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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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Burn Severity Analysis of the Chelaslie River Forest Fire

Anonym

In my previous blog post back in January of this year I discussed "Using SWIR and LWIR Imagery to Analyze Forest Fires" which involved using Landsat 8 OLI/TIR data to analyze the Chelaslie River forest fire in British Columbia last summer (2014). In the earlier blog post I describe utilization of ENVI's Spectral Indices tool to compute the Normalized Burn Ratio (NBR) index for a scene that was acquired while the fire was still actively burning. As I mentioned in this previous blog post "the real power of the Normalized Burn Ratio spectral index is exhibited when you create pre-fire and post-fire NBR images then subtract the post-fire NBR raster from the pre-fire NBR raster to create a Differenced Normalized Burn Ratio (DNBR) image that indicates burn severity". However, at that time I was unable to find a good post-fire Landsat scene as most had either clouds or snow obscuring the burned areas.

Now that several months of summer 2015 have passed it occurred to me that it's worth another search of the Landsat archive available on USGS EarthExplorer to see if a new scene is available that provides a good post-fire capture of the Chelaslie River forest fire area. As luck would have it Landsat 8 acquired a nice cloud-free (at least over the fire area) scene just in the past month on 05 July 2015 that happens to be the same exact path/row as a pre-fire Landsat 7 scene acquired on 09 July 2002. The DNBR index can be calculated using a series of simple processing steps followed by the PreFireNBR - PostFireNBR equation which can easily be executed using the Band Math tool within the ENVI software.

Here's a quick high-level synopsis of the processing steps:

  • Open both pre-fire and post-fire Landsat datasets
  • Run the Radiometric Calibration tool
  • Calibrate both datasets into top-of-atmosphere (TOA) at-sensor reflectance
  • Run the Spectral Indices tool
  • Calculate the Normalized Burn Ratio index for both datasets
  • Run the Band Math tool
  • Enter simple "b1 - b2" subtraction expression
  • Map pre-fire NBR to "b1" and post-fire NBR to "b2"
  • Apply a raster color slice to the output DNBR raster
  • Convert the raster color slice to a classification image with specific class names & colors

Below is a screenshot of the DNBR classification overlaid on top of the post-fire Landsat image with class names based on the "FIREMON BR Cheat Sheet V4 (June 2004)" document.

Image data downloaded from USGS EarthExplorer

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