Posts tagged ‘spectral reflectance’

Feb 20

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.

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.

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.

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). Its 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.

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.

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.

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 read his paper, but here we give you the equation in case you’re interested in using it.

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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Feb 13

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.

Field with crop seedlings starting to sprout

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

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.

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. In 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 a 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.

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.

Image depicts two equations one is NDVI and the other is SAVI

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.

Image depicts two equations SAVI is the top equation while the bottom equation is modified SAVI or MSAVI

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.

Depicts a compairson of MSAVI and SAVI in terms of dynamic range and noise level

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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Feb 6

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.

Flat map of the earth depicting NDVI amounts covering the contents

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 canopy growth that occurs over time as well as the spatial dynamics that occur across landscapes.

Diagram depicting seasonal growth plotted against spatiotemporal variation

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.

Chart reflecting data and electromagnetic radiation

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

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

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

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

How Do We Calculate the NDVI?

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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Jan 2

Lysimeters Determine if Human Waste Composting can be More Efficient

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

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

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

How Many Soil Moisture Sensors Do You Need?

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

Data loggers: To Bury, or Not To Bury

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Founders of Environmental Biophysics: Champ Tanner

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

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Soil Moisture Sensors In a Tree?

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

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Oct 10

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Measuring NDVI in a Greenhouse Presents Challenges (Part 2)

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

Researchers measuring the NDVI of green plants in a greenhouse

Shuyang’s experimental setup.

Fast Growth Causes Problems

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

Researchers measuring NDVI of petunias in a greenhouse

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

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

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

Summary and Future Studies

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

Six poinsettia plants with small flowers arranged with one in the middle and five around the middle one in a circle

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

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

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

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Oct 3

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Measuring NDVI in a Greenhouse Presents Challenges

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

Picture of green house full of bright red Poinsettia plants

Greenhouse plant canopies are highly variable.

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

Sensor-controlled, automated irrigation system in a greenhouse

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

Measuring Crop Size

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

Green plants being monitored in a greenhouse

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

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

Some species were more upward growing and some more sprawling.

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

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

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Oct 31

Water Potential/Water Content: When to Use Dual Measurements

In a previous post, we discussed water potential as a better indicator of plant stress than water content. However, in most situations, it’s useful to take dual measurements and measure both water content and water potential. In a recent email, one of our scientist colleagues explains why: “The earlier article on water potential was excellent. But what should be added is an explanation that the intensity measurement doesn’t translate directly into the quantity of water stored or needed. That information is also required when managing water through irrigation. This is why I really like the dual measurement approach. I am excited about the possibilities of information that can be gleaned from the combined set of water content, water potential, and spectral reflectance data.”

Field plantation with a sprinkler in the middle of it

Potato field irrigation

Managing Irrigation

The value of combined data can be illustrated by what’s been happening at the Brigham Young University Turf Farm, where we’ve been trying to optimize irrigation of turfgrass (read about it here). As we were thinking about how to control irrigation, we decided the best way was to measure water potential. However, because we were in a sandy soil where water was freely available, we also guessed we might need water content. Figure 1 illustrates why.

Turf farm data concerning water potential diagram

Figure 1: Turf farm data: water potential only

Early water potential data looks uninteresting; it tells us there’s plenty of water most of the time, but doesn’t indicate if we’re applying too much. In addition, if we zoom in to times when water potential begins to change, we see that it reaches a stress condition quickly. Within a couple of days, it is into the stress region and in danger of causing our grass to go into dormancy. Water potential data is critical to be able to understand when we absolutely need to water again, but because the data doesn’t change until it’s almost too late, we don’t have everything we need.

Turf farm data dual measurements data diagram

Figure 2: Turf farm data, volumetric water content only

Unlike water potential, the water content data (Figure 2) are much more dynamic. The sensors not only show the subtle changes due to daily water uptake but also indicate how much water needs to be applied to maintain the root zone at an optimal level. However, with water content data alone, we don’t know where that optimal level is. For example, early in the season, we observe large changes in water content over four or five days and may assume, based upon onsite observations, that it’s time to irrigate. But, in reality, we know little about the availability of water to the plant. Thus, we need to put the two graphs together (Figure 3).

Water potential and vol. water content diagram

Figure 3: Turfgrass data: both water potential and volumetric water content together.

In Figure 3, we have the total picture of what’s going on in the soil at the BYU turf farm. We see the water content going down and can tell at what percentage the plants begin to stress. We also see when we’ve got too much water: when the water content is well above where our water potential sensors start to sense plant stress. With this information, we can tell that the turfgrass has an optimal range of 12% to 17% volumetric water content. Anything below or above that range will be too little or too much water.

Soil water potential and volumetric water content diagram

Figure 4: Turfgrass soil moisture release curve (black). Other colors are examples of moisture release curves for different types of soil.

Dual measurements will also allow you to make in situ soil moisture release curves like the one above (Figure 4), which detail the relationship between water potential and water content. Scientists can evaluate these curves and understand many things about the soil, such as hydraulic conductivity and total water availability.

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Aug 22

Author Interview: Soil Physics with Python

The new book Soil Physics with Python: Transport in the Soil-Plant-Atmosphere System written by Dr. Marco Bitteli, Dr. Gaylon S. Campbell, and Dr. Fausto Tomei presents concepts and problems in soil physics as well as solutions using original computer programs.

Soil Physics with Python

In contrast to the majority of the literature on soil physics, this text focuses on solving, not deriving, differential equations for transport. Numerical methods convert differential equations into algebraic equations, which can be solved using conventional methods of linear algebra. Here, Dr. Campbell interviews about this update to his classic book Soil Physics with BASIC.

Why did you write the first book, Soil Physics with BASIC?

Soil physics classes were always frustrating for me because you would spend time writing fancy equations on the chalkboard, and in the end, you couldn’t do anything with them. You couldn’t solve any of the problems because, even though they involved difficult mathematics, the math was still so simplified that it didn’t apply to anything that went on in nature.

When I taught my first graduate soil physics class, I determined that we were going to be able to do something by the time we finished. Luckily, in the mid-1970s, personal computers were being developed, and I realized this was the answer to my problem. Numerical methods could solve any problem with any geometry in it. It wasn’t limited to problems that fit the assumptions needed to derive a complex differential equation. I could write computer programs that simplified the mathematics for the students and teach them how to solve those problems using numerical methods. By the end of the semester, my students would have a set of tools that they could use to solve problems in the real world.

Did this book come from class notes or some other source?

I wrote two textbooks and they both came the same way. When I first started teaching, I had a textbook that was inadequate, so I began writing notes of my own and handing them out to the students. After two years, I turned these notes into An Introduction to Environmental Biophysics. Soil Physics with BASIC came about by the same process, but I enlisted the help of my daughter, Julia, to type it up. It was in the early days of word processing so entering equations was quite difficult. It all went well for her until chapter eight, which was a nightmare of greek symbols. After she finished slogging for days through the material, we somehow lost the chapter. She retyped it, and we lost it again, making her type it three times! We didn’t have spreadsheets then either, so the figures were all hand-drawn by my daughter, Karine.

Red soil in the desert with trees and brush around

Marco [Bitteli] has added two and three-dimensional flow problems, so you can model whole landscapes and water behavior in an entire terrain.

What does Soil Physics with Python add to the conversation?

First, it updates the programming language. BASIC was a language invented at Dartmouth and intended to be a simple teaching language. It was never supposed to be a scientific computer language. Python (13:26.) is a newer language, and there are many open source programs for it, making it a better language to use for science.

Secondly, the old book had one-dimensional flow problems in it for the most part, but Marco [Bitteli] has added two and three-dimensional flow problems, so you can model whole landscapes and water behavior in an entire terrain.

In addition, Dr. Bitteli describes the process and analysis of soil treated as fractals as well as soil image analysis. There are a lot of extensions and updates that weren’t in the original book.

Will it be accessible across all disciplines?

To some extent, different disciplines speak different languages. A soil physicist talks about water potential, and a geotechnical engineer talks about soil suction. Thus, there may be some translation of discipline-specific terms, but it’s intended to be a book that people in the plant sciences can use along with people in the soil sciences.

Dr. Marco Bitteli earned his PhD at Washington State University and was Dr. Campbell’s former student. This book is a product of their continued collaboration. Dr. BBitteli is now a professor at University of Bologna, the oldest university in operation in the world. Soil Physics with Python is available at Amazon.com.

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Aug 14

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The Potential of Drones in Research

Someday soon, multi-rotors will execute pre-programmed flight paths over several hundred research plots collecting daily data and sending it back to a computer while researchers sip their morning coffee. Researchers and growers won’t need to know anything about flying: the drones will fly themselves. This is the dream.

One UAV (unmanned air vehicle) industry leader at the above drone demonstration commented, “The truth is that this is where agriculture (and research) is going, and I don’t mean ‘Tomorrowland’ going–I mean it’s pretty much there. The only thing that’s holding us back is a permit from the FAA for autonomy, and that’s because the FAA is slowly backing into this UAV piece because we have the busiest general aviation sky in the world. But really, what you should have in your mind is multiple units operating with a single operator in a control vehicle.” The above UAV was extensively tested in California’s NAPA valley with results soon to be published online.

In this blog, a METER scientist and an instrumentation engineer give their perspectives on what needs to happen before drones reach their full research potential.

Drone Hexacopter

What are the advantages of drones for researchers?

Dr. Colin Campbell, research scientist-

One of the biggest challenges of work in the field is variability: low spots, high spots, sandy soil, clay soil, hard pans beneath the surface in some areas and not in others. This results in highly variable performance in crops. In addition to that, even when you have good homogeneity in a field, you might have differences due to irrigation or rainfall. If we want to improve agriculture, one thing that we have to do is be able to come out with better tools to be able to visualize the field in more than a single dimension. In order to do this right now, students go out and take plant measurements all day, every day, all summer long. The advantage of a drone is that you could do flyovers of a field, monitoring the traits that you’re interested in using reflectance indices that would normally take days of work.

What are the obstacles to progress?

Greg Kelley, mechanical engineer, and drone hobbyist-

Recently, the FAA has come out with a set of guidelines for the industrial use of drones: flying machines have to stay under a certain ceiling (500 ft; 150 m), and they have to be flown in the line of sight of the operator. The naive thing about those policies is: how much control does the operator have over the drone anyway? It used to be that with your remote control, you were moving the control surfaces (flaps, rudder, etc) on the aircraft, but this is changing. The onboard computer performs things like holding a stable altitude, maintaining a GPS location, or auto-stabilization (it keeps the aircraft level, even when a gust of wind comes). Those are degrees of control that have been taken away from the operator. Thus, according to the level of automation that the operator has built into the system, he may not be in direct control at all times. In fact, these machines are being developed so that they can fly themselves. From my perspective, the FAA regulations are going to have to evolve along with the automation of drones in order to allow the development of this technology in an appropriate way.

Drone with eight rotors.

What needs to happen before drones reach their full potential?

Dr. Colin Campbell–

Even if we get the flexibility required with drones, we’ve got to get the right sensor on the drone. On the surface, this seems relatively simple. Sensors to measure spectral reflectance are available in a package size that should easily mount on a drone platform. But, there are still many challenges. First, current spectral reflectance sensors make a passive reflectance measurement, meaning we’re at the mercy of the reflected sunlight. Clouds, sun angle, and leaf orientation, among other things, will all affect the measurement. There are several groups working on this (just search “drone NDVI” on the internet), but it’s a difficult problem to solve. Second, drones create a spectral reflectance “map” of a field that needs to be geo-referenced to features on the ground to match measurements with position. Once data are collected, the behavior of “plot A” can only be determined by matching the location and spectral reflectance of “plot A.” Different from the first challenge, this is more related to programming than science but is still a major hurdle.

Despite these challenges, drones promise incredible benefits as an agricultural and environmental measurement tool. As one industry leader at the drone demonstration put it, “the complexity of the problems that agriculture faces and the opportunities for efficiencies are vast. It will require ongoing engagement, next year and the year after that. There are a lot of questions to be answered and the efficacy is yet to be determined, but it’s exciting to watch the UAV helicopter and where it’s going.” Both Campbell and Kelley agree that significant advances will be made within the next few years.

Read about an ROI calculator that’s been created to help growers quantify whether the benefits of using a drone will exceed their costs.

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May 15

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What is the Future of Sensor Technology?

Dr. John Selker, hydrologist at Oregon State University and one of the scientists behind the Trans African Hydro and Meteorological Observatory (TAHMO) project, gives his perspective on the future of sensor technology.

Researcher Pointing to Something while Walking through a Forest

Dr. John Selker (Image: andrewsforest.oregonstateuniversity.edu)

What sparked your interest in science?

I was kind of an accidental scientist in a sense. I went into water resources having experienced the 1985 drought in Kenya. I saw that water was transformative in the lives of people there. I thought there were lots of things we could do to make a difference, so I wanted to become a water resource engineer. It was during my graduate degree process that I got excited about science.

What was the first sensor you developed?

I’ve been developing sensors for a long time. I worked at some national labs on teams developing sensors for physics experiments. The first one I developed myself was as an undergraduate student in physics. I was the lab instructor for the class, and I wanted to do something on my own while the students were busy. I made a non-contact bicycle speedometer which was much like an anemometer. I took an ultrasonic emitter, trained it on the tire, and I could get the beat frequency between emitted sound and the backscatter to get the bicycle speed.

What’s the future of sensor technology?

Communication

Right now one of the very exciting advances in technology is communication. Having sensors that can communicate back to the scientists immediately makes a huge difference in terms of knowing how things are going, making decisions on the fly, and getting good quality data. Oftentimes in the past, a sensor would fail and you wouldn’t know about it for months. Cell phone technology and the ability to run a station on a few AA batteries for years has been the most transformative aspect of technological development. The sensors themselves also continue to improve: getting smaller and using less energy, and that’s excellent progress as well.

A Picture of a Orange Maple Leaf in the middle of Fall

What often happens is that you install a solar sensor, and then a leaf or a dust grain falls on it, and you lose your accuracy.

Redundancy

I think the next big thing in sensing technology is how to use what we might call “semi-redundant” sensing. What often happens is that you install a solar sensor, and then a leaf or a dust grain falls on it, and you lose your accuracy. However, if you had a solar panel and a solar sensor, you could then do comparisons. Or if you were using a wind sensor and an accelerometer you could also compare data. We now have the computing capability to look at these things synergistically.

Accuracy

What I would say in science is that if we can get a few more zeros: a hundred times more accurate, or ten times more frequent measurements, then it would change our total vision of the world. So, what I think we’re going to have in the next few years, is another zero in accuracy. I think we’re going to go from being plus or minus five percent to plus or minus 0.5 percent, and we are going to do that through much more sophisticated intercomparisons of sensors. As sensors get cheaper, we can afford to have more and more related sensors to make those comparisons. I think we’re going to see this whole field of data assimilation become a critical part of the proliferation of sensors.

What are your thoughts on the future of sensor technology?

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