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Posts tagged ‘Sensors’

Do Funding Agencies Favor Collaboration?

It’s an interesting question, and certainly one scientists need to think about. In a recent conversation a science colleague said, “I think in science right now, all the funding agencies are recognizing that to answer the problems that matter, you need to bring in people from different disciplines and even industry. If you look at the major funding focus of the National Science Foundation, when they consider bio-complexity, they’re not looking for a group of people with the same perspective. Science questions are becoming more complex, so you need to get input from people with varied backgrounds.”

funding

R.J. Cook Agronomy Farm at WSU (http://css.wsu.edu/facilities/cook/)

Examples of this are two projects that METER has collaborated on recently: the Specialty Crops Research Initiative – Managing Irrigation and Nutrients via Distributed Sensing (SCRI- MINDS) and the WSU Cook Farm project, both of which were able to get funding based in part on the use of METER’s technology, and both had a high-level of multidisciplinary involvement.

We got involved in the Cook Farm Project seven years ago because another scientist and I had an idea that fit in the context of a hot topic of the time which was to create a wireless sensor network that was densely populated in a relatively small area.  We did this because at that time, scientists were recognizing that many of the processes they were interested in were occurring when they were not out in the field measuring. In order to understand these processes, we needed in situ measurements collected continuously over a long period of time.

What we were trying to do is show that you could create a wireless sensor network in a star pattern, where you have a central point collecting data from a host of nodes surrounding it.  Our questions were:  can we create a sustainable star network in the field to get consistent and continuous measurements over several seasons, while densely populating the study area with sensors? The measurement network that we designed allowed us to investigate how topography, slope, and aspect interact to determine the hydrology of the soil in this intensely managed agronomic field.

Decagon collaborated with scientists at Washington State University, working with twelve sites across a 37-hectare field.  We installed five ECH2O-TE (now 5TE) sensors at 30, 60, 90, 120, and 150 cm below the soil surface.

funding

Wheat field

What we learned was that when wheat plants grow, their roots follow the water down a lot deeper than you might imagine.  We observed considerable water loss even 150 cm below the soil surface. Data on soil water potential suggested that, as water was depleted to the point where it was not easily extractable, plant roots at a given level would move deeper into the soil where water was more easily accessible. Soil morphology also came into play as hardpans occurred at several measurement locations and water uptake from layers above and below them showed amazing differences.

It was a really exciting thing scientifically, but also technologically.  We learned that the star network was easily possible.  It ran autonomously and was very successful, in spite of the fact that the cell phone we used to get the data back to the office never worked very well.

So it was the science question and the technology question together that was able to secure the funding.  With those twelve sites WSU was able to secure a grant from the USDA for 4.2 million dollars and the research is still ongoing today.  In fact, recently Cook Farm was established as one of the National long-term agroecosystem research sites (LTAR) which will help continue this kind of research well into the future.

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Despite Drawbacks, Scientific Collaboration Pays Off

Though collaboration can fuel innovation and increase the relevance and complexity of the scientific questions we study, I’ve noticed it does have its ups and downs.  The highs and lows we’ve run into on our research projects may help others avoid some of the pitfalls we experienced as many diverse groups tried to learn how to work together.

collaboration

Researchers discussing science at the Lytle Ranch Preserve, a remarkable desert laboratory located at the convergence of the Great Basin, Colorado Plateau, and Mojave Desert biogeographical regions.

There can be bumps in the road when collaborating with companies who want to test their product. Being at the forefront of innovation means that untested sensors may require patience as you work out all the bugs together. But from my perspective, this is part of the fun.  If we are late adopters of technology, we wouldn’t get to have a say in creating the sensors that will best fit our projects as scientists.

Collaborating scientists can also sometimes run into problems in terms of the stress of setting up an experiment in the time frame that is best for everyone.  During our experiment on the Wasatch Plateau, we had six weeks to get together soil moisture and water potential sensors, but our new GS3 water content, temperature, and EC sensors had never been outside of the lab. In addition, we planned to use an NDVI sensor concept that came out of a workshop idea my father Gaylon had.  We’d made ONE, and it seemed to work, but that is a long way from the 20 we needed for a long-term experiment in a remote location at 3000 meters elevation. In the end, it all worked out, but not without several late nights and a bit of luck.  I remember students holding jackets over me to protect me from the rain as I raced to get the last sensor working.  Then we shut the laptop and ran down the hill, trying to beat a huge thunderstorm that started to pelt the area.

collaboration

Desert-FMP Researchers at the Lytle Ranch Preserve

Other challenges of scientific collaboration present organizational hardships.  One of the interesting things about the interdisciplinary science in the Desert FMP project is the complexity of the logistics, and maybe that’s a reason why some people don’t do interdisciplinary projects.  We are finding in order to get good data on the effects of small mammals and plants you need to coordinate when you are sampling small mammals and when you’re sampling plants.  Communicating between four different labs is complicated.  Each of the rainout shelters we use cover an area of approximately 1.5 m2 .  That’s not a lot of space when we have two people interested in soil processes and two people interested in plants who all need to know what’s going on underneath the shelter.  Deciding who gets to take a destructive sample and who can only make measurements that don’t change the system is really hard.  The interesting part of the project where we’re making connections between processes has required a lot of coordination, collaboration, and forward-thinking.

In spite of the headaches, my colleague and I continue to think of ways we can help each other in our research.  Maybe we’re gluttons for punishment, but I think the benefits far outweigh the trouble we’ve had.  For instance, in the above-mentioned Desert FMP project we’ve been able to discover that small mammals are influential in rangeland fire recovery (read about it here).  We only discovered that piece of the puzzle because scientists from differing disciplines are working together.  In our Wasatch Plateau project, my scientist colleague said it was extremely helpful for him to be working with an instrumentation expert who could help him with setup and technical issues.  Also, we’ve been able to secure some significant grants in our Cook Farm Project (you can read about it in an upcoming post) and answer some important questions that wouldn’t have occurred to either one of us, if we hadn’t been working together.  In addition, solving problems that have cropped up in our projects has spurred us on to a new idea for analyzing enormous streams of data in near-real time.  (read about it here).

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TDR versus Capacitance or FDR

When we talk with scientists at conferences they often want to know the difference between TDR versus capacitance or FDR.  We’ve written a paper about this in our app guide that has been pretty popular, but it can be difficult to find on our website. Here is an introduction and a link if you are interested in learning more.

TDR Sensor Installation (Giulio Curioni, School of Civil Engineering, Univ. of Birmingham)

TDR Sensor Installation (Giulio Curioni, School of Civil Engineering, Univ. of Birmingham)

Capacitance and TDR techniques are often grouped together because they both measure the dielectric permittivity of the surrounding medium. In fact, it is not uncommon for individuals to confuse the two, suggesting that a given probe measures water content based on TDR when it actually uses capacitance.

TDR

10HS capacitance sensor

With that in mind, we will try to clarify the difference between the two techniques. The capacitance technique determines the dielectric permittivity of a medium by measuring the charge time of a capacitor, which uses that medium as a dielectric. We first define a relationship between the time, t, it takes to charge a capacitor from a starting voltage, Vi , to a voltage V, with an applied voltage, Vf.  Read more….

Watch the webinar

In this webinar, Dr. Colin Campbell discusses the details regarding different ways to measure soil moisture and the theory behind the measurements.  In addition, he provides examples of field research and what technology might apply in each situation. The measurement methods covered are gravimetric sampling, dielectric methods including TDR and FDR/capacitance, neutron probe, and dual needle heat pulse.

 

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Near Real-Time Data Analysis

We are entering an era of cheap data.  Sensor technology has advanced to the point where it has become easy to collect large amounts of measurement data at high spatiotemporal resolution.

real-time data analysis

Hydroserver map screen: Using an off-the-shelf open source informatics system like Hydroserver kept us from reinventing what’s already out there, but allowed flexibility to program to our own needs.

We are now to the point where we have gigabytes worth of data on soil moisture, plant canopy processes, precipitation, wind speed, and temperature, but the amount of data is so overwhelming that we are having a difficult time dealing with it. The cost of measurement data is dropping so quickly, people are forced to change from a historical mindset where they analyzed individual data points to the mindset of turning gigabytes of data into knowledge.

real-time data analysis

Because Bioinformatics students are used to working with DNA data, they understand how to write computer programs that analyze large amounts of data in near real-time.

One approach suggested by my colleague Rick Gill, a BYU Ecologist, is to collaborate with bioinformatics students.  Because they are used to working with DNA data, these students understand how to write computer programs that analyze large amounts of data in near real-time.  Rick came up with the idea to tap these students’ expertise in order to analyze the considerable information he anticipates collecting in our Desert FMP Project, an experiment which will use TEROS 21 and SRS sensors to determine the role of varying environmental and biological factors involved in rangeland fire recovery.

Rick and I are predicting that near real-time data analysis will give us several advantages. First, we need readily available information so we can tell that sensors and systems are working at the remote site.  Large gaps in data are common for sites that aren’t visited often, and sensor failures are missed when data are collected but never analyzed.  With our new approach, all data are databased instantly, and the results are visualized as we go.  Not only that, we’ll be able to control what’s being analyzed as we see what’s happening.  We can tell the bioinformatics students what we need as we begin to see the results come in.  If we see important trends, we can assign them to analyze new data that may be relevant right away.

These techniques have the potential to help scientists from all disciplines become more efficient at collection and analysis of large data streams. Although we’ve started the process, we have yet to determine its effectiveness.  I will post more information as we see how well it is working and as new developments arise.

Watch Dr. Gill’s data analysis webinar: Finding Insights in Big Data Sets

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Complex Scientific Questions Yield Better Science in Desert FMP Project

The Desert FMP project originated from a discussion between pretty divergent scientists: Rick Gill, a BYU ecologist, another scientist who works on soil microbes, a plant physiologist, and a mammalogist who researches small mammals.

Desert FMP

Tree fire in Rush Valley

In an interview Rick said, “We started talking one day about the transformations that have occurred in the arid West over the past 100 years.  One of the things we are really interested in is fire.  How do ecosystems recover after fire? What’s the role of water in rangeland recovery? And the unique piece of this is: what’s the role of small mammals in this process?  We may never have thought of that question, or the complexity of researching how all of our questions work together in a system, if scientists from different disciplines hadn’t decided to collaborate.”

Desert FMP

Rush Valley research site. Five replications with four treatments: burned/unburned and small mammal/no small mammal. What’s interesting for us is that you can see that in the burned plots (the light brown) there are strong differences in the amount of the bright green plant—halogeton—that was present and it is systematically associated with the presence of small mammals. Here is the logic: In the spring, the presence of small mammals suppressed the cheatgrass and to some extent halogeton; in the absence of halogeton, cheatgrass ran wild. The cheatgrass transpired away all of the water and the halogeton that had germinated all died before it could flower.

As the experiment unfolds it is becoming clear that small mammals play a larger role in ecosystem recovery from fire than originally thought.  The scientists have used their observations to hypothesize that small mammals eat the seeds and seedlings of two invasive species. This ends up setting the vegetation along a very different trajectory than when small mammals are absent following fire.  Rick says, “We have discovered this complex but interesting interaction between water, fire, and small mammals. The first year after the fire, a really nasty range forb moved in called halogeton, which is toxic to livestock. Halogeton also accumulates salts in the upper soil profile that will cause failure in native plant germination.  Cheatgrass has also moved in which makes the area more prone to fire as it connects the sagebrush plants with flammable material. But what’s interesting is in treatments where mammals were present, the densities of both halogeton and cheatgrass were much lower than where small mammals were absent.

Desert FMP

Plot water potential comparison using matric potential sensors between Mammal (blue) and no mammal (red) over time. With no mammals to control cheatgrass, it depleted soil water availability below no mammal treatment and consequently halogeten was not able to grow.

 “The other really important thing is that cheatgrass and halogeton have different growth patterns.  Cheatgrass germinates in the Fall.  It reaches peak biomass early in the growing season and then dies off leaving a blanket of dead, highly flammable vegetation.  Halogeton germinates early in the growing season and remains relatively small until early Autumn when it bolts.  These are things that will be really easy to pick up using NDVI sensors, which are sensitive to the amount of green vegetation within the field of view of the sensor.  We are also using a system that we’ve designed to manipulate precipitation input.   This will enable us to connect water availability to the success of two invasive plants that have negative impacts on rangelands.  And with these same treatments we’re going to be able to tease out when in the year and to what extent small mammals are influencing the ecosystem by eating the seeds or the plant and at what stage.”

“Until I saw it in the field, the question of mammals being influential in rangeland fire recovery had never occurred to me.  We only discovered that piece of the puzzle because scientists from differing disciplines are working together.”

Below are two virtual tours of the site:

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Volumetric Water Content: Keeping your Eye on the Goal

Most scientists agree that it’s productive to attend seminars and conferences in order to talk with peers, share ideas, and learn about what other scientists are doing. However, in both academia and industry, we need to be careful that we are not so easily influenced by other scientists’ opinions that we lose sight of the end goals of our own projects. This happened to us recently at Decagon. Here’s the background: our volumetric water content (VWC) sensors actually measure the dielectric permittivity of the soil and use a transfer function to predict VWC from the measured dielectric value. Most of our sensors receive a “dielectric calibration” during the production process where they are calibrated in five dielectric standards to make sure they all measure dielectric permittivity accurately, thus leading to accurate VWC measurements with our standard transfer function.

volumetric water content

Volumetric water content (VWC) is determined by measuring the charge storing capacity of the soil using capacitance/frequency domain technology.

We were doing a pretty good job calibrating these sensors in dielectric standards, and our default dielectric-to-VWC transfer function resulted in good VWC accuracy. Then we went to a series of meetings and talked to some of our researcher friends who work on instrumentation. They said, “Look, your water sensors aren’t reading as accurately as they should in dielectric permittivity.” Here’s where the trouble started…

Wanting to make the perfect instrument, we went back and re-evaluated the dielectric calibration standards for these water content sensors and tried to use the book values of dielectric permittivity. This was a bad idea because it fundamentally changed the sensor output. Now, despite the sensors giving a slightly more accurate value for dielectric permittivity, they gave less accurate measurements of VWC. Compounding the problem, we now had a population of sensors that didn’t read the same as earlier sensors of the same type. So when customers started replacing their old sensors they said, “Wait a minute, this sensor reads 4% higher water content than my old water content sensor.” That’s when we realized that we had a real problem.

Our underlying mistake here is that we failed to remember that 99% of the people who buy our VWC sensors don’t even care what dielectric permittivity is. They just want an accurate, repeatable measurement of soil moisture. Essentially, because we were so focused on trying to produce a theoretically perfect sensor for a vocal minority of technically savvy users, we lost sight of the practical matter. Did our sensors produce an accurate water content measurement?

I wonder how often this happens in academia and industry. Scientists are bombarded with input from so many different stakeholders, it’s sometimes difficult to maintain the original focus of their projects. We need to remember to focus on the end goal and filter out things that may distract from that goal.

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Spectral Reflectance and Water Content in the Wasatch Plateau Experiment

We chose to collaborate with Brigham Young University in an experiment on the Wasatch Plateau in 2009 because a scientist friend of ours had been working in that area the previous five years, and he noticed there were big grazing responses.  The plants growing in the long-term grazed areas were all drought tolerant, while ungrazed plots had plants that were often found only in wetter areas.  The only difference was the fence that kept sheep on one side and not on the other.   The big question was: how does water influence plants in this ecosystem that we understand relatively well? The story had always been the influence of grazers, when in fact, maybe the indirect consequence of grazing was mediated by water.

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The Wasatch Plateau above Ephraim Canyon, UT, USA.

METER donated some sensors in order to set up an experiment where we changed the amount of water in various plots of land. We had rain exclusion plots, and we had treatments where we collected all incoming rainfall and reapplied it either once a week or every three weeks.   This allowed us to say to what extent this system was controlled by water during the growing season.  To do this, we took measurements with our prototype NDVI Spectral Reflectance Sensor to measure canopy greenness. We also used our prototype volumetric water content sensors to measure soil moisture (this was a few years ago and the sensors were prototypes at the time).  Using these sensors, we found that water is critical in a system people have dismissed as being climate-controlled because it’s at the top of a mountain.

water

A very early prototype of a NDVI sensor measuring canopy greenness in experimental plots on the Wasatch Plateau.

It turns out the amount and timing of precipitation makes a big difference.  We were able to directly connect plant survival, not just to the grazing treatment, but to the actual amount of water that was in the soil. Also, using continuous NDVI data, we were able to look closely at the role of grazing on plant canopies.  When we looked at our NDVI data, we were able to see a seasonal signal, not just a single snapshot sample in time.  So by having the richer data from the data loggers, we obtained a more nuanced understanding of the impact of land use on these important ecological processes.

One of the mistakes we made was failure to include redundancy in the system.  We only had two replicates, so when one of them went down we ended up having just one little case study.  However, that mistake gave us new ideas on how to set up a better system using the right sensors for the job, and it generated a new idea on how to get real-time analysis of data.  In our new Desert FMP project, we have a much better-replicated system where more is invested in the number of sensors that we’re putting out. Each treatment combination will have five to ten water potential sensors.  We are also developing a system where we can analyze data in real-time, so this time we will know when a sensor goes out if a student accidentally kicks it.

 For more details on the Wasatch Plateau Experiment, watch for our published paper that we’ll link to when it comes out.

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Burn victim research leads to new method for measuring stomatal conductance

Measuring the stomatal conductance of a leaf should be a pretty straightforward problem.  The conductance is just the flux density of water vapor divided by the concentration difference between the leaf and its surroundings.  Common approaches to this problem involve either flowing air of known vapor concentration over the leaf and measuring how much water vapor is picked up, or sealing a cup of known capacity to the leaf surface and measuring how quickly the vapor concentration in the cup increases.  Both of these, though simple in concept, require quite a bit of expensive equipment to pull off.  We wanted a simpler approach.  We put a humidity sensor in a small tube, the end of which could be pressed against the leaf.  As vapor diffused through the tube the humidity in the tube increased.  The conductance of the tube is easily calculated.  It is the diffusivity for water vapor divided by the tube length.  The leaf conductance could be computed from the tube length, the humidity in the tube and the ambient humidity.  That worked, but it turned out that ambient humidity variations introduced too much error, so we later added a second humidity sensor toward the distill end of the tube. Our approach was very simple, and works well, but it wasn’t a new idea.

stomatal conductance

Cross section of METER’s Leaf Porometer

I read of a similar device in a conference proceedings (I don’t recall the name of the conference)  in 1977 when I was on sabbatical at University of Nottingham in England.  The device wasn’t for leaves.  It was developed by a medical researcher to assess severity of burn injuries, and for use on neonatal infants.  The skin of a non-sweating human is pretty impermeable to water.  A typical conductance is around 5 mmol m-2 s-1.  This is about half the value for a leaf with stomates closed, and about two orders of magnitude lower than leaves with open stomates.  Burned skin, however, is much more permeable, and the permeability is related to the severity of the burn.  A device that could measure the permeability of skin would therefore give information on the severity of the burn.  The researcher built an apparatus, similar to our porometer, with two closely spaced humidity sensors in a diffusion tube.  As I recall, it was somewhat successful, but I’m not aware of it ever having been commercialized or used much after that. The application for infants is also interesting.  Full-term babies have low skin conductances.  I haven’t seen measurements, but assume they are similar to adult conductances.  The skin of premature infants, though, has a much higher conductance.  I don’t know typical conductance values, but do know that, without intervention, the conductance can be so high that evaporative water loss from the baby will reduce body temperature to dangerously low levels, even in an incubator. I don’t know if later work has been done to measure skin conductance, but it is interesting that the first applications of the technology we now use in our porometer was for measuring conductance of the human epidermis, not the epidermis of leaves.

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