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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!



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 >

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

4/24/2025

This blog was written by Eli Dwek, Emeritus, NASA Goddard Space Flight Center, Greenbelt, MD and Research Fellow, Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA. It is the fifth blog in a series showcasing our IDL® Fellows program which supports passionate retired IDL users who may need support to continue their work... Read More >

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

2/25/2025

This blog was written in collaboration with Adam O’Connor from Wyvern.   As hyperspectral imaging (HSI) continues to grow in importance, access to high-quality satellite data is key to unlocking new insights in environmental monitoring, agriculture, forestry, mining, security, energy infrastructure management, and more.... Read More >

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A column sort routine

Anonym

In spreadsheet programs like Excel or LibreOffice, you can apply a sort on a column to every other column in the spreadsheet. IDL's SORT function doesn't provide this functionality, but with a little code, we can make it so. The function COLSORT (get the source code here) accepts a 2D array and the zero-based index of the column to sort on. By default, values are sorted in ascending order; a keyword can be set to sort in descending order. Here's an example of how the routine works. Start with a 4 x 5 array of numbers:

IDL> a = round(randomu(seed, 4, 5) * 20.0)
IDL> print, 'Original array:', a, format='(a,/,4(i))'
Original array:
           8           6          14          10
           4           9          18           5
           1          11          13          18
           8           9          11           9
           3          19           4          16

Use COLSORT to perform a reverse sort on column index 1 (the second column) and extend the sort to the other columns in the array:

IDL> sort_index = 1
IDL> b = colsort(a, sort_index, /reverse_sort)

Check the result:

IDL> print, 'Sorted (descending) array:', b, format='(a,/,4(i))'
Sorted (descending) array:
           3          19           4          16
           1          11          13          18
           8           9          11           9
           4           9          18           5
           8           6          14          10

This program could be extended to apply to rows and to arrays of higher dimensionality.

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