The purpose of this lab is to gain a basic understanding of Lidar data through its structure and processing. To gain this understanding, the lab required me to process and retrieve various surface terrain models and to create a couple of derivative products from using point clouds.
Methods:
PART 1 - Point Cloud Visualization in Erdas Imagine
I opened an LAS dataset of Eau Claire in ArcMap where I then assigned it a coordinate system of NAD 1983 HARN Wisconsin CRS Eau Claire (US Feet) for the XY coordinate system and NAVD 1988 US Feet for the Z coordinate system. I then brought in a shapefile of Eau Claire County that was given in the lab and noticed that the shapefile covered my study area so I knew the XY coordinate system I chose was correct.
PART 2 - Generate an LAS Dataset and Explore Lidar Point Clouds with ArcGIS
Section 1: Create Folder Connection
I created an LAS dataset of Eau Claire data files using ArCatalog and I observed the metadata to make sure I still had proper horizontal and vertical coordinate systems. I observed the surface menu options to look at the Aspect, Slope, and Contour of various features and noted the differences and similarities between each option. One feature I observed was a bridge crossing a river. To do this, I set 'Points' to 'Elevation' and 'Filter' to 'First Return'. Next I clicked on the 'LAS Dataset Profile View' tool and selected my AOI to be the length and width of the bridge. I then opened a new window and observed the bridge (Fig. 1).
PART 3 - Generation of Lidar Derivative Products
Section 1: Deriving DSM and DTM Products from Point Clouds
In this part of the lab I needed to determine the spatial resolution that derivative products should be produced at by estimating the NPS at which the point clouds were collected. This was obtained from the Point Spacing information given under the LAS Dataset Properties information screen. So I used the LAS Dataset to Raster tool in ArcToolbox in order to create a digital surface model of the first return. I set the Cell Type to Maximum and Void Filling to Natural Neighbor, and the Sampling Value field to 6.56168 feet which is roughly 2 meters. I then used the Hillshade tool under Raster Surface to create a hillshade of the DSM. This hillshade showed a more distinct topographic/geomorphic profile of the study area. I then used the same process to create a hillshade for the DTM model. I put both outputs on one screen in Erdas and used the swipe tool to notice the differences between the two models.
Section 2: Deriving Lidar Intensity Image from Point Cloud
I first set the LAS Dateset to Points and filtering to First Return. I then input the LAS dataset for the city of Eau Claire where I used the LAS Dataset to Raster tool and set the value field to Intensity, Binning cell assignment to Average, and the void fill to Natural Neighbor with a cell size of 2 meters.
Results:
Fig. 1: LAS first return point cloud of a bridge
Fig. 2: Hillshade image of the DSM
Fig. 3: Hillshade image of the DTM
Fig. 4: Intensity image obtained from the Eau Claire LAS dataset
Sources:
All data was given by Dr. Cyril Wilson in Lab 5
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