hillshade of UWEC DSM

The Digital Terrain Model shows a greyscale Raster Image of terrain at the ground level surrounding  UW- Eau Claire campus, as seen in Figure 5. The DTM was derived similarly to the DSM, but uses Ground as the observable value instead. This model removes the clutter existing above the ground surface, such as buildings from vegetation, so that surface elevation becomes the main focus of the resulting image. This type of data is good for examining flood plains and landscapes. Figure 6 shows the hillshade of the result.

Figure 5: UWEC Digital Terrain Model

Figure 6: Hillshade of UWEC DTM

The last of the LiDAR derivatives was the intensity image generated for the City of Eau Claire. Since the resulting image appeared in dark values using ArcMap software, the final product was transferred back into Erdas Imagine for a more clear visualization of the results. Pictured in Figure 7 below is a high resolution image of the City of Eau Claire using the Intensity function in ArcMap. 

Figure 7: Intensity Image of the City of Eau Claire, as seen in Erdas Imagine

Conclusions:

This exercise ultimately allowed students to experiment with LiDAR Outputs, point cloud data and manipulation, and LAS Datasets in order to visualize and prioritize different aspects of imagery for further study. In this lesson, students learn to derive DSM, DTM, Hillshade, and Intensity images for future manipulation in Remote Sensing study and interpretations of photo imagery.

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.