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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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The Truth about Vegetation Species Identification from Imagery

Anonym

Lately the topic of vegetation analysis, in particular species identification, has been coming up like gang busters. I spent a lot of time during my years at the Carnegie Institution department of Global Ecology looking at invasive species in tropical environments—think dense tropical forests. Even with hyperspectral data it was a huge challenge. Vegetation isn’t like minerals. It’s carbon, Hydrogen, nitrogen and oxygen. Absorption features are more driven by leaf composition and health than by a species having unique feature, whereas a mineral has absorption feature unique to it or a family of minerals caused by electron bonds or presence/absence of other compounds. Vegetation of the same species can look vastly different. Even leaves on the same plant can look completely different from one another. In other words in the intra species variability is greater than the inter species variability. Or as I say too often, vegetation is hard.

Take my chlorotic plant here. According to Wikipedia “ chlorosis is a condition in which leaves produce insufficient chlorophyll. As chlorophyll is responsible for the green color of leaves, chlorotic leaves are pale, yellow, or yellow-white. ”One leaf looks vastly different from the rest of the plant, but it’s the same plant. Species ID with spectral data is challenging because there are so many manifestations of how a single plant can appear. To create a spectral library of one species of plant is extremely difficult—there are literally endless permutations of how that species can appear. You really can’t use one spectrum to go find others. If you did, a classifier would go find others like that spectrum. But whatever state that spectrum is in (dry, wet, healthy) does not truly giving you a species ID. That’s not to say you can’t do some discrimination. Conifers and deciduous trees can be separated same for grasses and certain kinds of shrubs with certain data. But sub alpine fir from Douglas fir with only spectra is really challenging.

RayKokaly, USGS SpecLab, has a nice paper on vegetation biochemistry observable from imaging spectroscopy. My key take away from this is you can extract a ton of biophysical information from imagery and use that to study ecosystem health and productivity, but again plants have similar biochemistries, so it’s not like that can be applied to species identification. These chemistries are not unique to a species.

The four things that can be most helpful toward getting to species ID are the following:

  1. Temporal data: Certain species emerge before others. Plants bloom at different times, have different responses to water stress, die at different times, etc. Even just two images from different times can help and more can provide a higher degree of accuracy.
  2. Contextual information such as, “you aren’t going to find palm trees growing in the mountain west, so don’t look for them there, look for lodge pole, Douglas fir, and blue spruce. ”Narrow your options and you’re more likely to have some success with a library and target detection. Again I would recommend HSI data. MSI would probably still be confounded depending on the spatial resolution.
  3. Object based analysis: This method works well with high resolution data, so maybe the spectral signature isn’t enough information, but crown shape, clustering, and texture can all indicate certain species. ENVI’s Feature Extraction Tool can provide this type of analysis.
  4. Deep Learning: We’re just starting to scratch the surface on what deep learning can do in vegetation. We have a working tool in house and if you have a hard vegetation problem you need solved, give me a shout. This yelp article on identifying food pictures gives a good overview of how deep learning works.

As I said in the beginning, vegetation is hard. Not impossible, but hard. But there are a number of things you can try to improve your results. Invasive species detection, weed infestation, and finding endangered plants are all areas of research in vital need of solutions. If you have a hard vegetation problem and want to talk it through, please reach out to me. It’s one of my favorite topics, in case you couldn't tell.

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