NASA ARSET_ Techniques for Plotting and Analyzing ATL24 Coastal Bathymetry Part 2

Speaker 1: Welcome to part two of the ARSET training NASA's satellite laser altimetry for coastal and nearshore bathymetry. My name is Sean McCartney from NASA's Goddard Space Flight Center in Maryland, serving as your ARSET instructor wherever you are joining from. Welcome. The following slides provide an overview of the two part webinar series. So why would somebody want to take this training? Nearly eighty percent of the world's oceans are unexplored and unmapped. Successful coastal management depends on monitoring and mapping.

Speaker 1: Coastal bathymetry is essential for navigational hazards for vessel operations, tidal modeling and prediction, tsunami risk assessment and forecasting, underwater cultural heritage preservation, and environmental change monitoring. Satellite drive pathymetry enhances capacity for collecting high resolution, accurate depth data supporting varied applications. By the end of this two part training, participants will be able to identify NASA bathymetry data used for global coastal and nearshore bathymetry mapping for risk reduction relating to shipping and navigation, identify the applications and limitations of IAT tube athymetry data or ATL twenty four for coastal and nearshore bathymetry mapping, Plot and download IAT tube athymmetry data using slide roll web client in analyze IAT tube bathymetry data using the slide roll Python client.

Speaker 1: The prerequisites for the training are two are SET courses, the Fundamentals of Remote Sensing Course and the Mapping and Monitoring Lakes and Reservoirs with satellite observations. From December two to December fourth, there will be two one and a half hour sessions which will include presentations, demonstrations, and question and answer sessions. All materials and recordings from each session will be available from the training web page. If you are not able to attend one part, a recording will be made available within forty eight hours on the RSET training web page.

Speaker 1: Homework opens today December fourth and will be due on December thirty first. A certificate of completion will be awarded to those who attend all live sessions and complete the homework assignment before the given due date. Part two of the training is focused on techniques for plotting and analyzing ATL twenty four coastal bathymetry. The objectives for part two of the training follows. By the end of today, participants will be able to identify the applications and limitations of i AT two pathymetry data for coastal and nearshore bathymetry mapping plot and download i AT two pathymetry data using slideral Web client in analyze i AT two pathymetry using slide rule Python client.

Speaker 1: Please put your questions in the questions box and we will address them at the end of the webinar. Feel free to answer your questions as we go. We will try to get to all the questions during the Q and A session. After the webinar, the remainder of the questions will be answered in the Q and A document, which we will be posted to the training website about one week from today's training. It is not my pleasure to introduce the guest trainers for today's webinar, Doctor Christopher Parrish, Joseph Paul Swinsky, Gretchen Imohori, and Keana Kief.

Speaker 1: Doctor Parrish is a professor of geomatics in the College of Engineering, School of Civil and Construction Engineering at Oregon State University. He also serves as director of the Geospatial Center for the Arctic and Pacific, a university led advancing research and education in geodesy, geomatics, and geospential geospatial engineering. He is past president of the America Society for Photogrammetry and Remote Sensing. Prior to joining Oregon State University, he held a position of lead Physical Scientists in the Remote Sensing Division of Noah's National Geodetic Survey.

Speaker 1: He also serves as an affiliate faculty member in the Center for Coastal and Ocean Mapping Joint Hydrographic Center at the University of New Hampshire. JP Swinsky is a twenty four year NASA Goddard software developer who spent most of his career creating flight software for spacecraft and science instruments. He led flight software development for the Atlas instrument from twenty ten until twenty sixteen. In twenty eighteen, he transitioned to cloud based data processing solutions, and in twenty twenty co developed slide role with the ice AT two Project scientist and University of Washington, a public web service providing fast cloud access to ice AT two data.

Speaker 1: Gretchen Imohori is estabbed scientist and Satpathy Project manager at Noah's National Geodetic Survey with twenty six years at NOAH, she supports hydrographic and remote sensing operations. She holds a Bachelor of Science and Chemistry from State University of New York, Buffalo and a Master of Science in Earth Science Ocean Mapping from University of New Hampshire. Keana Keith is an analyst programmer with two plus years experience working Chris Parish's research group at Oregon State University.

Speaker 1: She has a Bachelor of Science and Computer Science from Oregon State University. She is a lead developer for noah's Comprehensive Bathometric LIGHTAR Uncertainty Estimator, SATPATHY Photogrammatic Shoreline Uncertainty Estimator, and a contributing developer of NASA's ATL twenty four. Chris over to you.

Speaker 2: Thanks Sean, and hi everyone again. My name is Chris Parrish. I'm with Oregon State University and I'm really honored to give a demo on ATL twenty four and how we're going to use ATL twenty four and Sentinel two spectral imagery alongside each other for mathemetric analysis and also maybe looking at mathemmetric change. So to set the stage for this, if you're going to follow along with me. There are some things that you need to set up ahead of time. So first of all, there are a couple of files that you should have been able to find on the training website.

Speaker 2: There's a shape file which is called hyannas AOI, and then there's also a Python script which is called psdblinear aggression dot PI. So you want to go and download those two files if you haven't already done. So, you also need to set up an EU Copernicus account in order to be able to download Sentinel two imagery. You'll also need to download and install qgis also known as QGIS. That's free and open source GIA software that we're going to use in this demo. And there's three sites that you want to go ahead and bookmark.

Speaker 2: Once is the Copernicus Browser, the second is the slide Rural Earth web client, and then the third is the Noah Webmap Tile Service Electronic Navigational Chart. So here's our project site. This is where we're going to be working for this demo. This is Hyhana's Harbor in Cape Cod, Massachusetts. This is a site that is meaningful to me because it's actually where I started my career. At the very beginning of my career, I worked on a Noah Hidrographic Service ship and this is where I first met the ship and where we spent a few weeks working before transmitting down south.

Speaker 2: So just to kind of take a quick look at the site here, one thing that you might notice is that there's a lot going on in terms of where we are. So this is on the south shore of Cape Cod you can see in the in the image up in the upper left where we are relative to Boston. But in terms of things going on in the harbor, there are a lot of coastal engineering project you know, engineered structures, some of them going back nearly two hundred years, such as the breakwater, the original breakwater. We've also got a jetty grindfield, other shoreline protection structures.

Speaker 2: There's a dredged channel that's used by the ferry. And then there are also a lot of storms that impacts this area, including a lot of times large no o' easters. So really a lot of different reasons why there could be changes to the bathymetry and the site and why we might be interested in monitoring with bathemetric change over time, and just to make the point that bathymetry in this area has been of interest for a long long time. Here we're looking at an eighteen ninety four US Coast and Ganetics Survey chart of Hyannas Harbor.

Speaker 2: So quickly, this is the workflow that we're going to go through. We're going to start with just a visual analysis of our project site. We'll do that in QGIS. I'm just a kind of gain familiarity with the site. Then we're going to go into the slide role web client, and this is where we're going to be able to view and download ATL twenty four with imetry. So I set to withymetry from our new ATL twenty four data products that Laurie talked about in the earlier session, and then we're going to integrate that ATL twenty four with inmetry with satellite drive with imetry from sentinel to imagery and generate final bathometric data layers for visual assessment.

Speaker 2: Before I jump into actually doing the dewnload, there's one step that I just want to talk about briefly so that it doesn't confuse anyone when we get to this step. So there's going to be a step where we're going to do this integration of the ATL twenty four with inmetry with Stanel two imagery, and the way we're going to do that. We're going to run a widely known algorithm known as the Stumf algorithm. This is actually going to parallel in some ways, I think a workflow that Gretchen is going to show and demonstrating the no asatpathy tool.

Speaker 2: I'll just say the version that we're going to do, it's going to be a little bit basic compared to what Gretchen is going to show, So we're not going to make use of some of the new elements that just compositing in the switching model and acolyte and other things that Gretchen might mention, But for visual analysis, this should be a pretty good workflow. So we're going to start by generating what's called pseudobythymmetry or PSDB, and that's going to be the logarithms the ratio of logarithms of two different spectral bands.

Speaker 2: We're going to use blue and green, and then in the next step we're going to do a linear regression of that pseudobythymmetry on ATL twenty four or regression of ATL twenty four reference pythymetry on this pseudobythymmetry, and that's going to give us the parameters of the linear transformation that we can use to transform that pseudopythymmetry to actual depths. So with that, let me go ahead and at this point and just start in on the demo. And the first thing we want to do is, I'm just going to bring up qgis.

Speaker 2: Again, this is free and open source GIS software. The version I've got is three point four zero, but any recent version should work reasonably well. And I'm going to go ahead and just open a new empty project. And at this point, I want to navigate to those files that I mentioned were on the training website and so hopefully you had a chance to download those. If you did. Some of the files that you grabbed, what point were the ones called hs AOI shape file. And in this case, I can just kind of drag and drop.

Speaker 2: I'm going to take the one with the extension of dot SHP and I'm just gonna drag and drop that right into qgis. There you can see it opened up. The first thing I want to do is probably just change the symbology. I'm going to double click that to bring up the symbology, and I want to change it to just a red outline something like that and say, okay, so there's my project site. The next thing I want to do is, let's see if I can move that screen move that screen sharing box out of the way. Okay. The next thing I want to do is just like usual and a gis I want to bring up some base map imagery, and so to do this, I'm actually going to bring up some Google imagery.

Speaker 2: If you're if you haven't set this up already, what you want to do is you want to go into plugins manage and install plugins, and that should pop up this list of plugins that are both installed and not installed. So in my case you can see this one called quick Map Services that's already been installed. If you don't have that yet, you want to go not installed, search for it in the search bar and install it. Because I've already got it. I'm gonna go ahead and just close this and then hopefully at this point you'll see over here on the on the right hand side of the screen, the search next gis q MS. If you don't see that, the way to get that to pop up is you want to go into UH.

Speaker 2: You should be able to go into just Web Quick Map Services and search next gis QMS. So in this box, I'm just gonna type Google Satellite

Speaker 2: and the one I want to pick is the one called Google Satellite Hybrid, and I'm gonna.

Speaker 3: Say ad.

Speaker 2: So that one ahead and opened up the imagery for my site. And at this point I can just kind of explore a little bit. I'll turn off the the project side outline just so we can see what's going on. Here are some of those engineered structures that I've talked about, the jetty, the breakwater, and then over here we've got a grind field. There are also some private revetments that have been installed for shoreline protection, the dredged channel that's used by the ferry, and so, like I mentioned, just a lot going on in a lot of reasons why the symmetry and the site might be changing and might be of interest.

Speaker 2: So in terms of looking at existing with mmetry, one thing we want want to do at this point is go ahead and bring in some Noah Medical chart data e n C data, and so to do this, I'm going to go up here and click on Layer Data Source Manager, and I'm going to scroll down here until I see w MS that stands for Webmap Service w MTS Webmap Tile Service. I'm going to do that and just click on new and I think I've already got this set up, but it shouldn't hurt. I'll go ahead and do it anyway. I'll call this.

Speaker 2: I'll call this Noah Webmap Tiles Service e n C. And then I'm going to need to put the U r L in here and to do that again. This was one of the three links that should have been available on the on the trending website, so it's actually this one here that starts with GIS chart Tools. I'm just going to grab that, that whole U r L and go ahead and put that in here and say, okay. It looks like I had already set that up, so it says do you want to overwrite it? And I'll just go ahead and say sure, overwrite okay, and then I can I can close out of this box and then back over here.

Speaker 2: Now when I scroll down in this in this browser, I scroll down to where I see w MS w MTS. Expand that pick the one that I just added. I want to expand it and expand it again until I see something called default zero two eight milimeter, and I'm going to write click that and say add layer to projects. And that worked, and went ahead and popped up the elect electronic navigational chart for this area, so we can zoom in and look at what the chart soundings look like for this area. One thing we might see.

Speaker 2: I found this kind of interesting just in exploring the e and say, if we get close to shore and for example, close to this breakwater here, you see that there are a lot of these rock awash symbols. But actually it looks like in the very shallowest areas maybe not a great density of sounding. So hopefully we'll be able to use ATL twenty four and Sentinel too withthymmetry to get a good idea of what's going on in those areas. So within mind, let's go ahead and just jump into the next part of this where I'm going to show you how to get I set to ATL twenty four with ymmetry, and to do so, I'm going to go to the next of the links that we shared and the demo and the training website, and so this time I want to go to the one that's called slide Rural Earth.

Speaker 2: That's going to take me to this website here. So this is how we're going to be able to get NASA ATL twenty four data through through slide rule and this web client. So I want to click on the link that says web client it's going to pop up. I want to make sure this is something that I sometimes forget to do. Actually, I want to make sure that I've selected the table it's called I set to coastal mathymmetry, and then we're going to be able to just zoom into our area and take a look at pathymmetry in that site.

Speaker 2: Before we do that, I just wanted to make a quick point here. So in picking out the site for this demo, I very specifically chose a site that was someone on the challenging side. So at mid latitude site, a lot of times when you see people do Sili drigathymmetry or pathymmetry, a lot of times we're focused on the very clear tropical waters where we just know things are gonna work great. So I purposefully tried to pick a little bit of a more typical project site. But just while we're here, I do kind of want to take this opportunity just to show you what it looks like if we you know, if we were to select a site that was in a uh a really you know, sort of amazing tropical area.

Speaker 2: Here, it looks like I thought I had a site selected, so I might have to do this kind of on the fly. So I'm just assuming in here on Turks and Caicos, and I'm just going to draw a project box. We'll do one maybe that goes about like maybe something like that, and I'm gonna we'll click ahead, run slide while this process is again the reason I'm doing this, This is not our site. I just want to show what it looks like when we've got a site where uh here. What's gonna happen is when it gets processing, we're going to see that it's selected all of the different track lines of I said to you, bothymetry, and for a side like this, what we should see is that throughout the entire site we've got really amazing pathymetry.

Speaker 2: I'll give this a second to just finish processing. Okay, there we go. So that worked. That came up and we can see it found all these different tracks of bythymmetry, and in any given track, I'm going to change the color scheme. I'm going to change it to this coool scheme.

Speaker 2: So any track that's within this site, I'm going to see this really just nice bathmetric profile. I'll see the depths dropping off to greater than fifteen meters depending on where where I am in the profile. I can pick another line. Basically, any line I pick, I imagine it's just going to look like pretty amazing pythymmetry. And in this case, another thing that I can put the actually do here is I can go to this three D v and win. This's loads if I can get this to work. But it should do is oh yeah, there we go.

Speaker 2: I should be able to just sort of drag this up and kind of rotate this around and look at the bathymetry for that site in three D. And in a case like this, I couldn't even made my side a bit bigger. In a case like this, you can imagine that we could generate a pretty good bathymetric DM even without incorporating any additional data. Okay, with that in mind, let's go ahead and go back to requests. And now I'm going to go up to the site that we actually are interested in for this demo. So I'm gonna I'm gonna scroll all the way back up to New England and to Cape Cod and to our Highness Project site.

Speaker 2: So here we are, here's our here's our actual side of interest. I'm gonna do the same thing. I'm gonna draw a box. I can make it pretty big. I just basically want to make sure I'm covering the whole area so I can do something. Maybe I'll do something maybe about like that, and then same process before I'm gonna run slide rule. It's going to go through and process again. It's finding all the tracks of I SAT to bathymmetry. So this one in this case, if you're to spend some time investigating these different track lines, basically what you'll see is it's not as amazing as when we were just looking at turks and caicos, but we do seem to have some pretty good bathymetry going through the going through the harbor, and so I can, you know, just try try a different couple of different track lines, and so it looks like we've got some good pometry.

Speaker 2: This is all great at this point. Let's go ahead and and and download it. And there's a few different ways I can do this. One way, if I want a little bit more control, What I could do is I could go ahead and select these individual track lines one at a time, and I could click on this thing that says three D view. Oh excuse me, not the three D V sorry, that table. I could click on table, and when this pops up. At this point, I can actually edit the commands that's shown here, and I can do things like I can pick exactly what parameters I want to export.

Speaker 2: I can I can pick different you know, filters, or do things in different ways. However, for the sake of this demo, I'm going to do things in an easier way. I'm just going to go kind of right in the middle of my screen. If you see this button that says export, I'm just going to go ahead and click that, and I'm gonna choose an export format, and I want to make that CSV comma separated values and say export. And I'm gonna go ahead and put this into the folder that I created for this training. So I created this g Hyannas Harbor Aziel twenty four demo.

Speaker 2: I'll make a new subfolder that I will call ATL twenty four, and I will put this CSP file right in that folder. Something I should point out at this point because I was kind of I wasn't very discriminate in picking out exactly what tracks I want. I just grabbed them all. That was the easiest way to go and grab the data. I am going to pay a slight price for that later, and I'll talk about that when I get there. For now, we're actually done in slab role, though, so I can go ahead and close the web browser and get back over into QGIS and back to our project site.

Speaker 2: And at this point, since I just grabs that ATL twenty four data layer, why don't I just go ahead and bring that into QGIS. So the way I'm going to do that is, I'm going to click on layer and under layer, I'm going to do AD layer, and there should be one that's called ad to limit text layer, so I'll click that. Now I want to just browse to find that file, so it's the file that I just created. There, I'm going to go to AHL twenty four folder, select that CSP file and say, okay, if I want, I can scroll along down here in the bottom and see the individual fields that came in in that file, so all the different information refraction corrected orthometric height, so all of the pothometry has been refraction corrected.

Speaker 2: It's got a confidence interval. I think that's something that Lori covered in her talk. But basically the confidence and the and the bathymetry classifications, spot granule, et cetera. Everything basically auto populated. It said that my ex field is longitude phyfield is latitude pH. I can go ahead and set my Z field to ortho underscore H that's my Z and I can go ahead and just say AAD and at that point I you'd be able to close this window. And if you can see all these orangeish dots that just got added, that's the ATL twenty four data.

Speaker 2: If I want to can change it symbol. I kind of like doing this as a blue dot. I think that looks pretty good. There's my ATL twenty four data. So at this point we're really doing well. I would make an observation here, though, and that is that even though this brought in a lot of ATL twenty four data, it still is kind of sparse in terms of you know, there's areas of the harbor where if we're interested in looking at nearshore withythymmetry, all we've got is these individual discrete tracks. So this is the point where we want to see how we can combine ATL twenty four pythymmetry with specially drive pathymetry from SEVENEL two.

Speaker 2: So to do this, I'm going to go to the third of the weblinks that we're shared in the trending website, and the third link it's the one that's called Pernicus Browser. So I'm gonna open this up. You'll need to create an account for this. It's free, uh so free to create an account, and then you'll have to log in. I happen to already be logged in, and you can tell that because it's got my name listed up here. But go ahead and sign in if you need to. Okay, So the next thing I want to do is just go over to our project site.

Speaker 2: So I'm gonna scroll over to Cape cod and into our Hynas Harbor project site. At this point, I want to draw an AOI and so I'm going to use this little polygon tool, and then once I select that, I want to grab the tool next to it. It looks like the pencil icon. I'll select that and I might scroll it a little bit, and I'm just gonna I'm just gonna go ahead and and digitize something it doesn't. It doesn't have to be perfect, and it's okay if I make this a little bit big than what it actually needs to be, so I'll maybe do something and just finish it up off like that.

Speaker 2: Okay, Now we want to put in a date, and this is the point where you can actually spend a lot of time looking through the different dates trying to find imagery that looks good. Cloud free, water looks reasonably clear. I'm going to do a little bit of a shortcut. I've already done some analysis and i found a data that looks good, so I'm just going to go ahead and put that in. The day that I found is twenty twenty three, twenty twenty three oh three, twenty twenty three oh three, twenty all right there, it is right there, so it brought up that date.

Speaker 2: I want to make sure I'm in the visualize tab here, and at this point over on the right hand side of the screen, I've got this download icon, so I'll click that download icon, and then here I want to click on the tab that says analytical, and I want to be careful with the settings here. I want my image format to be tiff thirty two bit float. I want to change my resolution to high. I'm gonna go ahead and keep the court the default coordinate system. And then I'm going to select three different images to download.

Speaker 2: I'm going to do true color, so that's going to be the RGB, and then I'm gonna pick just two of the bands. I'm going to do BAN two, which is blue, and BAN three, which is green, and I can just click the click the download icon, so that should be saving them to my downloads folder. I can go ahead and open that up there it is, it just downloaded. I'm going to say, we'll just do cut and I'm going to paste this into the to the folder that I set up specifically for this demo. We'll do a subholder. My new subholder will just be called Sentinel two.

Speaker 4: No.

Speaker 2: Two and copy in that ZIP file. I'll go ahead and extract all of the different images that are in that in that. Okay, So that worked, it extracted all the images. I'm going to do one more thing, just because these are fairly long and cumbersome file names. I'm going to rename these the thing that says banned too. I'm just going to rename that to S two for Sentinel to blue. The one that says banned three, I'm going to rename that to be S two winterscore green.

Speaker 2: And then the final one that's the true color RGB, I'm going to rename that to be S two underscore RGB.

Speaker 2: So I've got those three three different images, all redn ends. At this point, I can get out of that Copernicus browser and I can actually just, uh, this is another case where I can just drag and drop this, So I'll just I'll just drag and drop those right in. Whoops, So it looks pretty good here. I am back in in QG. I S I've got my uh my RGB image up at the top. If I want, I can go ahead and take my ATL twenty four both imagery and drag that up to the up to the very top so that we can see where the where the I saw two pythymmetry is on top of the Sentinel two imagery.

Speaker 2: Okay, at this point, we're going to move on to the next step, which is, uh that some prestio that I showed you earlier on the slides, and we're going to create that pseudo bythymmetry layer. So this is going to be the ratio of the logarithms of the two different spectral bands, the blue band and the green band. So the way I'm going to do this is I'm going to click on raster and then raster calculator, so that brings me up here to raster a calculator. I'm going to create an output layer, and I want to give this a name, so I'm going to put it right back in my in my demo folder, I'll do a new subfolder called PSDB.

Speaker 2: Again, that stands for pseudopathymmetry, and that denotes the fact that we're not going to have actual depths. It'll just be sort of pseudodepths or pseudobythymmetry. So I'm going to go in there and I'm going to make my file name. I'm going to do stb dot tef save, and then the next thing I want to do is I just want to go ahead and create that stump pray show. So this is going to be natural log of one thousand times blue band. So S two blow in parentheses divided by natural log of one thousand.

Speaker 2: I'ms the green band. This might look a little funny, what what is this again? This is just this is just the stump rash show that that we saw earlier back in the in the slide. So we've done log of blow over log of green. That factor of a thousand, that's just a constant that is designed to keep the keep the logarithms positive and linear linearly related to depth. So I'm just gonna go ahead and run this. I'm gonna say, okay, and it worked. It already generated my pseudopythymmetry later. All right.

Speaker 2: The next step I want to do at this point is, like I mentioned earlier, our ultimate goal here is we want to use the ISAT tubeothymmetry from ATL twenty four to calibrate our pseudopythymmetry. And so to do this, we need to create a layer that has both values. We want to We want a layer that has the at L twenty four heights. I should have mentioned, by the way, uh in the at L twenty four the heights or orthometric heights best on the e g M O eight GID model. So those are you know, actual datam best heights, and then the pseudopythymmetry that's not those are just sort of relative values.

Speaker 2: But what I want to do is create a layer that has both of those together and so two. Uh, to do this, I'm going to go up to the processing toolbox. I'm gonna to go processing toolbox. I've already got this up. So in this search window for the processing toolbox, I'm going to search for clip raster by mass layer, clip raster by mass layer. And in this case, uh, my input layer is going to be let's see, I want to do uh, I want to do It looks like I made a mistake. I called the thing that I wanted to be set up with the matory actually called SDB.

Speaker 2: That should say PSDB. But that's okay. My input layer is going to be just my SDB layer, and my mask layer is going to be Wait, I'm sorry, I say what I've done. I've done the I've done the wrong tool here, the tool that I actually wanted. Sorry, back in this search box. The tool that I want here is the sample raster values. That's the one that I meant to bring up sample raster values. So my input layer is going to be my Hotel twenty four data. My raster letter layer is going to be SDB, and again I should have called that PSDB.

Speaker 2: I'm going to create an output column. I'm going to call that sample sample underscore PSDB and just go ahead and run that

Speaker 2: and I can close and that ran, and that created this layer called sampled, which now that has all the ATL twenty four heights and it also has the pseudopythymmetry. So at this point we're actually really close to being able to generate our final SDB layer. We just need to do a linear regression in order to calculate linear transformation coefficients that are going to allow us to transform or pseudo bythymmetry to actual withymetry. So to do this, there's a Python script. I'm going to go plug ins Python console and then open up the Python editor and there's the scripts that's for me already open.

Speaker 2: But if you don't have this open, you just want to click on the open script and want to browse and finds the scripts that was made available on the Trening website. It's the PSDB linear regression dot pile, and go ahead and open that up. That will open up the scripts. The only thing that you should actually have to change here is if you go down to this it's line seven, and you'll see this path to where the where that CSV file lives that you created. Again, that's the CSV file that we need, the one that has the ATL twenty four data in the pseudo bythymetry.

Speaker 2: You want to change that to your path and make sure you've got your slashes going in the right direction. And at that point you should just be able to hit this s green run script icon and hopefully this will run and okay. So that went ahead, and that finish running, and it gave me the scatter plot. So what I see here is on the x axis, those are my pseudo pythymmetry values. On the why I that's my I saw to bithymetry from ATL twenty four and then it fit a linear aggression line. The first thing I want to note in this case is that our our square value is actually fairly lost, so we've got an R squared of point five to four.

Speaker 2: Usually in my research group, we wouldn't move on with using this for bathymetry unless we had an R squared of about point seven or higher. The reason that I happen to know that this is okay in this case is if you'll remember that there was that step when we were in the slab real Web client and I said that we could have been a little bit more selective and just picking out exactly which tracks we wanted to download, but we didn't do that. We went ahead and just sort of grabbed everything. So I know, because I actually tried this, that if I'd spent a little more time in that step of grabbing just the tracks that I knew had the best bathymetry, and or if I'd done a little bit of cleanup afterwards, I could get a much better our square value here.

Speaker 2: But actually it doesn't really change the presameters of this linear transformation the sloping and intercept constant. So that's what we're really interested in here, So we're not going to worry too much about the kind of lowish R square. We just need these the values the sloping intercept constants. So at this point we are actually very close. Uh, this is a point where we're going to go back into raster calculator. Let me see if I can do it this way. I need to keep this scatter plot up because I need to be able to see these numbers.

Speaker 2: So I'm going to go back to raster calculator and what I'm basically going to do is I'm just going to apply a linear transformation that is going to transform my pseudo bythymmetry to actual byth inmetry. So I'm going to do an output layer. I will call this. I'll create a new folder here that's just going to be called sdb SDB, and I'll give this file a file name. And because I accidentally called the other one SDB, I'm going to call this one. I'll just do something like SDB final, SDB final dot tif and I'll say save.

Speaker 2: And now I'm going to create an equation to compute this, and it's going to be I'm just going to look over here. This is why I keet the scatter plot up. I want to look at uh at the two constants. So I'm going to do negative twenty five point eight five zero times SDB, which really again that should have been our pseudo athymetry minus oh, I'm sorry, not minus plus plus twenty one point five to three zero, where those two constants again are just from overhearing our scatter plot, I can say you okay, and it looked like that that one ahead ran.

Speaker 2: So I've got a layer that's now called sd be final. I do probably want to clip that to just our project site so we can and I can I can get rid of the Python script at this point, this is where I want to clip it. So this is the point where I actually want to do the tool that I started to show last time. I'm gonna do this clip raster by mass player and I'll do I'm going to clip SDB final and the mass Player is going to be the hynas AOI and just let that run. Oh oh, I guess it didn't select it. Try one more time.

Speaker 2: Run. Okay, that ran. Now I can get rid of this one and I can turn off that and that, and I've just got my final SDB layer and if I want to change the symbology, I can double click it. I can change from single band great to single band pseudo color and just pick out a color ramp. I kind of like this one. This one looks kind of like pytymetry. Go ahead and apply and close. And so now I've got a bathymetric DM for this project site. And that was created using the combination of I set two bathymetry from ATL twenty four and centaal two values.

Speaker 2: It looks pretty interesting. I can clearly see the dredge channel through there. I can see some of the deeper areas like in the anchor in the in the anchorage behind the breakwater. Over here, I can see some of the really shallow areas that are closer to shore. So everything looks pretty good. Uh So at this point, if I wanted to, if I were interested in in bathymetric change and kind of seeing how the pathymmetry in this site changes over time, I could just repeat that process using multiple dates, multiple epics of Sentinel two imagery and then I could do differences of the d ms or just sort of look visually at how pathymmetry in this area is changing over time.

Speaker 4: So that's it.

Speaker 2: That is the uh that's the end of the demo. But I just wanted to say thanks and if you want to reach me, my contact information is here, Christopher dot Parrish at Oregon State dot ADU.

Speaker 1: Thanks Chris, thank you for the excellent demonstration of ATL twenty four integration with Sentinel two and generation of mathymetric data layers. We will now transition to JP Swinsky, who will provide a demonstration of slide roll Python client. JP over to you.

Speaker 5: Great well, thank you.

Speaker 6: I'm going to be talking about slide rule worth again and just as a refresher from what we talked about a couple of days ago. Slide Rule is a public web service with rest like APIs for processing and accessing science data. And specifically what we're talking about here is accessing the ATL twenty four with inmetry data. Last time we talked about the web browser and I gave a demo of that web client. Today we're going to be talking about the Python client that typically runs inside a jubiterin notebook, and I'm going to be given an example of how to access to ATAIL twenty four data from a jubiteren notebook using the side rule Python client.

Speaker 6: Now before I do that, though, I want to just show our landing page of our website. So the Python client can be a little intimidating. It's not as easy to use as the web client, and so we provide a lot of documentation to go along with it to help to help you out, to help you get started. So if you navigate to side rule worth do io in your browser, find you'll come here. And the first thing I want to point out is this contact us page, so you should always feel free to reach out to us. Here's this email here goes directly to me.

Speaker 6: I'm always excited when users email us and we do our best to get back to you as soon as possible. You're also free for people a little bit more on the developer side, if you want to go to GitHub and open up an issue if you find anything wrong, or want to feature requests. And certainly we're an open source project and we invite collaborations, so you're also free to open pool requests and start discussions on features that you'd like to see. But now about the documentation. So clicking on the documentation link takes you to our documentation site and this is it here on the left.

Speaker 6: You can kind of walk through it in the order of which you would typically want to approach it if you're a new user. So getting started, the first thing you're going to want to do is know how to install the Python client, and this is instructions on how to install it. We support Conda and pie Pie. You can also directly install it from GitHub. Then we have a really short, the simple Hello World type Python program that can just get you started and make sure everything is installed correctly and you're running okay, and then we move into examples.

Speaker 5: I don't know about you, but I learned best from example.

Speaker 6: So here's some examples that we've put together, and the one we're actually gonna be going through today is this ATL twenty four example. But then as you progress you can go through the user's guide. You're gonna find out all the different parameters and APIs that are available. There's some more information for specific to ice at two right here, and then going down even further, we have our developer's guide. So if you're really interested in how this all works, we have a map of our project why we develop side rule.

Speaker 6: Here's an under the hood. So here's an eye chart on all the different components and an explanation of those. But just wanted to let you know this is where to go when you have questions about using the Python client. So now I'm going to switch over and actually look at our ATL twenty four example.

Speaker 5: So this is just check. I've checked out the repository.

Speaker 6: And I've opened it up in vs code, and so under the slide rule repo from GitHub, I go to clients Python examples, and then under the examples you'll find this notebook that I'm going to walk through right here. So I've set this all up, so I'm gonna just run through this out. So this is I'm importing the slide rule client into my kind of environment. Well, I should point out this file right here is all of the packages you need to run all of our examples. You don't have to use it, but if you're wondering, man, I wish I had a quick way to get all the dependencies.

Speaker 5: This is what you do.

Speaker 6: Okay, So now I'm going to initialize the client. You will find one difference here. I'm running against a private developers cluster. Just so I don't pound the public cluster. This organizational setting is absent in the in the version that is on our repository, and it would go right to the public cluster. Here are some helper functions. I'm not going to talk too much about those, but you'll see them being used. It's really just to plot the data that we get back from the server. So I have functions to plot ATAIL twenty four, ATAIL six, and atil oh three.

Speaker 6: And now I'm also going to define an area of interest. It's just on the north shore of the Dominican Republic. And this is There are many ways that you can find an area of interest using our Python client.

Speaker 5: This is the simplest.

Speaker 6: It's just creating a list of lat longs. You can also use Goojson and shape files. But I've done this this way here for clarity. So now if you just want I just you just want all the data ATAIL twenty four data in this area of interest. This is one line here you run it it's going to it says that there's one hundred and ten ATAIL twenty four granules that are intersect this area of interest.

Speaker 5: Ran in eight seconds.

Speaker 6: It processed all one hundred and ten of those granules in parallel on the server. So so just to talk through what's happening here, the Python client built a request when you hit this run, and it's sent that request to the server. The server is sending back status messages which we see displayed here, and then it sends back a data frame encoded as a geopark file that the Python client then opens up. And so that's all what happened right there. And now I'm just going to look at that data frame and has forty four thousand rows.

Speaker 6: That means it's got forty four thousandthymmetry photons. And these are the columns that come back by default when you just do a simple access of the ATL twenty four data so you'll see here that each photon has the same classification. It's all withymetry. This is an encoding for which groundtrack it is. This is the height of the surface, the rithmetric height of the surface. This is the reference groundtrack. If you know anything about ice A two, all of the tracks are labeled and so it's reference groundtracks seven to fifty eight.

Speaker 6: Here's the rithmetric height of each of these photons. So this has a depth of about twenty meters here, and here's the confidence level that the ATAIL twenty four algorithm assigned to it. This is which spot it was on the six spots of the ice AT two ATLAS instrument. Here's the along track coordinate, a cross track coordinate. And then this source ide is allows you to look up which granule the data came from. So embedded in the metadata of the geodata frame is a dictionary that links source IDs to granule names.

Speaker 6: And then lastly, this is which cycle the data was collected at. Oh, and we have the time and the lat long and if you're familiar with geodata frames, that this lat long.

Speaker 5: Isn't coorted as a point geometry.

Speaker 6: So let's plot this and see what it looks like. Okay, this is just a quick view of what it looks like. And so I can see here underneath where it's white, this is probably land. And then this is where we get a lot of good pathymetry near to the shore. And then the algorithm is doing its best to find bathymetry as it goes farther offshore.

Speaker 5: So this is a lot of data. Now let's say, you.

Speaker 6: Know what, there's actually a particular track that I'm interested in, and so I've preconstructed this. So now instead of pulling all of the data, I just want the ground track three the right beam. I want the reference groundtrack to be two two, and I want it to be from cycle twelve of the ICE two collection. So now let's run that. It's gonna the client's going to construct a request, send it out to the server. The server is going to process that request and send the data back in. It happened now in three seconds.

Speaker 6: So I'm gonna look at this data frame. And so now I've only got eight hundred and sixty one but THEMTRY photons. So this set of photons is a subset of this one up here, and the subset is defined by these parameters here the beam, the reference groundtrack, and the cycle.

Speaker 5: You can use others.

Speaker 6: You can include just one or include others that are in the data set, all of which are going to be explained in our documentation.

Speaker 5: Okay, so now let's plot this. Ah.

Speaker 6: Okay, so this is now a side view of the photons.

Speaker 5: Let's go back up here again.

Speaker 6: This is a top down view looking plotting them all of the bathymetry based on its lat long coordinate and kind of color coding it to show some depth. But now we're turning this on its side and looking at just one of the tracks, and we defined which one of those tracks are and we get this nice plot here. So this is a profile of the seafloor. Okay, So now I'm like, oh, I really like this. I want to I want to dig in and get more information about it. So now I'm going to do a detailed access of this single track, similar to what we talked about a couple of days ago with the web client.

Speaker 5: I'm turning the compact flag off so I get all of the data.

Speaker 6: I'm going to now say I don't want you to I don't want the server to threshold on confidence at all. Give me all of the bathymetry, even if it's very low confidence, and then give me all the different classifications.

Speaker 5: So let's run this.

Speaker 6: It's going to construct this request, it's going to send it to the server. Serve is going to respond with the results. And it just didn't. And now we have thirty five thousand photons. Again, the reason we have thirty five thousand photons. So now we have thirty five thousand photons. The reason we have thirty five thousand photons instead of the eight hundred some up here is because we're asking for all the photons, not just the bithymmetry. So we're getting a lot of sea surface and unclassified photons.

Speaker 5: So let's plot that.

Speaker 6: And here you see it, so you can see the gray is the unclassified. This looks like land, and then we come down there's a little drop to the ocean, and then we've got the sea surface in blue, and we've got that same track now in red in the context of the other photons.

Speaker 5: Let's look at this based on confidence.

Speaker 6: And you can see here we have a bunch of different confidence values spread throughout the bothymmetry being identified there. Okay, so again something that we talked about a couple of days ago was using the web client. We were able to, instead of going directly to a TAIL twenty four, go to ATL three and use ATL twenty four to classify it. So this is a more sophisticated request. The ATAIL twenty fours is run against the release six ATLO three, so I have to manually specify that in the request parameters.

Speaker 6: I'm going to ask for everything because this is an ATL three request. I'm gonna turn off any filtering based on its own confidence values, and I'm going to ask for the same beam reference, groundtrack, and cycle.

Speaker 5: So this is going to go out and grab that. And we got that there.

Speaker 6: And so now this is all of the ATLO three data classified with the.

Speaker 5: ATL twenty four.

Speaker 6: You notice it's a bigger area here, and there's more data data that wasn't used by the classifier because it was outside of a band considered outside of the band in which there'd be valid bathymetry. So that's why there's even more data. Now I'm ATLO three, But I also want to point out back up here, we're going to get different fields. So now we get what the spacecraft velocity was at each of these photons. We get what the solar elevation is. See, we get the background rate and we get the quality pH flags.

Speaker 6: These are all fields that come in the ATLO three product but do not come in the ATL twenty four product. So therefore, if you want those fields, this is an easy way to get that data and overlay.

Speaker 5: The ATL twenty four classifications. Okay, now.

Speaker 6: Let's combine some features of ATLO three and again combine it with ATL twenty four to do some filtering at the ATLO three level. So here I'm going to do the same request as before, except I'm going to do two new filters. I'm gonna say, only give me ATLO three that has a high confidence.

Speaker 5: According to the ATLO three algorithm.

Speaker 6: And the ATLO three has this classifier called yapsy, yet another photon classifier which gives us score based on the local density of the photon how many photons are near it. And so I'm gonna say, only give me photons that have a high density. So this is saying filter the ATIL of three with these parameters and then overlay the ATL twenty four classifications. And so we run that request and we get the data back. Took a little bit longer, A second longer because it's it had to do some more processing with the A and the confidence filtering, and now we have much less photons than before, and we see, interestingly, we see some gaps here now where there were photons that were filtered out by the ETL three filtering but were classified by ATL twenty four.

Speaker 6: And we do see though some more photons that were classified by ATL twenty four. And then it cleans up all of this, all of the photons if you look back up here, all of this stuff that was obviously not a surface reflection. Some of these filters very quickly cleaned up all of that. And here we're now taking the same plot and we're overlaying color coding it with the yapsy score, so you can see what the aapsy score is of some of these photons that were identified as bothymetry. Okay, so here is now a final step to this process of using ATLO three, and that is we're going to take ATLO three data, we're going to classify it with ATL twenty four data, then we're going to specify to only use the bathymetry photons, and then we're going to run the ATLO six surface surface finding algorithm on it to produce an aggregated surface elevation of the bothymmetry photons.

Speaker 6: And we're all going to do that on the server making this request here. So this request is constructed. You can see I talked through what it's doing, and this is the magic to tell the server to use the ATL three surface finding algorithm. And so it ran. It took three seconds to run. And now this is the this is the data. We're going to get different fields in this data set because it's this is really now like an ATLO six product, but we've fooled it by giving it bathymetry instead of land ice elevator photons.

Speaker 6: So we get you know, number of fit photons, we get the surface window, we get the misfit, the armist of the misfit, we get the slope here.

Speaker 5: And so forth.

Speaker 6: Okay, so now let's plot this and we get a very nice smooth return of this of the seafloor. Now again you may say, well, there's some things here that that aren't right. You can go back and tweak the parameters here and say, oh, you know what, let me get rid of low confidence photons, or let me do an atail twenty four confidence level.

Speaker 5: That's that's higher.

Speaker 6: I only want the really high confidence photons based on ATAIL twenty four and you and you can tweak that to get a very clean But even just with these parameters, we've gotten a very clean surface that's easy to work with and is only a handful right eighty one. Right, this this data set represents the same surface, but as much smaller and easier to work with.

Speaker 5: And then, lastly, for.

Speaker 6: Power users that you just want access to the raw data, here's a request that does some different filters, just based on ATAIL twenty four. So again, now we've moved past the talking about the ATAIL of three. We're back to talking about ATL twenty four and some of the more corner case features that we support this set. Here, I've just chosen to turn off the low confidence filter. You could turn on a night filter. Say you want, say you have an area of interest and you're just interested in pathymetry that was collected at night, Well you could turn this on and then we'll only return pothymetry accepted.

Speaker 5: That was collected at night.

Speaker 6: Or there's we have a calculation of what the maximum sensor depth is for the Atlas instrument when measuring bothymmetry. Sometimes we get we think the algorithm will find bothymmetry beyond that sensor death. But if you want to just reject all of that, you can turn this one and say, if the sensor depth is exceeded, turn it to false, and that way you'll only get photons in which the sensor depth is not exceeded. So let's take a look at this and you'll see now fewer photons because we've done some filtering here.

Speaker 5: Just for completeness, I'll plot that there. Now. Lastly, an exciting.

Speaker 6: Feature that we've just added is the ability to directly query for AHL twenty four resources from the client, just hitting the Python, just hitting.

Speaker 5: The slide rule servers.

Speaker 6: So these are requests that do not use CMR in order to querry ATL twenty four resources, and therefore we expose some additional query parameters.

Speaker 5: So here is an area of interest. I'm gonna build a.

Speaker 6: Polygon string from that, and now I'm gonna make a request for retail twenty four granules with this polygon, and I'm gonna say I want the maximum. I want to have a maximum mean depth of ten meters. So nothing should have a max a mean depth of greater than ten meters, so only mean depths for granules less than ten meters, and I only want granules that are collected in the fall. So there's an encoding for which season. Winter is zero, spring is one. Actually, this is the that wrong, this is the this is the summer.

Speaker 6: I only want to collect in the summer. Summer's two and the fall is three. So I'll run this request. It's gonna go out to the servers. It's gonna make that query. And now this is all of the granules, have found fifty three tracks, and it's returning the granule names with the beam that meets this criteria, have.

Speaker 5: Mean depth, mind depth, max depth season.

Speaker 6: We have the number of bathymetric photons and a few other things that you can look at. And then also now if you want more information about a granule itself, we have an API that allows you to say, give me information just about this granule. And so we'll run that. It went out very quickly, ran out. It made a request to the server of the server queried our own database and said, okay, for this granule here it has these beams. This beam was collected in the summer. Oh, there's no bothymetry in any in that beam okay, beam three it was collected in the summer, had four photons and this was the mean depth, mind depth and max depth for that and so forth.

Speaker 1: Thank you, JP. Great demonstration for participates wanting to explore the slide roll Python client. Next, we'll hear from Gretchen and Kana, who will present on Noah's Satpathy tool and integration of NASA's ATL twenty four product. Gretchen and Kiana over to you.

Speaker 7: Thank you pretty much.

Speaker 4: We're very excited to talk about our work today on noah Stat Value Tool and the integration of NASA's ATL twenty four. To begin with, we're going to give a little background talk about sat baby and what SDB is and its importance, and then give a few examples from our SAT value desktop tool. I'll run through a quick demo and then pass that off to Canna to talk about our ATL ATL twenty four integration and her demo, and then if we have time, we'll talk about the future of sapathy. So, Noah Stat BATHI Testktop Tool version three point one is now official.

Speaker 4: This is the interface. The whole purpose of the value tool is to automate the creation of satellite dry bythymmetry. And I should say that this desktop tool is for Noah internal use only at this point, but I will talk about the future.

Speaker 4: So for those that did not catch Chris Parrish's talk in the morning, Satellite dry pathymmetry or SDB is an indirect method of bathometric measurement. And while it is not the most direct compared to multibede sonar and vathametric lighter, SDB is more direct than the gravity based pythymmetry. But given that, why do we care about STAB? The example on the left or the right give a good example of why it's important. The top right image is the batometric lighter example from our group and wrote sunsing division in the National g JIC Survey, and this is a saint shows if you look at the lower SDB example that doctor Rick Stump created, it fills in the gaps of the bathymetric lighter.

Speaker 4: So there are some uses for him. But what is Sallee drieth inventary in detail? For our purposes today and sat pathy, we use the method of using sentinel two resolution of ten meters and updated research from doctor Rick Stump and doctor Isabel Camelero and I should note that all SDBA methods can be grouped into three method major character categories. The first one is spectral SDP special radiance for example, radio transfer equation or RTE optimization, RTE empirical and VAN ratio. The second is photochrometric and the wave and the third is wave kinematics.

Speaker 4: For our purposes today, we're going to focus on the band ratio approach, which is based on a relationship between reflectance and depth and it often requires but not always, which I'll explain a little bit when we talk about our simplified calibration requires a known dep depth data to calibrate the composed compositing approach for the data. The compositing approach for the Van ratio solution inherently removes a lot of the faulty data. So we're asked a lot of times is why do we need u s dB for our purposes at NAWA, it's for intermonautical chart updates, hydrographic survey planning and reconnaissance change analysis, and to fill in atometric glider caps in non navigationally stigific areas.

Speaker 4: And then image below as an example from our NOAH chart. These areas of unsurveyed locations still exist in areas like Alaska, but I'm going to focus on the hydrographic survey planning in Wisconsin. The reason i SDB is so important, as we've been using it quite a bit, is it's become a vicious cycle where hydrographic survey data sometimes doesn't exist. We need the data, then the hydro data. The hydro vessels need to collect the data. However, if they're an area where there isn't any data to operate safely, it becomes very dangerous.

Speaker 4: This is where SDB is very has become very important to provide some amount of data so the surveys can be as safe as possible. So my first example is of Saint Matthew's. Saint Matthews is off of in Alaska, off the coast, and the e n C or Electronic Navigation chart. While it does.

Speaker 7: Show some areas where there are rocks and areas of.

Speaker 4: Danger, where to the a survey of vessel like the Fairweather need to stay away from.

Speaker 7: We were asked when the nose ship.

Speaker 4: Fairweather had to survey this area, we could run the sad bady tool to provide reconnaissance data. The pseudopathymetry read examples on the left and it shows some areas that are not captured in the E n C current chart.

Speaker 4: This helps keep the noise ship fair weather safe to the best extent possible. Another example is in Kotsubu, Alaska. That's in the northern part of Alaska, and if you can take a look at the E and C on the left, there's a lot of gray areas that basically says there are no charted depths here, So you're basically working blind when you're serving in this area.

Speaker 7: Very dangerous.

Speaker 4: Again, we were asked to run the sad Bady tool and to provide data that would provide some information on the thethymmetry. What we found was not just the thethymmetry, but channels, multiple channels, two channels to show where they can navigate somewhat safely. So the next I'm going to run into the sat patty dust optimum. So this is the the sat patty desktop tool interface, and I'm going to zoom in on our area of interest, which should be around the area of Miami.

Speaker 7: Each of these.

Speaker 4: Squares are the set of two tiles, and I will select the tile that I'm interested in and zoom in further intro and the area of interest. You can also provide an AOI that's a ZIP shape pile, and then I'm going to select the project directory. Next, we're going to figure out what start date we want. So for the Miami area it's relatively clear in Florida most of the year, except when there's hurricanes. We're going to go up to January to January first, and we'll do it in data a couple of weeks earlier.

Speaker 4: For the cloud cover, we'll just leave it as default as twenty percent. You can move it to eight, ten percent or less cloud shadows. We'll talk a little bit about that as we're processing some of the data. It's no and nice you don't have to worry about. And I'll show example of the no data cover what I mean by that. It's now we're set. We're going to search for the imagery. The little dial shows that it's searching, and while we're doing that, I'm going to talk a little bit about well, it's already finished.

Speaker 4: Actually, let's look at our search results.

Speaker 7: It was pretty clear.

Speaker 4: So this is what it looks like after the imagery is run through, and we can go through and select which ones are the least turbid have the least amount of clouds. This is quite a bit turbid, so we don't want to use something like that. We don't want to use the stuff with all the clouds. I'll explain a little bit more about them. I'm going to go back to the first one. This looks pretty clear compared to this one looks pretty turbid still in this area, So.

Speaker 7: I hadn't selected.

Speaker 4: In a perfect world, if we had more time, we would select ideally six to eight images to create the composite.

Speaker 7: And I'll talk a little bit more about that.

Speaker 4: But the interesting time one was going to select one and then I'm going to submit the scenes. The SAT value workflow and composite product procedure for a given area interest or AOI Multiple sentinel two alwe scenes are collected and atmospherically corrected to remote sensing reflectance or RSS. Red, green, and blue bands are used as inputs to the band ratio models PSDB red and PSTV green which stands for pseudo STP. In addition, turbidity proxies are specified in the red edge seven O four band and the OC three based chlorophylla A product.

Speaker 4: So this is the PSD red in the psd green, so the red edge For each scene, individual pseudopithymmetry layers are generated and then composited along the temple dimension by selecting the maximum PSTV value per pixel representing the deepest least turbid observation. The temporal index of this maximum pseudo SDP is recorded and applied to extract corresponding values from the red edge and chlorophyl a letters, producing composite turbity turbidity peroxy maps. These composite products provide context for any residual tibidity and service inputs to the subsequent steps within the SAP value processing cheap.

Speaker 4: As they spoke about before, when you're selecting imagery, there are free things you need to take into consideration. We want low turbidity. When there's turbidity in the water column, the calculated DUFF is going to be more shallow than the actual value, so that's important to know. They also want little or no sunland sunglint, or the reflection of the sun off the water surface obscures the ability of the satellite sensor to properly capture reflectance and then see little or no little or no shadows due to clouds.

Speaker 4: Those are the stark areas here, shadows cast by clouds obscure the finalized DEBA product by goverestimating death values and then d little or no clouds. In the AOI we talked a little bit about that the presence of clouds also affects the final STV results by underestimating death values. Snow and ice. There's another one, little or no snow and ice. Snow and ice on or near the shore line affects the final STV results and also underestimates the death results. So I'm going to see if it's ready and it is process is incomplete.

Speaker 4: Take a look at our map results. As you can see there is our items take off.

Speaker 7: Some of these a little easier to see. So this is the s the.

Speaker 4: PSP red, and the PSDB green. We're going to focus on the PSDB red first. So now I'm going to walk into the walkay through the simplified calibration procedure. I think that there we go, so Stafady's simplified calibration procedure. As you recall I said, you normally need the charted soundings or reference survey to help tie the the PSDB down to to have actual UH true drops and meters. So we're going to convert the pseudo SDB drive from spectral ratios into true drops intometers. This option is beneficial for areas where we can't we don't have reconnaissance, or we lack recent high quality reference data.

Speaker 4: And the relationship is defined by PSDB red I'm sorry. SDB equals m one, which is the slope times PSDB e minus m not which is the offset here, where M one represents the slope and m not represents the offset. Okay, all right, so the next thing I'm going to talk about it is the offset parameter or I'm not just a side, I'm here. The offset parameter is selected by sampling a near ship pixel of the landwater interface, which serves as an approximate local data. It represents the PSD value at depth of zero and serves this initial reference point for the relationship between PSDB and water depth.

Speaker 4: When tidal information is available, the offset parameter can be further refined with a well with a known vertical reference such as local sea level. Okay, so we're going to take the magnifying glass to hover over these area along the shoreline. We'll see these values here will change through our research we know that the PSDB offset range is between point nine three and one point zero three ahead towards the interior, not the channel. See if we can find something, looks pretty good.

Speaker 7: Okay.

Speaker 4: So they now that I've clicked the offset, it fills in automatically. We're going to do the same thing for the PSDB green. Put this down to PSD green. You can usually pick the same from the same areas. The range the research for the PSB green offset is between point eight five.

Speaker 7: And point nine five.

Speaker 4: See that looks pretty good, okay. So now that's filled into the offset, so point nine to eight. We're going to go ahead and submit the parameters zum out a little bit.

Speaker 8: Okay, so a little bit more slow parameters M one. The slope M one value is roughily indicative of how strongly light it tenuates with depth in the water column. Higher tenuation, for example, more turbid or scattering waters requires a steeper slope, while lower tenuation and clear water corresponds to a short, shallower slope. An initial value is provided based on the mean of slope estimates reported by publications from doctor Rick Stump and doctor Isabel cabell Aro, which drive the calibration parameters to the regression with reference slid our data sets and or charted sellings.

Speaker 7: Go ahead and something.

Speaker 4: Because the optical properties and water clarity can read very regionally, users can fine tune the slope in plus or mine plus or minus five percent increments to better match local water conditions. Adjustment is constrained within specific ranges where the PSDB red is between four point eight eight and sixty point five and SDB green is between forty two point seven and eighty eight point four five. Supporting tubidity indicate caters such as the seven O four reddit good band, which is here in our those literary products.

Speaker 7: There we go. So that's the red edge. This is our chlorophyll. Oops, there we go. Red edge in chlorophyll.

Speaker 4: Can guide this adjustment by providing context on prevailing water quality, for example clear, moderately turbid or extremely turbid. Okay, so now that we have that, we're gonna we'll now have the We should now have the STB red and the SDV green players not available. Okay, so now you'd want to do a quick check of the SDB red make sure there's no negative values.

Speaker 7: It's pretty good.

Speaker 4: We're going to just stick to the shore between zero and five because that's really SDB red generally where the limits are and it works the best. And then the green we're going to do the same thing. Everything from probably about five meters to on should be in the positive reade. You just want to do a double check. That looks pretty good, Okay, So that's the firstness we'll check on that. So the area in Miami has basically a low tithe that is optimal first as satpathy. So in this side, I'm going to talk about the importance of tides and macro and macro tidal areas.

Speaker 4: These red points that we created represent the tie aid stations and their values are calculated through NOASV data tooled by subtracting the mean mobile water value from the mean high water value for that station. The difference between the two value represents the tide range of that location. If the tide range is a meter or less, it simplifies things for sappathy. This means and what is case dependent and general tide adjustment is not necessary because it falls within the vertical uncertainty of STB generated through satpathy.

Speaker 4: When the tide range is larger than one meter, locations require a tide adjustine because the potential difference between the title stages exceeds inherent uncertainty of SAP badie. We'll talk more about this when we discuss the time model.

Speaker 7: I'm going to go back to.

Speaker 3: Our tool.

Speaker 4: And to select the different options. I'm going to add the reference data. That's another way to kind of see if we're in the ballpark. So this is remote sensing, our Remote Senstensing Division Topo Batty data under the National Genetic Survey, and that.

Speaker 7: Will show up when I select.

Speaker 3: There we go.

Speaker 4: So let me take off our interim products so you can see this. So this is a little snippet of our data reference data.

Speaker 7: I'm gonna expand this.

Speaker 4: Because okay, so the SDB red, so the auxilliary grid the values are shown here. That's for the reference data. And I'm gonna flip this s t BE red and as they move across to see that the numbers change. So I'm gonna stick to probably close to the shore.

Speaker 4: I'm just gonna run around and see how close we are,

Speaker 4: so we don't anything like a half a meters between is okay that's roughly okay, Okay, we'll do the same thing for the SDB green. Oops again, we're gonna go a little farther out for the green. We do sometime after five meters. We're just doing a cursory glance here, all right, So those values look generally okay, We're go ahead, and you can always come back and adjust it again, but we're going to go ahead and submit those parameters. So before I hand this over to Kenna, I'm gonna talk about our motivation at Noah for integrating the ATL twenty four into SATPATHI first, also an entirely independent reference data source.

Speaker 7: It's near global coastal.

Speaker 4: Coverage is ideal for US well. It has a maximum depth ranges which are generally quite comparable to one seci up and occasionally slightly deeper and are The ATL twenty four actually tests confirm and stability for STB calibration and melidation. So, like I was talking about with the the future of SAP ABBY, the reason why we're interested in things like near global coastal coverage is because we were asked to expand the sat AALBI, to not just create a themetry just for United States, but to go globally, to work with our federal partners and with cost survey, so the global coverage is very advantageous, as well as the accuracy tests to make us assured that we're using reference data that's suitable.

Speaker 4: So that's a really incredible option and we are working on Although the desktop tool is just available for Noah at this point, there is a public site that's going to be that's in work now that's going to take advantage of the A TALE twenty four. So we'll talk about that hopefully in another presentation. Now i'd like to pass this off to Canon to talk about her ATL twenty four demo, and yeah, thank you Canon,

Speaker 4: Thank you Gretchen.

Speaker 9: So we get ATL twenty four data in side role using side rules Python API, which allows us to request granule data from October twenty eighteen to November twenty twenty four, when release one of ATL twenty four was published. In satbathy we use a greater confidence threshold than the default value of zero point six, and this is to prevent some false pathymetry that occurs, especially around the SA surface, where the bottom of the sea surface can get falsely identified as pathymetry. The ATL twenty four implementation takes the satellite derived bathymetry photon data and then it regrids that photon data into a ten meter resolution raster to match the resolution of satbathy.

Speaker 9: This is to make it easy for use with Brian Eaters valalidation code, which produces air analysis heat scatters and histogram plots for SDB red, SDB green, and SDB merged. These values can be used to validate results or can be used to recalibrate the shoreline. If we go to an example here, I have set up a sat bathy run that is very similar to what Gretchen showed us just a moment ago. I've got a similar area of interest selected. I've gone through the tile selection and calibration steps, and so now all you need to do to get ATL twenty four products is check this ATL twenty four comparison checkbox before you click create final Products.

Speaker 9: If you look at the terminal while this is happening, you'll see the request to satbathy start to generate. It's going to return photons in a random temporal order, just due to the nature of how side rule gathers the data, So we do a sort on that data from oldest to newest. That way, if a photon shares a pixel in the final raster. It'll use the newest photon to generate that pixel's value. This can take a moment to process, so I will just go and take us to an already completed folder. So to get to your data, you're going to go into your output folder.

Speaker 9: You're going to go into the prod folder and into the ATL twenty four sub folder. In here you're going to see the aristatistics, heat scatter and histogram results for your SDB green, SDB merged, and SDB red. You're also going to have a data subfolder, and in here will both be the ATL twenty four dot TIF raster that we use in satbathy, but also a CSB file that contains all the photon data retrieved for this request. That way, because it is slightly lossy to generate the raster, you can access all of the ATL twenty four data, including any of those photons that didn't make it into the final raster.

Speaker 9: This ATIL twenty four TIF, for example, can be used to pull it into qgis. I've already loaded the data just by taking the file and dragging and dropping it onto qgis with a base map already loaded in, and then I've set the colors to go from shallow, which is a yellow color, to a darker color in purple, and we can zoom in and see these lines. Here are our granule tracks that relate to the photon data that we've returned. Just an example of what this output file looks like. We'll go ahead and look at the merged values.

Speaker 9: So in merged we have air statistics, and so this will give you the median absolute air, the mean absolute air, the bias, and then the number of photons relating to that bin that we've done for the statistics. So from zero to ten meters, the bins are in two meter increments, and then from ten meters onward the bins are in five meter increments, just because in those deeper values you can see like ten six and one photon returns, the returns just get smaller. This is an example of what the heat scattering looks like.

Speaker 9: On the y axis, we have the reference data set, which in this case is ATL twenty four, and on the X axis we have SDB merged, which is the SDB merged results from our satbaty run. As we can see, there's fairly good correlation on this one to one axis, which generally means that the ATL twenty four data is validating the results from Sabbath and then we can look into these histograms that give us an idea of how the photon spread is within those bins. Alrighty, and then that's my person and I'll pass it back to Gretchen.

Speaker 4: All right, So thank you Kenna. Now I'm gonna give some major updates for tak a little bout our major updates for the SATPADI desk top version three point one. So, as Kana mentioned, we have the validation code that should be ready to show you and our actual data. So I'm gonna that's in our validation folder and we can see how we did. Let's take a look at the sv merged heat scatter PT this.

Speaker 7: Okay, so this looks pretty good.

Speaker 4: This is the generally from like I said, the zero to four is the SDB red so they get merged. The green STB, which is from about five meters to looks like it's about a little over eight. So this aligns pretty well. The reference data is not we used was again our remote sensing division to a badly lighter under the nest g n X survey. And Noah, we have a lot more data compared to the ATL twenty four, but so you can see a lot of more data points, which is nice.

Speaker 7: So it looks like it lined up pretty well.

Speaker 4: So the items, the the values we picked looked like it was on. But if it is a little off, you would do adjustment in the in the tool, you can use the multiplier. Let's say the greening was a little the dark spot was a little below that line, you would to the adjustment for the green here down here, and we probably do the minus and and you can always go back. That's the nice ability of the tools to be able to like adjust and recreate the products.

Speaker 7: It's a nice manifit.

Speaker 4: Okay, I go back to So that's and then can I already talked about the histogram and the aero statistics. I won't go over that, but it's very similar.

Speaker 7: And then.

Speaker 4: I'll talk a little bit about the tide module is I spoke about earlier. For areas like Miami and Hatteras, those are below one meter tied our water level. But for areas in Alaska that we've been working quite a bit, sometimes it can be five meters. So in these cases, in other areas where we don't have information on tide we can use the on datum SDB composite product using the Vizio tide model. So the workflows as follows. We extract a sentinel two scene gradual sensing time, query the tide model at each x y t point, create gridded water level rasters at local means level, and composite the water level of rasters according to maximum PSD RAD or green, and then finally we adjust the calibrated sd B red or SDB green products using the water level grid composite.

Speaker 4: The examples that we have to the right are for Hatterus. That's one of our test regular test locations to make sure.

Speaker 7: Everything was working correctly.

Speaker 4: Normally, we already know what the tides are for Hatteras, but for places outside that don't or uh maybe globally that we may not know is that will be very helpful.

Speaker 7: And there is plans to update to the most recent of the ZIO.

Speaker 4: So going back to where everything is, if you want to access that, that's in the SDB UH composites folder. So if you see the local mean sea level and at the beginning of the tiff and the water level, I have that already up along with the final project SDB merged, so this is the SDB merged this is indicates deeper water.

Speaker 7: Here and then.

Speaker 4: The tie module local mean Sea level adjusted. That is the product for my the MIAMI so resources going forward for folks that are looking for more information on sat pavvy and actually the research that all went into it. These are papers from doctor Cabellero and doctor Rick Stump,

Speaker 4: and I really like to do a special thanks to the SAUT Valley team, especially Brian Atter who has been a huge help for all their continued support, and thanks to NASA for allowing us to do this presentation and training.

Speaker 1: Thank you Gretchen and Keana for the terrific presentation and demonstration on satpathya. The following slides provide a summary of the concepts covered in part two of the webinar series. ATL twenty four can be integrated with optical bands blue and green green wavelengths for driving satellite drive pathymetry. Slide roll is a public web service with low latency access to on demand data products stored in S three. Slide role provides on demand processing next to the data for generating customized data products using parameters supplied in the user's request.

Speaker 1: Slide roll Python client can be used to access ATL twenty four. Slide roll provides documentation for the Python client, including example scripts, user guide, and developers Guide. SATPATHI is a hybrid web desktop tool created by Noah for automating automating satellite derived pathymmetry. Satpathy utilizes ten meter resolution satellite imagery from the Copernicus Sentinel two mission, Amazon Web service and Acolyte Atmospheric Correction processor. Satpathy integrated the ATL twenty four product as an independent reference data source.

Speaker 1: The following is a summary of topics covered over both days of the training. NASA's i AT two carries a photon counting laser altimeter named Atlas. Atlas provides near contiguous a long track sample using six individual beams of green light at five hundred and thirty two nanimeter wavelength, providing high vertical resolution. Every photon detected by Atlas has a latitude, longitude, and elevation associated with it. National Snow and Ice Data Center provides access to ATL twenty four data metadata and tools.

Speaker 1: Slide roll is a public web service with low latency access to on demand data products stored in S three. Slide roll Python Api can be used to access ATL twenty four. Satbathi is a hybrid web desktop tool created by Noah for automating satellite drive bathymetry. Satpathy utilizes ten meter resolution satellite imagery from the Copernicus Sentinal two mission Amazon Web service and Acolyte. Satpathy integrates the ATL twenty four product as an independent reference data source. Before we transition to the Q and A session, I want to remind you there will be one homework assignment accessible from the training page starting today.

Speaker 1: Answers must be submitted by Google Form with a due date of December thirty first. To receive a certificate of completion, you must attend all live webinars and complete the homework assignment by the deadline. You will receive a certificate via email approximately two months after the completion of the course. We want to thank once more doctor Christopher Parish from Oregon State University, JP Swinskey from NASA's Goddard Spaceflight Center, Gretchen Emilhore from Noah, and Keana Key from Oregon State University for their presentations and instructive demos.

Speaker 1: Below is the contact information for Chris, JP, Gretchen and Keana, along with links to the urset website and social media. If you enjoyed today's webinar, we hope you will sign up on the r set list serve to receive notifications of future trainings, and please follow us on social media for other relevant announcements pertaining to NASA's Earth Sciences. We will now transition to the question and answer portion of today's training.

Speaker 3: Okay, can you guys hear me?

Speaker 1: Okay, yes, Gretchen, Yes, we can hear you.

Speaker 3: Yes, thank you. I can start on the questions, starting with the question nineteen. Does that sound okay?

Speaker 1: Actually we start from the first, so we're going to.

Speaker 3: Jump Oh, I beg your pardon.

Speaker 2: So yeah.

Speaker 1: So question one, When integrating ATAIL twenty four data sets from ice AT two for shallow water but symmetry, do you typically use machine learning approaches? If yes, which techniques are most frequently applied example given random forest, convolutional neural network XG, BOOST, et cetera. And why are they preferred over traditional methods?

Speaker 2: I think that was my answer to that question, and then sorry, my answer ended up in a couple of different pluses on the on the screen there. That's a really active research topic. It seems like their paper is being published every month on looking at different machine learning methods for STB, So I would just point to a couple of recent review papers that were published on STB. And also we did a recent I st to bothymmetry review paper that was by John at All, just published this year. It contains a lot of references.

Speaker 1: Hey, Chris, thank you so much. Question number two, could you suggest some case studies or I sad to ETL twenty four imagery has been applied for ecosystem monitoring or restoration projects, for example, chloral refmapping, seagrass monitoring, or coastal habitat restoration.

Speaker 2: Yeah, I can take that one too. There was a paper bile Aqualic at All. Mike Sinskay of NASA was also a co arthor on that. Sorry, I was trying to pull up the exact reference for it, but that was a paper specifically looking at I said tube pothymetry for benic habitat mapping. And then our research group is actually currently looking at use of INTL twenty four for core refrustration site monitoring in the Florida case. That's a collaboration with gender Extra at the University of New Hampshire. Center for coastalin Ocean Mapping.

Speaker 1: Okay, Chris, thank you so much. Question number three. If we are integrating I said to Atail twenty four data sets with multi spectral remote sensing data such as sentinels the imagery, what are the best practices to improve accuracy, Which remote sensing data sets are most frequently used for this purpose? And why are there any tips for pre processing, filtering, or modeling that can enhance the symmetry estimates.

Speaker 2: I can keep going because I put the first part of the answer in the chat, but maybe Gretchen or others will want to chat in here as well, so and I think this was covered in part in Gretchen's presentation. But careful selection of the input scenes, trying to find scenes that avoid clouds, turbidity, artifacts, compositing of several scenes can improve the accuracy or blessed atmospheric correction, which was a step that Gretchen mentioned. And then another thing that we recommend is performing accuracy tests and using independent reference data.

Speaker 2: We're available as shown and Gretchen's and Cannas demos.

Speaker 1: Chris, thank you again. Question number four, Will this work for the purpose of archaeological diving?

Speaker 2: I can keep going, but obviously anybody else jump in at any time. Archaeological marine archaeology is potentially a really interesting application of issatude with tymmetry and ATL twenty four, and it's one that I've heard mentioned in the peer reviewed literature. To date, I haven't seen a published study dedicated to marine archaeology, but I know that again, it's an area that people are interested in and I think are maybe currently actively working on. Very cool.

Speaker 1: I look forward to reading some of his papers when they're published. Question number five, Are I too ATL twenty four data sets used for applications like natural resource exploration or disaster monitoring, such as tracking sediment deposition and can they be applied in hydrocarbon exploration as well?

Speaker 2: I can take just the first part of that. Are the second part I guess on disaster monitoring. So a former student, John Herman, did an excellent study on use of solely satellite based methods. So I set to you with tymmetry and specially drive pathymetry or looking at a hurricane change analysis. And in Joe's study it was shown that we could actually see sort of at least broad patterns of special change, for example, moving sandbars that occurred to the hurricane. I think the resource exploration again, that's another kind of topic that I think people are interested in right now.

Speaker 2: It's one that I've certainly heard people mention. I don't have any studies to point to off the top of my head.

Speaker 1: Chris, thank you so much again. Question number six, how does the vertical accuracy of HL twenty four compare to standard single beam hydrographic surveys in clear water? Can I use ATL twenty four to validate or even correct my existing field data?

Speaker 2: This one was also my response, and again anybody else, any of the other panelists this time, and I was trying to answer some of the questions in the in the chats as other four presenting my answer to this one, I said, too, is generally not as accurate as a single beam or multi beam ecosounding. We did a study of ATL twenty four accuracy and that was just published in a paper an Earthen Space Science earlier this year. What we found from the ATL twenty four pothymetry we had eight different test sites.

Speaker 2: The RMSS of the ATL twenty four pythymmetry was zero point six eight or sixty eight centimeters using all points, and if we filtered by just high confidence points. It was point four to three meters or forty three centimeters, So I think you know, half meter or better than half is really quite good for a lot of applicussions, but still not approaching what you can get from sonar.

Speaker 1: Great Chris, thank you again. Question number seven, did you use Sentinel two data instead of Lance hat for this for the spatial resolution for the model? Does this improve the model?

Speaker 2: We've used both. We've used both Lance eight nine and Sentinel two both can both can be great sources of input data for for SDB. The Sentinel two special resolution is a little bit better in the in the red, grin and blue spectral bands, which can be helpful.

Speaker 1: Hey, great Chris, thank you. Question number eight, My area of interest is a coral reef with high morphological complexity such as rough slopes, seven drop offs, and narrow values. Since the ice that two laser footprint is roughly eleven to seventeen meters, does it effectively capture these sharp changes or does it smooth out the the athymmetry too much for precise coastal mapping.

Speaker 2: Yeah, that one again was a great question, and that's correct. I said two footprint is around eleven meters, and that can be a limiting factor and extracting a really high frequency spatial detail. Something that's important is that the along track special resolution of I said two is quite good. Points in the along track direction and nominal points spacing is about seventy centimeters, so that can help and resolving features. But really my main suggestion here is just investigated and several of these talks by you know, JP and others, you saw examples of using that slide real web client.

Speaker 2: That's a really fast and easy way to go in and visually grab some ATL twenty four data for your site, and I would just start there in terms of looking at how good the resolution is and if it's going to support your needs.

Speaker 1: Awesome, Chris, thank you so much. Question number nine, when do you change when you do the change analysis when Sentinel two? Do you calibrate the reflectance with ATL again or use the regression coefficient from the original calculation and apply it to all the sentinel imagery.

Speaker 2: My recommendation is if you're doing multi temporal analysis, so change detection or something like that where you've got input scenes from different times, I would, if at all possible, I would do the calibration separately for each epic, but if you wanted to, you could start with the calibration parameters for the first from the first epic and and just rEFInd them as needed.

Speaker 1: Awesome, thanks Chris. Question number ten data collection model for water turbidity based on size, weight, composition, settling rate, overlay with world currents and closest coastal impact. Question mark does it need to include baseline TURBIDIV levels possible to make moving animated data overlay over seasonal title and trade wind change prediction models for cumulative impacts?

Speaker 2: My answer here was this sounds like an amazing tool. To the best of my knowledge, that's not something that currently exists, but if there are people who want to take the lead on developing something like that, I can see it being beneficial to a lot of different people.

Speaker 3: Great.

Speaker 1: Thank you again, Chris. Question number eleven, how can tide be applied or moved from pseudo satellite drive asymmetry.

Speaker 2: I'll give my answer here and Kretschener theres may want to jump in, so regarding tide correction, and if you're doing single scene STV, so you're not compositing multiple scenes, and if your calibration data are already reference to a vertical data, then your output STB are going to be in cparently a reference to that same vertical datam as the reference data that you're using for calibration. And so for that reason, in that specific case, you don't need a TI a dedicated tide correction step. And to kind of explain that if you're doing if you're doing a linear transformation from your pseudo SDB to SDB and the transformation parameters come from something that's already referenced to a vertical datam any kind of vertical shift, for example, digit tides is just going to be inherently accounted for the time that you do need a tide correction is if you're using if you're sort of compositing or pulling together multiple stands collected at multiple different stages of tide.

Speaker 1: Hey, Chris, thank you. Question twelve, Since the interest is in the symmetry, would you want to sample satellite data just after ice off to avoid photoplankton that might be growing in the water column in the later spring and summer which could mask the bethymmetry.

Speaker 2: Quite possibly, Yes, I would monitor global turbidity or you know kdie four ninety data sets to try to find optimal times for optimal water clarity. That is generally going to be the driving factor and whether you get good with ymmetry.

Speaker 5: Or not.

Speaker 1: Gay question number thirteen. Does the stump approach only work with the ratio of the log of blue and green or could other band ratios be applied?

Speaker 2: Absolutely, you can do other band ratios, and I think this was shown in Gretchen and Cannis talks for example, they're SDB green and SDB red. Those refer to, respectively, ratios of log blue to log green and then log blue to log red. So those are those are both combinations that can be used and really any any other combination essentially as well, if you're working with hyperspectrol imagery for example.

Speaker 1: All right, Chris, thank you again. Question fourteen. Have you tried to compare your depth data or the data source being discussed against real world bethymmetry data products such as the Noah Blue topo to assess its accuracy?

Speaker 2: We have in that accuracy test that I think I put in the in the chat that was a comparison against Noah data. A lot of it was a lot of it was airborne Tobo Bethy Ladder data available on Noah Digital Coast, and a lot of that same data is available in Blue Topo. For the next release, we're going to try to do an automated accuracy test using all of the Blue Topo, all the Blues, all the Noah Blue TEPA data, but the development of that is something that's still on the works.

Speaker 1: Okay, thank you, Chris. Question fifteen, what quality criteria in ATL twenty four would you specify to filter out and noisy data? Since I we'll be working with an inland water body, can you specify a comparable quality criteria for ATL thirteen data?

Speaker 2: For ATL twenty four, we've typically recommended a confidence threshold, So one of the parameters is that confidence parameter It was discussed in Laurie mcgroder's presentation that provides a confidence of the symmetry classification. We've used a threshold of point six as a good starting point for filtering out some of the lower confidence data, but that's kind of site specific, so you might want to sort of play around with that and fine tune it. The second part of the question is correct ATL thirteen is the is the corresponding products for inland waters thirteen provides a lot of other parameters, but there is a there is a pythymmetry parameter.

Speaker 2: To the best of my knowledge, there is not a similar confidence parameter. But I would definitely encourage rating the algorithm. Theoretical Basis document which provides all the technical specifications for ANTIL thirteen data product.

Speaker 1: Terrific. Question number sixteen, how would you integrate the SDB layer with the point the themetry data?

Speaker 2: This may have been where Gretchen was going to step step into this. Gretchen or somebody else want to want to pick up the questions from this point or would you all like me to keep going?

Speaker 5: Allright?

Speaker 3: Not hearing anyone else, I'm sorry because I was muted. Go ahead, I was going to pick up at nineteen.

Speaker 2: Okay, all right, I'll I'll turn it over. You integrate the STB layer with the point the theymmetry.

Speaker 4: I'm not.

Speaker 2: I'm not totally sure I understand the question. But if it's referring to how do you actually how do you take your pseudobythymmetry and then integrate that with the individual points from ETL twenty four, that was one of the steps that I should how to do that in my demo just using QGIS and and basically it was using that sample raster values tool to have the pseudo SDB and then just at each at each raster or each location of ATL twenty four point to sample the corresponding pseudo STB value.

Speaker 1: Okay, Question seventeen, what about the difference in pseudo STB per S two detector?

Speaker 2: Yeah, I think that's I think that's interesting. One of my recent students, Art mcculluugh, I looked at actually using small sets where you've got you know, each each individual satellite has its own imaging sensor on it, so you've got just differences between the different sensors. I think that's just a I don't have detailed information other than I think that's a great question and most definitely an area for ongoing research.

Speaker 1: Great question number eighteen. I've collected hydrographic survey data across my steady area. How should I combine field based hydrographic data with ice AT to a long track measurements to create a more robust validation data set. What are the trade offs between them?

Speaker 2: My answer to this one just in terms of things to consider, you know, need to pay attention to different special resolutions and accuracies. Different datums can definitely be a factor when you're trying to combine data. So for data collected in the US knows vertical datum transformation utility be datum can be a useful utility for that.

Speaker 1: Great question number nineteen, Where can I access the satpathy tool?

Speaker 3: Okay, yeah, Now the satvati tool is for noah internal use. Is a desktop tool, but we are working on a larger cloud effort, public cloud effort. This will incorporate lessons we have learned over the years as we've worked with Noah's Office of cod Survey for our reconnaissance projects with them and our other federal mapping partners. But I think you gave my sean as my information on the slide, so folks that are interested, you can feel free to contact me for updates on our status.

Speaker 1: Great Gretchen, thank you so much. Question twenty the example in Alaska was fascinating. Have there been any studies looking at the efficacy of these approaches in polar regions in particular given the challenge is presented by snow and ice.

Speaker 3: Yeah, that's a really good question. We've found that working in Alaska has been very challenging and we have been trying to do, where possible some parallel research along with the SDB reconnaissance operational work for NOS office A coast survey. Alaska alone is challenging areas, so there's more work needed there as well as the other northern areas and polar regions.

Speaker 1: Okay, christ thank you. Last question twenty one, I missed the definition of SDB underscore merged and SDB on the score read. Could you explain.

Speaker 5: H questions?

Speaker 2: Should I answer this?

Speaker 3: Yeah, Brian, thanks, that'd be great.

Speaker 2: Great.

Speaker 10: So, S tob red and SB green are the two products that are created from the or else SDB algorithm or s A coach. We found the S to b read typically works better in shallow regions, while SMB green is more suited for deeper water. So s to be merged us as a combination of those two algorithms, relying on s tob red for the shallow water and then transitions to s tob green for the deeper areas. More information about how that works could be found in some of the publications that were listed.

Speaker 1: Brian, thank you and thank you to everybody that asked questions, and I want to thank everybody that attended today's training, actually both parts of the training. Today was the last day of this two part webinar series, so for wherever you were joining from, thank you so much. We also want to thank once again our amazing presenters, Doctor Christopher Parish from Oregon State University, JP Swinsky from NASA's Goddard Space Light Center, Gretchen Imohorami from NOAH, and also Key from Oregon State University.

Speaker 1: We also want to thank subject matter expert Brian Edter from NOAH from joining today to help as a subject subject matter expert answering some of the questions, and also want to thank Amy Neely from NASA Goddard Space Flight Center as well for her vision in having this training take place. So thank you to all the wonderful presenters and we look forward to seeing you in the future at an upcoming our set training. Thank you

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