Colour composite image

Indicator : NDVI – Vegetation health & density


To identify the ‘vegetated health and density’ from remote sensing images, an indicator, called NDVI or ‘Normalised Difference Vegetation Index’ is frequently used.
Healthy vegetation absorbs most of the incident visible red light (emitted by the sun), and reflects a large portion of the near-infrared light. Unhealthy (non-green) or sparse vegetation reflects more visible red light and less near-infrared light. This difference in reflectance for different wavelengths, allows remote sensing instruments to measure the relative presence (or absence) of healthy, green vegetation, by simply measuring and comparing the reflectances. Typically, this is done by evaluating the following formula:

  • NDVI = (NIR - RED)/(NIR + RED)

where NIR is the near-infrared reflectance and RED is the reflectance of visible light.


The physical NDVI values are between -0.1 and 0.92, where higher values indicate denser and healthier (higher green density) vegetation. NDVI values of 0.1 and below, for instance, typically correspond to areas with little to no vegetation (rocks, ice, desert). Moderate values (around 0.2 and 0.3) correspond to shrub and grasslands and high values (0.5 and above) typically correspond to dense vegetation like rainforests. In the legend of the image viewer, the NDVI is not given as physical value, but as a qualitative indicator.

Vegetation health & density
Physical NDVI value

Very good

0.72 till 0.92


0.42 till 0.72


0.22 till 0.42


0.12 till 0.22

Very poor

-0.10 till 0,12

‘NDVI’ or ‘Vegetation health and density’ indicator can be a very interesting tool for observing a range of changes in vegetation condition or cover. It can e.g. be used to monitor agricultural crops. Over the course of a growing season, we first see a steady increase in the ‘Vegetation health and density’ values as the young, green vegetation grows (the growth makes the surface appear more and more green, which is described by the ‘Vegetation health and density’). This increase reaches a maximum value just before it drops suddenly at harvest time or when the plants die naturally, which can easily be explained by the harvesting of the healthy, green plants or their senescence, which makes the surface appear less green. Another example is the clear-cut or the re-establishment of forested areas. Forests in tropical areas typically have high ‘Vegetation health and density’ values all year round, as it exists of dense and evergreen vegetation. After a forest is cleared, the ‘Vegetation health and density’ drops dramatically. If the forest has the chance to re-establish, the ‘Vegetation health and density’ will increase gradually over the years.
See examples in case studies.

Product specification sheet
See NDVI Product Specification Sheet for further details.

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Indicator : DMP – Vegetation growth rate


To measure the ‘vegetation growth rate’, the DMP or ‘Dry Matter Productivity’ can be used.
When vegetation (crops or natural vegetation) is healthy and water and nutrients are not limiting, DMP is proportional with the amount of light intercepted by a crop canopy. Estimates of the productivity of terrestrial vegetation can be made by combining remote sensing imagery with meteorological data (solar radiation and temperature information). The calculation is based on the classical Monteith (1972) approach.


This indicator gives the daily increase of the dry matter biomass (growth rate) of the overall vegetation and is expressed in kilograms of dry matter (kg DM) per hectare per day. The physical DMP values range between 0 and 327.67 kg dry matter per hectare per day, where higher values indicate a higher growth rate, so more production of dry matter biomass. In legend of the image viewer, the DMP is expressed as qualitative values in stead of physical values.

Vegetation growth rate
Physical DMP value
[kg DM/ha/year]

Very high

148 till 328


92 till 148


46 till 92


23 till 46

Very low

0 till 23

The ‘DMP’ or ‘vegetation growth rate’ reflects only the above-ground dry matter (no fresh matter) biomass. Stress factors as water surplus or deficit, poor availability of nutrients or occurrence of pests are not directly considered in the ‘vegetation growth rate’ calculation, and as such ‘vegetation growth rate’ should be interpreted as indicative for “potential” production.
The ‘vegetation growth rate’ can be useful for rangeland monitoring, to provide an indication on the available vegetation biomass. A possible application could be to investigate the effectiveness of grazing management, by investigating the average ‘vegetation growth rate’ for buffer and core conservation areas. Another use is the evaluation of crop growth. The ‘vegetation growth rate’ typically increases at the start of the growing season to reach its maximum after which it decreases towards the end of the season. The ‘vegetation growth rate’ gives no indication about the quality of the harvested crops. It describes the total biomass production of the crop and makes no difference between those parts of the plants which are harvested (e.g. in case of root or fruit crops) or not.
See examples in case studies.

Product specification sheet
See DMP Product Specification Sheet for further details.

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Types :


Each ‘Vegetation health & density’ and ‘Vegetation growth rate’ product exists as a composite of 10 daily images. Single day  images are often partly covered by clouds, resulting in missing values. Combining 10 consecutive images allows to fill most of these gaps. To obtain a composite, for each individual pixel, the highest value within that period is retained.
Each month is split in 3 periods : a ‘beginning’ (start on the 1st day), ‘middle’ (start on the 11th day) and ‘end’ (start on the 21st day)

Comparison to ‘previous 10 days’

Over the course of the growing season, the values of the vegetation indicators vary. The ‘comparison to previous 10 days’ is calculated from the difference between the current and the previous vegetation indicator values and reveals if the indicator value is increasing or decreasing.
Variations are related to the natural response of the vegetation to the different seasons. At the beginning of the wet season, the vegetation will become greener, denser and more productive, leading to an increase of the indicator values , while at the end of the wet season the vegetation will senescence resulting in a decrease of the indicator values . However, if those changes occur at an unusual moment in the season, this indicates exceptional situations. An early increase may e.g. indicate an early start of the growing season, or an unexpected dip in the middle of the season may e.g. indicate that plants are suffering from drought or pests.

Comparison to ‘last year’
The values of the indicators for the same period of the year may also differ between different years. The ‘comparison to last year’ is calculated from the difference between the current vegetation indicator and the vegetation indicator of the previous year for the same period of the year. Higher values may e.g. indicate better conditions or an advanced start of the season compared to last year.
However, this comparison does not specify whether in the current or previous year exceptional conditions occured. This can be assessed by the ‘comparison to average condition.

Comparison to ‘average condition’
For each period of the year, the ‘average condition’ (or ‘long term average’) of the Vegetation health & density’ (NDVI) and ‘Vegetation growth’ (DMP) is calculated. Each pixel of the ‘average condition’ contains the mean value for that period since 1999 up till the most recent year. It is updated every year. Bad measurements (snow/cloud/error) are not taken into account.
The ‘average condition’ reflects the condition that normally occurs and is considered as a reference. Calculating the difference between the ‘current’ vegetation condition and the ‘average condition’ can reveal anomalies.  Higher values may e.g. indicate that the plant conditions are much better compared to a normal situation. If higher values are present during most of the season, the crop production can be expected to be higher as usual. Lower values may e.g. indicate droughts.

Product specification sheets
Comparison previous 10 days
Comparison same period last years
Comparison average conditions same period

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Colour composite image

A digital image recorded by a digital camera is displayed on a computer screen very logically, that is the display system’s red channel is associated with the camera’s red channel, its green channel with the camera’s green channel, and so on. The resulting image is thus a faithful copy of what the direct observer’s eye would have seen: a red-coloured object is shown in red, etc.

In remote sensing systems it is possible to detect and record parts of the electromagnetic spectrum that cannot be detected by the naked eye, for example, the infrared band. To visualise this information we pair up display colours (red-green-blue) with spectral bands in the observation system that do not necessary correspond to them. In so doing, we create coloured composites.

NIR colour composite
In Near Infrared (NIR) colour composites the green band of the sensor is displayed in blue, the red band is displayed in green, and the near infrared band in red. Such a composition is very effective for analysing vegetation and offers the advantage of having practically the same properties as the colour infrared photographs with which photo-interpreters have been familiar for years. On such image vegetation that has high photosynthetic activity will appear in bright red (near-infrared peak), water will appear practically black (as this material absorbs practically all wavelengths), and mineral surfaces (bare ground, concrete) will show up in hues ranging from blue to white.


NIR colour composite image

SWIR colour composite
In Short Wave Infrared (SWIR) colour composites the red band of the sensor is displayed in blue, the NIR band is displayed in green, and the SWIR band in red. For vegetation, reflectance is high in NIR part of the electromagnetic spectrum and low in the red part of the spectrum. Therefore vegetation will appear green in SWIR colour composite images.
The reflectance in the SWIR portion of the spectrum is determined primarily by the moisture content of the surfaces being measured. Vegetation that is under stress (due to drought, pests, climate change, pollution, etc) will generally have less moisture content than healthy vegetation.


SWIR colour composite image

BELSPO remote sensing tutorial. http://eoedu.belspo.be/

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