Eyes on Earth Episode 16 – Predictive Modeling
Land cover and land use across the United States are the culmination of a complex web of interwoven factors: Climate, landscape types, and economic factors among them. Remotely-sensed data from satellites like Landsat and a variety of other sources are useful for documenting and monitoring land cover and land use. When used alongside other data sources, however, remote sensing data can offer a glimpse at future land use under a range of scenarios. In this episode of Eyes on Earth, we learn how EROS scientists project future landscape change.
Hello everyone. Welcome to this episode of Eyes on Earth. Our podcast that focuses on our ever-changing planet, and on the people here at EROS and across the world who use remote sensing to monitor and study the health of earth. I’m your host, Steve Young. Today’s guest is Terry Sohl. Terry is a Research Physical Scientist at EROS who works in the fascinating area of projecting how land cover across the United States may change in the future. Here is what that means. In a lot of places around the world, today’s forest could be a soybean field tomorrow. It could be a suburb. A wheat field this season could turn into a sea of switchgrass next year if a farmer decides there is more money to be made in biofuel. When aquifers decline, maybe livestock herds begin to graze where corn and soybeans once grew. The economics of land use change is part of this. There can be environmental impacts as well as landscapes change, and habitat does with it. So, Terry is here to talk about all of that. Welcome, Terry.
Terry Sohl: I’m glad to be here.
So, you’ve helped to develop a model called Forecasting Scenarios of Land Use Change, or FORE-SCE. What exactly is that and what does it do?
This is a work that started about 12 years ago. The idea was that EROS has this wonderful archive or remote sensing data that can look at the current and the past land cover. What we wanted to know is, “Is there a way to take these data and project what may happen in the future or even go back in time before the days when we had remote sensing data?” So the FORE-SCE model was developed specifically to take advantage of USGS land cover data, use that to parameterize the model and then kind of stretch out these land cover databases that we produce for the current and the past out into the future and towards the historical period.
Steve: How important are remote sensing systems like Landsat in all of this and why are they important?
Terry: They are vitally important. One of the things that makes our model unique is our heavy reliance on remote sensing data. And that’s why it’s a perfect location here at USGS EROS to do this work. The model is so heavily reliant on looking at Landsat imagery and other satellite imagery that we get in the building to look at spatial patterns for example. So, historical spatial patterns of change, we can look at from a historical perspective and use that to parameterize our model for how things will look in the future.
You may be able to tell through remote sensing and the reflectivity off the Earth that the change is there, but how do you get to the reasons why it changes? Is there a way to gauge that or measure that?
The driving forces of change are really what are the most important factor with land cover modeling. What we can do is we can take two dates in the past and extrapolate the past out into the future and call that a projected land use model. But those really aren’t what are valuable. What's really valuable is understanding the economics, understanding the demographics. You know, how are people moving on the landscape? Or, how is migration changing populations in a given area? Or climate, you know how is climate impacting the landscape? And so, those driving forces of change and understanding them are really a key to the model. Just as much as the historical remote sensing data is.
You could perhaps explain the migration of corn crops and all that based on things like temperature, rainfall and all of that over time?
That’s actually why the historical period is so valuable for the modeling. We can look at a thing like corn, and we know from a soils perspective and from a climate perspective and from a topography perspective the types of areas corn likes. Well we can also look at projected climate change in the future and we can look at how precipitation patterns have changed, we can look at how irrigation water availability may change, if an aquifer is declining for example, and we can input those into the model. And so that historical information helps us understand the factors that drive landscape change. As we project those factors out into the future the model responds. So that we can actually look at corn and how that distribution might change in the future as climate changes.
I imagine to project how land cover and land use may change in the future you have to understand how it has changed in the past. How do you do that?
What we try to do is to compare what we have modeled with a past period. So, if we model out into the future, what we do is we typically start the model about 15 or 20 years in the past. The reason that we do that is we can look at that 15- or 20- year period, compare the model performance to actual performance of land use on the ground as measured from remote sensing data. And so, we kind of calibrate our model that way. We look at how the model performed over a historical period and compare back to the historical land cover databases that are produced at EROS. So we have these national scale projects such as the National Land Cover Database that measures land cover change over time. When we model back in time, we can really go back as far as we want. It is just the uncertainty increases because the data quality gets less as you go back in time. But we can use things like agricultural census data from the USDA. We can look at census data from the past. We can look at old satellite imagery or old aerial photography, even going all the way back to the 1930s to help us parameterize the model.
Landsat takes this big moderate resolution look at regional scales and larger scales. How detailed can you get with this model? Can you see change from region to region? State to state? Field to field? How detailed can you get?
We're looking at change all the way down to the parcel level. And so we have ownership boundaries for the United States. Things like house lots, things like farm fields. And we try to model each one of those individually but do it collectively across a really broad area. And just for example the area that I am working on right now, is one region that covers a couple of states in the eastern U.S. That one region has about 10 million of these individual polygons that we are modeling individually. So it really is a challenge to try to model at a fine spatial resolution but do it across a broad scale. That’s something that the FORE-SCE model is actually pretty good at.
Steve: How might government policy change land use, land cover?
Terry: I’ll give an example of right around here in South Dakota. You know, most people think of an area like South Dakota where it's mostly agricultural land as being rather immune from these policy shifts. When people think of land use change, they tend to think of things like urbanization and cities growing. Well, back in 2007, just for one example, there was a government policy that passed: the Energy Independence and Security Act. This was a law that mandated that we will produce 32 billion gallons of biofuel by 2022. Well, because of that policy, you had an uptick in demand for corn for ethanol, an uptick in soy for biodiesel, and because of that you’ve seen a tremendous expansion of agricultural land, grassland being converted to cropland, in eastern South Dakota in the last 10 years. Part of that is due to government policy. And then there are many examples of that going further back in time you have things like the Conservation Reserve Program that really had a large impact, not only on the great plains, but across the U.S.
Steve: Do economics change land use and land cover?
Terry: Very much so. And just for an example, if you look at what’s happened with agricultural prices and tariffs and demand for things like soybeans, from South Dakota again, there’s been a tremendous impact on policy, there’s been a tremendous impact on economics, and it’s not just what happens locally. It’s what happens nationally and even internationally. All the burning in the Amazon right now is really directly related to economic conditions and the demand for commodities such as soybeans. So, you have things that are happening at a global level that can impact land cover at even a local scale right here in South Dakota.
Steve: Talk a little bit about how a change in the use of land can alter the environment. Things like habitat, biodiversity. Are those changes sometimes long term?
Terry: Very much so. When people think of things that impact animals for example, they tend to think of climate change, but land use really has more of an impact including for the longer term. We had a partnership with Audubon where we are modeling land use change for the past, present and future. The Audubon Society is taking that information and using it to model how bird species may change in the future. They just came out with a report that shows how bird distributions are likely to respond to both climate change and land use change in the future. And it’s not just things like birds and biodiversity, it’s hydrology, water quality and water quantity and flow, flooding frequency, climate and local weather variability, carbon and greenhouse gases that impact climate. All of those are very heavily affected by land use, and that is one of the reasons why projecting what happens with land use out in the future is so important.
Steve: Who uses this information? How might they use it?
Terry: We have partners from across the federal agencies such as EPA has used our work. We have groups like Audubon, from NGO’s that use our work. We have quite a few partners in academia right now. I’ll give an example of one partnership. A project that was funded by the National Science Foundation with the University of South Dakota, University of Wyoming, Montana State, and it’s looking at biofuels in the northern great plains, how biofuels is tied into economics and how these scenarios of biofuels going out into the future could impact things like bird populations, climate, weather and hydrology.
Steve: When we talk about who uses it, Main Street would be interested in all of this wouldn’t it?
Terry: Yes, that is one of the challenges in trying to relate the value of this work is that most of our partners frankly are in academia in other federal agencies, and so how do you translate what we do here to the layperson in terms of how it impacts them. There is a whole field of work called ecosystems services that is looking at the value that the landscape provides, from not only an economic perspective, from an agricultural perspective, from a standpoint of forests and the products that are provided. Things like recreation. How the landscape impacts pheasant populations in South Dakota and hunting. Or how it impacts hiking and things like that. These are all things that are impacted by land use. So, what we try to do is not only explain the relevance of what we do towards these ecological scientific processes, but also try to relate it to the economics of how that’s going to impact people on the ground and also things like recreation.
Steve: You’ve been at FORE-SCI for over a decade now. How are your refining that model to make it better and more accurate?
Terry: One of the things about modeling is you always start with a concept and it’s always one small core element that you begin with and it always grows. That’s the one true element of most models, is they tend to grow over time. And ours is no different. So, what we do is we try to keep adding elements that improve our representation of what happens on the landscape. Just for an example, one of the things we’re working on right now is something that’s been missing from the FORE-SCE model: a fire model. By this time next year, we’ll have an element included in the model that looks at not only how land use changes but how fire might impact the landscape.
Steve: You are projecting what may happen in the future, so people come to you and they’re interested in different scenarios, and for a single parcel or a single area, you may have to come up with 4,5,6 projections?
Terry: Yes, you know it’s a little bit different than being a weather forecaster. We’re not trying to predict the future. What we are trying to do is say, “There’s a lot of uncertainty.” Some of the things that we’ve talked about today, things like the economics or demographics or energy, that’s another one. Where is your energy going to come from in the future? Is it going to be renewables, is going to be fossil fuels? All of these things have a huge impact. They all interrelate, and they all make it very difficult to predict what’s going to happen in the future. And so, what we do, is we produce these scenarios. We’ll have multiple scenarios that try to capture that uncertainty. So that, if I am a farmer in eastern South Dakota, I can look at a climate change scenario for example, that’s quite severe, and try to figure out how I might adapt to that. Or, I might have a less severe climate change scenario. And so, having multiple scenarios helps people to adapt to potential change and hopefully to mitigate any negative consequences before they actually happen.
Steve: We’ve been talking to Terry Sohl, a research physical scientist at EROS who is in the business of projecting how the country’s landscapes may look different in the future, based on a whole of things, climate, economics, government policies and the like. Thanks for joining us, Terry.
Terry: You bet, thank you.
Steve: We hope you come back for the next episode of Eyes On Earth. Thanks for joining us.
This podcast is a product of the US Geological Survey, Department of the Interior.