ABC,s of Infrared Imagery for Crops
Eileen Perry, WSU Center for Precision Agricultural Systems
You’ve probably heard the term infrared images or infrared photography. But what are they and what do they tell you about your crop? Actually, there are two really different technologies, color infrared and thermal infrared. Color infrared cameras record the amount of light reflected from plants in the green, red, and near infrared wavelengths. Plants reflect light in the green wavelengths (that’s why we see them as green), they absorb in the red wavelengths (for photosynthesis), and they really reflect a lot of light in the near infrared. This is the region just beyond our visible range, which is a good thing, because if we could see out in that region we’d need sunglasses to look at plants. Color infrared imagery is generally displayed in a format called ‘false color’. In this format, vegetation with high chlorophyll density shows up in the image as bright red. In the 1980’s and 1990’s, color infrared imagery was mainly generated using film cameras. Today, color infrared is usually generated using special digital cameras. Thermal infrared imagery measures the energy from plants in much longer wavelengths beyond the visible and near infrared region. This recorded energy correspond s to the temperature of the plants. Thermal infrared camera systems tend to be more expensive and have lower image resolutions (that is, fewer total pixels per image) than color infrared cameras.
So what does each of these technologies tell us about crops? Color infrared imagery is really tuned for highlighting healthy vegetation versus stressed vegetation, soil, pavement, irrigation ponds, and other parts of the orchard and vineyard landscape. With processing of the imagery, we can make comparisons of the health of trees or vines throughout a block or over time. Figure 1 shows a color infrared image, and an image product created from the color infrared. The false color infrared map on the left clearly shows the shapes of the trees as well as the vegetation growing between the trees. You can see areas where the trees are more vigorous, and areas with less vigor. The color infrared image has also been geometrically corrected so you use it with your GPS and find areas of interest in the block. Many enhancements can be made to color infrared imagery to help us see more subtle differences in the canopy. These enhancements generally involve comparing the relative amounts of red and near infrared reflectance. Urs Schulthess of CropMaps LLC has been applying a proprietary algorithm to tree fruit to generate what he calls a Canopy Density map. The image on the right side in Figure 1 shows the Canopy Density map based on the color infrared imagery on the left. In this map, the vegetation growing between the trees was filtered out and the individual pixels were merged into zones with similar densities. Canopy density can have a big impact on fruit set, yield, and quality. Color infrared can also be used near the end of the growing season. Figure 2 shows a Chlorophyll Index map, which was created by CropMaps based on a color infrared image taken on October 25, 2004 when the leaves were turning. The Chlorophyll Index is sensitive to the amounts of chlorophyll still present in the leaves. Trees with an ample supply of water and nutrients should stay green longer, while deficient trees should senesce earlier. This Chlorophyll Index map generated at the end of the growing season can be used to create zones for soil sampling and fertilizer applications.
Figure 1. The left image is a color infrared image displayed as ‘false color’. The more vigorous trees show up as bright red. The right image is a map which breaks the block into zones of similar canopy density. In this map the vegetation between the trees is deleted so we are just looking at the tree canopies.
Figure 2. This map was made using color infrared imagery taken in October, and shows the amount of chlorophyll in the canopy, which can be an indication of nutrient needs.
Thermal infrared imagery is all about surface temperature. Research tells us that leaves on well watered, healthy plants are generally within a couple of degrees Celsius of the air temperature. So in thermal infrared imagery, we are going to see big differences in temperature between vegetation and other surfaces like bare soil, pavement, water, and so on. But we will also see differences across the canopy, especially when some plants are water stressed. In Figure 3, we see a before and after set of images of an apple block where there was leak in the drip irrigation system that went unnoticed for a couple of weeks. In the left panel, you can actually see the location of the leak. The right panel shows the same field three days later, after the irrigation was repaired and the remainder of the block was well watered. What’s interesting is that although this thermal infrared imagery doesn’t have the same high spatial resolution of the color infrared, there is still enough detail to make out a problem with irrigation. These thermal images were not corrected to map coordinates before they were given to the grower, but he could still locate the leak.
Figure 3. These are thermal infrared images taken on the same field a few days apart. The left image shows the location of an irrigation leak, the right image shows the same block after the leak was found (thanks to the first image) and fixed.
So what is the cost of color infrared and thermal infrared imagery? That depends a lot on the amount of processing that has been done to deliver a product. While raw imagery may cost pennies per acre, image products may cost a few dollars per acre. If you are looking into purchasing imagery, you might consider asking the following questions: Has the imagery been corrected for differences in lighting or camera settings? Is the imagery or product in map coordinates? Can you compare the image values from one image to another, or are they only relevant within that image? The answers will help you better understand the value of the imagery for your particular needs.
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