In scientific speak, light sensors are used to gather information about an object without actually coming into contact with said object. As humans, we do this all the time. Our sight, smell and hearing are good examples of “remote sensing.” Digital cameras are another tool we use to gather information remotely. Now, technology offers us more sophisticated cameras, or spectral sensors, that can be used to gather information about turf quality.
These spectral sensors measure light in specific wavelengths, usually the red and near-infrared. In healthy vegetation, for example, red light is absorbed for photosynthesis. Perhaps more telling, healthy plants are particularly “bright” in the near-infrared, which is not visible to the unaided eye. Using these spectral sensors, a vegetative index made up of red and near-infrared reflectance can be used to quantify turf vigor, sometimes prior to visually obvious signs of stress or recovery. Mapping reflected near-infrared and red light from turfgrass can be a powerful management tool allowing turf managers to visually display patterns in greenness, quantify response and recovery, streamline management strategies and take preventive action.
Until recently, these tools were not generally available to the public and required skills in the physics of remote sensing and geographic information system (GIS) analysis. TurfScout is one company that’s making access to this information possible. Below are two case studies that demonstrate the TurfScout system as a tool for monitoring golf greens (or fairways) and promoting more efficient research and development of turf products and management strategies.
Golf green application
As a test of the TurfScout system, one Georgia golf course monitored three greens using a cart-mounted spectral sensor. The sensor used in this example is an “active” light sensor, which means it has its own light source and records the amount of light that’s reflected back. The reflected energy in the red and near-infrared is then used to calculate a vegetation index. More than one type of index may be calculated, but the two most commonly used indices are the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI). In either case, as turf quality improves the index increases.
With this new information on his greens and by uploading digital photos to the TurfScout Web site, Ray Meredith, superintendent at the Fort Benning Golf Club, used the TurfScout system to monitor response and recovery, identify recurring hot-spots, and detect sub-visual cues of turfgrass stress.
“Having walked the course for years, I know my greens,” Meredith says. “But the maps were like a report card showing me exactly where I needed improvement and exactly where I was successful. Each green is unique, TurfScout maps and charts allowed me to see changes in turf quality and adjust the way I was managing each green accordingly.”
In one instance Meredith observed a green recover from an existing, visibly stressed condition. When multiple mapping days were compared, he noticed that the average vegetation index was increasing over time and that those increases corresponded to an intensified fertilization schedule for this particular green.
More recently, Meredith noted that TurfScout Specific maps were capable of predicting recurring hot-spots. Specific maps are normalized to enhance small differences in turf quality, and are given a categorical legend ranging from poor quality to excellent quality. When Specific maps are paired with Standard maps, which use a fixed legend (1 = bad to 10 = Excellent), these maps provide sub-visual indicators of pending stress. In the figure below, the Specific map shows potential problem areas as turf that is lower in quality. While the Standard map suggests that current conditions (blue to green) are acceptable, Specific maps show the location of recurring hot-spots. In this case, Standard maps could be used to establish minimum thresholds of acceptable quality using the numeric legend.
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Research and development application
The superintendent isn’t the only beneficiary of light sensing technology and the power of GPS. More and more researchers are looking to spectral data analysis to find the answers. Spectral data are unbiased, highly quantitative and easy to collect. However, without the right software and expertise in remote sensing, turning pixels into useful information can not only be time consuming but in some cases misleading and frustrating.
Now novice and expert users alike can collect and analyze spectral data in minutes. The TurfScout system removes erroneous data points, identifies plot outliers and produces treatment summaries in minutes. The TurfScout web tool is being used to evaluate turfgrass products, new turf varieties, drought resistance, shade tolerance, and mowing height. Scientists and technical staff can quickly access and review experimental results in real time. The online system provides a consistent and rapid overview of results, freeing up time for good science and the labor needed to maintain experiments. With real-time feedback corrective actions during the collection season, rather than re-active, are possible.
“We have begun to integrate TurfScout into our turf research evaluation program as part of standard turf quality assessments,” says David Spak is the biology manager at the Bayer Environmental Science Development and Training Center in Clayton, N.C. “TurfScout allows us to rapidly and objectively screen new plant health promoters and compare the performance of disease management programs. We are steadily growing in confidence with this technology and believe that it will take us to the next level when it comes to non-destructive methods of evaluating turfgrass performance.”
Another turfgrass scientist, Brian Schwartz at the University of Georgia, is currently using the TurfScout system on more than 300 experimental golf green and fairway plots.
“A goal of the turfgrass breeding program in Tifton, Ga., is to develop stress tolerant cultivars that maintain quality even when grown in environments where drought, disease, and insect pressures have limited other grasses. But, measuring turfgrass response to these stresses is often time consuming and difficult,” Schwartz says. Research using the NIR and red sensor technology indicate that estimates of turf health during artificially induced drought cycles correlate very well with visual ratings of leaf “firing” or browning, but with about 25 percent of the effort.
“Plant breeders often compromise between screening more germplasm less rigorously or testing fewer plants with more in-depth methods,” Schwartz says. “The services provided by TurfScout may allow four times the number of experimental lines to be evaluated with the same effort, making the identification of the next released cultivar less like finding a ‘needle in the haystack.’”
Where do we go from here?
The science of remote sensing is not new and great strides have been made in the past two decades demonstrating the linkages between vegetative health and reflectance. The challenge today is in learning how to apply and deliver this knowledge to the turfgrass manager. With the advent of a newer, “greener” industry and the rising costs of course maintenance, these tools can be used to promote proactive, preventive and precision turf management.
Dana Sullivan is a remote sensing specialist with TurfScout. You can reach her at dana@turfscout.com.
