Remote Sensing Basics: Normalized Difference Vegetation Index
By: Cameron Bronstein
Tags: Ecology, Satellite Technology, Machine Learning, Towards Data Science, Remote Sensing

Remote Sensing Basics: Normalized Difference Vegetation Index

NDVI visualization of continental US (Source: GIS Geography)

If you haven’t already, please check out my previous post that summarizes my capstone project from General Assembly’s Data Science Immersive course: Land-use and Deforestation in the Brazilian Amazon. It is a good introduction to some of my interests in machine learning, remote sensing, and ecology — and a good preface to this article.

So, what’s next for me?

I am thinking of this post as a promise to myself . I promise to continue to find ways to build my engineering and machine learning prowess and to advance the research and understanding of ecological systems.

I am deeply invested in learning about these tools and all we can do with them, and am so inspired by the companies that are both advancing the technology and increasing the access for scientists to use it for conservation purposes. Their efforts allow me to continue pursuing my interests.

Normalized Difference Vegetation Index

One application of satellite imagery is the Normalized Difference Vegetation Index (NDVI). This is a basic but deeply powerful index that can be calculated in any satellite image that contains a near-infrared color channel. The index compares reflected near-infrared light to reflected visible red light, and is calculated simply with the following equation:

The scale for NDVI is -1 to 1, with values near -1 representing water and those near 1 representing dense and healthy vegetation.

Vegetation index varies across images based on an image’s contents. This is because land objects respond differently to different wavelengths of light (see my technical post on the contents of satellite images). Vegetation reflects high amounts of near-infrared light, so the vegetation index will be greater for images with a greater density of vegetation.

This has implications for object or land-type identification in satellite image classification. One idea would be to calculate NDVI for each pixel in an image and use those values as an additional color channel in the image. This could be an additional layer of information passed into a neural network and potentially, increase the ability for the network to detect edges in an image. For example, detection of areas where the image shifts from dense to sparse vegetation, city-scape to rural surroundings, or forest to clear-cut agriculture.

Another application of NDVI is to use it to track changes to an ecosystem over time. We can measure the impacts of forestry on an ecosystem by calculating the change in vegetation index over time. Now that we can access imagery of the entire planet on a daily basis, we can track impacts of climate change through metrics like vegetation index. This is valuable for understanding impacts of climate change at varying scales. Changes to the vegetation index will vary locally and regionally. This could direct conservation efforts to areas that are subject to greater changes, or aid in management and planning efforts.

Local Effects of Wildfire on Vegetation Index in California

I would like to build tools using vegetation index on a greater level to further my experience in data engineering and data science. Before completing my capstone project, I became interested in using satellite imagery to understand the effects of the 2018 California wildfires on surrounding vegetation. Intuitively, areas burned by wildfire experience severe changes to their vegetation density. These changes would be reflected (no pun intended) in the NDVI. Using remote sensing, we can track the response of the vegetation over time, and answer questions like: “How long does it take for ecosystems to recover from wildfire?”.

Satellite Image from USGS of the Camp Fire in November 2018: Paradise, California

This could be applied to the recent California fires, as well as those from previous years. Thanks to open access data programs like Planet’s Open California, we can build out these applications immediately, using imagery that dates back 2015.

I’m excited to explore!