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Posts from the ‘Canopy’ Category

Irrigation Curves—A Novel Irrigation Scheduling Technique

This week, guest author Dr. Michael Forster, of Edaphic Scientific Pty Ltd & The University of Queensland, writes about new research using irrigation curves as a novel technique for irrigation scheduling.

Growers do not have the time or resources to investigate optimal hydration for their crop. Thus, a new, rapid assessment is needed.

Measuring the hydration level of plants is a significant challenge for growers. Hydration is directly quantified via plant water potential or indirectly inferred via soil water potential. However, there is no universal point of dehydration with species and crop varieties showing varying tolerance to dryness. What is tolerable to one plant can be detrimental to another. Therefore, growers will benefit from any simple and rapid technique that can determine the dehydration point of their crop.

New research by scientists at Edaphic Scientific, an Australian-based scientific instrumentation company, and the University of Queensland, Australia, has found a technique that can simply and rapidly determine when a plant requires irrigation. The technique builds on the strong correlation between transpiration and plant water potential that is found across all plant species. However, new research applied this knowledge into a technique that is simple, rapid, and cost-effective, for growers to implement.

Current textbook knowledge of plant dehydration

The classic textbook values of plant hydration are field capacity and permanent wilting point, defined as -33 kPa (1/3 Bar) and -1500 kPa (15 Bar) respectively. It is widely recognized that there are considerable limitations with these general values. For example, the dehydration point for many crops is significantly less than 15 Bar.

Furthermore, values are only available for a limited number of widely planted crops. New crop varieties are constantly developed, and these may have varying dehydration points. There are also many crops that have no, or limited, research into their optimal hydration level. Lastly, textbook values are generated following years of intensive scientific research. Growers do not have the time, or resources, to completely investigate optimal hydration for their crop. Therefore, a new technique that provides a rapid assessment is required.

How transpiration varies with water potential

There is a strong correlation between transpiration and plant water potential: as plant water potential becomes more negative, transpiration decreases. Some species are sensitive and show a rapid decrease in transpiration; other species exhibit a slower decrease.

Plant physiologist refer to P50 as a value that clearly defines a species’ tolerance to dehydration. One definition of P50 is the plant water potential value at which transpiration is 50% of its maximum rate. P50 is also defined as the point at which hydraulic conductance is 50% of its maximum rate. Klein (2014) summarized the relationship between transpiration and plant water potential for 70 plant species (Figure 1). Klein’s research found that there is not a single P50 for all species, rather there is a broad spectrum of P50 values (Figure 1).

Figure 1. The relationship between transpiration (stomatal conductance) and leaf water potential for 70 plant species. The dashed red lines indicate the P80 and P50 values. The irrigation refill point can be determined where the dashed red lines intersect with the data on the graph. Image has been adapted from Klein (2014), Figure 1b.

Taking advantage of P50

The strong, and universal, relationship between transpiration and water potential is vital information for growers. A transpiration versus water potential relationship can be quickly, and easily, established by any grower for their specific crop. However, as growers need to maintain optimum plant hydration levels for growth and yield, the P50 value should not be used as this is too dry. Rather, research has shown a more appropriate value is possibly the P80 value. That is, the water potential value at the point that transpiration is 80% of its maximum.

Irrigation Curves – a rapid assessment of plant hydration

Research by Edaphic Scientific and University of Queensland has established a technique that can rapidly determine the P80 value for plants. This is called an “Irrigation Curve” which is the relationship between transpiration and hydration that indicates an optimal hydration point for a specific species or variety.

Once P80 is known, this becomes the set point at which plant hydration should not go beyond. For example, a P80 for leaf water potential may be -250 kPa. Therefore, when a plant approaches, or reaches, -250 kPa, then irrigation should commence.

P80 is also strongly correlated with soil water potential and, even, soil volumetric water content. Soil water potential and/or content sensors are affordable, easy to install and maintain, and can connect to automated irrigation systems. Therefore, establishing an Irrigation Curve with soil hydration levels, rather than plant water potential, may be more practical for growers.

Example irrigation curves

Irrigation curves were created for a citrus (Citrus sinensis) and macadamia (Macadamia integrifolia). Approximately 1.5m tall saplings were grown in pots with a potting mixture substrate. Transpiration was measured daily, between 11am and 12pm, with a SC-1 Leaf Porometer. Soil water potential was measured by combining data from an MPS-6 Matric Potential Sensor and WP4 Dewpoint Potentiometer. Soil water content was measured with a GS3 Water Content, Temperature and EC Sensor. Data from the GS3 and MPS-6 sensors were recorded continuously at 15-minute intervals on an Em50 Data Logger. When transpiration was measured, soil water content and potential were noted. At the start of the measurement period, plants were watered beyond field capacity. No further irrigation was applied, and the plants were left to reach wilting point over subsequent days.

Figure 2. Irrigation Curves for citrus and macadamia based on soil water potential measurements. The dashed red line indicates P80 value for citrus (-386 kPa) and macadamia (-58 kPa).

Figure 2 displays the soil water potential Irrigation Curves, with a fitted regression line, for citrus and macadamia. The P80 values are highlighted in Figure 2 by a dashed red line. P80 was -386 kPa and -58 kPa for citrus and macadamia, respectively. Figure 3 shows the results for the soil water content Irrigation Curves where P80 was 13.2 % and 21.7 % for citrus and macadamia, respectively.

Figure 3. Irrigation Curves for citrus and macadamia based on soil volumetric water content measurements. The dashed red line indicates P80 value for citrus (13.2 %) and macadamia (21.7 %).

From these results, a grower should consider maintaining soil moisture (i.e. hydration) above these values as they can be considered the refill points for irrigation scheduling.

Further research is required

Preliminary research has shown that an Irrigation Curve can be successfully established for any plant species with soil water content and water potential sensors. Ongoing research is currently determining the variability of generating an Irrigation Curve with soil water potential or content. Other ongoing research includes determining the effect of using a P80 value on growth and yield versus other methods of establishing a refill point. At this stage, it is unclear whether there is a single P80 value for the entire growing season, or whether P80 shifts depending on growth or fruiting stage. Further research is also required to determine how P80 affects plants during extreme weather events such as heatwaves. Other ideas are also being investigated.

For more information on Irrigation Curves, or to become involved, please contact Dr. Michael Forster: michael@edaphic.com.au

Reference

Klein, T. (2014). The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Functional Ecology, 28, 1313-1320. doi: 10.1111/1365-2435.12289

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Apply now for the 2018 Grant A. Harris Fellowship

University of Idaho graduate student and past Grant A. Harris Fellowship recipient, Adrianne Zuckerman, is taking a different approach to stream restoration than the traditional approach, channel manipulation, which often requires heavy equipment and major disruption to the riparian area.

 

 

Zuckerman set out to understand how vegetation lining the stream bank impacts habitat quality for anadromous salmon and steelhead in Washington’s Methow River, which flows through the eastern Cascades. Zuckerman wanted to know how tree species composition affects the amount of nutrients available to the benthic insect community, since they are a critical food source for young salmonid fish.

When Zuckerman began investigating methods for measuring leaf contribution to the stream, she found that leaf litter traps were the standard equipment. Leaf litter traps are time consuming to set and maintain, and data analysis consists of frequent visits to the field followed by extensive time in the lab processing leaf material.

Looking for an alternative method, she discovered the LP-80 ceptometer: a lightweight, field-portable instrument for measuring leaf area index. Using the LP-80, Zuckerman was able to rapidly assess the leaf area contribution of each tree species along the riparian corridor. Using this information, it was relatively straightforward for her to estimate the contribution of each tree species to the stream food web.

Zuckerman’s research will help land managers and other researchers understand the importance of riparian vegetation for maximizing the food available to salmonid fish species. Improvement and maintenance of optimal stream-side vegetation composition should ultimately help to enhance salmon populations in the Pacific Northwest.

Receive $10,000 in METER research instrumentation

The Grant A. Harris Fellowship awards $40,000 in research instrumentation (four $10,000 awards) annually to graduate students studying any aspect of agricultural, environmental, or geotechnical science.  METER is now accepting applications for the 2018 award. Learn more about how to apply for the Grant A. Harris Fellowship here.

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How to Assess Maximum Potential Biomass Production—Simplified

The conversion of light energy and atmospheric carbon dioxide to plant biomass is fundamentally important to both agricultural and natural ecosystems.

biomass

Potato field

The detailed biophysical and biochemical processes by which this occurs are well understood. At a less-detailed level, however, it is often useful to have a simple model that can be used to understand and analyze parts of an ecosystem. Such a model has been provided by Monteith (1977). He observed that when biomass accumulation by a plant community is plotted as a function of the accumulated solar radiation intercepted by the community, the result is a straight line. Figure 1 shows Monteith’s results.

biomass

Figure 1. Total dry matter produced by a crop as a function of total intercepted radiation (from Monteith, 1977).

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Can a Leaf Wetness Sensor be a Rain Detector?

The PHYTOS 31 Leaf Wetness Sensor was designed to measure the presence and duration of water on leaf surfaces. However, Dr. Bruce Bugbee, professor of Crop Physiology at Utah State University, noticed that his leaf wetness sensor revealed interesting phenomena associated with some precipitation events. Here is what he observed on a recent day at the USU Environmental Observatory in Logan, Utah

leaf wetness sensor

It is possible to have a day with numerous 0.1 mm increments of rain, followed by some evaporation, in which a rain gauge would not record any rain during the day.

“Recent data from our weather station provided two examples of the offset in measurement associated with tipping bucket rain gauges. It started raining on campus last night at exactly 20:00 hours, as indicated by the response of the leaf wetness sensor (Figure 1). The first 0.1 mm tip of the rain gauge occurred about 25 minutes later (Figure 2). The resolution for most high-quality tipping bucket rain gauges is listed as 0.1 mm, but this is not the resolution for the first 0.1 mm of rain.

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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. http://dx.doi.org/10.1890/ES15-00170.1

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

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Avocado Growers in Kenya Fight Drought with Recycled Water Bottle Irrigation (Part 2)

Dr. Brent Clothier, Dr. Steve Green, Roberta Gentile and their research team from Plant and Food Research in New Zealand are working in Kenya to alleviate the poverty of the many small-holder farmers who grow avocados in the Central Highlands of Kenya (see part 1). This week, read about an inexpensive irrigation solution for these farmers and how the researchers are developing a plan to manage nutrients.

avocado

The period of water stress in October is at the time of main flowering.

Recycled Water Bottles Provide a Solution

When the team was visited Kenya in early March, the Long Rains had not arrived, and the trees were under water stress. The researchers sought to reduce the impact of drought by using a prototype of a portable drip-irrigation system they developed. They used ‘old’ 20 liter drinking water bottles to deliver water to the trees at 4 L/hr.

avocado

20 L water bottles used for tree irrigation.

The bottles can be refilled and moved from tree to tree. By measuring water content in the soil, the team found that the 20 L of drip irrigated water lasted in the soil about 2 days. When the period was increased to 4 days, the root water uptake was reduced over days 3 and 4 after wetting. Thus they recommended the bottle be recharged and reapplied every two days. This enables the bottle to be used on another tree on the intervening day and should help the farmers to reduce the worst impacts of the drought while waiting for the Long Rains to arrive.

avocado

Refilling the water bottles.

Replacing Low Soil Nutrients

In another phase of the experiment, Dr. Clothier’s team surveyed soil and plant nutrient contents in the main avocado production regions to assess the current fertility status of the farms. Soils in this region are classified as Nitisols, deep red soils with a nut-shaped structure and high iron content (Jones et al. 2013). These soils have low levels of organic matter and low pH. Soil sampling revealed a decrease in pH and increase in organic matter with altitude in the Kandara valley. This observed gradient is likely attributable to the higher amounts rainfall received in the higher altitudes of the valley, which can increase organic matter production and leach base cations from the soil. Soil and leaf nutrient analyses of the monitoring farms showed similar trends in nutrient availability. There are also low levels of the macronutrients nitrogen and phosphorus and the micronutrient boron in these soils. These nutrients are essential for avocado growth and production. One challenge to improve avocado productivity is finding ways to improve soil nutrient availability and tree nutrition.

avocado

An example of the benefits of a secure revenue-stream: One farmer purchased a new cow, which enables him to meet the nutrient requirements of more avocado trees.

A Plan for Managing Nutrients

The majority of the small-holder farms supplying avocados to Olivado use organic production methods. This means organic amendments such as plant residues, composts and animal manures are required to replenish the nutrients that are exported from the farms and improve soil fertility. Livestock have the potential to provide nutrient amendments for a considerable number of avocado trees. Even better, the input of organic materials will build-up soil organic matter levels, which benefit soil conservation, water holding capacity, pH buffering, and soil biological activity.

The researchers are developing simple nutrient budgets for these avocado trees using yield and fruit nutrient concentration data to assess the quantity of nutrients being exported off-farm in the harvested crop. Using the nutrient concentrations of locally available organic amendments, they will provide recommendations on the amount of organic material needed to sustain soil fertility.

Nutrient balances will be incorporated into a decision support tool to assist small-holder farmers in enhancing their soil and plant nutrition. These budgets will be enhanced by further characterizing the nutrient composition and quantities of available organic matter amendments in the region. The researchers are working to improve these nutrient budget estimates with data specific to the avocado farms in the region. They will also set up demonstration farms to evaluate the production responses to recommended nutrient management practices.

To find out more about Kenyan avocado research contact Brent Clothier: brent.clothier@plantandfood.co.nz .

(This article is a summary/compilation of several articles first printed in WISPAS newsletter)

References:

Jones, A., Breuning-Madsen, H., Brossard, M., Dampha, A., Deckers, J., Dewitte, O., Gallali, T., Hallett, S., Jones, R., Kilasara, M., Le Roux, P., Micheli, E., Montanarella, L., Spaargaren, O., Thiombiano, L., Van Ranst, E., Yemefack, M., Zougmore, R., (eds.) 2013. Soil Atlas of Africa. European Commission, Publications Office of the European Union, Luxembourg. 176 pp.

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Avocado Growers in Kenya Fight Drought with Recycled Water Bottle Irrigation

Dr. Brent Clothier, Dr. Steve Green, Roberta Gentile and their research team from Plant and Food Research in New Zealand are working in Kenya to alleviate the poverty of the many small-holder farmers who grow avocados in the Central Highlands of Kenya. These farmers have old and very large avocado trees. The fruit from these trees are purchased by the company Olivado EPZ who presses over 1300 small-holders’ avocados for oil. Dr. Clothier and his team are investigating how to increase the productivity of the farmers’ avocado trees and increase the quality of the fruit so they yield more oil.

avocado

Small-holder farmers grow avocados in the Central Highlands of Kenya.

Reducing Leaf Area to Avoid Water Stress

Because of the age and size of these trees, harvesting of the avocados is difficult and time consuming, and through dropped fruit, the quality of the avocados can be comprised. In addition, any dry season water-stress negatively impacts fruit filling. The research team performed some initial remedial pruning of these trees to develop a more manageable and productive tree form. They sought to assess whether the reduced leaf area would enable the trees to avoid water stress during the dry season of January through March between the short and long rainy seasons. They removed 30-40% of the central limbs of the avocado tree to create a more open canopy form.

The team instrumented two trees with heat-pulse sap-flow probes. One tree was left unpruned and the tree in the photo above was pruned. The tree that was pruned was using between 300-400 liters per day, as expected for a tree of that large size. The unpruned tree was smaller in size, and it was using between 150-250 liters per day during May and June. The selective limb pruning resulted in the rate of water-use dropping to 200-300 liters per day, a drop of 100 liters per day.

avocado

The more open canopy form of the pruned avocado tree.

Determining Tree Water Use During Rainy and Dry Seasons

The team also measured the water-use of four trees of different sizes during the entire season using the compensation heat-pulse method and soil water content. They found the trees’ water-use doubled with the arrival of the Short Rains and then began to decline in early January after the rains ended. The trees were under a degree of water stress prior to the arrival of the (short) Short Rains, and as the weak Short Rains ended early, the trees again went into water stress with only occasional respite due to isolated rainstorms in January and February.

This pattern of water stress presents a challenge for sustaining high levels of avocado production. The period of water stress in October is at the time of main flowering, and researchers who were there noted a carpet of aborted flowers on the orchard floor. They also noticed that the fruit were smaller at one farm than those higher up in the Central Highlands where rainfall is higher and more frequent. Thus, to improve production it is imperative to mitigate the impacts of drought, and this needs to be done without reference to any infrastructure for irrigation.

Next week: Read about an inexpensive irrigation solution for these farmers and how the researchers are developing a plan to manage nutrients.

(This article is a summary/compilation of several articles first printed in WISPAS newsletter)

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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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