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Vegetation Monitoring using Remote Sensing

A normal, healthy plant will absorb blue and red light and reflect green light, which is why they appear green to our eyes. With the green visible light, plants also reflect Near-Infrared (NIR) light. This type of light, which is invisible to the human eye, also isn’t actively used for the photosynthesis process, and the healthier the plant, the more NIR light is reflected. When a plant becomes dehydrated or stressed, the spongy layer of the plant collapses and its leaves reflect less NIR light, yet they still reflect the same amount of light in the visible range.

As you can see, a stressed leaf and a healthy leaf reflect nearly the same amount of blue, green, red light, but a healthy leaf reflects more near-infrared light.

NDVI values between -1 and 0 correspond to non-plant surfaces that have a reflectance in the Red that is greater than the reflectance in the NIR. These could be surfaces such as equipment, water, or soil. Soil’s value is close to 0. Plant values range from 0.1 to nearly 1, and like stated earlier, the higher the NDVI value, the greater their density and health.

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 light and less near-infrared light. The numbers on the figure above are representative of actual values, but real vegetation is much more varied.

NDVI is a perfect tool for tracking plant health. Many agronomists, forestry engineers and farmers capture a series of NDVI maps to track the health of their crops throughout the growing season and even from year to year. NDVI values can be averaged to establish the normal growing conditions for the crops in a given area for a given time of the year.

NDVI gives powerful insights and makes it easier to visualize crop health that the naked eye can’t see. It shows you where the problem is in advance so you can fix it faster. NDVI technology does not replace humans, but it does help make your job easier. And with drone mapping software, it’s becoming one of the most successful methods to easily and quickly assess plant and crop health and and improve farm yields.

The vegetation monitoring methods, such as NDVI, can be used in several fields:


The series of images below show the development of crop growing cycles under a central pivot irrigation system at the central north region in Minas Gerais State from January to August 2019. We can observe the small changes within the parcels indicating a deficiency in the irrigation system and/or ground fertilization problem reflecting in the crop health.

This is only one of the possible applications of this methodology.

Pasture health

Monitoring pasture quality is very important for the cattle development and gain of weight. These procedures permit the monitoring of pasture health and help in forecasting the appropriate time to introduce pasture rotation and complementary feeding.

See below the pasture health in three distinct dates of a year:

  • 04/02/2019
  • 06/26/2019
  • 09/14/2019

  • Forestry.

    Monitoring the forest vegetation cover health and humidity level.

    The example below shows a regional view of an eucalyptus forestation in Minas Gerais central north region. We selected three different dates: one during the dry season peak (mid October), another during the start of the dry season (end of June) and the last one during the peak of the wet season (beginning of April). The images order are: Visible, NDVI and NDWI.

    The series was done to illustrate the contrasts between wet and dry seasons and the continuous monitoring of these parameters can help in a more efficient forestry inventory and wildfire control by identifying dryer areas within the forested area.

  • 04/02/2019
  • 06/26/2019
  • 10/14/2019

  • Environment

    The vegetation analysis NDVI can be used to measure deforestation growth. See below the resulting images using NDVI to quantify the deforestation during a period in the south western portion of Pará State.

    Deforested areas in July 2018.

    Deforested areas in July 2019.

    Image showing the resulting 14% increase in deforestation from July 2018 to July 2019.