One of the most useful outputs available in the Geospatial Forestry Platform are the NDVI images that are derived from satellite imagery. But what is NDVI? This abbreviation stands for Normalised Difference Vegetation Index, and it is a measure of photosynthetic activity in vegetation. Simply put, it is a measure of how well a plant is growing.

NDVI - How does it work?

How does it work?

NDVI is based on the way vegetation responds to two specific bands in the electromagnetic spectrum, namely the Red band (in the visible light portion of the spectrum), and the Near-Infrared band (just beyond the visible spectrum, so that it is not visible to the human eye).

Chlorophyll, a pigment in the leaves of vegetation, absorbs energy from the Red band, but very strongly reflects energy from the Near-Infrared band. Therefore, the more chlorophyll present in the leaves, the more actively a plant is growing, and the more strongly these absorption/reflection characteristics occur. This phenomenon allows optical satellite sensors to record this interaction to derive NDVI imagery.

How I NDVI Measured - Kenya

How is NDVI measured?

NDVI is calculated as the ratio of Red and Near-infrared values, using the formula:

NDVI = (NIR —Red)/(NIR + Red)

The derived values for this ratio lie between -1 and +1, where the higher the value, the more actively the plant is growing, or healthy, i.e., low values indicated problems in growth, while higher values indicate good growth.

While there are no absolute values that can be assigned to these ratio outputs, the following can be used as a guide in interpreting NDVI values:

-1 – 0

No vegetation or dead vegetation

0 – 0.33

Unhealthy/sparse vegetation

0.33 – 0.66

Moderately healthy vegetation

0.66 – 1.0

Very healthy vegetation

Limitations of NDVI

While NDVI is a recognised measure of plant health and growth, and is very widely applied, it does have limitations that need to be taken into account when using this technique.

NDVI outputs are directly related to the raw values within an individual image, and these values are affected by several factors, such as atmospheric conditions, which alter the raw image values, such that different values can be recorded over the same scene, even if nothing has changed on the ground. Therefore, NDVI values acquired at different times are not directly comparable, unless measures such as atmospheric corrections have been first applied to the imagery, prior to NDVI being calculated.

NDVI also tends to become “saturated”, i.e. the ratio values peak at a certain point and can no longer record differences in the ratio values. This occurs when vegetation growth is very heavy or dense and so NDVI tends to be less useful under these conditions, i.e. it cannot detect variation in growth patterns between very dense or strongly growing vegetation.

Applications of NDVI

NDVI is a very useful indicator of vegetation health and can highlight different growth conditions across a forest stand or agricultural field. It can pin-point areas of concern where growth is not what it should be, is a good indicator of possible drought conditions and can be used to monitor crop condition over a normal growth cycle, from emergence through active growth to senesce or harvest states. It is also used as an indicator of biomass. Although it is only one of many similar vegetation indices that can be applied to monitor vegetation state, it is certainly the most widely used. Sudden drops in NDVI values (especially to values around 0) between consecutive images can indicate loss of vegetation through harvesting activities, fire damage or other catastrophic events.

When applied, with an understanding of its limitations, it is an extremely useful tool for users needing to monitor vegetation.

Dr Mark Norris-Rogers (PhD) PrGIScP

Dr Mark Norris-Rogers (PhD) PrGIScP

Having originally trained as a Forester, and spending over 10 years in forest management, Mark subsequently specialized in GIS and Remote Sensing, and has over 25 years’ experience in this field. He has a keen interest in applying spatial technologies to provide integrated forest management information for the Forest Managers.

Mark has considerable experience in applying Remote Sensing technologies, such as optical, Radar and Lidar, into forest planning and management. This has included applying Lidar technology to Enhanced Forest Inventory systems which have greatly enhanced the effectiveness of clients’ management plans and operations. Mark provides specialist Remote Sensing and GIS skills to several South African, Canadian and UK companies involved in forestry and natural resources management.

Apart from his forestry qualifications (Diploma in Forestry; NHD Forestry), Mark has a BA Hons in GIS and a PhD in Environmental Science, where his research involved monitoring forestry operations using medium and high-resolution satellite imagery. He is a registered Professional G.I. Science Practitioner with the South African Geomatics Council. Mark has also co-authored several papers in international journals and presented papers at various international conferences.