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Posts from the ‘Ecology’ Category

Soil Moisture Sensors: Why TDR vs. Capacitance May Be Missing the Point

Time Domain Reflectometry (TDR) vs. capacitance is a common question for scientists who want to measure volumetric water content (VWC) of soil, but is it the right question?  Dr. Colin S. Campbell, soil scientist, explains some of the history and technology behind TDR vs. capacitance and the most important questions scientists need to ask before investing in a sensor system.

TDR vs. Capacitance

TDR began as a technology the power industry used to determine the distance to a break in broken power lines.

Clarke Topp

In the late 1970s, Clarke Topp and two colleagues began working with a technology the power industry used to determine the distance to a break in broken power lines.  Time Domain Reflectometers (TDR) generated a voltage pulse which traveled down a cable, reflected from the end, and returned to the transmitter. The time required for the pulse to travel to the end of the cable directed repair crews to the correct trouble spot. The travel time depended on the distance to the break where the voltage was reflected, but also on the dielectric constant of the cable environment.  Topp realized that water has a high dielectric constant (80) compared to soil minerals (4) and air (1).  If bare conductors were buried in soil and the travel time measured with the TDR, he could determine the dielectric constant of the soil, and from that, its water content.  He was thus able to correlate the time it took for an electromagnetic pulse to travel the length of steel sensor rods inserted into the soil to volumetric water content. Despite his colleagues’ skepticism, he proved that the measurement was consistent for several soil types.

TDR vs. Capacitance

TDR sensors consume a lot of power. They may require solar panels and larger batteries for permanent installations.

TDR Technology is Accurate, but Costly

In the years since Topp et al.’s (1980) seminal paper, TDR probes have proven to be accurate for measuring water content in many soils. So why doesn’t everyone use them? The main reason is that these systems are expensive, limiting the number of measurements that can be made across a field. In addition, TDR systems can be complex, and setting them up and maintaining them can be difficult.  Finally, TDR sensors consume a lot of power.  They may require solar panels and larger batteries for permanent installations. Still TDR has great qualities that make these types of sensors a good choice.  For one thing, the reading is almost independent of electrical conductivity (EC) until the soil becomes salty enough to absorb the reflection.  For another, the probes themselves contain no electronics and are therefore good for long-term monitoring installations since the electronics are not buried and can be accessed for servicing, as needed.  Probes can be multiplexed, so several relatively inexpensive probes can be read by one set of expensive electronics, reducing cost for installations requiring multiple probes.

Many modern capacitance sensors use high frequencies to minimize effects of soil salinity on readings.

Advances in Electronics Enable Capacitance Technology

Dielectric constant of soil can also be measured by making the soil the dielectric in a capacitor.  One could use parallel plates, as in a conventional capacitor, but the measurement can also be made in the fringe field around steel sensor rods, similar to those used for TDR.  The fact that capacitance of soil varies with water content was known well before Topp and colleagues did their experiments with TDR.  So, why did the first attempt at capacitance technology fail, while TDR technology succeeded? It all comes down to the frequency at which the measurements are made.  The voltage pulse used for TDR has a very fast rise time.  It contains a range of frequencies, but the main ones are around 500 MHz to 1 GHz.  At this high frequency, the salinity of the soil does not affect the measurement in soils capable of growing most plants.  

Like TDR, capacitance sensors use a voltage source to produce an electromagnetic field between metal electrodes (usually stainless steel), but instead of a pulse traveling down the rods, positive and negative charges are briefly applied to them. The charge stored is measured and related to volumetric water content. Scientists soon realized that how quickly the electromagnetic field was charged and discharged was critical to success.  Low frequencies led to large soil salinity effects on the readings.  This new understanding, combined with advances in the speed of electronics, meant the original capacitance approach could be resurrected. Many modern capacitance sensors use high frequencies to minimize effects of soil salinity on readings.  

TDR vs. Capacitance

NASA used capacitance technology to measure water content on Mars.

Capacitance Today is Highly Accurate

With this frequency increase, most capacitance sensors available on the market show good accuracy. In addition, the circuitry in them can be designed to resolve extremely small changes in volumetric water content, so much so, that NASA used capacitance technology to measure water content on Mars. Capacitance sensors are lower cost because they don’t require a lot of circuitry, allowing more measurements per dollar. Like TDR, capacitance sensors are reasonably easy to install. The measurement prongs tend to be shorter than TDR probes so they can be less difficult to insert into a hole. Capacitance sensors also tend to have lower energy requirements and may last for years in the field powered by a small battery pack in a data logger.   

In two weeks: Learn about challenges facing both types of technology and why the question of TDR vs. Capacitance may not be the right question.

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Where Will the Next Generation of Scientists Come From?

The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international science and education program that provides students and the public worldwide with the opportunity to participate in data collection and the scientific process.

GLOBE

GLOBE has a huge impact in schools around the world.

Its mission is to promote the teaching and learning of science, enhance community environmental literacy and stewardship, and provide research quality environmental observations.  The GLOBE program works closely with agencies such as NASA to do projects like validation of SMAP data and the Urban Heat Island/Surface Temperature Student Research Campaign.  The figure below shows the impact GLOBE is having in schools worldwide.

Dixon Butler, former GLOBE Chief Scientist, is excited about the recent African project GLOBE is now participating in called the TAHMO project.  He says, “Right now, in Kenya and Nigeria, GLOBE schools are putting in over 100 new  mini-weather stations to collect weather data, and all that usable data will flow into the GLOBE database.”

GLOBE

Participating in real science at a young age gets youth more ready to be logical, reasoning adults.

Why Use Kids to Collect Data?

Dixon says kids do a pretty good job taking research quality environmental measurements.  Working with agencies like NASA gets them excited about science, and participating in real science at a young age gets them more ready to be logical, reasoning adults.  He explains, “The 21st century requires a scientifically literate citizenry equipped to make well-reasoned choices about the complex and rapidly changing world. The path to acquiring this type of literacy goes beyond memorizing scientific facts and conducting previously documented laboratory experiments to acquiring scientific habits of mind through doing hands-on, observational science.”

Dixon says when GLOBE started, the plan was to have the kids measure temperature.  But one science teacher, Barry Rock, who had third grade students using Landsat images to do ozone damage observations, called the White House and said, “Kids can do a lot more than measure temperature.” He gave a presentation at the White House where he showed a video of two third grade girls looking at Landsat imagery. They were discussing their tree data, and at one point, one said to the other, ‘That’s in the visible. Let’s look at it in the false color infrared.’  At that point, Barry became the first chief scientist of GLOBE, and he helped set up the science and the protocols that got the program started.

GLOBE

GLOBE uses online and in-person training and protocols to be sure the students’ data is research quality.

Can GLOBE Data be Used by Scientists?

GLOBE uses online and in-person training and protocols to be sure the students’ data is research quality.  Dixon explains, “There was a concern that these data be credible, so the idea was to create an intellectual chain of custody where scientists would write the protocols in partnership with an educator so they would be written in an educationally appropriate way.  Then the teachers would be trained on those protocols. The whole purpose is to be sure scientists have confidence that the data being collected by GLOBE is useable in research.”

Today GLOBE puts out a Teacher’s’ Guide and the protocols have increased from 17 to 56.  The soil area went from just a temperature and moisture measurement to a full characterization.  Dixon says, “We’ve been trying to improve it ever since, and I think we’re getting pretty good at it.”  

GLOBE

GLOBE students were the only ones going around looking up at the sky doing visual categorization of clouds and counting contrails. It was just no longer being done, except by these students.

What About the Skeptics?

If you ask Dixon how he deals with skeptics of the data collected by the kids, he says, “I tell them to take a scientific approach.  Check out the data, and see if they’re good.  One year, a GLOBE investigator found a systematic error In U-tube maximum/minimum thermometers mounted vertically, which had been in use for over a century, that no one else found. The GLOBE data were good enough to look at and find the problem.  There are things the data are good for and things they’re not good for. Initially, we wanted these data to be used by scientists in the literature, and there have been close to a dozen papers, but I would argue that GLOBE hasn’t yet gotten to the critical mass of data that would make that easier.”

GLOBE did have enough cloud data, however, to be used in an important analysis of geostationary cloud data where the scientist compared GLOBE student data with satellite data Dixon adds, “GLOBE students were the only ones going around looking up at the sky doing visual categorization of clouds and counting contrails. It was just no longer being done, except by GlOBE students. Now GLOBE has developed the GLOBE Observer app that let’s everyone take and report cloud observations.”

GLOBE

Young minds need to experience the scientific approach of developing hypotheses, taking careful, reproducible measurements, and reasoning with data.

What’s the Future of GLOBE?

Dixon says GLOBE’s goal is to raise the next generation of intelligent constituents in the body politic. He says, “I thought about this a lot when I worked for the US Congress.  In addition to working with GLOBE, I now have a non-profit grant-making organization called YLACES with the objective of helping kids to learn science by doing science.  Young minds need to experience the scientific approach of developing hypotheses, taking careful, reproducible measurements, and reasoning with data. Inquiries should begin early and grow in quality and sophistication as learners progress in literacy, numeracy, and understanding scientific concepts. In addition to fostering critical thinking skills, active engagement in scientific research at an early age also builds skills in mathematics and communications. These kids will grow up knowing how to think scientifically. They’ll ask better questions, and they’ll be harder to fool.   I think that’s what the world needs, and I see the environment and science as the easiest path to get there.”

Learn more about GLOBE and its database here and about YLACES at www.ylaces.org.

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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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German Researchers Directly Measure Climate Change Effects Using Lysimeter Network (part 2)

In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters (see part 1).  This week, read about the intense precision required to move the soil-filled lysimeters, how problems are prevented, and how the data is used by scientists worldwide.

climate change

Moving the lysimeters

Moving the Lysimeters is not Easy

As noted previously, one TERENO lysimeter weighs between 2.5 and 3.5 tons depending on the soil and the water saturation, so the problem of  transporting it without compacting the soil or causing cracks in the soil column caused Georg many sleepless nights.   He explains, “We found a truck with an air venting system, which could prevent vibrations in a wide range. We made a wooden support structure, bought 100 car springs, and loaded the lysimeter on this frame.  After some careful preparation and design adjustments, I told the truck driver, ‘take care, I’m recording the entire drive with my acceleration sensor and data logger so I can see if you are driving faster than I allow.”  Each lysimeter soil surface level was marked to check if the lysimeter was rendered useless due to transport, and the truck was not allowed to go over a railway or a bump in the road faster than 2 km per hour to avoid the consequences of compaction and cracking.

climate change

Tensiometers and soil moisture sensors monitor the hydraulic conditions inside the lysimeters.

Preventing Problems

Understanding the water potential inside the intact lysimeter core is not trivial. Georg and his team use maintenance-free tensiometers, which overcome the typical problem of cavitation in dry conditions as they don’t need to be refilled. Still, this parameter is so critical they installed 3 of them and took the median, which can be weighed in case one of the sensors is not working. Georg says, “There is a robust algorithm behind measuring the true field situation with tensiometers.”

What Happens With the Data?

Georg hopes that many researchers will take advantage of the TERENO lysimeter network data (about 4,000 parameters stored near-continuously on a web server). He says, “Researchers have free access to the data and can publish it. It’s wonderful because it’s not only the biggest project of its kind, each site is well-maintained, and all measurements are made with the same equipment, so you can compare all the data.”  (Contact Dr. Thomas Puetz for access). Right now, over 400 researchers are working with those data, which has been used in over 200 papers.

climate change

Lysimeter plant with CO2 fumigation facility in Austria.

What’s the Future?

Georg thinks 40,000 data points arriving every minute will give scientists plenty of information to work on for years to come. Each year, more TERENO standard lysimeters are installed to enlarge the database. The ones in TERENO have a 1 m2 surface area, which is fine for smaller plants like wheat or grass, but is not a good dimension for big plants like trees and shrubs. Georg points out that you have to take into account effort versus good data. Larger lysimeters present exponentially larger challenges. He admits that, “With the TERENO project, they had to make a compromise. All the lysimeters are cut at a depth of 1.5 m. If there is a mistake, it is the same with all the lysimeters, so we can compare on climate change effects.”  He adds, “After six years, we now have a standard TERENO lysimeter design installed over 200 times around the world, where data can be compared through a database, enhancing our understanding of water in an era of climate change.”

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German Researchers Directly Measure Climate Change Effects Using TERENO Lysimeters

In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters.

lysimeters

The goal of these lysimeters is to measure energy balance, water flux and nutrition transport, emission of greenhouse gases, biodiversity, and solute leaching into the groundwater.

In 2008, the Karlsruhe Institute of Technology began to develop a climate feedback monitoring strategy at the Ammer catchment in Southern Bavaria. In 2009, the Research Centre Juelich Institute of Agrosphere, in partnership with the Helmholtz-Network TERENO (Terrestrial Environmental Observatories) began conducting experiments in an expanded approach.  

Throughout Germany, they set up a network of 144 large lysimeters with soil columns from various climatic conditions at sites where climate change may have the largest impact.  In order to directly observe the effects of simulated climate change, soil columns were taken from higher altitudes with lower temperatures to sites at a lower altitude with higher temperatures and vice versa. Extreme events such as heavy rain or intense drought were also experimentally simulated.

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Lysimeter locations in Germany

Georg von Unold, whose company (formerly UMS, now METER) built and installed the lysimeters comments on why the project is so important. “From a scientific perspective, we accept changes for whatever reason they may happen, but it is our responsibility to carefully monitor and predict how these changes cause floods, droughts, and disease. We need to be prepared to react if and before they affect us.”

How Big Are the Lysimeters?

Georg says that each lysimeter holds approximately 3,000 kilograms of soil and has to be moved under compaction control with specialized truck techniques.  He adds,The goal of these lysimeters is to measure energy balance, water flux and nutrition transport, emission of greenhouse gases, biodiversity, and solute leaching into the groundwater. Researchers measure the conditions of water balance in the natural soil surrounding the lysimeters, and then apply those same conditions inside the lysimeters with suction ceramic cups that lay across the bottom of the lysimeter.  These cups both inject and take out water to mimic natural or artificial conditions.”

lysimeters

Researchers use water content sensors and tensiometers to monitor hydraulic conditions inside the lysimeters.

Researchers monitor the new climate situation with microenvironment monitors and count the various grass species to see which types become dominant and which might disappear. They use water content sensors and tensiometers to monitor hydraulic conditions inside the lysimeters. The systems also use a newly-designed system to inject CO2 into the atmosphere around the plants and soil to study increased carbon effects.  Georg says, “We developed, in cooperation with the HBLFA Raumberg Gumpenstein, a new, fast-responding CO2 enrichment system to study CO2 from plants and soil respiration. We analyze gases like CO2, oxygen, and methane. The chambers are rotated from one lysimeter to another, working 24 hours, 7 days a week.  Each lysimeter is exposed only for a few minutes so as not to change the natural environment.”

Next week:  Read about the intense precision required to move the soil-filled lysimeters, how problems are prevented, and how the data is used by scientists worldwide.

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