Color, NIR and NDVI imagery According to Iowa State

Color, NIR and NDVI imagery According to Iowa State

The Integrated Crop Management News, and Iowa State University Extension and Outreach program recently published an informative article on how to choose the right imagery, in regards to best management practices for Color, NIR, and NDVI imagery. Read the full article, here or continue on below:
May 17, 2016 via Iowa State University Extension and Outreach: Integrated Crop Management 

Figure 1. Shown from left to right are examples of Color, near infrared (NIR), and Normalized Difference Vegetative Index (NDVI) images. The images were captured with a Rotary Platform small Unmanned Aerial System (sUAS). 

Key Points:

  • RGB (color) imagery is similar to viewing a digital photograph taken from a plane.
  • Near infrared (NIR) imagery provides a greater assessment of plant health than traditional photos by visualizing color bands outside of what the human eye can see.
  • Normalized Difference Vegetative Index (NDVI) is a commonly provided index that assesses crop vigor based on a mathematical interpretation of color and near infrared data.
  • Imagery is very useful to identify areas of crop variability, but field scouting is often still required to verify the cause of variability.

As spring leads into summer, don’t forget to consider aerial imaging as part of a continuous improvement plan for crop production. Remote sensing and the use of aerial imagery has been used for decades in agriculture, but we’ve seen the number of imagery providers grow extensively since 2010. The use of imagery can vary from farm to farm but several common uses include: variable rate fertility recommendations, assessing water management performance, quantifying soil compaction and machinery induced yield limiters, locating late season weed outbreaks, and generally evaluating the consistency of crop vigor across a field.

Most producers will source imagery from an input service provider or a technology service provider. Service providers work with a range of different platforms to capture crop imagery. After an image is captured and processed, these service providers often give more than one final image back to a grower. It is important to know the differences between the types of images and how each can be used to benefit a grower. Three of the most common images provided are color (RGB), Near Infrared (NIR) and the Normalized Difference Vegetative Index (NDVI).

Comparing Imagery Types:

Color RGB (Red, Green, Blue): also known as color imagery are images that most closely represent how the human eye would see a field from a plane.

  • Provide shape and definition to problem areas that would be difficult to define at ground level.
  • Available from most aerial imagery platforms.
  • Typical uses: Color imagery provides an opportunity to identify areas potentially in need of greater water management and the effects of management systems; i.e., turnarounds in the headlands and planter skips. Additionally, it can be used to do an initial quantification of lost production acres.

The figure below shows a typical RGB image of a corn field that was captured on June 25, 2014 by a contract flight. The image had a pixel size of 0.8 ft.  In this field we can identify the following production features: planter skips, drowned out spots, and areas damaged by turning equipment.

Color imagery does have limitations.  Generally, the crop needs to be significantly stressed in order to see a visual difference that can be identified in a color image. Additionally, color imagery provides little opportunity to distinguish small differences in areas of high yield. Color imagery also has less value prior to a full canopy due to excessive soil saturation in the image.

Near Infrared Imagery (NIR): also known as color infrared imagery, uses a false color composite to display information that would normally be invisible to the human eye. The NIR map shows areas of highly vigorous crops in bright red and weak crops or bare soil in gray.

  • Plant health is displayed with a greater range of detail than the color image. Plants will often show response to damage or disease in NIR images before the same response is visible with a color image.
  • NIR is commercially available from most imagery vendors and is typically part of the base imagery package.
  • Typical uses: Quantify machinery induced crop limiting factors and weed detection. Provides higher levels of detailed assessment for defining management zones, making fertilizer recommendations, quantifying ponding or water management effectiveness, and generally assessing crop vigor across a field.

The figure below shows a typical NIR image of the same corn field that was captured on June 25, 2014 by a contract flight. The image had a pixel size of 0.8 ft. In this field we can identify the following production features: drowned out spots, turnarounds in the headlands, machinery induced crop limiting factors, and areas where water damage occurred, but did not kill the plant population are visible.

NIR maps have a key advantage over color RGB in that they show crop performance and vigor in much greater detail. The goal in agriculture is to have a uniform emergence and vigor throughout a field. NIR maps can provide key information related to whether this goal has been met across a field. In the example image below some artifacts are controllable while others are not. The wet spots in the field are likely not controllable without significant investments in drainage. These areas are already likely well understood by the producer.  The higher resolution issues from machine traffic and individual row emergence can be resolved and are actionable through an increased focus on planter management.

Normalized Difference Vegetative Index (NDVI): is a calculated index used to monitor crop health and photosynthetic activity. The higher the index value the greater the crop vigor.  A color gradient is applied to make the image easier to interpret. A commonly used gradient is red to green; red being the low values and green being the high. Typically four colors are used with each representing approximately 25% of the field. This is similar to how a yield map represents data.

  • One of the simplest indices, commonly provided by imagery providers as part of a standard imagery package.
  • Two different types of NDVI image: Calibrated and Uncalibrated/Maximum Variation Scaling.
    • Calibrated NDVI images can be used to show changes in vegetation due to management systems or other factors over time.
    • Uncalibrated or Maximum Variation NDVI images can be used to show crop vigor at a particular point in time. It’s the most common type currently provided by imagery providers.
  • Typical Uses: NDVI imagery has been widely used to assess crop vigor across a field, areas of ponding and changes in field conditions over time.

The figure below shows a typical uncalibrated NDVI image that was calculated using the contracted flight imagery captured of the corn field on June 25, 2014. The image was calculated with a 0.8 ft pixel size. In this field we can identify the following production features: planter skips, drowned out spots, areas damaged by turning equipment, variation in the amount of water damage for some areas, and boundaries for where damage occurred.

Similar to NIR the NDVI map shows variability in crop vigor in greater detail than a standard color image. Color scaling for the NDVI map does need to be considered when making recommendations based on the imagery. Uncalibrated NDVI which is the most common form will always results in areas of red and green within the field, even if the entire field is relatively strong or relatively poor. As a result, NDVI is a good information source for quickly evaluating different production zones or artifacts within a field but scouting is still required to assess the magnitude and cause of the variability.


Links to this article are strongly encouraged, and this article may be republished without further permission if published as written and if credit is given to the author, Integrated Crop Management News, and Iowa State University Extension and Outreach. If this article is to be used in any other manner, permission from the author is required. This article was originally published on May 17, 2016. The information contained within may not be the most current and accurate depending on when it is accessed.

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