Global Mapper for Wind Energy Development

3D models of wind turbines in the 3D Viewer in Global Mapper.

The development of a wind energy  project, big or small, is a complex process that considers several factors. From measuring the actual wind resources in an area to researching potential zoning and ordinance conflicts, it’s not a project that’s easily simplified. But in the beginning stages of planning, whether you’re considering bringing wind energy to your own property or to a larger community, creating a rough visualization of a wind project can be relatively easy.

In this blog entry, we explain the online resources and tools available through Global Mapper that can help estimate resources and terrain modifications, and create a visualization of the preliminary plans of a wind project. We’ll do this by simulating a simplified planning process for a wind farm to arrive at a 3D visualization.

Importing & Analyzing Online Data in Global Mapper

In the planning of an actual wind project, we would want to know the annual average wind energy potential of our property, any legal limitations, and so much more information before even beginning plans for development. But for this simple simulation, our purpose is to introduce how relevant data can be accessed, analyzed, and visualized in Global Mapper.

One online source that we are using is the National Renewable Energy Lab, which is a federally owned and contractor-operated facility that provides data and maps for energy-focused purposes. The data set we are downloading shows the wind energy potential of areas across the state of Maine on a relative scale ranging from values of 0 to 7, with 7 representing the greatest potential.

Running a Simple Query to Target Specific Attribute Values

If we determine the required value for our wind farm plans, we can build a query that targets those specific areas that match our requirement. For instance, if we wanted to find areas that are greater than or equal to the value of 6, we can run a simple query to find those areas within this data set. We can also use the Info tool to explore the wind energy potential of properties within an area.

NREL data
The freely available raw data downloaded directly from the NREL website. By either running a simple query or using the Infor tool, polygons can be selected in this data to explore their attributes.

Applying Color to Visualize Patterns in Data

Another way we can visualize the distribution and range of values in this data set is by applying a color scheme. As we can see, this visualization makes it easy to target those areas of maximum wind potential. If we wanted, we can add a legend to our map to further illustrate what values the colors actually represent. But in this instance, we are interested in visualizing which areas have the highest potential.

Colorized data
NREL data with colors applied, allowing for a more immediate understanding of the range of values in the data set.

We can bring in some additional data to add more context, such as county outlines and town boundaries within the state. If we were looking to develop wind energy in a particular geographic location, for instance in a particular town, we have the background data that shows those boundaries. We can also pull in road data to see the road access to areas being considered for development.

For our simulation, we are choosing an area based on this very quick visualization of the NREL data we imported into Global Mapper.

Accessing Free Terrain and Land Cover Data Through Global Mapper’s Online Data Service

With our area of interest chose, we can find more relevant data through Global Mapper’s free online data service. For our simulation, we are choosing to use a specific area of a 10-meter National Elevation Data (NED) data set that we streamed into the application and exported to a local Global Mapper grid file.

Online data in Global Mapper
Terrain and land cover data accessed through Global mapper’s online data services.

We streamed the data through the online data service, which has a wide range of data options categorized geographically as well as by data type and theme. In this instance, we are interested in terrain data to give us visual context and also a functional base for some of the modification processes we will run later.

We are also interested in land cover data, which will help us visualize the roughness of the terrain. We can find a raster representation of our area under the land cover section in the online data options.

Generating a Roughness Grid from Land Cover Data

Areas with less friction, or surface roughness, are better suited for wind energy production. From our land cover data, we can generate a grid to visualize areas where roughness could reduce energy potential.

To create this roughness grid, we can open locally saved land cover data that we had previously exported from the online data service. Either by right clicking the land cover layer or from our analysis menu, Global Mapper gives us the option to generate a roughness grid and to choose a shader with which to render the grid. For this visualization, we prepared a custom shader beforehand that illustrates the range of roughness through the gradients of a single color – lighter tints representing less roughness, darker shades representing greater roughness.

Roughness grid in Global Mapper
A roughness grid showing the distribution of open and forested areas through the gradients of a single color.

This visualization allows us to see open areas such as fields or bodies of water that may provide ideal conditions for a wind farm.

Finding Ridge Lines & Isolating a Single Ridge

Another ideal location for a wind farm is on a ridge. We can find a ridge line or high point within the focus area by using the Find Ridge Lines tool, which is a function that works similarly to a watershed analysis, but in reverse. Instead of looking for areas where drainage would accumulate, the tool finds the highest points on our terrain.

Ridge lines in Global Mapper
Ridge lines generated using the Find Ridge Lines function in Global Mapper.

After choosing specific parameters, such as the width threshold of the lines, we can see a variety of ridge lines appear in the area visible on our screen. These lines are actually segmented, so in order to isolate a ridge we want, we can combine the segments of that ridge into a single line by selecting the desired segments and using the Combine Features tool.

Plotting Points Along a Ridge to Represent Wind Turbines

With our new ridge line selected, we can generate point features to represent our wind turbines along the ridge by using the Create New Points from Selected Lines tool. We can specify that we want ten vertices to represent ten wind turbines evenly spaced along the ridge, and discard vertices that may have already been part of our original ridge line. Once these parameters are set up, we can see that the ten vertices have been generated that represent the wind turbines in our simulation.

We can then edit these inherently generic point features and choose a Feature. For this simulation, we prepared a custom feature type called Wind Turbine which has a 3D visual representation of a wind turbine assigned to it. This 3D model is actually pre-configured in Global Mapper. We can also edit the attributes of these, but for this simulation, we are only assigning our customized feature type.

Points on a ridge line in Global Mapper
Evenly spaced points representing the locations of wind turbines on a ridge line.

Once these points have been edited, we can view them in the 3D Viewer and see the 30-meter height attribute of the 3D models we prepared in advance, and the even spacing between each model along our ridgeline.

Creating Buffers Around Wind Turbine Locations

After we have placed our wind turbines, we can then generate a buffer around each point in preparation for creating flattened areas, or site pads, in the terrain. With our points selected, we can click the Buffer tool in our toolbar. In this simulation, we are choosing to have buffer areas with a 10-meter radius around each of our wind turbines. Once the buffer areas are defined and generated, we see the concentric ring that represents the physical area that will be flattened around each point in the terrain-modification process.

Buffers around points in Global Mapper
Circles around each point represent a 10-meter buffer that was created using the Buffer tool.

Generating an Elevation Grid from LiDAR Data

In order to generate a more accurate terrain model for our simulation, we can import pre-cropped LiDAR data that was originally streamed from the U.S. Geological Survey through Global Mapper’s online data service. This higher quality elevation data allows us to create more precise modifications and visualization than the lower-resolution terrain data we had originally imported.

Raw LiDAR data in Global Mapper
Raw LiDAR data from the U.S. Geological Survey.

To create an elevation grid from this LiDAR point cloud, we can simply click the Elevation Grid button with our LiDAR data layer selected. In this simulation, we are choosing to grid only ground points. Once the new grid has been generated, we can open the Elevation Options to feather, or blend, the edges of our higher quality grid into the lower-resolution terrain data.

Calculating Cut and Fill Values & Creating Pad Sites

With our buffers selected, we can use the Flatten Site Plan tool to flatten those buffer areas of the LiDAR-based elevation grid. The tool calculates the volume of material that must be shifted in order to achieve a flattened site – giving a cut volume and a fill volume. Not only does Global Mapper give these helpful calculations, it also modifies the elevation grid so we can visualize what the cut and fill alterations would look like.

Cut and fill calculation in Global Mapper
The results of a cut and fill calculation in the Path Profile window. The original profile of the terrain is shown in red and the flattened terrain is represented by a yellow line and green shading. This shows the cut and fill that would be required for this pad site.

Viewing the Visual Impact of a Project with the View Shed Tool

With one of our wind turbine points selected, we can click the View Shed tool to see the extent at which our wind turbine is visible in the distance. We can base our analysis on the height of our selected wind turbine and on the height of an average person — 2 meters or so. Global Mapper calculates the areas at which our wind turbine will be visible to an average person, and displays these areas in red. This analysis allows us to see the visual impact of our wind farm in the area of development.

View Shed tool in Global Mapper
The areas displayed in red are locations where the selected wind turbine is visible.

Creating a Fly-through of a Wind Energy Project

After setting up our wind turbines and modifying our terrain surface, we can create a 3D fly-through to further visualize the project. We can do this by drawing a line for our flight path using the Digitizer tool. With this line selected, we can set up the specifications of our fly through by using the Create Fly-through tool.

Once we’ve established the height, bank angle, and duration of our flight, we can preview it in the 3D Viewer. If we’re happy with this fly-through, we can also save it from the 3D Viewer. If we aren’t happy with it, we can go back and edit the flight or segments of the flight line again.

Creating a fly-through is a great way to present a project, particularly one like a wind energy project that may need to be proposed to government officials or multiple stakeholders.

Global Mapper: A Robust Tool for Any Development Project

While this simulation involves some behind-the-scenes preparation, such as the creation of a custom point feature type and the cropping of LiDAR data, it’s still a prime example of how simple data visualization and terrain modification can be in Global Mapper. It can be easy, not only in the context of a potential wind energy project, but for any development plan that requires quick access to terrain data and robust digitizing tools.

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