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It shouldn’t surprise anyone that scientists and engineers are constantly pushing the envelope to improve remote-sensing data and their utility, says Matt Hansen, a Landsat Science Team (LST) member and professor in the Department of Geographical Sciences at the University of Maryland in College Park.

Color photo of Landsat Science Team member Matt Hansen
2018-2023 Landsat Science Team member Matt Hansen, professor in the Department of Geographical Sciences at the University of Maryland in College Park.

Looking for ways to get a finer spatial scale, to increase revisit rates, to push for hyperspectral looks at the planet—those conversations, Hansen says, “will never end, really.”

Yet here’s where a bit of frustration enters the conversation for Hansen, a specialist in large area land cover and land use change mapping whose work has influenced the management of natural resources across the globe in everything from deforestation to biodiversity monitoring.

Technology and the benefits of it, he says, fall short if they aren’t used to inform local, regional, even global policies. For example, Brazil slowed its deforestation rates from almost 30,000 square kilometers annually to below 5,000 square kilometers, Hansen said. When stakeholders from private industry, civil society and the Brazilian government got behind a policy to balance development with protection of ecosystem services, Landsat 5 was the lone piece of remote-sensing technology they used to quantify success.

He wishes other countries got onboard and used remote sensing more to regularly guide and support their policy decisions. Even Brazil has been backsliding since its early success, he said.

“So, when you ask me what I have got going on now, my whole focus really is on, how relevant can we be?” Hansen said. “We can be a closed shop and pat each other on the back at the really cool things we are doing. But it is more about, ‘What is the material impact on society, the societal benefits side?’ That is really, I think ... and not just for me, but for our whole community ... what is interesting and what we should be focused on moving into the future.”

Hansen recently sat down for a conversation with the USGS’ “Eyes on Earth” podcast. Here are a few other outtakes from that discussion.

Eyes on Earth Episode 33 - Global Land Change

In your investigations of global land change, are there any surprises that have changed the way you think about the state of the Earth?

“It seems like our appropriation of land, either to convert it from a natural land cover to a land use, or to intensify existing land uses, appears inexorable. We are really aggressive at appropriating land and efficient at squeezing economic productivity out of the land. I guess that is sort of surprising, maybe not, but it’s remarkable. One of the big challenges is, ‘How much of that can the planet sustain without feedbacks like climate change from the emissions of tropical forests burning, or water quality degradation due to intensification of agricultural landscapes?’ We are an impressive species, and I think that’s the biggest surprise, is how industrious we are, I guess, in terms of taking economic advantage of land.”

Take out your crystal ball and share some of your ideas on future trends in global land remote sensing.

“Global land remote sensing, if I were in charge, it would really be at the top of a lot of important themes. I would try to do a more top-down approach. I alluded to this before. I think land change is the big one. And, understanding the impacts of land change on systems, environmental systems and human systems, is the biggest deal. We can do a lot of what’s needed right now with Landsat. The degree to which we incorporate other datasets to reduce our uncertainties is great, and we will. The future trends for remote sensing are inevitably going to be better data.”

What will better data get us?

“One of the interesting things for the future is, ‘To what degree can we put ourselves out of business?’ If you are in research, you want to do research leading to operations. Operations means it just runs and we are supporting decisions. That means we have solved some problems methodologically. So, I think a really important trend that I would like to see is that we solve some problems, and we move out of research and development for some basic monitoring tasks, whether it’s agriculture, forests, or other dynamics.”

Do you see other important potential trends out there?

“The other future trend, which I think is up in the air right now, is the idea of whether or not we need people with geographic backgrounds, or we just need computer programmers. This is a point of contention with me, where a machine learning computer programmer can outsource calibration of a model and just run mapmaking processes without really being geographically knowledgeable and understanding whether or not the map that is expressed in front of him or her makes sense.”

So, you lean toward geographers?

“I think geographers are still needed. There is an iterative active learning process to mapping that I think is still required. You have to have geographers to troubleshoot the data inputs, both on the training data and the feature space. And also to critique the outputs and iterate, and that’s the active learning side of it. In the future, I am very curious which path it goes down. I feel like domain experts in geography, knowing environments and knowing drivers and putting them into landscapes and understanding those dynamics ... is critical to making maps. But, conversely, maybe there are algorithms smart enough that with training that is outsourced, you can do the same thing at the same level of accuracy.”

You are a member of the USGS NASA Landsat Science Team. What do you consider to be the priorities for improving the value of the nearly 50-year-old Landsat archive?

“One of the tricks with Landsat that we face when we are doing our long-time series studies is the tension between continuity and improving the observations themselves in terms of quality. I think a challenge for Landsat is, you know you go from MSS (Multispectral Scanner System) to TM (Thematic Mapper) to ETM-plus (Enhanced Thematic Mapper-Plus) to OLI (Operational Land Imager). As we get to Landsat 10, what happens to continuity? The Landsat instruments themselves are slightly different. The strength of Landsat is the time series. It is our baseline. This is what we will use as a reference to compare what’s going to happen now and into the future. I am strongly an advocate for continuity ... that the observations don’t get so far off of a standard that you can’t compare them.”

But don’t improvements in technology improve the overall products?

“It’s hard to resist improving your technology, right? So, the pushbroom of Landsat 8 is such a great improvement over the whiskbroom that, yeah, sure, it’s a different instrument. It’s a better instrument. I think this is the challenge. What do we do to maintain the ability to integrate the entire time series so that we can track the dynamics? For me, that’s a key management point.”

Describe the Landsat program of the future from your perspective.

“For me, Landsat is a piece of global input infrastructure. You can talk about GPS and a few other systems, like our Navy, which keeps the ocean trading lanes free and open. There are examples of global public goods that individual countries pay for and everybody benefits. Landsat is one of those. I really think that is such a great gift, and we need to maintain that. I’m a little concerned about going backwards in terms of private versus public data provisioning. So, I think that’s a big thing we have to hold the line on, that the Landsat program represents this information source for which everyone can avail themselves and work toward solving problems.”

Why is it important to make it available to everyone?

“By having many, many eyeballs on the same data, we can work towards a kind of convergence of evidence. It is great to have a bunch of people looking at the same data and working on the same problems at different scales, different time periods, maybe different themes. But this is how we come to consensus, and we solve problems. And I think if we don’t maintain that ... if you go to more private models, there’s going to be fewer people looking at each problem. There are going to be fewer ideas thrown at the data, and it will be an underused resource. So, I think for me, the future has got to be keeping this free-and-open perspective and, as a global good, it will just never die because it will have this built-in value and built-in audience and user base that will just rely on it.”

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