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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!



Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

9/8/2025

Geohazards such as slope instability, erosion, settlement, or seepage pose ongoing risks to critical infrastructure. Roads, railways, pipelines, and utility corridors are especially vulnerable to these natural and human-influenced processes, which can evolve silently until sudden failure occurs. Traditional ground surveys provide only periodic... Read More >

Geo Sessions 2025: Geospatial Vision Beyond the Map

Geo Sessions 2025: Geospatial Vision Beyond the Map

8/5/2025

Lidar, SAR, and Spectral: Geospatial Innovation on the Horizon Last year, Geo Sessions brought together over 5,300 registrants from 159 countries, with attendees representing education, government agencies, consulting, and top geospatial companies like Esri, NOAA, Airbus, Planet, and USGS. At this year's Geo Sessions, NV5 is... Read More >

Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

6/3/2025

Rethinking the Reliability of Type 1a Supernovae   How do astronomers measure the universe? It all starts with distance. From gauging the size of a galaxy to calculating how fast the universe is expanding, measuring cosmic distances is essential to understanding everything in the sky. For nearby stars, astronomers use... Read More >

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

5/26/2025

Whether you’re new to remote sensing or a seasoned expert, there is no doubt that large language models (LLMs) like OpenAI’s ChatGPT or Google’s Gemini can be incredibly useful in many aspects of research. From exploring the electromagnetic spectrum to creating object detection models using the latest deep learning... Read More >

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

4/28/2025

When every second counts, the ability to process geospatial data rapidly and accurately isn’t just helpful, it’s critical. Geospatial Intelligence (GEOINT) has always played a pivotal role in defense, security, and disaster response. But in high-tempo operations, traditional workflows are no longer fast enough. Analysts are... Read More >

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Using the ASSOC function

Anonym

The ASSOC function provides a basic technique for random access to data in a file. It's particularly useful for files that have a repeating data structure, like a series of images. For example, I have an ENVI file that holds the six multispectral bands (1-5 and 7) of a Landsat 7 ETM+ scene. ENVI, by default, uses band sequential (BSQ) interleaving, meaning the six band images are laid out in memory something like this:

The organization of multispectral Landsat 7 bands in an ENVI file

I'd like to create a normalized difference vegetation index (NDVI) image from these data, so I'll need to read bands 3 (red) and 4 (near infrared) into IDL. One technique would be to read the entire file into IDL, then subscript the result to get bands 3 and 4. This may not be a good idea, though, because a Landsat scene can be large (I've used a 13000 x 12000 pixel scene in the past); it could consume all the memory available to IDL. A better approach is to read only bands 3 and 4 from the file. There are a few ways to do this, but the problem lends itself nicely to a solution with ASSOC. Start by getting a path to the ENVI file, assuming the file is in your IDL path, with:

 IDL> file = file_which('boulder-ETM.dat')

Next, open the file for reading with OPENR:

 IDL> openr, u, file, /get_lun

Now, noting that each image band is 700 x 575 pixels with an 8-bit depth, use ASSOC to set up an associated variable to represent a single band in the file:

 IDL> tm = assoc(u, bytarr(700, 575, /nozero))

At this step, no data have been read into IDL: file access only occurs when the associated variable tm is subscripted. (Interesting, yes?) Calculate NDVI, using the associated variable to pull the red and near-infrared bands from the file, which, by the diagram above, are represented by indices 2 and 3 in the associated variable:

 IDL> ndvi = (float(tm[3]) - tm[2]) / (float(tm[3]) + tm[2])

Again, file access only occurs when the associated variable is subscripted. After the expression on the right-hand side is evaluated, the variable ndvi resides in IDL's memory. Now that all file access has been performed, close the file with FREE_LUN:

 IDL> free_lun, u

and do a quick check on the NDVI variable that's been created:

 IDL> help, ndvi NDVI            FLOAT     = Array[575, 700] IDL> print, min(ndvi), max(ndvi) -0.626374     0.650224

Finally, display the NDVI image with a colorbar:

 IDL> g_ndvi = image(ndvi, $ >       /order, $ >       /interpolate, $ >       title='Landsat NDVI image of Boulder, Colorado') IDL> g_cb = colorbar(target=g_ndvi, $ >       orientation=1, $ >       title='NDVI value', $ >       textpos=1, $ >       font_size=10, $ >       position=[0.80, 0.2, 0.82, 0.8], $ >       ticklen=0.1)

Here's the result from the Windows side of my (new!) laptop:

An NDVI image of Boulder, Colorado, from Landsat 7

Note that I could have used READ_BINARY or READU + POINT_LUN to read the data from the file, but the approach with ASSOC is much easier.

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