The main goal for this lab is to learn about Lidar data structure and processing. The specific objectives for this lab are processing and retrieval of various surface and terrain models and processing and creation of intensity image and other derivative products from point cloud. In this lab, we will work with Lidar point clouds in LAS file format.
Methods:
In this lab, ArcMap and ERDAS Imagine 2015 were used in order to analyze information pertaining to the Eau Claire area. The process began with point cloud visualization in ERDAS Imagine. ERDAS was used to add lidar point cloud files to access their information. Once the point clouds were loaded and displayed on ERDAS, ArcMap was opened. After navigating to Tile Index in Lab 5, QuarterSections_1.shp was displayed and observed. After this, ERDAS and ArcMap were closed out.
Next, an LAS dataset was generated and lidar point clouds with ArcGIS were explored. First, a folder connection was created by opening ArcMap and opening ArcCatalog. From here, we connected to the Lab_5/LAS folder and creating a new LAS Dataset. After renaming the file Eau_Claire_City.lasd, we navigated to LAS Dataset Properties. Under LAS Files, click Add Files and add the individual LAS files. The files are added to ArcMap. View Statistics and click Calculate to see the statistics for all the files, which can be viewed under the tab LAS Files. Next, assign coordinate information to the LAS dataset by first clicking on the XY Coordinate System. Consult the Metadata for Lab 5 by selecting Edit with Notepad++. In ArcMap, view the XY Coordinate System and navigate to NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet) and Apply. Go to the Z Coordinate tab and navigate to NAVD 1988 US Feet and Apply. Units are now applied to the image. Close the Properties window and bring in the image of Eau Claire, which should appear as the image below.
Zoom into individual tiles to view its detail. Examine the Surface pull down menu and observe its features.
Click on the Contour listing and change the index factor by changing the numbers. Return to Layer Properties and change the properties under Filter and observe their differences. Back in the main viewer under the LAS Dataset, set points to Elevation and First Return. Click on the LAS Dataset Profile View tool and use it to view the bridge pictured below.
When accessing the LAS Files in ArcMap, the statistics on the files display that the Min Z and Max Z for the entire LAS Dataset are 517.85 and 1845.92 respectively. When viewing whether these values are realistic for the city of Eau Claire, it appears as though the highest value makes sense, but the lowest value seems obscure because the sensor may not be able to sense through multiple layers of an area that may be less than 517.85. At this time, we are unsure of the unit of measurement of these numbers. However, after consulting the Metadata, it appears as though the horizontal coordinate system is through D_North_American_1983 with a unit of feet, and the vertical coordinate system is through the North American Vertical Datum of 1988 also with a unit of feet. Viewing the range of the image, the X and Y ranges are 20995.8 feet and 13347.02 feet respectively. There are some areas with limited amounts of points when viewing this image when zoomed in. These limited points could be due to the large amount of data/land that the sensor must calculate in a brief amount of time because of its high elevation. This would result in wide point spacing during the on fly interpolation. When it comes to the distribution of slope for natural land surface features and man made features, the man made features have sharp edges and specific patterns of elevation, whereas the natural surface features appear more random and sporadic. When choosing a filter under Layer Properties, the Ground and Non Return options make use of classification, First Return relies on return number, while All (Default) uses both. There is a difference between these two options because return number does not classify what the object is, whereas the classification does. Looking back at the LAS Dataset under point spacing, the average NPS of the point clouds appear to be 1.485 after calculation. When viewing the nature of features in the first return hillshade derived product, it is apparent that the product is in grayscale and illustrates the tops of every object in the image. It shows great amounts of detail in every building, tree, road, etc. for texture. After creating a bare Earth raster, the image appears much smoother, and objects such as buildings and trees are taken away. What is left in the DTM is what the sensor predicts is the ground in the image; strictly the terrain. The impact from a visual perspective of removing vegetation and buildings from the map view is a contrast between the smooth view of the DTM image and the first return image. Viewing the intensity image, the spectral channel of the intensity image is under the middle infrared band. Overall, however, there are differences in the spectral characteristics of the intensity image. The image is visibly sharper due to ERDAS, but the image is only on the grayscale, instead of the full spectrum of colors.
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.
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