Lidar Applications

A return is a data point that is recorded by Lidar sensors. The amount of light received by the sensor for a given return is referred to as return intensity. There are different kind of returns as well...

Single return: For solid features, like buildings, a single laser pulse usually generates a single return.

Multiple returns: For porous features, like a leaf-off tree, a pulse will usually generate multiple returns as the laser energy passes through the canopy.

The intensity of a return can indicate the material properties or the porosity of a feature. Features with high intensities, such as grass, have high reflectivity and no porosity.

As you can see from the image to the right, the field in the middle of the buildings is lighter in color, since it has a high reflectivity and no porosity. The building tops, meanwhile, have a low reflectivity, showing us their solid state.

Ground returns are generated when a pulse reaches the ground.

Ground returns will be consistently generated for fields, pavement, and grass. They are common but less frequent for leaf-off forests and they will rarely occur in leaf-on forests, and there are no ground-returns associated with buildings.

Ground returns are used to create a digital elevation model, or DEM, that estimates the ground elevation at any given location.

In the figure to the right, you can see that we are in a large flat surface, because there is a lot of similar ground returns along the surface. In this case, the image is captured from the University of Rhode Island Fine Arts parking lot.

You may also notice that there are white sections on the ground along the left of the image, and that is where buildings would be found.

First returns correspond to the tops of features (i.e. rooftops, treetops, etc.) in an environment. These are used to create Digital Surface Models (DSM).

While first returns are used to generate DSMs, they are not the same thing as data used to generate a Digital Height Model (DHM). DHMs estimate the height of features by subtracting the DEM raster from the DSM raster.

You can see the areas in red represent the highest points of elevation, where the first returns would be located. The areas in red correspond to rooftops and higher treetops, as can be seen in the aerial view depicted above.

You can measure height using Lidar data as well. By selecting the Create Profile tool under the Classification menu, you can draw a line perpendicular from the ground to a target feature, and you will create a profile in ArcPro.

Once the profile is created, you can select the "Measure" tool and "Measure Vertical" to assess the height of a feature in relation to ground points.

In the figure on the right, you can see we measured the water tower just northeast of the University of Rhode Island campus. In our example, the water tower stands at approximately 111 meters tall.

Another tool that is useful is measuring slope. Once can accomplish this by creating a profile, and measuring distance. This tool will provide the length of the feature you are analyzing as well as the slope of the feature as well.

Once you've defined your area, you can open up the profile and see the feature on the screen. By identifying a start and end point on the feature, you can see the program will provide the measurements you need to calculate slope.

In the figure above, you can see our area of interest is defined by a yellow outline along Flagg Rd. on URI's campus. Once your area is defined, you can use the measure distance tool to calculate the length of your area, along with the vertical change of the landscape. The equation would be percent slope = (vertical change / horizontal distance) * 100

In our Flagg Rd. example, the math to find the slope would be as follows: (12.57 / 296.06) * 100 = approximately 4% slope.

Another tool that is useful when it comes to Lidar data is measuring polygon volume. In this example, under the "Data" menu, you will find an option to calculate the area and volume of "plane by bounded feature." If you have polygon data of buildings in the area, you can utilize the polygons and your Lidar data to find out how much volume each building has.

You can see the Coastal Institute building in the figure above, and on the right, you can see the building's corresponding polygon highlighted in blue. After measuring volume and opening the attribute table, you can see that the volume and surface area of the polygon is now associated with the building shape.

Digital Elevation Model (DEM)

Digital elevation models, or DEMs, are defined by the USGS as arrays of regularly spaced elevation values referenced horizontally either to a Universal Transverse Mercator (UTM) projection or to a geographic coordinate system. In other words, they are made up of cell values that represent the elevation within the cell. Ground elevation can be estimated using DEMs.

Analysis tools can be run on DEMs to produce new surfaces such as slope and aspect. They can also be used to study surface properties such as visibility and water flow.

To make a DEM yourself, you first need to extract the ground returns from the data in your area of interest. In the example provided on the right, I downloaded the Lidar point cloud data for the area that encompasses the University of Rhode Island campus. You can see the two areas I selected here:

Once you have your Lidar point cloud data, you can use that as the input for the Make LAS Dataset Layer tool in ArcPro. In a classified point cloud, you're going to want to select class 2, which represents the ground returns. Now that you've isolated the ground returns, you can use them as input for the tool "LAS Dataset to Raster" and create a DEM raster. Make sure to select "elevation" for the field value, "binning" for the interpolation type (this will tell the tool to determine the pixel value based on points that fall within each pixel), and "average" for the cell assignment (this will reduce noise in the DEM). Then, select "Linear" for the Void Fill Method, and "Floating Point" for the Output Data Type (so you can use decimal number to represent elevations). Lastly, make sure to choose "Cell Size" for the Sampling Type to specifically set the cell size of the cells you want for the output raster. Make sure that the cell size of your DEM matches the cell size of the other data you are using in your project.

Digital Height Model

A Digital Height Model (DHM) is used to estimate the height of features such as trees, buildings, bridges, etc. at any given location. In the figure to the right, you will see a DHM of the center of the University of Rhode Island's campus. See below for the aerial view of the same location:

To create a DHM, you will need a Digital Surface Model (DSM) to start. Then use your Lidar dataset as the input for the "LAS Dataset to Raster" tool. Your output should be "DSM" for the Output Raster, and the "Elevation" should be selected for your value field. "Binning" should be your Interpolation Type (this would change depending on the goals of your application), "Maximum" should be your Cell Assignment, and "Linear" should be your Void Fill Method. Then, choose "Floating Point" for the Output Data Type and 1 x 1 cell size to match our previous data.

A DHM is made by simply subtracting a DEM raster from a DSM raster. The "Minus" tool is quite easy to use, just use your DSM as input 1, and DHM as input 2.

Intensity is the amount of energy received by the sensor for a Lidar return. Feature material and the portion of the pulse footprint intercepted by the feature can affect return intensity. Intensity rasters are used to determine the material of the features in the area of interest. Solid features return 100% of the energy back to the sensor. As you can see in the image to the right, the black features are low intensity features, and the white features are high intensity features. The white features may reference buildings, because they are solid features that would reflect a lot of energy back to the sensor. The black features may be trees, since they are porous objects that would reflect less energy back to the sensor.

Mapping Buildings

You can use the same Lidar dataset to classify buildings from the point cloud. You can convert the extracted points to a raster and dataset, and then use the raster features to create polygons. As an example, refer to the image on the right, which shows the buildings from the University of Rhode Island's campus in dark pink polygons.

Mapping Risk Trees

Risk trees are trees that are potentially capable of striking a building. The rule of thumb to follow is if the height of the tree is greater than the distance to the building, the tree is capable of hitting the building if it were to fall. In the image to the right, you can see the risk trees in purple on the campus of the University of Rhode Island.

Interactive Map

On the right, you can see an interactive web map that displays the buildings and risk trees on the campus of the University of Rhode Island. Feel free to explore the map! You can navigate the map just how you would navigate a map using ArcPro software.

On the right is the model I've used throughout this project thus far.

Five applications for Lidar that I find interesting include:

• Pollution Modelling: LiDAR can detect particles in both air and water, which makes it particularly adept at identifying pollutants like carbon dioxide, sulphur dioxide and methane. Together with a building or terrain model, researchers can use this data to observe and reduce pollutant build-up in a given area.
• Gas composition data: DIAL is used to measure particular gases in the atmosphere, such as ozone, carbon dioxide or water vapour. We can use LiDAR to study the gas composition of the atmosphere, necessary for weather forecasting, climate modeling and environmental monitoring.
• Biodiversity: LiDAR remote sensing can be used to assess biodiversity by monitoring habitat structure; a key indicator of species diversity. Much like wildlife, the technology has evolved over the years to provide detailed information about the forest, looking at factors crucial to survival, such as vegetation structure.
• Coastline Management: There are two different types of LiDAR: topographical and bathymetric. The former uses an  infrared  laser to map the land, while the latter uses water-penetrating green light. In tandem, they can be used to form coastal surveys, giving maximum overlap between land and sea and in doing so, minimising data gaps.
• Wind Turbines: Scanning wind before it hits the wind turbine can help to maximise efficiency. LiDAR attached to the turbine itself is used to calculate the direction and strength of wind, and if necessary will change the direction of the blade to in order to generate more power.

These applications and the corresponding information were sourced from  this website.