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Posts from the ‘Ecology’ Category

Stem Water Content Changes Our Understanding of Tree Water Use

In an update to our previous blog, “Soil Moisture Sensors in a Tree?”, we highlight two current research projects using soil moisture sensors to measure volumetric water content (VWC) in tree stems and share why this previously difficult-to-obtain measurement will change how we look at tree water usage.

stem water content

Researchers explore the feasibility of inserting capacitance soil sensors in tree stems as a real-time measurement.

Soil Moisture Sensors in Tree Stems?

In a recent research project, Ph.D. candidate Ashley Matheny of the University of Michigan used soil sensors to measure volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple.  Though both tree types are classified as deciduous, they have different strategies for how they use water. Oak is anisohydric, meaning the species doesn’t control their stomata to reduce transpiration, even in drought conditions.  Isohydric maples are more conservative. If the soil starts to dry out, maple trees will maintain their leaf water potential by closing their stomata to conserve water.  Ashley and her research team wanted to understand the different ways these two types of trees use stem water in various soil moisture scenarios.

Historically, tree water storage has been measured using dendrometers and sap flow data, but Ashley’s team wanted to explore the feasibility of inserting a capacitance-type soil sensor in the tree stems as a real-time measurement.  They hoped for a practical way to make this measurement to provide more accurate estimations of transpiration for use in global models.  

Scientists measured volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple.

Measurements used

Ashley and her team used meteorological, sap flux, and stem water content measurements to test the effectiveness of capacitance sensors for measuring tree water storage and water use dynamics in one red maple and one red oak tree of similar size, height, canopy position and proximity to one another (Matheny et al. 2015). They installed both long and short soil moisture probes in the top and the bottom of the maple and oak tree stems, taking continuous measurements for two months. They calibrated the sensors to the density of the maple and oak woods and then inserted the sensors into drilled pilot holes.  They also measured soil moisture and temperature for reference, eventually converting soil moisture measurements to water potential values.

Results Varied According to Species

The research team found that the VWC measurements in the stems described tree storage dynamics which correlated well with average sap flux dynamics.  They observed exactly what they assumed would be the anisohydric and isohydric characteristics in both trees.  When soil water decreased, they saw that red oak used up everything that was stored in the stem, even though there wasn’t much available soil moisture.  Whereas in maple, the water in the stem was more closely tied to the amount of soil water. After precipitation, maple trees used the water stored in their stem and replaced it with more soil water.  But, when soil moisture declined, they held onto that water and used it at a slower rate.

stem water content

Researchers want to figure out the appropriate level of detail for tree water-use strategy in a global model.

Trees use different strategies at the species level

The ability to make a stem water content measurement was important to these researchers because much of their work deals with global models representing forests in the broadest sense possible.  They want to figure out the appropriate level of detail for tree water-use strategy in a global model. Both oak and the maple are classified as broadleaf deciduous, and in a global model, they’re lumped into the same category. But this study illustrates that if you’re interested in hydrodynamics (the way that trees use water), deciduous trees use different strategies at the species level.  Thus, there is a need to treat them differently to produce accurate models.

Read the full study in Ecosphere.

Reference: Matheny, A. M., G. Bohrer, S. R. Garrity, T. H. Morin, C. J. Howard, and C. S. Vogel. 2015. Observations of stem water storage in trees of opposing hydraulic strategies. Ecosphere 6(9):165.

Next week: Learn about more research being done using soil moisture sensors to measure volumetric water content in tree stems.

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Electrical Conductivity of Soil as a Predictor of Plant Response (Part 2)

Salt in soil comes from the fertilizer we apply but also from irrigation water and dissolving soil minerals.  If more salt is applied in the irrigation water than is leached or taken off in harvested plants, the soil becomes more saline and eventually ceases to support agricultural production (see part 1).  This week, learn an effective way to measure electrical conductivity (EC) in soil.

Electrical conductivity

Salt in irrigation water reduces its water potential, making it less available to the plant.

How to Measure Electrical Conductivity of the Soil Solution

As mentioned above, the earliest measurements of solution conductivity were made on soil samples, but it was found to be more reliable to extract the soil solution and make the measurements on it. When values for unsaturated soils are needed, those are calculated based on the saturation numbers and conjecture about how the soil dried to its present state. Obviously a direct measurement of the soil solution conductivity would be better if it could be made reliably.

Two approaches have been made to this measurement. The first uses platinum electrodes embedded in ceramic with a bubbling pressure of 15 bars. Over the plant growth range the ceramic remains saturated, even though the soil is not saturated, allowing a measurement of the solution in the ceramic. As long as there is adequate exchange between the ceramic and the soil solution, this measurement will be the EC of the soil solution, pore water EC.

Salt in soil comes from the fertilizer we apply, irrigation water and dissolving soil minerals.

The other method measures the conductivity of the bulk soil and then uses empirical or theoretical equations to determine the pore water EC. The ECH2O 5TE uses the second method. It requires no exchange of salt between soil and sensor and is therefore more likely to indicate the actual solution electrical conductivity. The following analysis shows one of several methods for determining the electrical conductivity of the saturation extract from measurements of the bulk soil electrical conductivity.

Mualem and Friedman (1991) proposed a model based on soil hydraulic properties. It assumes two parallel conduction paths: one along the surface of soil particles and the other through the soil water. The model is

Equation 1

Here σb is the bulk conductivity which is measured by the probe, σs is the bulk surface conductivity, σw is the conductivity of the pore water, θ is the volumetric water content, θs is the saturation water content of the soil and n is an empirical parameter with a suggested value around 0.5. If, for the moment, we ignore surface conductivity, and use eq. 1 to compute the electrical conductivity of a saturated paste (assuming n = 0.5 and θs = 0.5) we obtain σb = 0.35σw. Obviously, if no soil were there, the bulk reading would equal the electrical conductivity of the water. But when soil is there, the bulk conductivity is about a third of the solution conductivity. This happens because soil particles take up some of the space, decreasing the cross section for ion flow and increasing the distance ions must travel (around particles) to move from one electrode of the probe to the other. In unsaturated soil these same concepts apply, but here both soil particles and empty pores interfere with ion transport, so the bulk conductivity becomes an even smaller fraction of pore water conductivity.

Electrical conductivity

When water evaporates at the soil surface, or from leaves, it is pure, containing no salt, so evapotranspiration concentrates the salts in the soil.

Our interest, of course, is in the pore water conductivity. Inverting eq. 1 we obtain

Equation 2

In order to know pore water conductivity from measurements in the soil we must also know the soil water content, the saturation water content, and the surface conductivity. The 5TE measures the water content. The saturation water content can be computed from the bulk density of the soil

Electrical conductivity

Equation 3

Where ρb is the soil bulk density and ρs is the density of the solid particles, which in mineral soils is taken to be around 2.65 Mg/m3 . The surface conductivity is assumed to be zero for coarse textured soil. Therefore, using the 5TE allows us to quantify pore water EC through the use of the above assumptions. This knowledge has the potential to be a very useful tool in fertilizer scheduling.

Electrical Conductivity is Temperature Dependent

Electrical conductivity of solutions or soils changes by about 2% per Celsius degree. Because of this, measurements must be corrected for temperature in order to be useful. Richards (1954) provides a table for correcting the readings taken at any temperature to readings at 25 °C. The following polynomial summarizes the table

where t is the Celsius temperature. This equation is programmed into the 5TE, so temperature corrections are automatic.

Electrical conductivity

Soil salinity has been measured using electrical conductivity for more than 100 years.

Units of Electrical Conductivity

The SI unit for electrical conductance is the Siemen, so electrical conductivity has units of S/m. Units used in older literature are mho/cm (mho is reciprocal ohm), which have the same value as S/cm. Soil electrical conductivities were typically reported in mmho/cm so 1 mmho/cm equals 1 mS/cm. Since SI discourages the use of submultiples in the denominator, this unit is changed to deciSiemen per meter (dS/m), which is numerically the same as mmho/cm or mS/cm. Occasionally, EC is reported as mS/m or µS/m. 1 dS/m is 100 mS/m or 105 µS/m.


Richards, L. A. (Ed.) 1954. Diagnosis and Improvement of Saline and Alkali Soils. USDA Agriculture Handbook 60, Washington D. C.

Rhoades, J. D. and J. Loveday. 1990. Salinity in irrigated agriculture. In Irrigation of Agricultural Crops. Agronomy Monograph 30:1089-1142. Americal Society of Agronomy, Madison, WI.

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Soil Moisture Sensors: Why TDR VS. Capacitance May Be Missing the Point (Part 2)

Dr. Colin S. Campbell discusses whether TDR vs. capacitance (see part 1) is the right question, the challenges facing soil moisture sensor technology, and the correct questions to ask before investing in a sensor system.

It’s easy to overlook the obvious question: what is being measured?

What are You Trying to Measure?

When considering which soil water content sensor will work best for any application, it’s easy to overlook the obvious question: what is being measured?  Time Domain Reflectometry (TDR) vs. capacitance is the right question for a researcher who is looking at the dielectric permittivity across a wide measurement frequency spectrum (called dielectric spectroscopy). There is important information in these data, like the ability to measure bulk density along with water content and electrical conductivity. If this is the desired measurement, currently only one technology will do: TDR. The reflectance of the electrical pulse that moves down the conducting rods contains a wide range of frequencies.  When digitized, these frequencies can be separated by fast fourier transform and analyzed for additional information.

The objective for the majority of scientists, however, is to simply monitor soil water content instantaneously or over time, with good accuracy. There are more options if this is the goal, yet there are still pitfalls to consider.


Considerable research has been devoted to determining which soil moisture sensors meet expectation.

Each Technology Has Challenges

Why would a scientist pay $100+ for a soil volumetric water content (VWC) sensor, when there are hundreds of soil moisture sensors online costing between $5 and $15? This is where knowing HOW water content is measured by a sensor is critical.

Most sensors on home and garden websites work based on electrical resistivity or conductivity. The principle is simple: more water will allow more electrons to flow. So conductivity will change with soil water content. But, while it’s possible to determine whether water content has changed with this method, absolute calibration is impossible to achieve as salts in the soil water will change as the water content changes. A careful reading of sensor specs will sometimes uncover the measurement method, but sometimes, price is the only indication.

Somewhere between dielectric spectroscopy and electrical resistance are the sensors that provide simple, accurate water content measurement. Considerable research has been devoted to determining which of these meet expectation, and the results suggest that Campbell Scientific, Delta-T, Stevens, Acclima, Sentek, and METER (formerly Decagon Devices), provide accurate sensors vetted by soil scientists. The real challenge is installing the sensors correctly and connecting them to a system that meets data-collection and analysis needs.

Installation Techniques Affect Accuracy

Studies show there is a difference between mid-priced sensor accuracy when tested in laboratory conditions. But, in the field, sensor accuracy is shown to be similar for all good quality probes, and all sensors benefit from site specific soil calibration. Why? The reason is associated with the principle upon which they function. The electromagnetic field these sensors produce falls off exponentially with distance from the sensor surface because the majority of the field is near the electrodes. So, in the lab, where test solutions form easily around sensor rods, there are differences in probe performance.  In a natural medium like soil, air gaps, rocks, and other detritus reduce the electrode-to-soil contact and tend to reduce sensor to sensor differences. Thus, picking an accurate sensor is important, but a high quality installation is even more critical.


Improper installation is the largest barrier to accuracy.

Which Capacitance Sensor Works Best?

Sensor choice should be based on how sensors will be installed, the nature of the research site, and the intended collection method. Some researchers prefer a profile sensor, which allows instruments to be placed at multiple depths in a single hole. This may facilitate fast installation, but air gaps in the auger pilot hole can occur, especially in rocky soils. Fixing this problem requires filling the hole with a slurry, resulting in disturbed soil measurements. Still, profile sensor installation must be evaluated against the typical method of digging a pit and installing sensors into a side-wall. This method is time consuming and makes it more difficult to retrieve sensors.

New technology that allows sensor installation in the side of a 10 cm borehole may give the best of both worlds, but still requires backfill and has the challenge of probe removal at the end of the experiment.

The research site must also be a consideration. If the installation is close to main power or easily reached with batteries and solar panels, your options are open: all sensors will work. But, if the site is remote, picking a sensor and logging system with low power requirements will save time hauling in solar panels or the frustration of data loggers running out of batteries.


Often times it comes down to convenience.

Data Loggers Can Be a Limitation

Many manufacturers design data loggers that only connect to the sensors they make. This can cause problems if the logging system doesn’t meet site needs. All manufacturers mentioned above have sensors that will connect to general data loggers such as Campbell Scientific’s CR series. It often comes down to convenience: the types of sensor needed to monitor a site, the resources needed to collect and analyze the data, and site maintenance. Cost is an issue too, as sensors range from $100 to more than $3000.

Successfully Measure Water Content

The challenge of setting up and monitoring soil water content is not trivial, with many choices and little explanation of how each type of sensor will affect the final results. There are a wealth of papers that review the critical performance aspects of all the sensors discussed, and we encourage you to read them. But, if soil water content is the goal, using one of the sensors from the manufacturers named above, a careful installation, and a soil-specific calibration, will ensure a successful, accurate water content measurement.

For an in-depth comparison of TDR versus capacitance technology, read: Dielectric Probes Vs. Time Domain Reflectometers

For an understanding of how capacitance sensors compare to other major contemporary sensor technologies, watch our Soil Moisture 201 webinar.

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 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.”


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 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 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.”


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

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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.

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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.


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.


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.


Figure 3: Spectral Reflectance Data. (Figure and Images:

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?


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.


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

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.”


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:

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.”


Some ranchers are happy to see the pines go (Image:

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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