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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



New ENVI Agent, IDL Agent, and GeoAgent Quick Guides

New ENVI Agent, IDL Agent, and GeoAgent Quick Guides

6/9/2026

The recent release of ENVI® Agent, IDL® Agent, and GeoAgent™ revolutionize how users interact with geospatial software. These agentic AI applications act as partners to plan, simplify, and execute complex workflows. Knowing where to start can be challenging for new users. To this end, we developed three new quick guides to... Read More >

Introducing NISAR Data Support

Introducing NISAR Data Support

6/5/2026

The release of ENVI® SARscape 6.3 in April 2026 includes preliminary support for NASA-ISRO SAR (NISAR) data. The NISAR mission is a joint Earth-observing satellite project between NASA and the Indian Space Research Organization designed to monitor changes in the planet’s land and ice surfaces using advanced radar imaging. It... Read More >

Monitoring Illegal Mining in the Amazon: Turning Persistent Data Into Actionable Insight

Monitoring Illegal Mining in the Amazon: Turning Persistent Data Into Actionable Insight

5/28/2026

Illegal mining over decades has constituted one of the most persistent and complex socio-environmental problems in the Brazilian Amazon. In recent years, with the increasingly intensive use of mechanized extraction, the associated environmental impacts—such as deforestation, intense soil disturbance, river siltation, and mercury... Read More >

From Answers to Action: Why ENVI and IDL Agents Go Beyond General AI

From Answers to Action: Why ENVI and IDL Agents Go Beyond General AI

4/20/2026

As generative AI tools like Claude and Gemini continue to gain traction, many organizations are asking the same question: Can general purpose AI actually support real geospatial workflows, or does it stop at surface-level answers? That question was front and center in our recent webinar, Meet Your New Partners in Science: ENVI... Read More >

Mapping Earthquake Deformation in Taiwan With ENVI

Mapping Earthquake Deformation in Taiwan With ENVI

12/15/2025

Unlocking Critical Insights With ENVI® Tools Taiwan sits at the junction of major tectonic plates and regularly experiences powerful earthquakes. Understanding how the ground moves during these events is essential for disaster preparedness, public safety, and building community resilience. But traditional approaches like field... Read More >

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Viewshed analysis for planning a sensor network

Anonym

Have you ever been curious as to what you might see from an overlook before planning to go there? Or perhaps why it's difficult to get cell phone reception in your area? These are the types of questions that viewshed analysis tries to answer, by taking a surface (including ground, trees, buildings) and analyzing each line of sight from the view point. 

Often this analysis assumes you can look in any direction. Sensors however are usually limited by their field of view, pointing direction, effective range, and surface or terrain over which they operate. A fixed security camera will be able to image the area within its horizontal and vertical field of view. Areas outside this region, or areas occluded by buildings, trees, and hills, will not be visible. In a similar way, cell phone transmitters and receivers, as well as radar and sonar systems, have a limited beam width, direction, and effective range.

Before deploying a remote sensing or communications network, it is common to model the effect of different choices in operating parameters, as well as the number and distribution of sensors.

Just as viewshed analysis can help predict what you may see from a particular vantage point, it can also be a quick way to estimate the coverage of a network, before undertaking a more extensive and rigorous sensor model and operating environment analysis.

ENVI's viewshed analysis tools can estimate the effective coverage area of multiple sensors each with varying fields of view, locations and heights, effective ranges, and pointing directions. With ENVI's programming interface many scenarios can be evaluated and then visualized.

In this example, I use a surface elevation model derived from LiDAR and a satellite image over Boulder Colorado. The LiDAR data was obtained from Neon (National Ecological Observatory Network) and the imagery was obtained from DigitalGlobe and then orthorectified in ENVI.

To perform a viewshed analysis on a network of sensors, launch the viewshed workflow from the toolbox, and add view points representing the locations of the sensors. Then in the view sources list right click or double left click to edit the view source parameters. Modify the range, height, horizontal and vertical field of view, pitch, and azimuth as needed. Points can be moved at any time by selecting them in the display and dragging them to a new location. Preview (as used in the following image) can be used to quickly view the results of parameter changes before producing the final result. 

 

The following picture illustrates the impact of sensor height, pitch, vertical field of view, and terrain on the area that is visible (green) and hidden (red) by line of sight. The limited vertical field of view means that the area closest to the sensor as well as the top of the highest peak are hidden. By reducing the pitch (pointing the sensor downwards) the visible area can be brought closer to the sensor at the cost of loosing visibility to the higher terrain. The green arrow indicates one of the lines of sight within the field of view, specifically the line of sight that just clears the first peak and that defines the hidden area behind the peak.   

 

To evaluate multiple configuration scenarios it is valuable to switch to ENVI's application programming interface. The RasterViewshed task can be run multiple times to evaluate network options.

The viewshed task is created using the following command.

task = ENVITask('RasterViewshed') 

Then for each scenario, you can set one or many simultaneous view sources and parameters sets. 

task.Pixel_Location = locations
task.Horizontal_Field_of_View = HFOVs
task.Vertical_Field_of_View = VFOVs
task.Pitch = pitches
task.Azimuth = azimuths
task.Range = ranges

task.Execute

Once complete the viewshed rasters can be combined into a raster series for visualization.

 task = ENVITask('BuildRasterSeries')

So next time you want to pick a campsite with the perfect view, or set up a network of sensors, transmitters, and receivers, remember that viewshed analysis can be a powerful tool.

 

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