Remote Sensing Lab 5

Remote Sensing Lab 5

Goals and Background:

In this lab exercise, the user will gain experience manipulating and generating LIDAR point cloud data and derivative products. Various manipulation includes examining the data in 2D or 3D, or examining contours, elevation, etc. Products created by the user will include Digital Surface Models and Digital Terrain Models, as well as the hill shaded visual enhancements of both.

Methods:

Part 1: Visualization

Because of the large file of LIDAR data files, they are often split into individual tiles of a certain area. By doing this division of the data, the computational cost of tools and simple point drawing is greatly lessened, and the ease of transfer between users is greatly increased. However, in order to produce derivative products later on, we will need the data in a uniform structure. The easiest way to do this is to create a LAS dataset.

Part 2: Generating LAS dataset and exploring point cloud with ESRI's ArcGIS software

To create the LAS dataset, we will use ESRI's ArcCatalog program. Within it, we can easily create a LAS dataset and add in our 40 tiles of LIDAR point cloud data. These 40 tiles form a AOI around Eau Claire, Wisconsin. 

figure 1: The created LAS dataset with the 40 tiles added. 

We will need to compute statistics for the new LAS dataset. This is completed in statistics tab in the dataset's properties. While not useful for us at the moment, they prove a valuable resource in QA/QC processes. 

Unfortunately, we will need to assign a coordinate reference system as well since our dataset is missing the reference information. However, such information is often also contained in the metadata. 

figure 2: The related metadata. Included is the information for both the planar and vertical coordinate systems. 

After obtaining the information, we can then define the CRSs in the dataset properties. The CRSs are NAD 1983 with the Lambert Conformal Conic projection and North American Vertical Datum of 1988 respectively. 

To double check that the data is in the right location, we can overlay on top of a shapefile of Eau Claire county. 

figure 3: The overlaid LAS dataset on the Eau Claire County Shapefile. The data is in its correct location centered on the city of Eau Claire. 

Using the LAS dataset toolbar in ArcMap, we can explore the data in various ways. The points can be changed to show elevation, contours, aspect, or slope. In addition, we can filter out and set what returns we want. Classification can also be done to only display returns which represent the ground. 

Expanding upon basic visualization, ArcMap also has 2D and 3D viewers the user can make use of, to better comprehend/visualize the data. 

figure 4: The 2D viewer. Displayed is one of the bridges close to the UWEC Campus.

figure 5: The 3D viewer.

Part 3: Generation of Derivative Products

Section 1: Creating DSMs and DTMs from point clouds 

We also create various products from this point cloud data. Namely, Digital Surface Models (DSM) and Digital Terrain Models (DTMs).

DSMs are a model that show the elevation of the various surfaces across the point cloud. This is created by filtering the point cloud data to only show first returns, and then using the LAS dataset to Raster tool found under conversion tools in Arc Toolbox.

For this model, we will use the following settings*:
Value field: Elevation
Interpolation Type: Binning
Cell Assignment Type: Maximum
Void Fill Method: Natural Neighbor
Sampling Type: Cellsize
Sampling Value: 6.56168

*All other settings not mentioned were left with default options.

The DSM can be enhanced using the Hillshade function found under Raster in 3D Analyst Tools to provide better visual clarity for the user.

DTMS are models which show the elevation of the ground surface of a point cloud. They can be created by filtering the point cloud data to show only ground classified returns, and then using the LAS dataset to Raster tool found under conversion tools in Arc Toolbox. 

For this model, we will use the following settings*: 

Value field: Elevation

Interpolation Type: Binning 

Cell Assignment Type: Minimum 

Void Fill Method: Natural Neighbor 

Sampling Type: Cellsize

Sampling Value: 6.56168

*All other settings not mentioned were left with default options. 

The DTM can be enhanced using the Hillshade function found under Raster in 3D Analyst Tools to provide better visual clarity for the user. 

Section 2: Creating an Intensity Image from a point cloud

We will also create an intensity image, which will show the intensity of the various returns (and in turn the reflectivity of surface features). It can be created by filtering the point cloud data to show only first returns, and then using the LAS dataset to Raster tool found under conversion tools in Arc Toolbox. 

For this model, we will use the following settings*: 

Value field: Intensity

Interpolation Type: Binning 

Cell Assignment Type: Average 

Void Fill Method: Natural Neighbor 

Sampling Type: Cellsize

Sampling Value: 6.56168

*All other settings not mentioned were left with default options. 

Results: 

figure 6: The created DSM. Note the changes in elevation around the river. 

figure 7: The created hill-shaded DSM. Clarity has been greatly increased in terms of interpretation and feature identification. 

figure 8: The created DTM. Elevation is much easier to discern. 

figure 9: The hill-shaded DTM.

Sources: 

Eau Claire County. (n.d.). Retrieved April 19, 2019, from https://www.co.eau-claire.wi.us/departments/departments-l-z/planning-development/gis-division

Price, M. (2014). Mastering Arcgis (6th ed.). Mcgraw Hill Higher Education.