Why NDVI
By Warren Witte
Why NDVI
By Warren Witte
Multispectral Sensors and NDVI
Integrated Aerial Systems have completed hundreds of successful crop health surveys in South Africa and Africa. Our UAVs have been fitted with the multispectral camera, the Micasense Red Edge MX. The Micasense Red Edge MX has two sensors, one facing the plants capturing the light they reflect in five bands; three visible, green, red and blue and two infrared bands, red edge and near infrared. The other sensor, the DLS 2 otherwise known as the sunshine sensor, records the intensity of the light emanating from the sun in these five same bands of light.
The most well-known and used index map produced from multispectral imagery is NDVI, an acronym for Normalized Difference Vegetation Index. NDVI is used to identify vegetated areas and their associated health. When sunlight strikes an object certain wavelengths are absorbed and others reflected. The NDVI algorithm is calculated from the visible and near-infrared light reflected by vegetation. The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 μm). The cell structure of leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 μm). Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation (right) reflects more visible lights and less near-infrared light. So vegetation having high values indicates healthy plant material, while low values represent unhealthy plant material (Illustration by Robert Simmon).
< 0 Inanimate/ dead material, e.g roads, buildings, soil or dead plants 0 -> 0.33 Unhealthy plant material 0.33 -> 0.66 Healthy plant material -> 0.66 Very healthy plant material
NDVI = (NIR-RED) / (NIR+RED)
NDVI has been available to famers for some time through remote sensing, while this gives an entry level perspective, drones are able to provide a very high degree of granularity on demand which has been otherwise been unobtainable. The value of NDVI lies in the ability to detect variability in your fields quickly. NIR bands are able to detect stress in crops before it is apparent in the visual spectrum (RGB). So using NIR data we are able to find that stress earlier minimizing potential yield losses. Multispectral cameras also provide increased accuracy when comparing plant health data over time, such as throughout the course of a season, which is a major benefit over visible spectrum cameras.
While NDVI is able to give you a good indication of possible variability in your fields you still need to get boots on the ground to investigate what may be the cause of that variability. In order to use this NDVI image for management decisions, or diagnose what the issue is, a farmer needs to understand the crop varietal, history of the field, growth stage, fertilization, pesticides, and the plant growth environment (primarily moisture and temperature). Drone imagery really just helps make crop scouting easier and more efficient. The infield-scouting application, apart of the service, means that you can enter your field and make notes on your device.