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Posts from the ‘Canopy’ Category

Best Research Instrument Hacks

Sometimes, brilliant ideas are born out of necessity.  We wanted to highlight innovative ways people have modified their instrumentation to fit their research needs.  Here, Georg von Unold, founder and president of UMS (now METER) illustrates ingenuity in a story that inspired the invention of the first UMS tensiometer and what could be one of the greatest scientific instrument hacks of all time.

Instrument hacks

The Bavarian Alps

An Early Penchant for Ingenuity

In 1986, graduating German students were required to join the military or perform civil service.  Von Unold chose to do a civil service project investigating tree mortality in the alpine region of the Bavarian Mountains.  He explains, “We were trying to understand pine tree water stress in a forest decline study related to storms in certain altitudes where trees were inexplicably falling over. The hypothesis was that changing precipitation patterns had induced water stress.”  

To investigate the problem, von Unold’s research team needed to find tensiometers that could measure the water stress of plants in the soil, which was not easy. The tensiometers von Unold found were not able to reach the required water potential without cavitating, so he decided to design a new type of tensiometer.  He says, “I showed my former boss the critical points. It must be glued perfectly, the ceramic needed defined porosity, a reliable air reference access, and water protection of the pressure transducer. I explained it with a transparent acrylic glass prototype to make it easier to understand. At a certain point my boss said, “Okay, please stop. I don’t understand much about these things, but you can make those on your own.”

Instrument hacks

Two snorkels protected a data logger predecessor from relative humidity.

Snorkels Solve a Research Crisis

The research team used those tensiometers (along with other chemical and microbial monitoring) to investigate why trees only in the precise altitude of 800 to 1100 meters were dying. One challenge facing the team was that they didn’t have access to anything we might call a data logger today.  Von Unold says, “We did have a big process machine from Schlumberger that could record the sensors, but it wasn’t designed to be placed in alpine regions where maximum winter temperatures reached -30℃ or below. We had to figure out how to protect this extremely expensive machine, which back then cost more than my annual salary.“

Von Unold’s advisor let him use the machine, cautioning him that the humidity it was exposed to could not exceed 80%, and the temperature must not fall below 0℃.  As von Unold pondered how to do this, he had an idea. Since the forest floor often accumulated more than a meter of snow, he designed an aluminum box with two snorkels that would reach above the snow.  The snorkels were guided to a height of two meters.  Using these air vents, he sucked a small amount of cold, dry air into the box. Then, he took his mother’s hot iron, bought a terminal switch to replace the existing one (so it turned on in the range of 0-30℃), and mounted a large aluminum plate on the iron’s metal plate to better distribute the heat.

Von Unold says, “Pulling in the outside air and heating it worked well. The simple technique reduced the relative humidity and controlled the temperature inside the box. Looking back, we were fortunate there wasn’t condensing water and that we’d selected a proper fan and hot iron. We didn’t succeed entirely, as on hot summer days it was a bit moist inside the box, but luckily, the circuit boards took no damage.”

Instrument hacks

Tree mortality factors were only found at the precise altitude where fog accumulated.

Finding Answers

Interestingly, the research team discovered there was more to the forest decline story than they thought. Fog interception in this range was extremely high, and when it condensed on the needles, the trees absorbed more than moisture.  Von Unold explains, “In those days people of the Czech Republic and former East Germany burned a lot of brown coal for heat. The high load of sulphur dioxide from the coal reduced frost resistivity and damaged the strength of the trees, producing water stress.  These combined factors were only found at the precise altitude where the fog accumulated, and the weakened trees were no match for the intense storms that are sometimes found in the Alps.”  Von Unold says once the East German countries became more industrialized, the problem resolved itself because the people stopped burning brown coal.

Share Your Hacks with Us

Do you have an instrument hack that might benefit other scientists?  Send your idea to kcampbell@metergroup.com.

How to Get More From Your NDVI Sensor (Part 3)

In the conclusion of our three-part series on improving NDVI sensor data (see part 2), we discuss how to correct for limitations which occur in high leaf area index (LAI) conditions.

NDVI Sensor

Where there’s a large amount of vegetation, NDVI tends to saturate.

NDVI Limitations – High LAI

NDVI is useful in the midrange of LAI’s as long as you don’t have strong soil effects, but as you approach an LAI above 4, you lose sensitivity. In figure 6, loss of sensitivity is primarily due to a saturation in the red band. Measurements were taken in a wheat canopy and a maize canopy. The near infrared reflectance is sensitive across the entire spectrum of the wheat and maize canopies, but the red saturates relatively quickly. Where the red starts to saturate is where the NDVI starts to saturate.

NDVI Sensor

Figure 6: Gitelson (2004) J. Plant Phys

Note: NDVI saturates at high LAI’s, however, if your purpose is to get at the fractional interception of light, NDVI tends not to have the saturation issue. In Figure 7, Fpar or the fractional interception of light of photosynthetically radiation is nearly complete far before NDVI saturates. This is because canopies are efficient at intercepting light, and once we get to an LAI of about 4, most of the light has been intercepted or absorbed by the canopy.  Thus, incremental increases in LAI don’t significantly affect the FPar variable.

NDVI Sensor

Figure 7: Fractional interception of light is near complete at an LAI around 4. (Gamon et al. (1995) Eco. Apps)

Solution 3- WDRVI

One solution for the NDVI saturation issue is called the Wide Dynamic Range Vegetation Index (WDRVI). It’s formulation is similar to NDVI, except for a weighting coefficient that can be used to reduce the disparity between the contribution of the near infrared and red reflectance.  

NDVI Sensor

In the WDRVI, a is multiplied by the near infrared reflectance to reduce its value and bring it closer to the red reflectance value. In doing so, it balances out the red and the near infrared contribution to the vegetation index.

NDVI Sensor

Figure 8: (Gitelson (2004) J. Plant Phys)

a can range anywhere from 0 to 1. Figure 8 shows that as we use a smaller value of a, we get an increasing linear response of the wide dynamic vegetation index to LAI.

The only drawback of the WDRVI is that the selection of a is subjective. It’s something that you experiment on your own until you find a value for a that is optimal for your solution.  People tend to err on the side of a very low value simply because they’ll get closer and closer to a linear response to LAI as a decreases.

Solution 4 – Enhanced Vegetation Index

The enhanced vegetation index (EVI) was designed to enhance sensitivity in high biomass ecosystems, but it also attempts to reduce atmospheric influences.  This was a vegetation index created for the purposes of a satellite based platform. There’s a lot of atmosphere to look through from a satellite to the ground, and sometimes the aerosols in the atmosphere affect the reflectances in the red and the near infrared regions causing spurious observations.  The EVI also tries to reduce sensitivity of the index to soil. Thus the EVI is a kind of solution to both extremes.

NDVI Sensor

In the EVI equation, the two major inputs are near infrared and red reflectances.  C1 , C2, and L are all parameters that can be estimated, but the blue band is something that has to be measured. Most NDVI sensors are two band sensors, so you don’t have that information in the blue.  Plus, with satellites, the blue band is relatively noisy and doesn’t always have the best quality data, thus EVI has limited value.

Solution 6: EVI2 (Enhanced Vegetation Index 2)

Those problems led a scientist named Jiang to come up with a solution.  Jiang observed quite a bit of autocorrelation between the red band and the blue band, so he decided to try and formulate EVI without the blue band in what he called the EVI2 (Enhanced Vegetation Index 2).  if you’re interested in the mathematics, we encourage you to go read his paper, but here we give you the equation in case you’re interested in using it.

NDVI Sensor

Figure 9

When Jiang calculated his EVI2 and compared it to the traditional EVI (Figure 9), it was nearly a one to one relationship. For all intents and purposes EVI2 was equivalent to EVI.  Since this avoids blue band, it offers some exciting possibilities as it reduces to just using the two inputs of NIR and red bands to calculate NDVI.

NDVI Sensor Summary

NDVI measurements have considerable value, and though there are extremes where NDVI performs poorly, even in these cases there are several solutions.  These solutions all use the near infrared and the red bands, so you can take an NDVI sensor, obtain the raw values of NIR and red reflectances and reformulate them in one of these indices (there are several other indices available that we haven’t covered). So if you’re in a system with extremely high or low LAI, try to determine how near infrared and red bands can be used in some type of vegetation index to allow you to research your specific application.

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Get More From Your NDVI Sensor (Part 2)

Last week we discussed Normalized Difference Vegetation Index (NDVI) sampling across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves (see part 1).  This week, learn about NDVI applications, limitations, and how to correct for those limitations.

NDVI Sensor

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum.

NDVI Sensor

NDVI Applications

People use NDVI to infer things like leaf area index (LAI) or fractional light interception (FPAR) of a canopy.  Some scientists also associate NDVI with biomass or yield of a crop. People also use NDVI to get a sense of phenology (general temporal patterns of greenness), as well as where vegetation occurs or how much vegetation is in a particular location.

In Figure 4, you can see how the reflectance spectrum at a given canopy LAI changes with leaf area index, decreasing in the visible range while increasing in the near infrared.

NDVI Sensor

Figure 4

At very low LAI’s, the reflectance spectrum is relatively undifferentiated between red and NIR (black line), but when LAI is high, there’s a strong absorption of red light by chlorophyll with a strong reflectance in the NIR. If fact, as LAI increases, there’s an ever-increasing reflectance in the near infrared around 800 nm.

NDVI Limitations

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum. Any time there’s very low vegetation cover (majority of the scene is soil), NDVI will be sensitive to that soil. This can confound measurements.  On the other extreme, where there’s a large amount of vegetation, NDVI tends to saturate. Notice the negligible difference between spectra at an leaf area index (LAI) of 3 (purple) versus 6 (green). Indeed, in a tropical forest, NDVI will not be sensitive to small changes in the LAI because LAI is already very high.  However, several solutions exist.

Solution 1-Soil Adjusted Vegetation Index

Figure 5 shows the results of a study taking spectral measurements of different vegetation indices across a transect of bare soil.  Moving from dry clay loam to wet clay loam, we see a very strong response of NDVI due to the wetness of the soil; undesirable if we’re measuring vegetation.  We’re not interested in an index that’s sensitive to changes in soil or soil moisture. However, there are a few other indices plotted in figure 5 with much lower sensitivities to variations in the soil across the transect.

NDVI Sensor

Figure 5: Qi et al. (1994) Rem. Sens. Env.

The first one of those indices is the Soil Adjusted Vegetation Index (SAVI). The equation for SAVI is similar to NDVI. It incorporates the same two bands as the NDVI–the near infrared and the red.

NDVI Senso

Soil Adjusted Vegetation Index (Huete (1988) Rem. Sens. Env.)

The only thing that’s different, is the L parameter.  L is a soil adjustment factor with values that range anywhere from 0 to 1.  When vegetation cover is 100%, L is 0 because there’s no need for a soil background adjustment. However, when vegetation cover is very low, that L parameter will approach one. Because it is difficult to measure exactly how much vegetation cover you have without using NDVI, we can modify the NDVI so it’s not sensitive to soil by guessing beforehand what L should be. It’s common practice to set L to an intermediate value of 0.5.   You can see in Figure 5 the Soil Adjusted Vegetation Index or SAVI has a much lower sensitivity to the soil background.

Solution 2- Modified SAVI

The next vegetation index is the modified SAVI (MSAVI). The SAVI equation contains an L parameter that we have to estimate–not an accurate way of handling things.  So a scientist named Key developed a universal optimum for L. We won’t get into the math, but he was able to simplify the SAVI equation to where there’s no longer a need for the L parameter, and the only inputs required are the reflectances in the near infrared and the red.  

NDVI Sensor

Modified SAVI (Qi et al. (1994) Rem. Sens. Env.)

This was a pretty significant advance as it circumvented the need to estimate or independently measure L. When Key compared SAVI to MSAVI, there was virtually no difference between the two indices in terms of their sensitivity to the amount of vegetation and their response to the soil background.

NDVI Sensor

MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)

Next week:  Learn about solutions for high LAI.

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Get More From Your NDVI Sensor

Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves.

NDVI

Figure 1: NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Global

The broadest way to think of NDVI is data obtained from an earth orbiting satellite. In the figure above, you can see highly vegetated areas that have high NDVI values represented by dark green colors across the globe.  Conversely, areas of low vegetation have low NDVI values, which look brown.  NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Local

How might NDVI be useful at the plot level? Figure 2 below shows a successional gradient where time zero is a bare patch of soil, or a few forbs or annual grasses. If we leave that patch of ground for enough time, the vegetation will change: shrubs may take over from grasses and eventually we might see a forest. Across a large area, we may also move from grasslands to forest. In an agricultural system, there is yearly turnover of vegetation–from bare field to plant emergence, maturity, and senescence. This cycle repeats itself every year.  Within these growth cycles NDVI helps to quantify the the canopy growth that occurs over time as well as the spatial dynamics that occur across landscapes.

NDVI

Figure 2: Seasonal growth plotted against spatiotemporal variation

Spectral Reflectance Data

So where does NDVI come from? In Figure 3, the x-axis plots wavelength of light within the electromagnetic spectrum; 450 to 950 nm covers both the visible region and a portion of the near infrared. On the y-axis is percent reflectance.  This is a typical reflectance spectrum from green vegetation.

NDVI

Figure 3: Spectral Reflectance Data. (Figure and Images: landsat.gsfc.nasa.gov)

The green hyperspectral line is what we would expect to get from a spectral radiometer.  Reflectance is typically low in the blue region, higher in the green region, and lower in the red region. It shifts dramatically as we cross from the visible to the near infrared. The two vertical bars labeled NDVI give you an idea of where a typical NDVI sensor measures within the spectrum.  One band is in the red region and the other is in the near infrared region.  

NDVI capitalizes on the large difference between the visible region and the near infrared portion of the spectrum. Healthy, growing plants reflect near infrared strongly.  The two images on the right of the figure above are of the same area.  The top image is displayed in true color, or three bands–blue, green and red. The image below is a false color infrared image.  The three bands displayed are blue, green, and in place of red, we used the near infrared. The bright red color indicates a lot of near infrared reflectance which is typical of green or healthy vegetation.

The reason NDVI is formulated with red and near infrared is because red keys in on chlorophyll absorption, and near infrared is sensitive to canopy structure and the internal cellular structure of leaves.  As we add leaves to a canopy, there’s more chlorophyll and structural complexities, thus we can expect decreasing amounts of red reflectance and higher amounts of near infrared reflectance.

How Do We Calculate the NDVI?

NDVI

The Normalized Difference Vegetation Index takes into account the amount of near infrared (NIR) reflected by plants. It is calculated by dividing the difference between the reflectances (Rho) in the near infrared and red by the sum of the two.  NDVI values typically range between negative one (surface water) and one (full, vibrant canopy). Low values (0.1 – 0.4) indicate sparse canopies, while higher values (0.7 – 0.9) suggest full, active canopies.  

The way we calculate the percent reflectance is to quantify both the upwelling radiation (the radiation that’s striking the canopy and then reflected back toward our sensor) as well as the total amount of radiation that’s downwelling (from the sky) on a canopy.  The ratio of those two give us percent reflectance in each of the bands.

Next Week: Learn about NDVI applications, limitations, and how to correct for those limitations.

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Piñon Pine: Studying the Effects of Climate Change on Drought Tolerance (part 2)

Henry Adams, a PhD student at the University of Arizona, is studying the effect of climate change and drought on Piñon Pines in the university’s Biosphere 2 lab (see part 1).  This week, find out how the researchers made comparisons at leaf level, transplanted the trees, and future implications for the Piñon Pine.

drought

The Piñon Pine, a conifer with an extensive root system, grows at high elevations in the Southwest. (Image: naturesongs.com)

Sensitivity to Dry Conditions

Another part of the drought study involved a hydrologist who was interested in using weighing lysimeter data to parameterize some models used by hydrologists to model water loss during drought. “The lysimeters are a pain to run, but they’re pretty sensitive,” says Adams. “They can measure with a 0.1 kg precision, so that sounds like a good way to quantify water loss. It turns out that stomatal conductance from the porometer actually appears more sensitive than the weighing lysimeter data. Water loss from the scale hits zero pretty quickly, and we can’t measure any loss after a couple of weeks, but we can still see water loss with our porometer data from the morning and the evening.”

drought

The Piñon Pine’s root system makes the it remarkably drought tolerant, but an extended drought in combination with a bark beetle outbreak killed 12,000 hectares of the trees in 2003. (Image: naturesongs.com)

Expanding the Experiment

At the peak of the experiment, Adams had undergraduates and lab techs running up to three porometers at a time all day long, and although he’s still buried in data from the first experiment, he’s looking forward to accumulating even more data. “One limitation of our study is that the trees had pretty small root balls when they arrived. We’ve transplanted some trees [at different elevations at a site] in northern Arizona using a full-sized tree mover to get as big a root to shoot ratio as possible in the transplant. We’ll be using the porometers to try to understand the physiology of how these trees die and to predict their temperature sensitivity in the light of global climate change, using elevation change as a surrogate for temperature. We also have trees at the site that are not transplanted to serve as a control for the transplants.”

drought

Some ranchers are happy to see the pines go (Image: travelforumboard.com)

Implications for the Future

Adams acknowledges that not everyone in the Southwest is worried about the Piñon Pine. “We work in a system that doesn’t have a lot of economic value. A lot of the ranchers are happy to see the pines go. They just think there will be a lot more grass for the cattle, and firewood cutters are out there cutting up the dead trees and selling them.” But if temperature alone makes trees more susceptible to drought, the implications go far beyond economics. Adams puts it succinctly, if somewhat mildly: “It’s kind of scary.”

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Piñon Pine: Studying the Effects of Climate Change on Drought Tolerance

In the name of science, Henry Adams has killed a lot of trees. Adams, a PhD student at the University of Arizona, is studying the effect of climate change and drought on Piñon Pines. The Piñon Pine, a conifer with an extensive root system, grows at high elevations in the Southwest. Its root system makes the Piñon Pine remarkably drought tolerant, but in 2002- 03, an extended drought in combination with a bark beetle outbreak killed 12,000 hectares of the trees. It was a 100 year drought, the driest period on record, and interestingly it coincided with temperatures 2 to 3˚C above recorded averages.

Piñon Pine

Biosphere 2. Image: wickipedia.org.

Research in Biosphere 2

Adams and his advisors wondered if increasing temperatures due to climate change might exacerbate the effects of drought and accelerate tree die-off. The University of Arizona has an unusual opportunity to test drought conditions and temperature change in its Biosphere 2 lab. Biosphere 2, a unique 3-acre enclosed “living laboratory” in the high Arizona desert, once hosted 8 people for two years of self-contained survival living. Now it hosts research projects, and Adams was able to use space inside to induce drought in two separate treatments of transplanted Piñon pines, one at ambient temperatures and one at temperatures 4˚C above ambient.

Sobering Outlook for the Piñon Pine

“Obviously, the warmer trees should die first,” says Adams. “But we want to test whether temperature change, independent of other factors, accelerates mortality.” If that acceleration in fact occurs, a shorter drought, the kind the Piñon Pine has historically been able to wait out, might cause a significant die-off.

Piñon Pine

Piñon Pine. Image: Naturesongs.com

Measuring Drought Response

Naturally, Adams and his colleagues did more than just watch how fast trees would die without water. They also studied the trees physiological response to drought, measuring gas exchange, water potential, and stomatal conductance. To measure stomatal conductance, they used a leaf porometer, making almost 9,000 separate measurements in sessions that lasted from sunup to sundown on one very long day once each week.

Stomatal Conductance in Conifers

There isn’t much guidance in the porometer manual for people who want to use it on conifers, so Adams “played around with it a little bit” on non-drought stressed trees before he started his study. He found that the best way to get good readings was to cover the aperture with a single layer of needles. “Needles are this three-dimensional thing,” he explains. “They have stomata on several sides, depending on the species. If you imagine that the fingers on your hand are needles sticking up from a branch, we just took those and pushed them together to make sure that there was just a one needle thick covering over the aperture. If you spread your fingers, that’s what it would be like if you didn’t totally cover the aperture-then you underestimate the conductance. We also found that if we stuck several layers in there, we could drive the conductance number up.

Next week: Find out how the researchers made comparisons at leaf level, transplanted the trees, and future implications for the Piñon Pine.

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Top Five Blog Posts in 2016

In case you missed them the first time around, here are the most popular Environmental Biophysics.org blog posts in 2016.

Lysimeters Determine if Human Waste Composting can be More Efficient

Top five blog posts Environmental biophysics

In Haiti, untreated human waste contaminating urban areas and water sources has led to widespread waterborne illness.  Sustainable Organic Integrated Livelihoods (SOIL) has been working to turn human waste into a resource for nutrient management by turning solid waste into compost.  Read more

Estimating Relative Humidity in Soil: How to Stop Doing it Wrong

Top five blog posts Environmental biophysics

Estimating the relative humidity in soil?  Most people do it wrong…every time.  Dr. Gaylon S. Campbell shares a lesson on how to correctly estimate soil relative humidity  from his new book, Soil Physics with Python, which he recently co-authored with Dr. Marco Bittelli.  Read more.

How Many Soil Moisture Sensors Do You Need?

Top five blog posts Environmental biophysics

“How many soil moisture sensors do I need?” is a question that we get from time to time. Fortunately, this is a topic that has received substantial attention by the research community over the past several years. So, we decided to consult the recent literature for insights. Here is what we learned.

Data loggers: To Bury, or Not To Bury

Top five blog posts Environmental biophysics

Globally, the number one reason for data loggers to fail is flooding. Yet, scientists continue to try to find ways to bury their data loggers to avoid constantly removing them for cultivation, spraying, and harvest.  Chris Chambers, head of Sales and Support at Decagon Devices always advises against it. Read more

Founders of Environmental Biophysics:  Champ Tanner

Top five blog posts Environmental biophysics

Image: http://soils.wisc.edu/people/history/champ-tanner/

We interviewed Gaylon Campbell, Ph.D. about his association with one of the founders of environmental biophysics, Champ Tanner.  Read more

And our three most popular blogs of all time:

Do the Standards for Field Capacity and Permanent Wilting Point Need to Be Reexamined?

Top five blog posts Environmental biophysics

We asked scientist, Dr. Gaylon S. Campbell, which scientific idea he thinks impedes progress.  Here’s what he had to say about the standards for field capacity and permanent wilting point.  Read more

Environmental Biophysics Lectures

Top five blog posts Environmental biophysics

During a recent semester at Washington State University a film crew recorded all of the lectures given in the Environmental Biophysics course. The videos from each Environmental Biophysics lecture are posted here for your viewing and educational pleasure.  Read more

Soil Moisture Sensors In a Tree?

Top five blog posts Environmental biophysics

Soil moisture sensors belong in the soil. Unless, of course you are feeling creative, curious, or bored. Then maybe the crazy idea strikes you that if soil moisture sensors measure water content in the soil, why couldn’t they be used to measure water content in a tree?  Read more

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Measuring NDVI in a Greenhouse Presents Challenges (Part 2)

University of Georgia researcher, Shuyang Zhen, wanted to find out if she could optimize greenhouse irrigation with reference evapotranspiration calculated from environmental factors and a crop coefficient, using NDVI measurements to adjust for canopy size (see part 1). Learn the results of the experiment and how fast growth and flowering caused problems with the NDVI measurement.

setup-1

Shuyang’s experimental setup.

Fast Growth Causes Problems

Shuyang says because the plants grew so large, the canopy filled in beyond what the sensor could see.  That meant there was additional leaf area that participated in vapor loss which wasn’t identified by the NDVI sensor.  As the canopies approached moderate-to-high canopy densities, Shuyang observed that the NDVI readings became less responsive to increases in canopy size. To work around this problem, Shuyang tried to calculate a vegetation index called the Wide Dynamic Range Vegetation index with the spectral reflectance outputs of the two wavebands measured by the NDVI sensor. Shuyang says, “This index was supposed to improve the sensitivity at higher canopy density, so I transformed all my data and was surprised that it actually improved the sensitivity when the canopy density was lower.  But at a higher canopy density it wasn’t as effective.”

setup-2

The red flowers reflected a lot of red light compared to the leaves, which confused the NDVI measurement.

Plant flowering also caused problems with the NDVI measurement.   Shuyang explains, “We had one cultivar of petunia with red flowers which formed on top of the canopy. The red flowers reflected a lot of red light compared to the leaves, which confused the NDVI measurement.  The NDVI value gradually decreased when the plants started to flower. There was no way I could get around that issue, so in some of the replicates, I removed the flowers, and in some I kept the flowers so I could compare the different responses and characterize why it happened.”

NDVI

The NDVI was very sensitive to the increase in crop size when the canopy was relatively small, but when you reach a certain canopy size and the canopy closure was nearly complete, then the sensitivity decreased.

Summary and Future Studies

During the early stages of growth, the research team saw a linear relationship between NDVI and crop coefficient. However, when the crop coefficient reached higher values, the response leveled off.  Shuyang says, “The response failed to change with further increases in the crop coefficient. The NDVI was very sensitive to the increase in crop size when the canopy was relatively small, but when you reach a certain canopy size and the canopy closure was nearly complete, then the sensitivity decreased.”  

NDVI

Lack of NDVI sensitivity during canopy closure and flowering translated to a problem with under-irrigation,

Shuyang adds that the lack of NDVI sensitivity during canopy closure and flowering translated to a problem with under-irrigation, so the team is thinking about developing separate models for different canopy stages.  She explains, “When the canopy reaches high canopy closure we may have to add an additional coefficient to compensate for that underestimation, but it’s difficult to evaluate what kind of coefficient we should use without more data. We need to do more studies to get an idea of what kind of adjustments will make the prediction more precise.”

Learn more about Shuyang’s work on the University of Georgia horticulture blog.

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Measuring NDVI in a Greenhouse Presents Challenges

Greenhouse growers need irrigation strategies to maintain high plant quality, but it’s difficult to obtain quantitative information on exactly how much water will produce the highest-quality growth.

Greenhouse

Greenhouse plant canopies are highly variable.

Estimating irrigation needs by using reference evapotranspiration calculated from environmental factors and a crop coefficient is standard for controlling field crop irrigation, but in a greenhouse this method can be challenging.  Greenhouse plant canopies are highly variable, and there’s limited information on the crop coefficient values for ornamental crops.  

greenhouse

Researchers used a sensor-controlled automated irrigation system with soil moisture sensors.

Measuring Crop Size

University of Georgia researcher, Shuyang Zhen, wanted to find out if she could solve this problem for greenhouse growers using NDVI measurements to adjust for canopy size. In a greenhouse setting, she and her team planted four types of fast growing herbaceous plants in small containers on top of greenhouse benches.  They set up a small weather station to monitor environmental parameters and used that data to calculate reference evapotranspiration.  

greenhouse

NDVI measurements are a non-destructive, continuous monitoring method to get information as to how big a crop is.

Using a sensor-controlled automated irrigation system with soil moisture sensors, the team determined the amount of water the plants used, which allowed them to calculate a crop coefficient on a daily basis.  They then used NDVI measurements to monitor crop size.  Shuyang says, “It’s easy to monitor environmental factors such as light, temperature, relative humidity, and wind speed, but it’s much harder to determine how big the crop is because many methods are destructive and time-consuming.  We chose NDVI measurements as a non-destructive, continuous monitoring method to get information as to how big our crop was. We were specifically interested in looking at how NDVI changes with the crop coefficient and how those two parameters correlate with each other.”

greenhouse

Some species were more upward growing and some more sprawling.

Shuyang mounted multiple NDVI sensors on top of the benches, approximately four feet from the plants. Each sensor had a field of view of about .6 square meters and tracked the changes in plant size and NDVI values for over 8 weeks.  Shuyang says, “Each species had different growth habits.  Some species were more upward growing and some more sprawling. They also had different leaf chlorophyll content. Over the course of my study, three species reached reproductive stages, producing flowers. All of these factors had an effect on the NDVI measurements.”

Next week: Learn the results of the experiment and how fast growth and flowering caused problems with the measurement.

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Measuring Light and Photosynthesis (PAR): Complicated, but Worth It (Part 2)

In part 2 of our PAR Measurement Series (read part 1), Dr. Gaylon S. Campbell discusses the impact of leaf arrangement, measuring light in a canopy, and why we measure PAR.

PAR

Vertical leaves absorb less radiation when the sun is at a high angle, and more radiation when the sun is at a low angle; the converse is true for horizontal leaves.

Leaf Arrangement

Leaf display (angular orientation) affects light interception. Strictly vertical or horizontally oriented leaves are extreme cases, but a large range of angles occurs. Vertical leaves absorb less radiation when the sun is at a high angle, and more radiation when the sun is at a low angle; the converse is true for horizontal leaves. The greatest photosynthetic capacity can be achieved by a change from nearly vertical to nearly horizontal leaves lower down. This arrangement leads to effective beam penetration and a more even distribution of light.

PAR

The highest LAI’s usually occur in coniferous forests.

Leaf area index (LAI), a measure of the foliage in a canopy, is the canopy property that has most effect on interception of radiation. LAI usually ranges between 1 and 12. Values of 3-4 are typical for horizontal-leafed species such as alfalfa; values of 5-10 occur in vertical leafed species such as grasses and cereals, or in plants with highly clumped leaves, such as spruce. The highest LAI’s usually occur in coniferous forests, which have overlapping generations of leaves. These forests have a photosynthetic advantage due to longevity of individual needles.

PAR

PAR must be measured at a number of locations and then averaged.

Measuring Light in a Canopy

Variability of leaf distribution in canopies results in wide variations in light. To determine light at any height in the canopy, PAR must be measured at a number of locations and then averaged. Direct methods of measurement include using the horizontal line sensors whose output is the spatial average over the sensor length. The appropriate sensor length or number of sampling points depends on plant spacing.

Indirect methods for measuring canopy structure rely on the fact that canopy structure and solar position determine the radiation within the canopy. Because it’s hard to measure three dimensional distribution of leaves in a canopy, models for light interception and tree growth often assume random distribution throughout the canopy; however, leaves are generally aggregated or grouped.

PAR

Models for light interception and tree growth often assume random distribution throughout the canopy; however, leaves are generally aggregated or grouped.

Why Measure Photosynthesis or PAR?

The ability to measure PAR assists with understanding the unique spatial patterns that different plants have for displaying photosynthetic surfaces. Since effective use of PAR influences plant production, knowledge of the structural diversity of canopies aids research on plant productivity. One result: researchers can use information about different plants’ abilities to intercept and use PAR to engineer canopy structure modifications that significantly improve crop yield.

View our LAI application guide, or learn more about how researchers and growers use PAR measurement to improve crop yields.

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