What is NDVI?
Normalized Difference Vegetation Index (NDVI) was created back in 1973 by a Texas A&M research team after studying data from some of the earliest Earth Observation (EO) satellites. NDVI is one of the most popular vegetation indices and acts as a valuable method to understand vegetation health and land use remotely. NDVI is helpful in applications from helping farmers with precision agriculture to allowing conservationists to understand ecosystem changes.
Year after year, we see an increase in the amount of remotely sensed imagery from satellites. Indices like NDVI allow you to extract information from this large data pool. There’s a lot of information out about NDVI, but it can be challenging to know where to start. Here are the top five things you need to know to get started.
The electromagnetic spectrum is fundamental to NDVI. Basically, NDVI is an indicator for plant health based on how the plants reflect certain ranges of electromagnetic spectrum. When we look at plants, they are green because the chlorophyll pigment absorbs red waves and reflects green waves.
Cell structures in plants reflect near-infrared (NIR) waves. When photosynthesis occurs, the plant is able to grow and develop more cell structures. This means that a healthy plant with lots of chlorophyll and cell structures actively absorbs red light and reflects NIR. In relation, an unhealthy plant will do the exact opposite.
To fully understand how healthy a plant is, we must compare the values of absorption and reflection of red and NIR light. The formula for NDVI equals the subtraction of the reflectance in the red spectral band from that in the NIR and then, it divides this by the sum of red reflectance and NIR (NDVI=(NIR-RED/NIR+RED)). NDVI values will always fall between -1 and +1; 1 being the healthiest and 0 being the unhealthiest. Range 0 to -1 indicates dead plants or inorganic matter. NDVI is a pixel-smart mathematical calculation rendered on an image using GIS tools. This means a single value can be determined for every pixel in an image.
This relationship between chlorophyll and light allows us to determine a healthy plant from an unhealthy one. Sensors in space measure wavelengths of light absorbed and reflected by green plants. The index detects the presence of live green vegetation using this reflected light in the visible and NIR bands.
After transforming satellite data into NDVI values, we can then create visuals that indicate the measure of vegetation type, quantity and condition. The benefit to having a value for every pixel means you can assign different colors to different ranges. GIS tools like QGIS, GRASSGIS, ArcGIS and many others make this possible.
There is no standard color map for NDVI but most commonly, one that accurately represents reality is used. No matter what scale is chosen, the ability to assign colors and create maps of regions allows you to monitor plant health over time.
NDVI all starts with satellites in outer space painting the picture for us. Around the clock satellites like Landsat, Sentinel-2, Pleiades and SPOT produce images with red and NIR bands ideal for NDVI. One important factor to consider is that different satellites have different wavelengths, which could mean slightly different results when compared to others. Thankfully on the UP42 Platform, all band information for the satellites are readily accessible on their respective block cards. It can be helpful to compare other data streams such as weather data to gain further insight.
Processing terabytes of satellite imagery into NDVI color maps is no simple task. We rely heavily on NDVI algorithms to translate the immense amount of data from satellite imagery. The NDVI algorithm is able to calculate the value for each pixel and produce a manageable georeferenced output as a result. The UP42 Marketplace has three ready-to-use processing blocks.
NDVI maps are extremely helpful in global-scale vegetation monitoring. By using these maps we can instantly gain an understanding of fiel and vegetation health, enabling professionals in agriculture to maximize yield potential, minimize environmental impact and improve precision agricultural practices.
Want to learn more about NDVI mapping? Let us know how we can help.