Eyes on Earth Episode 128 – 2024 EROS Fall Poster Session

[00:04] Hello everyone and welcome to another episode of Eyes on Earth, a podcast produced at the USGS Eros Center. Our podcast focuses on our ever-changing planet and on the people here at Eros and across the globe who use remote sensing to monitor and study the [00:30] Fall Poster Session. [00:32] A poster session is essentially a way for scientists to share their work with their colleagues in a public forum. [00:38] About 30 posters were on display in the Eros Atrium from Eros staff and several students from South Dakota State University and the University of South Dakota. [00:49] The posters covered a wide range of topics. [00:52] Let's start with some of the students. Locally in South Dakota, invasive species is an area of concern. Two students are studying invasive carp in water bodies in the region. [01:05] And another student is measuring water loss and gain across the prairie pothole region. [01:12] Go ahead and introduce yourself. - My name's Natalie Liberati. I'm a graduate student at South Dakota State University in the Natural Resource Management Department. [01:22] I'm going to be looking at the Landsat, Dynamic Surface Water Extent data, to evaluate flooding and how that could facilitate carp movement into adjacent watersheds. [01:33] So currently the James River in South Dakota is invaded with carp from the Missouri River. We're looking at

[01:41] which connections that flooding is creating, which ones are most at risk, [01:47] for carp invasion. [01:48] So a couple of my images here. [01:51] I looked at the discharge data from the James River from a specific gauge [01:55] and we have data ranging from [01:59] no flooding at all to high flooding events. [02:03] And I have an image here. [02:05] showing how these specific wetlands that are adjacent to the river [02:10] in a non-flood year, they're not connected. [02:12] When it does flood, [02:14] The water from the river then spills over and connects these wetlands to the river that are usually isolated. [02:22] So they have not been subject [02:24] to carp invasion until now. They could potentially become invaded. [02:29] Can you explain a little bit more what [02:32] Dynamic surface water extent. [02:35] data is. [02:36] So the DSWE or dynamic surface water extent data is [02:40] satellite imagery of surface water. It's just showing me where water is present on the landscape. [02:47] And I can distinguish them from what is a permanent water body versus what could be flood water using different... [02:54] water body polygons as well from the USGS website and [03:00] the National Hydrography Data Set. [03:03] So, [03:03] It's showing you where surface water is present. [03:07] and then you can take that data and [03:10] manipulate it [03:11] to figure out what's flood water and what's

[03:13] not flooded. [03:15] So we're using the dynamic surface water extent data to do that in ArcGIS and we're looking at [03:21] multiple metrics of flood water connection, so the surface area of each connection, [03:26] how many habitats are being connected to the James River, [03:29] the type of habitat being connected, so whether it's a lake, a pond, a stream. [03:33] a wetland [03:35] and where these habitats specifically are [03:38] in the watershed. Well, that makes sense. Do you have any idea how someone could use this data? [03:44] Yeah, so we're hoping that with the results I come up with, this would give managers [03:50] the ability to look at these connections and see which ones are more important to block carp movement with [03:57] So if there's like a really prominent, permanent wetland that's becoming [04:01] connected to the river. [04:03] they might be able to use deterrents to block carp movement into that wetland that's usually isolated. [04:08] especially if [04:10] it's really good habitat for them. [04:12] Well, thank you. Thank you. [04:16] Go ahead and introduce yourself for us. I'm Brendan Elwer. I'm a master's student at South Dakota State University. Okay, sounds good. Can you summarize your study for us here? Yeah, absolutely. So I'm working with using habitat quality as a tool for informing invasive species management. So in North and South Dakota, two invasive species that are threatening lots of water bodies are the invasive silver and bighead carp. [04:36] They compete with native species like walleye, perch, [04:39] and potentially even up to species like waterfowl through food competition. My work is focusing on informing preventative actions.

[04:46] The tool we're using for that is Habitat Quality. [04:49] looking at the way that that might shift through space and time. [04:52] as most of these invasive individuals are going to spend [04:55] spend their time in the highest quality habitat. [04:57] So knowing where that quality habitat is at, [05:00] can make these preventative actions more efficient and more effective at detecting individuals as they become invaded into these new water bodies. [05:09] So to do that, we're going to be using an energetics-based model. So it takes food consumption, so energy the fish consume, [05:15] versus what they expend based on temperature, flow, and other environmental characteristics. [05:20] So I actually went and physically collected temperature... [05:23] Chlorophyll A, which is a surrogate for phytoplankton. [05:26] zooplankton, and those are two food resources for these silver and bighead carp, as well as velocity in the rivers I sampled. [05:33] And so using that, we can predict [05:35] how well officials survive, grow, reproduce using this energetics-based model. [05:40] However, I can't collect hundreds if not dozens of samples on some of these water bodies. [05:45] So using remote sensing data, we're going to try to make this on a very fine scale. So using Landsat, trying to get this on a [05:52] 30 by 30 meter scale. [05:53] So using temperature from Landsat, we can divide our lake or wetland or river into these 30 by 30 meter grids. [06:01] that we can assign a temperature to. [06:03] And then the last one is looking at that Chlorophyll A. [06:06] looking at reflectance values whether it be from both just the Landsat surface reflectance or [06:11] we're going to explore the aquatic reflectance product that's being developed or in the provisional stages

[06:16] to [06:17] try to predict chlorophyll A using regression based off satellite and what we [06:21] physically observed to assign those values. [06:24] We can run our energetics model for each individual cell. [06:27] that based on that output we can map out habitat quality to see how those [06:32] patches of quality habitat if there are patches. [06:35] shift through space and time to inform these preventative [06:39] actions for managing agencies. [06:41] Okay, that sounds really good. You can't use your ground studies [06:45] or satellite. [06:46] by themselves. What's the real value in using both combined? [06:49] So both combined there, it's helping us get that finer resolution where [06:55] taking the [06:56] five samples out of a 6,000 acre lake versus [06:59] upwards of 50,000 for however many it comes to you can just refine that spatial scale down and make it [07:05] add more detail to what we're trying to do. [07:08] Thank you for doing this. Okay, go ahead and introduce yourself. Hi, I'm Madison DeJarlis, and this is my preliminary time series analysis of surface water change in the prairie pothole region. Describe really quick what the prairie pothole region is. I mean, pothole sounds like a funny term. So what's going on here? Okay, so the prairie pothole region is [07:30] One giant, they do call it kind of a wetland complex. [07:33] but it is overall hydrology so there's a lot of [07:36] lakes, rivers, streams, all those things, and it's from glaciation. [07:40] So it is a direct byproduct of the glacial period that we have. And all of these wetlands, all these bodies of water are actually kind of connected and feed off of each other.

[07:48] in this huge area. [07:50] which crosses Manitoba, Saskatchewan, [07:52] Alberta, North Dakota, even into Montana and Minnesota, Iowa. [07:56] So this is the beginning steps of my actual thesis project. [08:00] in which I am analyzing the Prairie Pothole region to try and see what surface water change is doing overall using Landsat. [08:07] So I'm comparing in this specific instance [08:09] '84 to 2023 to try and get those bookend dates to see if there is a trend that's identifiable. [08:15] And there does appear to be one. However, it is important to note that it is the end date. [08:20] So there could be other things kind of [08:22] impacting this trend, whether it's the actual trend itself or not. [08:26] So far we're able to see that there is a loss happening on the [08:29] Manitoba end as well as the Iowa end of the Prairie Pothole Region. [08:33] And we're seeing gain within Manitoba and the Dakotas. [08:37] which this does actually track with a lot of climate data, which I have in my literature. [08:42] of where we're seeing snowfall increases and loss of precipitation otherwise. [08:46] And I'm using Google Earth Engine and the Modified Normalized Difference Water Index. [08:53] MNDWI and that just kind of seems to be the best fit for identifying water overall with the most consistency. Yeah, this is a pretty large area so it's a lot of Landsat data. How do you manage that? [09:03] Google Earth Engine is very helpful. It is very, very, very helpful. I've been able to create a [09:09] cloud free composite over the growing season, which then I apply that MNDWY. [09:14] And so I get my binary which can be exported as one kind of image.

[09:19] the image looks really neat you know we can't see that on audio here but and you've zoomed into some specific areas to show those [09:26] water gains, water losses, so it's really nice to see it at that level. [09:31] What are the next steps? So the next step for this thesis project is actually going to be looking at it on a pixel by pixel base. [09:37] So I have an R package that's able to track the trend [09:40] rather than looking at the bookend date. So from year to year we'll be able to see if it's consistent gain, consistent loss, [09:46] or if there is a trend to it where it's oscillating every five years or so on and so forth. [09:50] So that'll be the next step is looking from year to year. That's even more data to work with. This isn't just 1984 and 2023, everything in between now. Everything in between, yep. Going all the way through it year to year. [10:02] That's going to be great. [10:03] Thank you. [10:04] Thank you very much. [10:07] Another student has his eyes on a different part of the world. [10:10] A University of South Dakota student is mapping grassland over Mongolia using cloud computing, because he is using 34 years worth of Landsat data. His study can help keep local livelihoods sustainable. [10:25] Go ahead and introduce yourself for us. Hi, I'm Abhinav. I'm from University of South Dakota. I am working in sustainability program. [10:33] So my project here is on analyzing grasslands [10:37] So currently I'm focusing on Mongolian grasslands and trying to analyze [10:41] biomass and canopy cover [10:43] for our time series from 1990 till 2023. [10:46] So I'm currently using [10:48] some machine learning models.

[10:50] and a Google Earth Engine platform, which is cloud computing, [10:53] helping me to play around with [10:56] huge amount of Landsat data [10:59] So my whole area is getting covered by around 130 lancet tiles. [11:03] So I'm just using [11:05] Google Earth Engine to process all this data. [11:07] It's a lot of data that you have to work with. [11:09] Yeah, so like if I have to do it in a traditional way, so I have to download all this [11:14] land side tiles [11:16] and each style goes for like [11:17] 1 GB to like 2 GBs. [11:20] So getting that much amount of data for a single timestamp will be huge because I'm working for 34 years. [11:26] So that will be something humongous. [11:29] What makes Landsat ideal for this large of an area? [11:33] The main idea was because since last 10 years [11:36] There has been a lot of extreme weather events which are happening. [11:39] So due to that, in last year itself, 40-50% of the livestock in this area died. [11:45] and in grasslands we know like lot of [11:47] things are depending on grazing and people earn a lot of [11:51] from that only that is the only way [11:53] they have their bread and butter from. [11:55] So that's weird. [11:56] since 1990 gave me a [11:58] ideal way like up till 30 years how things have changed [12:01] and then what's the reason behind this last 10 years where we are experiencing a lot of [12:06] extreme events. Can you describe real quick what above ground biomass is exactly? [12:12] When we talk about green covers [12:13] It's either trees, grasses or croplands. [12:16] So crop lens. [12:17] from the sowing phase till the harvesting phase, the biomass keeps on increasing. So it's the amount of

[12:22] carbon the store in [12:23] and [12:25] that's like really feeds into the mass which they have. [12:28] and when we accumulate that whole number, it becomes an above-ground biomass. [12:32] Do you have a good idea of who will find this valuable? Who will use this? [12:37] So I think the government obviously first of all and some kind of NGOs are also working in this area. [12:43] who want to improve this [12:46] scenario like where grassland is being decreased over the years. [12:50] So they can find this useful to identify which areas they can put in their [12:54] efforts to plant more and like revive the green cover. [12:58] so that the livelihood of the people over there still [13:02] stays. [13:03] the way it was since the [13:05] traditionally how it was, they can restore all that stuff better. [13:09] to keep that sustainable. [13:10] Okay. [13:11] Thank you for your time. [13:13] Thank you. [13:14] Now we'll turn to Eros staff and see what they're working on. [13:18] The USGS Eros recently released a new generation of land cover mapping in the annual NLCD products. [13:27] NLCD is the National Land Cover Database. [13:30] A big part of that is making sure that land cover data is accurate. [13:36] Go ahead and introduce yourself. My name is Jo Horton. I'm a land remote sensing scientist [13:42] at the USGS Eros. [13:43] and I'm working on the annual NLCD project. [13:47] Go ahead and summarize what you have on your poster here. Oh, my poster is focusing on the part of the work that I do for annual NLCD, which is collecting the reference data set that will be used to validate the land cover and land cover change products.

[13:59] So basically it covers [14:01] the collection of data, we're collecting up to 10,000 points across the entirety of CONUS, which is the lower 48. [14:10] and we're collecting annual [14:12] data for all the land cover, land use, and land change processes from 1984 through 2023. [14:18] and then that [14:19] data that we collect, which is considered ground truth, [14:23] will be compared to the annual NLCD [14:26] products at those same points [14:28] same locations and the same time. [14:30] to see how they compare and see how accurate the annual NLCD Land Cover products are. [14:35] It's really important that NLCD be accurate. So this is a big deal, right? This is a very big deal. NLCD has been the [14:42] standard. [14:43] for land cover mapping in the United States for [14:46] decades at this point. [14:48] and this new product [14:49] with the annual [14:51] time series. We really want to make sure the people understand how accurate it is, how consistent it is, [14:56] and have the confidence they've always had in NLCD continue. The thing that I really like is [15:02] I mentioned we're collecting those 10,000 points. [15:04] Yeah. [15:05] Every point has at least one interpreter that looks at it and collects that data from 1984 to 2023. [15:11] But what we're also doing is taking 50% of those points and assigning them to a second person. [15:16] who does an independent [15:18] duplicate interpretation. [15:20] And then we compare those labels. [15:22] and we can get information not only on interpreter consistency and interpreter agreement, [15:27] for the collection of the reference data, but we can also use those metrics

[15:31] for quality, [15:32] Assurance. [15:34] identifying edits or corrections in the final data set and it can be used for all sorts of other research related to reference data collection consistency, which is an interesting area of research. Okay, that sounds great. Thank you, Joe. [15:51] Research scientist Hua Xi had an interesting poster about how the urban heat island effect, [15:57] warmer temperatures in cities, affects vulnerable populations the most, [16:02] and how his study can help inform city planners on decision making. Okay, go ahead and introduce yourself. My name is Hua Shi and [16:12] I'm the research scientist at FDS. [16:16] contract to USGS Aeros [16:19] We used to [16:20] A lens set. [16:21] to the derived [16:23] land surface temperature. [16:25] and because [16:26] urbanization in urban areas have higher [16:30] temperature in the non-urban area, [16:33] temperature. [16:34] So when we calculate the intensity that defined by [16:38] Urban temperature. [16:40] but [16:41] non-urban. [16:42] you can show the [16:43] the intensity of Haight Island. [16:45] We know organization [16:47] coupled with the climate change [16:49] drive the land surface temperature, [16:52] LST higher by intensifying [16:55] the heat waves. [16:57] resulting in increased heat [16:59] related [17:01] energy costs

[17:02] illness and mortality. This study [17:05] investigate whether and how [17:08] urban induced warming varied across seven [17:12] national climate assessment regions [17:15] So we select the eight [17:17] urban [17:17] center to represent [17:19] this NCA region. [17:21] The figure one, you can see here. - Several different cities that you focused on to kind of analyze this. [17:27] urban heat data that you have, right? Yeah. And what's the most interesting thing that you found out in this? [17:34] The most interesting part of this study is how it shows the complex relationship between [17:40] urbanization. [17:42] climate change and eco [17:44] social economic difference. [17:46] It is revealed that urban induced worm [17:51] varies. [17:52] significantly depend on the [17:54] regional climate and neighbourhood [17:57] the study focuses on the different [17:59] social economic factors [18:01] like income. [18:02] diversity and their correlation. [18:05] We're still here. [18:06] The Weaves. [18:07] This understanding can help inform urban planning to reduce the heat [18:13] Waves. [18:14] especially in vulnerable communities. [18:17] leading to more equitable [18:19] strategy to address the challenge of urbanization and climate changes. [18:25] Yeah, these vulnerable populations in cities [18:28] mostly get these [18:29] heat intensity areas. [18:31] What can we do to help alleviate that?

[18:34] decision maker or [18:36] city planning. [18:37] for future, they need to consider this. [18:40] like a more like an increased [18:42] Green [18:43] spaces and water body make like artificial lakes or ponds. [18:48] you know, can cool down this kind of thing. That sounds really good. This can help inform those decisions. [18:54] Okay. [18:54] Thank you, Hua. [18:57] Senior scientist Lei Ji mapped historical agricultural water use in the U.S. in irrigated cropland. [19:06] Okay, well, I'll let you introduce yourself. My name is Lechi. [19:09] I'm a senior scientist, I work with the LSRC, contract to [19:14] USGS. [19:15] error center. [19:16] Go ahead and summarize your poster for us. OK. [19:20] host here, which is a [19:21] pronounce the study. [19:24] about how to map [19:26] historical agricultural [19:28] water use [19:29] across continental United States. [19:32] using remote sensing method. [19:34] Okay, this is across the entire conterminous U.S., the 48 states. [19:39] Yeah. [19:40] In the United States, 15% of crop lands are irrigated land. [19:46] So traditionally, [19:48] The estimation of groundwater is based on [19:51] report. [19:52] surveyed. [19:53] received from farmers. [19:55] data is [19:56] collected and compiled by [19:59] government agency. [20:00] So this work is kind of very time and cost consuming.

[20:06] So, [20:07] Now we are working on the [20:09] using the remote sensing method. [20:12] Creator. [20:13] a data set [20:14] that is called blue water evapotranspiration, in short it's blue water ET product. [20:21] to map water consumption. You called it blue water [20:25] evapotranspiration. Evapotranspiration just refers to water that's evaporating [20:31] and transpiring off of the land. [20:33] So then blue water evapotranspiration, [20:36] That is [20:37] water use [20:38] for irrigated cropland, is that correct? [20:40] That's correct, yeah. [20:42] I like how you have this [20:43] figure here that shows a few examples [20:46] in different parts of the country. [20:48] I see darker green and lighter green. What do the different shades of green mean in the imagery here? [20:54] The darker green means higher. [20:57] water use the [20:58] from irrigation water. [20:59] by the cross. [21:01] So lighter means less water used by crops. [21:04] only for irrigation water. [21:06] Okay, so we can visualize that here in the imagery. What other data was this using? We have a surface temperature, [21:12] Uh, [21:13] Vegetation indexes. [21:15] and the phenology. [21:16] and the climate data also. [21:18] soil data, so all the data input, [21:21] And I... [21:22] calculate the final product is blue water. [21:27] evapotranspiration. [21:29] That sounds good, Leigh. Thank you. [21:31] Oh, thank you. [21:31] - No matter what. [21:33] Besides the more than 52-year archive of Landsat data,

[21:37] Eros also has vast holdings of historical aerial images and declassified satellite imagery. [21:44] And we keep adding more to the collection. Drone images? Yep, we've got that too. [21:51] Thank you. [21:51] Okay, go ahead and introduce yourself. [21:54] My name is Brent Nelson. I'm the work manager for the DMID operations group. [21:59] We were responsible nine years ago for the scanning of the Oregon [22:04] Aerial film is where our project started. [22:07] We bid it out nine years ago and got that for about 232,000 frames. [22:12] And through time now, we have now scanned all of the Bureau of Land Management film, [22:18] on our scanners in the digital lab. [22:21] which totaled just under 970,000 frames of film that was scanned. [22:26] And so we also took some of the imagery that they had scanned by other outside vendors [22:31] and integrated that into our database, had to change the resolution on it [22:35] So we now have just a little over a million frames [22:39] of BLM film that is available to the World to Earth Explorer. [22:43] What are the dates of these images then? How far back does it go? This film goes back to the 1949 era. [22:51] So it's a lot of historical data. [22:53] This is useful for all kinds of land change studies. [22:57] There's already been quite a bit of demand for it. [22:59] and we have all that film now boxed up. [23:02] Ready to ship 38 pallets of film to ship to Nara [23:06] for long-term storage.

[23:08] They have went through all their offices, their places, and they said we all have all of their film and it's all scanned. [23:14] and for nine years this was a [23:17] virtually a six day, 24 hour operation to get this taken care of. So this was [23:23] for the digital lab for DMID and the center, a major undertaking. [23:26] Yeah. [23:27] Yeah, we've got pictures of the multitudes of rolls of film [23:31] And pallets, which are, well now that they're scanned, they can go back to long-term storage, right? Correct. They'll go to Kansas. [23:39] in the Nara salt mines. [23:40] and be stored there because most of this is color. [23:44] so it'll be frozen for long-term storage and [23:47] because it's all digitized. [23:50] BLM now has access to all of it, whether it be for research or legal issues, it's all there. [23:55] None of it was [23:57] modified in any which way. I mean, it was all scanned to specific requirements. [24:02] as it is. [24:03] is all of the high res [24:05] to download from Eros right now? [24:07] Yes. [24:08] All of this is high risk. [24:10] scanned at 14 microns. [24:12] and it is all available free of charge through Earth Explorer. Very nice. [24:17] Hey thank you Brent. [24:18] No problem. [24:22] Okay, go ahead and introduce yourself for us. Hi, I'm Tim Smith. I've been at Aero since 1979. I'm the archive task lead for [24:30] Data Management Information Division. Sounds good. What do we have on your poster right here? [24:35] This is highlighting the declassified satellite program

[24:38] from 1960 to 1984. [24:40] This imagery was all the spy world that [24:43] we were used to worry about and you had to have a classification to even be able to use this imagery. [24:48] And then in about 19... [24:50] 92 timeframe. [24:52] Release. [24:53] And then we've had subsequent declassifications over the years [24:56] This being the highest, most [24:57] worldwide coverage of the D-Class Hexagon program. [25:01] Okay, so the latest release is from Hexagon. [25:04] images came in that release. [25:06] - It's right around 600,000 images that were sitting on, right around 14,000 reels of film that came in. This is second generation imagery, [25:15] All the original film is at the National Archives in College Park. [25:18] One of the more interesting [25:20] photos that you have on display here, of course we have things like [25:24] pressure ridges in Antarctica. Very interesting. There's a lake in South Dakota. I also see one that says, [25:31] Cuba Missile Site. So that was very interesting in the 60s. This is declassified now. It is just [25:38] seen as more valuable to the public for research. That's correct. [25:42] What do we do with it here? From here we're making an available ranking, True Weekend, [25:47] have this imagery [25:48] digitized to create the browse resolution images that are posted on Earth Explorer [25:53] and then from there people are using that imagery to select what [25:56] Images need to be scanned at a higher resolution, and once that's scanned, we keep that image forever. Okay, and then it's out there, high resolution, anybody can download it. This has become public domain information, right? Yes, it has, yes. It works out very well that way, and again...

[26:10] marries up nicely with all the other National Land Remote Sensing Data Archive imagery, of which Landsat's a part of that too. [26:16] We have that congressional mandate. [26:18] to keep that imagery forever and make sure the public can get access to it. [26:21] Thanks, Tim. [26:25] Go ahead and introduce yourself for us, please. My name is Brent Johnson. I work at AeroStata Center here. Summarize for us real quick what you have on the poster here. Okay, the poster is quantifying and telling us [26:37] what we have archived for [26:39] unfavorable. [26:40] Crude. [26:42] aerial systems okay that's you US you a asses how we have it on here yep you a s [26:48] We started off collecting, gathering data, UAS data for different government agencies that were using drones, that's what it is. [26:58] And now we've [26:59] combined, you know, concatenated that now. It's just USGS we're gathering stuff for. Okay. [27:05] We're distributing that from here at Eros? We are distributing it and archiving this data. [27:11] there was an edict or a mandate by the government saying that [27:14] any federal dollars used for [27:17] federal projects would be [27:20] made public [27:21] And then [27:22] archived. [27:24] so it could be [27:25] retained and pulled back out if they needed. [27:28] We made this so that it's dynamic in the sense that [27:32] not only imagery but because UAS are now used for [27:36] volcanic eruptions. [27:38] taking gassing readings in

[27:42] the plumes of the volcanoes [27:44] THE BIG THING NOW IS [27:46] which is a very good thing. [27:48] In this case we're looking at yellow scans, people have used [27:51] That's the cheapest and most [27:53] reasonable [27:54] sensor they can hook onto the UAS to gather [27:58] LiDAR data, its elevation models, [28:01] point clouds [28:03] So many things are becoming discovered now, especially in the archaeology uses it a lot, LIDAR. [28:10] And they're discovering so many [28:12] ancient different things out there so [28:15] How many of these UAS images do we have then in our archive? [28:19] The total UAS imagery [28:21] We call it RAW. That's coming from right off the camera. [28:25] is 806. [28:27] thousand seven hundred and twelve in our archive. However, [28:31] that doesn't mean that's [28:33] all that's out there. That's what we've gotten here so far anyway. Are we adding to it? We're hoping to add many, many more, yeah, obviously. [28:41] Very nice. This is all part of what we do, studying land change with [28:45] whatever means we have. Remote sensing, different remote sensing, land change, and the systems are used to gather that data. There we go. This is really cool. Thank you, Brent. You bet. Thank you, Tom. [29:00] Thank you for joining us on this episode of Eyes on Earth, where we touched on a lot of research that uses remote sensing data. The university students and aeroscientists who attended had lively discussions and learned a lot from one another.

[29:15] Check out our social media accounts to watch for all future episodes. You can also subscribe to us on Apple and YouTube podcasts. [29:30] Geological Survey, Department of Interior. [29:36] you