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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!



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 >

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

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Positioning plots

Anonym

Last week, I gave an example of creating a multi-panel plot (or multiplot) using the LAYOUT keyword. Today (with a nod to a comment from Paul Young), I'll show an alternate technique using the POSITION keyword. The Solar Physics Group at NASA's Marshall Space Flight Center provides information on the sunspot cycle. You can download the group's monthly sunspot number data—dating back to 1749!—here (TXT). I'd like to display, side-by-side, a plot of the sunspot series since 1970 and a histogram of the series. Start by reading the data from the file, assuming it's included in your IDL path. I prefer the astrolib READCOL procedure for reading text files.

 file = file_which('spot_num.txt') readcol, file, year, month, sunspots

Next, create a time vector and use it to restrict our analysis to sunspot activity since 1970:

 time = year + (month-1.0)/12.0 i_ge1970 = where(time ge 1970.0, /null) time_recent = time[i_ge1970] sunspots_recent = sunspots[i_ge1970]

Now use HISTOGRAM to calculate a discrete frequency distribution of the sunspot numbers since 1970:

 sunspot_histogram = histogram(sunspots_recent, $ binsize=10, $ locations=sunspot_bins)

I've empirically chosen a bin size of 10, and returned the locations of the bins into the variable sunspot_bins. The first plot, positioned on the left, is of the sunspot series:

 xr = minmax(time_recent) yr = minmax(sunspots_recent) series = plot(time_recent, sunspots_recent, $ dimensions=[800,600], $ position=[0.10, 0.15, 0.75, 0.90], $ xrange=xr, yrange=yr, $ xtitle='Year', ytitle='Sunspots', $ title='Sunspot Activity (1970-present)')

The key in this call to PLOT is the POSITION keyword: this four-element array describes the lower left [0.10, 0.15] and upper right [0.75,0.90] corners of the bounding box of the plot, in normalized coordinates. The plot fits within this box. I've also used the convenient astrolib MINMAX function to set up axis ranges for the plot, and the DIMENSIONS property to set the size of the plot window, in pixels. The second plot, positioned to the right of the first, displays the histogram of the sunspot series:

 histoplot = plot(sunspot_histogram, sunspot_bins, $ position=[0.80, 0.15, 0.95, 0.90], /current, $ /histogram, $              ; IDL 8.2.1 yrange=series.yrange, $    ; match yrange of first plot ymajor=0, $                ; no axis text /fill_background, fill_color='light gray', $ xtitle='Frequency', title='Histogram')

Swapping the order of the parameters to PLOT transposes the histogram. POSITION is used again to position the plot. CURRENT ensures this plot appears in the same window as series. The HISTOGRAM property tells IDL to draw discrete blocks instead of point-to-point lines (hi, Haje!). Here's my result: Sunspot series and histogram

I like this pair of plots because scanning horizontally across the time series gives a visual estimate of the histogram displayed on the right. Next week, I'll perform some simple time series analyses on these data.

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