Truecolor satellite imagery is an incredibly valuable tool in remote monitoring. However, relying solely on what we can see with our own eyes in a standard satellite image may not tell the whole story. This is where the Normalized Difference Vegetation Index (NDVI) comes in. By using NDVI alongside truecolor imagery, we can identify subtle changes and gain a more complete understanding of what's happening on the ground.
In Lens, you can access a variety of vegetation datasets that are derived from NDVI and serve as a powerful complement to monitoring with truecolor imagery.
If you ask us, NDVI is pretty neat — it allows us to literally see photosynthesis in action, and measures the health and density of vegetation. Healthy, dense vegetation strongly absorbs red light for photosynthesis and strongly reflects near-infrared light. Unhealthy or sparse vegetation, on the other hand, reflects more red light and less near-infrared light.
To keep things straightforward in Lens, we clip NDVI values to a range of 0 to 1, and use a color ramp for easy visualization of the data. A value closer to 1 is displayed in dark green and indicates very healthy, dense vegetation, while values closer to 0 are displayed in white and indicate non-vegetated areas like water, roads, and structures. Values ranging between 0 and 1 fall in the light green, yellow, and orange areas of the color scale and can indicate stressed, thinned, or sparse vegetation.
We process NDVI datasets to make them easily accessible via Lens, and empower the user to pick which dataset to use and when. We call all NDVI data “Vegetation”, and currently work with three sources:
The National Agricultural Imagery Program (NAIP) is a U.S. Department of Agriculture program that acquires high-resolution aerial imagery during the agricultural growing season across the contiguous United States.
One of the most common sources for high-quality NDVI data is the Sentinel-2 satellite constellation, part of the European Union's Copernicus Programme.
With a balance between high capture frequency and resolution, we chose Sentinel-2 data to power the preset vegetation drop policy in Lens Lookout, our automated change detection tool
The Landsat program, a joint effort by NASA and the U.S. Geological Survey (USGS), has been collecting imagery for decades.
While truecolor imagery is great for identifying obvious features and large-scale changes, it can fall short when it comes to detecting subtle shifts in vegetation health. Changes like disease, drought, or degradation can be hard to spot with the human eye until the impact is severe.
This is where NDVI shines. Because it's sensitive to the specific light reflectance properties of chlorophyll, it can reveal these changes long before they become visible in a truecolor image. An area of forest that looks green and healthy in a truecolor image may have a lower-than-expected NDVI value, signaling early signs of stress.
NDVI can also act as a great proxy for identifying changes before ordering high-resolution truecolor imagery. For example, a new building or a road being constructed will result in a sharp drop in NDVI values for that specific area, which then may spur an order of high resolution imagery to follow up in more detail.
Ready to unlock the power of NDVI for your monitoring needs? Get started with Lens today or contact our team to see how vegetation data can enhance your remote sensing workflows.
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