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New Weather Station Technology in Africa-3

The Trans African Hydro and Meteorological Observatory (TAHMO) project expects to put 20,000 microenvironment monitors over Africa in order to understand the weather patterns which affect that continent, its water, and its agriculture. In the conclusion of our 3 part series, we  interview Dr. John Selker about his thoughts on the project.

TAHMO

The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food.

In your TEDx talk you estimate that US weather stations directly bring U.S. consumers  31 billion dollars in value per year. Can Africa see that same kind of return?

Even more.  The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food.  Africa, one could argue, is the breadbasket for this coming century.  Thus, the value of information about where we could grow what food could be astronomical.  It’s very difficult to estimate.  One application of weather data is crop insurance.  Right now, crop insurance is taking off across Africa. The company we’re working with has 180,000 clients just in Kenya.  When we talked about 31 billion dollars in the U.S., that is the value citizens report, but you need to add to that protection against floods, increased food production, water supply management, crop insurance and a myriad of other basic uses for weather data.  In Africa, the value of this type of protection alone pays for over 1,000 times the cost of the weather stations.

Another application for weather data is that in Africa, the valuation of land itself is uncertain. So if, because of weather station data, we find that a particular microclimate is highly valuable, suddenly land goes from having essentially no value to becoming worth thousands of dollars per acre.  It’s really difficult to estimate the impact the data will have, but it could very well end up being worth trillions of dollars.  We have seen this pattern take place in central Chile, where land went from about $200/hectare in 1998 to over $3,000/ha now due to the understanding that it was exceptionally suited to growing pine trees, which represented a change in land value exceeding $3 billion.

Does the effect of these weather stations go beyond Africa?

There’s limited  water falling on the earth, and if you can’t use weather data to invest in the right seeds, the right fertilizer, and plant at the right time in the right place, you’re not getting the benefit you should from having tilled the soil.  So for Africa the opportunity to improve yields with these new data is phenomenal.  

In terms of the world, the global market for calories is now here, so if we can generate more food production in Africa, that’s going to affect the price and availability of food around the world.  The world is one food community at this point, so an entire continent having inefficient production and ineffective structures costs us all.

TAHMO

If we can generate more food production in Africa, that’s going to affect the price and availability of food around the world.

You’re collecting data from Africa. Is it time to celebrate yet?

I think this is going to be one of those projects where we are always chilling the champagne and never quite drinking it.  It is such a huge scope trying to work across a continent.  So I would say we’ve got some stations all over Africa, we’re learning a lot, and we’ve got collaborators who are excited.  We have reason to feel optimistic.  It will be another five years before I’ll believe that we have a datastream that is monumental.  Right now we’re still getting the groundwork taken care of.  By September of this year we expect to have five hundred of stations in place, and then two years from now, over two thousand. This will be a level of observation that will transform the understanding of African weather and climate.

TAHMO

This is a project of hundreds of people across the world putting their hands and hearts in to make this possible.

How do you deal with the long wait for results?  

In science there is that sense you get when you want to know something, and you can see how to get there.  You have a theory, and you want to prove it.  It kind of captures your imagination.  It’s a combination of curiosity and the potential to actually see something happen in the world: to go from a place where you didn’t know what was going on to a place where you do know what’s going on.  I think about Linus Pauling, who made the early discoveries about the double helix.  He had in his pocket the X-ray crystallography data to show that the protein of life was in helical form, and he said, “In my pocket, I have what’s going to change the world.”  When we realized the feasibility of TAHMO, we felt much the same way.”  

Sometimes in your mind, you can see that path: how you might change the world.  It may never be as dramatic as what Pauling did, but even a small contribution has that same excitement of wanting to be someone who added to the conversation, who added to our ability to live more gracefully in the world.  It’s that feeling that carries you along, because in most of these projects you have an idea, and then ten years later you say, “why was it that hard?”  

Things are usually much harder than your original conception, and that energy and curiosity really helps you through some of the low points in your projects.  So, curiosity has a huge influence on scientific progress.  Changing the world is always difficult, but the excitement, curiosity, and working with people, it all fits together to help us draw through the tough slogs.  In TAHMO, I cannot count the number of people who have urged us to keep the effort moving forward and given a lift just when we needed it most.  This is a project of hundreds of people across the world putting their hands and hearts in to make this possible.  Having these TAHMO supporters is an awesome responsibility and concrete proof of the generosity and optimism of the human spirit.

Learn how you can help TAHMO.

New Weather Station Technology in Africa (Part 2)

Weather data improve the lives of many people. But, there are still parts of the globe, such as Africa, where weather monitoring doesn’t exist (see part 1). John Selker and his partners intend to remedy the problem through the Trans African Hydro Meteorological Observatory (TAHMO).  Below are some challenges they face.

weather station

TAHMO aims to deploy 20,000 weather stations across the continent of Africa in order to fill a hole that exists in global climate data.

Big Data, Big Governments, and Big Unknowns

Going from an absence of data to the goal of 20,000 weather stations offers hope for positive changes. However, Selker is still cautious. “Unintended consequences are richly expressed in the history of Africa, and we worry about that a lot. It’s an interesting socio-technical problem.”  This is why Selker and others at TAHMO are asking how they can bring this technology to Africa in a way that fits with their cultures, independence, and the autonomy they want to maintain. 

TAHMO works with the government in each country stations are deployed in; negotiating agreements and making sure the desires of each recipient country are met. Even with agreements in place, the officials in each country will do what is in the best interest of the people: a gamble in countries where corruption is a factor which must be addressed. Selker illustrates this point by recalling an instance in 1985 when he witnessed a corrupt government official take an African farmer’s land because the value had increased due to a farm-scale water development project.

Most TAHMO weather stations are hosted and maintained by a local school, making it available as an education tool for teachers to use to teach about climate and weather. Data from TAHMO are freely available to the government in the country where the weather station is hosted, researchers who directly request data, and to the school hosting and maintaining the weather station. Commercial organizations will be able to purchase the data, and the profits will be used to maintain and expand the infrastructure of TAHMO.

weather station

Selker says it’s all about collaboration.

Terrorism, Data, and Open Doors

“When I wanted to go out and put in weather stations, my wife simply said, ‘no, you will not go to Chad.’ … because it is Boko Haram central,” Selker says.

The Boko Haram— a terrorist organization that has pledged allegiance to ISIS— creates an uncommon hurdle. Currently the Boko Haram is most active in Nigeria, but has made attacks in Chad, Cameroon, and Niger.

Selker also mentioned similar issues with ISIS, “When ISIS came through Mali, the first thing they did is destroy all the weather stations. So they have no weather data right now in Mali.” Acknowledging the need for security, he adds, “we’re  completing the installation of  eight stations [in Mali] in April.”

“We have good contacts [in Nigeria] and they’re working hard to get permission to put up stations right now in that area. We’ve shipped 15 stations which are ready to install. With these areas we can’t go visit, it’s all about collaboration. It’s about partners and people you know. We have a partnership with a tremendous group of Africans who are really the leading edge of this whole thing.”

weather station

Most TAHMO weather stations are hosted and maintained by a local school.

A Hopeful Future

Despite the challenges of getting this large-scale research network off the ground, Selker and his group remain hopeful.  About his weather data he says, “It’s not glamorous stuff, you won’t see it on the cover of magazines, but these are the underpinnings of a successful society.”

Selker optimistically adds, “We are in a time of incredible opportunity.”

Learn more about TAHMO

Next Week:  Read an interview with Dr. John Selker on his thoughts about TAHMO.

New Weather Station Technology in Africa

Weather data, used for flight safety, disaster relief, crop and property insurance, and emergency services, contributes over $30 billion in direct value to U.S. consumers annually. Since the 1990’s in Africa, however, there’s been a consistent decline in the availability of weather observations. Most weather stations are costly and require highly trained individuals to maintain. As a result, weather stations in African countries have steadily declined over the last seventy years. Oregon State University’s, Dr. John Selker and his partners intend to remedy the problem through his latest endeavor— the Trans African Hydro Meteorological Observatory (TAHMO).

weather stations

Weather data improve the lives of many people. But, there are still parts of the globe where weather monitoring doesn’t exist.

Origins of TAHMO

TAHMO is a research-based organization that aims to deploy 20,000 weather stations across the continent of Africa in order to fill a hole that exists in global climate data. TAHMO originated from a conversation between Selker and Dr. Nick van de Giesen from Delft University of Technology while doing research in Ghana. Having completed an elaborate study on canopy interception at a cocoa plantation in 2006, they hit a “data wall.” There was virtually no weather data available in Ghana, a problem shared by most African countries. This opened the door to what would later become TAHMO.

weather stations

The majority of weather stations are being installed at local schools where teachers are using the data in their classroom lessons.

Logistics and Equipment

Originally Selker and van de Geisen set out to make a $100 weather station, which Selker admitted, “turned out to be harder than we thought.” Not only was making a widely-deployable, affordable, research-grade, no-moving-parts weather station difficult, but additional challenges presented themselves.

“The model of how we might measure the weather in Africa, the whole business model, the production model, infrastructure support, the data-base and delivery system, the agreements with the countries, agreements with potential data-buyers, that all took us a long time to sort-out.” Despite these challenges, in 2010 it started to look feasible. “That’s when we really started to figure out what the technology we were going to use was going to look like.”

After giving a lecture at Washington State University, Selker spoke with Dr. Gaylon Campbell about the project, which led to a long development-deployment-development cycle. Eventually the final product emerged as a low-maintenance, no-moving-parts, cellular-enabled, solar-powered weather station.

weather stations

An estimated 60 percent of the African population earn their income by farming.

Agricultural Benefits of Weather Stations

Crop insurance, a service that is widely used in developed countries, relies on weather data. Once historical data exists, insurance rates can be set, and farmers can purchase crop insurance to replace a crop that is lost to drought, weather, wildfire, etc. On a continent with the largest percentage of the total population subsistence farming, this empowers farmers to take larger risks. Without insurance, farmers need to conserve seed, saving enough to eat and plant again if a crop fails. With crop insurance, crop loss is not as devastating, and farmers can produce larger yields without worrying about losing everything. Hypothetically, this would lead to more food available to the global market, stabilizing food prices year over year.

Crop insurance aside, weather data provide growers with information like when to plant, when not to plant, what crops to plant, and when and if to treat for disease. For rainfed crops, this can mean the difference between a successful yield and a failure.

“Currently in most African countries, the production per acre is about one-sixth of that in the United States. That is the biggest opportunity, in my opinion, for sustainable growth without having to open up new tracts of land. The land is already under cultivation, but we can up productivity, probably by a factor of four, by giving information about when to plant,” Selker comments.  

Despite the social benefits, Selker makes it clear that the TAHMO effort is based on mutual benefit: “We are here for a reason, we want these data to advance our research on global climate processes.  This is a global win-win partnership.”

Learn how you can help TAHMO by getting active.

Next week:  Read about some of the challenges facing TAHMO

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.

New Infiltrometer Helps City of Pittsburgh Limit Traditional Stormwater Infrastructure (Part 2)

To save the aesthetics of Dellrose Street, an aging, 900 ft. long, brick road, the city of Pittsburgh wanted to limit traditional stormwater infrastructure (see part 1). Jason Borne, a stormwater engineer for ms consultants and his team decided permeable pavers was a viable option, and used two different types of infiltrometers to determine soil infiltration potential.  Here’s how they compared.

double ring infiltrometer

Setting up the infiltrometers.

Shortened Test Times Allow Design Changes on the Fly

Though most of the subsoil was a clay urban fill, there was a distinct transition between that clay material to a broken shale/clay mixture.  Borne says, “After excavation, it rained, and we saw that the water was disappearing through the broken shale/clay material.  When we did the infiltration tests, the broken shale/clay showed a higher infiltration potential than the clay fill material.  That led us to modify the design of the subsurface flow barriers based on specific observed infiltration rates of the subsoils. Where the tests showed higher hydraulic conductivity values, we were able to rely on infiltration entirely to remove the water from behind the check dams.”  Borne adds that in the areas where infiltration was poor, they augmented infiltration with a slow release concept. “We put some weep holes in the flow barrier and let the water trickle out down to the next barrier and so on.  Basically, the automated SATURO infiltrometer allowed us to do many tests in a short amount of time to establish a threshold of where good infiltrating soils and poor infiltrating soils were located.  This enabled us to change the design on the fly.  The double ring infiltrometer takes significantly more time to do a test, and time is of the essence when the contractor wants to backfill the area and get things moving. It was nice to have a tool that got us the information we needed more rapidly.”

double ring infiltrometer

SATURO Infiltrometer

How did the Double Ring and SATURO Compare?

Borne says the SATURO Infiltrometer was faster and reduced the possibility of human error.  He adds, “We liked the idea of it being very standardized. The automated plot of flux over time was also of great interest to us, because we could see a trend, or anomalies that might invalidate the results we were getting. The double ring infiltrometer takes a long time to achieve a state of equilibrium, and it’s hard to know when that occurs. You’re following the Pennsylvania Department of Environmental Protection suggested guidelines, but they’re very generalized.  To me it doesn’t suit all situations.  What we found with the SATURO infiltrometer is it records information at very discreet intervals, plots a curve of the flux over time, and when it levels out, you basically achieve equilibrium.  You get to that state of equilibrium faster.  There’s a water savings, but there’s also a time savings.  And there’s the satisfaction of getting standardized results rather than the possibility of each technician applying the principles in a slightly different way, as they might with the double ring infiltrometer.”

Borne and his team were ultimately able to prepare a permeable paver street design which allowed for the exclusion of traditional storm sewer infrastructure, reducing both capital costs and long-term maintenance life cycle costs. The permeable paver concept is intended to provide a template for the city of Pittsburgh to apply to the future reconstruction of other city streets.

Get more information on applied environmental research in our

New Infiltrometer Helps City of Pittsburgh Limit Traditional Stormwater Infrastructure

Though difficult and expensive to repair, the brick-paved streets that still exist in some Pittsburgh, Pennsylvania neighborhoods are worth saving. Dellrose Street, an aging, 900 ft. long, brick road, was in need of repair, but the city of Pittsburgh wanted to limit traditional stormwater infrastructure, such as pipes and catch basins.

Infiltrometer

Dellrose Street permeable paver system

To save the aesthetics of the neighborhood, they hired ms consultants, inc. to design a permeable paver solution for controlling stormwater runoff volumes and peak runoff rates that would traditionally be routed off-site via storm sewers.  Jason Borne, a stormwater engineer for ms consultants who worked on the project says, “What we try to do is understand the in situ infiltration potential of the subsoils to determine the most efficient natural processes for attenuating flows; either through infiltrating excess water volume back into the soil or through slow-release off-site.”  He used the SATURO Infiltrometer to get an idea of how urban fill material would infiltrate water.

Green Infrastructure Aids Natural Infiltration

As Borne and his team investigated what they could do to slow down the runoff, they decided permeable pavers would be a viable solution.  He says, “There’s not much you can do once you put in a hardened surface like a pavement.  Traditional pavement surfaces accelerate the runoff which requires catch basins and large diameter pipes to carry the runoff off-site. We were interested in investigating what some of the urban subsoils, or urban fill would allow us to do from an infiltration perspective.  As we started looking at some of these subsoils, we decided a permeable paver system would be ideal for this particular street.”

Infiltrometer

Subsurface flow barrier installation

Infiltrometers Determine Natural Infiltration Potential

Once the water flowed into the aggregate, the team began to figure out ways to slow it down and promote infiltration.  Borne says, “Basically we came up with a tiered subsurface flow barrier system.  We had about 60 concrete flow barriers across the subgrade within the aggregate base of the road. We needed so many because the longitudinal slope of the road was fairly significant. Behind each of these barriers we stored a portion of the stormwater that would typically run off the site.  The ideal was to remove the stored water through infiltration–to get it down to the subgrade and away, so we used infiltrometers to help us establish where we could maximize infiltration and where we might need to rely on other management methods.”

A Need for Faster Test Times Inspires a Comparison

Borne says that USDA soil surveys are too generalized for green infrastructure applications in urban areas and only give crude approximations of the soil hydraulic conductivity. Understanding the best way to promote natural infiltration requires a very specific infiltration rate or hydraulic conductivity for the location of interest.  He says, “The goal is to excavate down to the desired elevation before construction and find out, through some kind of device what the infiltration potential of the subsoil is.  Typically we use a double ring infiltrometer, but it’s a very manual device. We’re constantly refilling water, and it requires us to be on-site and attentive to what’s happening.  We can’t really multitask, especially in areas of decently infiltrating soils where the device might run out of water in 30 minutes or less. So, in the interest of saving water and time, we used the automated SATURO infiltrometer and the manual double ring infiltrometer concurrently for comparison purposes.”

Next week:  Find out how the two infiltrometers compared.

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How to Get More From Your NDVI Sensor (Part 3)

In the conclusion of our three-part series on improving NDVI sensor data (see part 2), we discuss how to correct for limitations which occur in high leaf area index (LAI) conditions.

NDVI Sensor

Where there’s a large amount of vegetation, NDVI tends to saturate.

NDVI Limitations – High LAI

NDVI is useful in the midrange of LAI’s as long as you don’t have strong soil effects, but as you approach an LAI above 4, you lose sensitivity. In figure 6, loss of sensitivity is primarily due to a saturation in the red band. Measurements were taken in a wheat canopy and a maize canopy. The near infrared reflectance is sensitive across the entire spectrum of the wheat and maize canopies, but the red saturates relatively quickly. Where the red starts to saturate is where the NDVI starts to saturate.

NDVI Sensor

Figure 6: Gitelson (2004) J. Plant Phys

Note: NDVI saturates at high LAI’s, however, if your purpose is to get at the fractional interception of light, NDVI tends not to have the saturation issue. In Figure 7, Fpar or the fractional interception of light of photosynthetically radiation is nearly complete far before NDVI saturates. This is because canopies are efficient at intercepting light, and once we get to an LAI of about 4, most of the light has been intercepted or absorbed by the canopy.  Thus, incremental increases in LAI don’t significantly affect the FPar variable.

NDVI Sensor

Figure 7: Fractional interception of light is near complete at an LAI around 4. (Gamon et al. (1995) Eco. Apps)

Solution 3- WDRVI

One solution for the NDVI saturation issue is called the Wide Dynamic Range Vegetation Index (WDRVI). It’s formulation is similar to NDVI, except for a weighting coefficient that can be used to reduce the disparity between the contribution of the near infrared and red reflectance.  

NDVI Sensor

In the WDRVI, a is multiplied by the near infrared reflectance to reduce its value and bring it closer to the red reflectance value. In doing so, it balances out the red and the near infrared contribution to the vegetation index.

NDVI Sensor

Figure 8: (Gitelson (2004) J. Plant Phys)

a can range anywhere from 0 to 1. Figure 8 shows that as we use a smaller value of a, we get an increasing linear response of the wide dynamic vegetation index to LAI.

The only drawback of the WDRVI is that the selection of a is subjective. It’s something that you experiment on your own until you find a value for a that is optimal for your solution.  People tend to err on the side of a very low value simply because they’ll get closer and closer to a linear response to LAI as a decreases.

Solution 4 – Enhanced Vegetation Index

The enhanced vegetation index (EVI) was designed to enhance sensitivity in high biomass ecosystems, but it also attempts to reduce atmospheric influences.  This was a vegetation index created for the purposes of a satellite based platform. There’s a lot of atmosphere to look through from a satellite to the ground, and sometimes the aerosols in the atmosphere affect the reflectances in the red and the near infrared regions causing spurious observations.  The EVI also tries to reduce sensitivity of the index to soil. Thus the EVI is a kind of solution to both extremes.

NDVI Sensor

In the EVI equation, the two major inputs are near infrared and red reflectances.  C1 , C2, and L are all parameters that can be estimated, but the blue band is something that has to be measured. Most NDVI sensors are two band sensors, so you don’t have that information in the blue.  Plus, with satellites, the blue band is relatively noisy and doesn’t always have the best quality data, thus EVI has limited value.

Solution 6: EVI2 (Enhanced Vegetation Index 2)

Those problems led a scientist named Jiang to come up with a solution.  Jiang observed quite a bit of autocorrelation between the red band and the blue band, so he decided to try and formulate EVI without the blue band in what he called the EVI2 (Enhanced Vegetation Index 2).  if you’re interested in the mathematics, we encourage you to go read his paper, but here we give you the equation in case you’re interested in using it.

NDVI Sensor

Figure 9

When Jiang calculated his EVI2 and compared it to the traditional EVI (Figure 9), it was nearly a one to one relationship. For all intents and purposes EVI2 was equivalent to EVI.  Since this avoids blue band, it offers some exciting possibilities as it reduces to just using the two inputs of NIR and red bands to calculate NDVI.

NDVI Sensor Summary

NDVI measurements have considerable value, and though there are extremes where NDVI performs poorly, even in these cases there are several solutions.  These solutions all use the near infrared and the red bands, so you can take an NDVI sensor, obtain the raw values of NIR and red reflectances and reformulate them in one of these indices (there are several other indices available that we haven’t covered). So if you’re in a system with extremely high or low LAI, try to determine how near infrared and red bands can be used in some type of vegetation index to allow you to research your specific application.

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Get More From Your NDVI Sensor (Part 2)

Last week we discussed Normalized Difference Vegetation Index (NDVI) sampling across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves (see part 1).  This week, learn about NDVI applications, limitations, and how to correct for those limitations.

NDVI Sensor

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

NDVI Sensor

NDVI Applications

People use NDVI to infer things like leaf area index (LAI) or fractional light interception (FPAR) of a canopy.  Some scientists also associate NDVI with biomass or yield of a crop. People also use NDVI to get a sense of phenology (general temporal patterns of greenness), as well as where vegetation occurs or how much vegetation is in a particular location.

In Figure 4, you can see how the reflectance spectrum at a given canopy LAI changes with leaf area index, decreasing in the visible range while increasing in the near infrared.

NDVI Sensor

Figure 4

At very low LAI’s, the reflectance spectrum is relatively undifferentiated between red and NIR (black line), but when LAI is high, there’s a strong absorption of red light by chlorophyll with a strong reflectance in the NIR. If fact, as LAI increases, there’s an ever-increasing reflectance in the near infrared around 800 nm.

NDVI Limitations

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum. Any time there’s very low vegetation cover (majority of the scene is soil), NDVI will be sensitive to that soil. This can confound measurements.  On the other extreme, where there’s a large amount of vegetation, NDVI tends to saturate. Notice the negligible difference between spectra at an leaf area index (LAI) of 3 (purple) versus 6 (green). Indeed, in a tropical forest, NDVI will not be sensitive to small changes in the LAI because LAI is already very high.  However, several solutions exist.

Solution 1-Soil Adjusted Vegetation Index

Figure 5 shows the results of a study taking spectral measurements of different vegetation indices across a transect of bare soil.  Moving from dry clay loam to wet clay loam, we see a very strong response of NDVI due to the wetness of the soil; undesirable if we’re measuring vegetation.  We’re not interested in an index that’s sensitive to changes in soil or soil moisture. However, there are a few other indices plotted in figure 5 with much lower sensitivities to variations in the soil across the transect.

NDVI Sensor

Figure 5: Qi et al. (1994) Rem. Sens. Env.

The first one of those indices is the Soil Adjusted Vegetation Index (SAVI). The equation for SAVI is similar to NDVI. It incorporates the same two bands as the NDVI–the near infrared and the red.

NDVI Senso

Soil Adjusted Vegetation Index (Huete (1988) Rem. Sens. Env.)

The only thing that’s different, is the L parameter.  L is a soil adjustment factor with values that range anywhere from 0 to 1.  When vegetation cover is 100%, L is 0 because there’s no need for a soil background adjustment. However, when vegetation cover is very low, that L parameter will approach one. Because it is difficult to measure exactly how much vegetation cover you have without using NDVI, we can modify the NDVI so it’s not sensitive to soil by guessing beforehand what L should be. It’s common practice to set L to an intermediate value of 0.5.   You can see in Figure 5 the Soil Adjusted Vegetation Index or SAVI has a much lower sensitivity to the soil background.

Solution 2- Modified SAVI

The next vegetation index is the modified SAVI (MSAVI). The SAVI equation contains an L parameter that we have to estimate–not an accurate way of handling things.  So a scientist named Key developed a universal optimum for L. We won’t get into the math, but he was able to simplify the SAVI equation to where there’s no longer a need for the L parameter, and the only inputs required are the reflectances in the near infrared and the red.  

NDVI Sensor

Modified SAVI (Qi et al. (1994) Rem. Sens. Env.)

This was a pretty significant advance as it circumvented the need to estimate or independently measure L. When Key compared SAVI to MSAVI, there was virtually no difference between the two indices in terms of their sensitivity to the amount of vegetation and their response to the soil background.

NDVI Sensor

MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)

Next week:  Learn about solutions for high LAI.

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Get More From Your NDVI Sensor

Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves.

NDVI

Figure 1: NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Global

The broadest way to think of NDVI is data obtained from an earth orbiting satellite. In the figure above, you can see highly vegetated areas that have high NDVI values represented by dark green colors across the globe.  Conversely, areas of low vegetation have low NDVI values, which look brown.  NDVI is sensitive to the amount of vegetation cover that is present across the earth’s surface.

NDVI – Local

How might NDVI be useful at the plot level? Figure 2 below shows a successional gradient where time zero is a bare patch of soil, or a few forbs or annual grasses. If we leave that patch of ground for enough time, the vegetation will change: shrubs may take over from grasses and eventually we might see a forest. Across a large area, we may also move from grasslands to forest. In an agricultural system, there is yearly turnover of vegetation–from bare field to plant emergence, maturity, and senescence. This cycle repeats itself every year.  Within these growth cycles NDVI helps to quantify the the canopy growth that occurs over time as well as the spatial dynamics that occur across landscapes.

NDVI

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.

NDVI

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?

NDVI

The Normalized Difference Vegetation Index takes into account the amount of near infrared (NIR) reflected by plants. It is calculated by dividing the difference between the reflectances (Rho) in the near infrared and red by the sum of the two.  NDVI values typically range between negative one (surface water) and one (full, vibrant canopy). Low values (0.1 – 0.4) indicate sparse canopies, while higher values (0.7 – 0.9) suggest full, active canopies.  

The way we calculate the percent reflectance is to quantify both the upwelling radiation (the radiation that’s striking the canopy and then reflected back toward our sensor) as well as the total amount of radiation that’s downwelling (from the sky) on a canopy.  The ratio of those two give us percent reflectance in each of the bands.

Next Week: Learn about NDVI applications, limitations, and how to correct for those limitations.

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Piñon Pine: Studying the Effects of Climate Change on Drought Tolerance (part 2)

Henry Adams, a PhD student at the University of Arizona, is studying the effect of climate change and drought on Piñon Pines in the university’s Biosphere 2 lab (see part 1).  This week, find out how the researchers made comparisons at leaf level, transplanted the trees, and future implications for the Piñon Pine.

drought

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

Sensitivity to Dry Conditions

Another part of the drought study involved a hydrologist who was interested in using weighing lysimeter data to parameterize some models used by hydrologists to model water loss during drought. “The lysimeters are a pain to run, but they’re pretty sensitive,” says Adams. “They can measure with a 0.1 kg precision, so that sounds like a good way to quantify water loss. It turns out that stomatal conductance from the porometer actually appears more sensitive than the weighing lysimeter data. Water loss from the scale hits zero pretty quickly, and we can’t measure any loss after a couple of weeks, but we can still see water loss with our porometer data from the morning and the evening.”

drought

The Piñon Pine’s root system makes the it remarkably drought tolerant, but an extended drought in combination with a bark beetle outbreak killed 12,000 hectares of the trees in 2003. (Image: naturesongs.com)

Expanding the Experiment

At the peak of the experiment, Adams had undergraduates and lab techs running up to three porometers at a time all day long, and although he’s still buried in data from the first experiment, he’s looking forward to accumulating even more data. “One limitation of our study is that the trees had pretty small root balls when they arrived. We’ve transplanted some trees [at different elevations at a site] in northern Arizona using a full-sized tree mover to get as big a root to shoot ratio as possible in the transplant. We’ll be using the porometers to try to understand the physiology of how these trees die and to predict their temperature sensitivity in the light of global climate change, using elevation change as a surrogate for temperature. We also have trees at the site that are not transplanted to serve as a control for the transplants.”

drought

Some ranchers are happy to see the pines go (Image: travelforumboard.com)

Implications for the Future

Adams acknowledges that not everyone in the Southwest is worried about the Piñon Pine. “We work in a system that doesn’t have a lot of economic value. A lot of the ranchers are happy to see the pines go. They just think there will be a lot more grass for the cattle, and firewood cutters are out there cutting up the dead trees and selling them.” But if temperature alone makes trees more susceptible to drought, the implications go far beyond economics. Adams puts it succinctly, if somewhat mildly: “It’s kind of scary.”

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