Cameras compatible with DroneDeploy capture three bands of light. The Plant Health maps demonstrate the relative health of vegetation within the map by comparing the value of each band.
Healthier vegetation reflects more of certain types of light than unhealthy vegetation. Your DroneDeploy Plant Health map creates a scale to highlight differences within your area of interest.
Plant Health map options vary according to your DroneDeploy plan
Please see our Precision Agriculture Package to access advanced algorithms, such as NDVI, and camera filter types.
For more information on the basics behind Plant Health Maps, please see our Identifying Crop Variability with Drones blog post.
Cameras compatible with DroneDeploy capture three bands of light as input. They capture either: Red, Green, and Blue light OR some combination of Near-Infrared, Green, and Blue light.
Cameras that capture Red, Green and Blue light (RGB) apply the VARI algorithm to assess plant health.
DroneDeploy now supports processing and analysis for select Sentera cameras. Read this guide to learn more.
VARI was designed and tested to work with RGB data rather than near-infrared (NIR) data. It is a measure of "how green" an image is. VARI is not intended as a substitute for a NIR camera, but it is meaningful when working with non-NDVI imagery. VARI also measures the reflectance of vegetation versus soil.
Please visit our blog post for more information on the differences between VARI and NDVI here.
VARI algorithm used with an RGB camera.
The traditional formula for NDVI compares Near Infrared and Red light. It is great for measuring healthy, green vegetation over a wide range of conditions.
Below is an NDVI map calculated with the standard formula. We'll use this to compare to the other formulas in order to present different aspects of your data.
ENDVI includes a comparison of Green and Blue light in addition to the NIR and Red in order to give a more sensitive result. This isolates the indicators of plant health and can be used to assess the presence and health of a crop.
Notice the darker reds that create more contrast compared to the above NDVI image.
GNDVI is a modified version of the NDVI algorithm that combines Green and NIR light to better indicate the variation of chlorophyll content in the vegetation. It is also useful to analyze deficit/excess of water and nitrogen in the crop.
SAVI uses a soil brightness correction factor to analyze areas of the crop that are not covered with vegetation and the soil surface is exposed.
OSAVI is a variation of SAVI, with a lower calibration factor. Using OSAVI, there is more variation with the soil than with SAVI, but it is also more sensitive to vegetation. In the example below, we see more subtle vegetation differences with OSAVI than with SAVI above. OSAVI would work well in an area with a dense canopy and a wider spectrum of vegetation.
OSAVI is more sensitive to vegetation than SAVI and would work well in denser canopies.
RDVI is similar to SAVI in that it suppresses the effects of soil and sun. Unlike SAVI, however, it does not work as well in sparsely vegetated or dry areas.
Where to from here?
Check our Camera Filters for NDVI mapping support documentation to learn more about the different filter types.