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Nov 20

New Applications in Archeology for TDR Probes Measuring Water Content

Recently, I spent a day at the University of Birmingham in the UK where I talked with Dr. Nicole Metje and researchers in the Civil Engineering department. They are working on a project called, “Mapping the Underworld,” (Curioni G., Chapman D.N., Metje N., Foo K.Y., Cross J.D. (2012) Construction and calibration of a field TDR monitoring station. Near Surface Geophysics, 10, 249-261) where they are using TDR probes to help locate buried objects that require maintenance.

University of Birmingham Clock Tower

Currently, people use rudimentary tools to poke around and figure out where the buried object is. A more effective high-tech solution is GPR (Ground Penetrating Radar) that is pulled over the top of the soil and creates a 2D image of permittivity below the ground surface. The problem is GPR only provides relative depth information and must have ancillary data to produce actual values. To address this issue, their group uses TDR probes (time domain reflectometry) which measure dielectric permittivity to ground truth the GPR. Using this method they hope to be able to predict the depth to anomalies that are observed in the 2D GPR output.

Sensor Installation Pit

After working on this for some time, the engineers at the University of Birmingham continue to deal with challenges related to TDR signal, interpretation, and maintenance. One challenge is that TDR systems are complex and power hungry. Thus, the researchers were interested in learning more about soil moisture sensing and different technologies that would help them meet their project goals. My first inclination was to solve their problem with water potential sensors. Many people who work in environmental applications want to know the fate and distribution of water where water potential is the driver. Interestingly, this is one of the few cases where people actually do need permittivity measurements (the value used to derive volumetric water content, VWC) instead of water potential because they use the actual permittivity signal to ground truth the GPR. This realization spawned a four-hour discussion on the frontiers of permittivity measurement in soil and the use of advanced analysis techniques to tease out important soil properties such as bulk density, electrical conductivity, and mineralogy.

I hadn’t given much thought to using soil science instrumentation to locating buried infrastructure. I’m excited to see what the combination of a new technology like GPR and dielectric measurement can do to help us solve everyday problems like where to start digging.

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Nov 17

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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.

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.

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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Nov 13

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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.

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.”

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.

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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Nov 10

Solving the Problem of Disappearing Science Lab Technicians

One of the hardest issues university researchers face today is the lack of funding for lab technicians. Although it’s frustrating that universities are no longer able to support this type of personnel, can technology close the gap? This is a question we’ve tried to answer in our Desert FMP project in collaboration with BYU.

Source: Simplyhired.com. Job listings for Science Lab Technicians have decreased 38% from March 2013-March 2014

I was talking to my colleague, Rick Gill, several weeks ago, and he had this to say about the disappearance of the previously indispensable lab technician: “We have fewer people in the lab, and the people we have are more expensive. We need to be deliberate in how we use their time. If we can make the entire system more efficient using technology, we’ll use the people we have in a way that is meaningful. In ecology right now, one of the things that we’re beginning to recognize is that the typical process where the lab tech would go out and take ten samples and average them is not what’s interesting. What’s interesting is when it’s been dry for four weeks, and you get a big rain event. This is because the average for four weeks is really low for almost all processes, but the data three days after it rains swamps the previous four weeks. So the average condition means almost nothing in terms of the processes we’re studying for global change. We need technology to take the place of the technician who would be monitoring the weather and trying to guess when the big events will occur.”

To capture these pulses in the Desert FMP project, we’re using a continuous monitoring system that communicates feedback directly to us as the principal investigators. Using advanced analysis techniques, we can painlessly assure that data are being collected properly and important events are never missed. Although we don’t have a technician, the goals of the project are still being met.

What do you think? How have you dealt with the disappearance of the lab tech?

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Nov 6

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 (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.

Download the “Researcher’s complete guide to water potential”—>

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Nov 1

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.

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.

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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