NASA ARSET_ Monitoring Earthquakes_ Volcanoes_ and Landslides with NISAR_s InSAR Capability

Speaker 1: Hello, and welcome to this webinar series harnessing NYSSAR next

Speaker 1: Generation Radar observations for Earth applications. I'm Erica Potis. I'm

Speaker 1: a scientist that NASA's Jet Propulsion Laboratory, and I'm also

Speaker 1: an instructor with the RSET program. Today's session will focus

Speaker 1: on the use of nisar's InSAR capability that's interferometric synthetic

Speaker 1: aperture radar to monitor earthquakes, volcanoes, and lass lights, which

Speaker 1: will be delivered by invited expert doctor Eric Fielding from

Speaker 1: NASA's Jet Propulsion Laboratory. This is a training outline. Today

Speaker 1: is the third and last session of this three part

Speaker 1: webinar series. There is a homework associated with this training

Speaker 1: and it can be accessed through the training web page.

Speaker 1: The do date for the homework is August six, and

Speaker 1: a certificate of completion will be awarded to all of

Speaker 1: those participants who attended all three sessions live and complete

Speaker 1: the homework assignment by the do date. How to ask questions.

Speaker 1: To ensure we see your question, please write your question

Speaker 1: in the Q and A box which you can locate

Speaker 1: it by doing a click on the three dots in

Speaker 1: the bottom right of the window in the platform, and

Speaker 1: there you'll see a slider, a Q and A option,

Speaker 1: and you can also see a standalone slideer, tap or

Speaker 1: app write your questions. Are all your questions there, and

Speaker 1: we will answer them during the Q and A session

Speaker 1: at the end, and we will try to answer all

Speaker 1: the questions during the Q and A session. The remaining

Speaker 1: questions will be answered in a Q and A document

Speaker 1: which we will post on the training website about a

Speaker 1: week after the training. Today's guest instructor is doctor Eric Fielding,

Speaker 1: who's a research scientist that NASA's Jet Proportion Laboratory. He's

Speaker 1: a geophysicist and an expert in the use of radar

Speaker 1: interferometry or InSAR for studying tectonic movements, landslides, and other phenomena.

Speaker 1: Doctor Fielding is also part of the NISAR science team.

Speaker 1: He's a globally recognized expert in the use of InSAR

Speaker 1: and has supported many or all of the InSAR sessions

Speaker 1: that our set has offered. So I'm extremely grateful for

Speaker 1: his involvement today as a guest instructor in this training.

Speaker 1: Welcome back, Doctor Fielding.

Speaker 2: My name is Eric Fielding. I'm a geophysicists at the

Speaker 2: Jet Propulsion Laboratory operated by the California Institute of Technology

Speaker 2: in Pasadena, California. Today, I'm going to be talking about

Speaker 2: how NISAR can be used to monitor geologic hazards, including earthquakes, volcanoes,

Speaker 2: and landslides using the InSAR capability of NISAR.

Speaker 1: Here's the.

Speaker 2: Overview what we're going to cover. Interferometric SAR or InSAR

Speaker 2: measures the displacement of the surface. We're going to concentrate

Speaker 2: in this part on the geologic hazards. Earthquakes, volcanoes, and

Speaker 2: landslides are the main ones, and I'm going to be

Speaker 2: showing that. This image on the right here is just

Speaker 2: a eye candy image of the interferogram for the volcanic

Speaker 2: eruption of this volcano and Ethiopia called Hailey Goopy that

Speaker 2: erupted in November twenty twenty five, fortunately just after nine

Speaker 2: Star was ready to start a collecting science date. The

Speaker 2: prerequisites for this training are the fundamentals of remote sensing training.

Speaker 2: The prerequisites for this training, assuming if you have no background,

Speaker 2: that would be to follow these previous r set trainings

Speaker 2: on fundamentals of remote sensing. There was one on SAR

Speaker 2: processing and data analysis in twenty seventeen, and a more

Speaker 2: extensive introduction to SARN deffrometry in twenty seventeen, or some

Speaker 2: other equivalent experience in understanding SARN defrometry. I'm going to

Speaker 2: give a here, but not all the details, and then

Speaker 2: of course the two previous sessions in this series. Okay,

Speaker 2: so I'm going to be covering now the how SAR

Speaker 2: diffromatory works. By the end of this training you should

Speaker 2: be able to understand what InSAR products are in their characteristics,

Speaker 2: have some idea about the uses and limitations of INSER

Speaker 2: data from NISAR, and see how we can use NICAR

Speaker 2: insert to study large displacements of volcanoes and earthquakes, and

Speaker 2: also how to use that InSAR to study more gradual

Speaker 2: displacements of landslides and other more slow moving phenomena. So

Speaker 2: I'm just going to give a quick review of what

Speaker 2: was in that twenty seventeen R set training on the

Speaker 2: sarrow diffrometry theory. In sorrow defrometry, the key thing is

Speaker 2: the phase of the SAR signal. And many other applications

Speaker 2: for mapping vegetation or floods or biomass those use the

Speaker 2: radar amplitude, sorry amplitude, but we're going to be using

Speaker 2: the phase of the star signal. The SAAR phase is

Speaker 2: a measure base of the range between the satellite antenna

Speaker 2: and the ground, plus something about the complexity of the

Speaker 2: objects within a given radar pixel, because these objects are

Speaker 2: essentially randomly located within the pixel. The way that we

Speaker 2: separate those random effects from the actual measurement, which is

Speaker 2: the range from the satellite to the ground that we

Speaker 2: want to measure by using sorrow deffrometry or differencing of

Speaker 2: the radar phase. So it's just a simplified version of

Speaker 2: the SAR phase. We have a phase observation five one,

Speaker 2: which is equal to four high over the radar wavelength

Speaker 2: Lambda times the range row one plus other constants which

Speaker 2: are those distribution of scatterers within the pixel, and then

Speaker 2: plus in one which is noise system noise. And then

Speaker 2: for the second image, which is five two, again we

Speaker 2: have the same constant at the beginning for bio verlambda

Speaker 2: the radar wavelength times the range of the second acquisition

Speaker 2: between the satellite in the ground and other constants which

Speaker 2: we are going to assume are the same. And then

Speaker 2: there's some noise, which we hope is small. Because these

Speaker 2: other constants can't be directly measured, what we do is

Speaker 2: use sarn defrometry to basically we subtract phase one from

Speaker 2: phase two or phase two from phase one to cancel

Speaker 2: out those other constants. This is a simplified view of

Speaker 2: what we're looking at. If for what we call differential interfrometry,

Speaker 2: where we're trying to measure the displacement of the surface,

Speaker 2: if we're lucky to have our two satellite passes exactly

Speaker 2: the same location, then then T one and T two

Speaker 2: at T one and T two, the two antennas are

Speaker 2: in the same place, and then the range at T

Speaker 2: one and range at T two can directly measure the

Speaker 2: change in range, which is what we want to measure. Again,

Speaker 2: we get this, We want to look at this change

Speaker 2: in phase, which is then proportional to four pi over lamb.

Speaker 2: At times the change in range grow. Another key aspect

Speaker 2: of Sarron defrometry is that we can only measure the

Speaker 2: radar phase modulo two pi. Basically, the phase is going

Speaker 2: around in a circle and it's in the complex numbers,

Speaker 2: and we can only measure the phase modulo two pie.

Speaker 2: So This top graph here shows the actual phase that

Speaker 2: we want to measure on them. It's the distribution of

Speaker 2: phase across a line across the program. And after we

Speaker 2: do the measurement, then we end up with this wrap

Speaker 2: phase where all the values are between minus pi and

Speaker 2: plus pi. And you can see then we have these

Speaker 2: discontinuities where it suddenly goes from minus pi to plus pie.

Speaker 2: And there's several of these discontinuities here, and that's what

Speaker 2: we call the wrapped phase. And then what we do

Speaker 2: in the processing is use a computer program that specialized

Speaker 2: for doing what we call phase unwrapping. It tries to

Speaker 2: go through the whole image and figure out where these

Speaker 2: discontinuities are and how to reconnect them and get back

Speaker 2: to the original phase. Although the original phase, I mean

Speaker 2: the unwrapped phase will be different from the original phase

Speaker 2: by some arbitrary constant that we will have to deal

Speaker 2: with later. These other equations here we're not going to

Speaker 2: go into in detail, but there is some bird pairs

Speaker 2: of satellites where there's some difference between the two antenna locations.

Speaker 2: There's also some phases to the topography, and we will

Speaker 2: subtract that out by knowing the topography. Another key aspect

Speaker 2: of sorrow defrometry is that it's there's a coherence and

Speaker 2: that's basically a measure of that noise term that we

Speaker 2: had in the in the first equations, where there's some

Speaker 2: amount of noise in the phase one in phase two measurements,

Speaker 2: and if that noise is high, then the coherence is low,

Speaker 2: and if the noise is small, then the coherence is high.

Speaker 2: In the past, many people have used the word correlation

Speaker 2: because this coherence is is measured by the correlational phase

Speaker 2: between adjacent pixels. If you look at papers from a

Speaker 2: decade ago or more, you almost always see the word correlation.

Speaker 2: But in the last ten years people have started using

Speaker 2: the word coherence for this measurement. It's really the same thing,

Speaker 2: but they are all the recent papers and the InSAR

Speaker 2: products from NISAR use the word coherence. So the key

Speaker 2: things that cause this noise are thermal and process or noise.

Speaker 2: This is generally very small different differential, geometric and volumetric scattering.

Speaker 2: This is where the radar waves are bouncing off of

Speaker 2: a volume of scattering objects. Typically this is a forest

Speaker 2: where the radar may be bouncing off different layers in

Speaker 2: the forest, and that's heavily dependent on the radar wavelength.

Speaker 2: Another thing that can cause noise is rotation of the

Speaker 2: viewing geometry. With modern satellites, that's not a concern. And

Speaker 2: then there's the issue of random motions over time, which

Speaker 2: are maybe a tree fell down, or a rock rolled

Speaker 2: down the hill, or there's a landslide that completely disrupted

Speaker 2: the hill. So these our examples of the types of

Speaker 2: things that cause a loss of coherence or will be

Speaker 2: also cause decorrelation, and we can relate this coherence or

Speaker 2: correlation to the standard deviation of the phase or an

Speaker 2: it's basically an estimate of the standard deviation of the phase,

Speaker 2: so that if affects how accurately we can estimate height

Speaker 2: from InSAR. There are ways of using InSAR to measure elevations,

Speaker 2: although not with the nicar satellite or displacements which we're

Speaker 2: going to be talking about here. And the loss of coherence.

Speaker 2: If the coherence is low, then that also affects our

Speaker 2: ability to unwrap phase because it's more difficult to know

Speaker 2: which phase jumps need to be connected. So now I'm

Speaker 2: going to be talking about then insur data products and

Speaker 2: their characteristics, So this is a simplified processing diagram for

Speaker 2: the NiCr system. The NIC processing system. Level zero A

Speaker 2: is the data that came down from the satellite that

Speaker 2: gets reformatted and with additional information becomes a level zero B.

Speaker 2: These are the raw data products not usually used by

Speaker 2: regular users, although some power users do their own processing

Speaker 2: with the levels zero data. That's then processed into the

Speaker 2: Level one which is in radar coordinates. The R here

Speaker 2: means radar coordinates or range Doppler coordinates. The r SLC

Speaker 2: is the range Doppler single look complex image. This is

Speaker 2: then the key product that's used to make all the

Speaker 2: level two products. The Level two products are all in

Speaker 2: geographic coordinates, so they're geocoded. The first one is a

Speaker 2: geocoded single look complex image. This is a new data

Speaker 2: type that is as far as I know, not been

Speaker 2: produced by any other satellite mission, although the Opera project

Speaker 2: that JPL has been reprocessing Sentinel one data to a

Speaker 2: similar product that they call the CO registered SLC Courridge

Speaker 2: single complex as a post processed from the original ISA

Speaker 2: data products. That's an r as a Range Doppler SLC.

Speaker 2: There's a geocoded covariance matrix, the g COVE that's the

Speaker 2: amplitude images with radiometric terrain correction that's used by ecosystems

Speaker 2: and other other studies of the radar amplitude. And then

Speaker 2: there's these two types of interferometric products. The geocoded offsets

Speaker 2: which are used for cryosphere or the very large motions

Speaker 2: of ice, and the geocoded unwrapped into programs. The gui

Speaker 2: W with this red circle here, which is what we're

Speaker 2: going to be talking about today, that's the useful for

Speaker 2: solid earth and the parts of the criosphere that are

Speaker 2: not moving so fast. And these other are these other

Speaker 2: boxes here show the intermediate products that that they're used

Speaker 2: in the processing where the UH to make the final

Speaker 2: GEW coded products. There's a range Doppler offset field range

Speaker 2: Doppler interfer wrapped inter program. Then that's unwrapped to a

Speaker 2: Arrange Doppler unwrapped INTERFERGRAM and that's finally jew coded to

Speaker 2: make did GEW coded unwrapped into the g u n W.

Speaker 2: So the nice R Level two g u n W

Speaker 2: file is it's an HDF five file like all the

Speaker 2: other nice R products and has multiple layers inside of it.

Speaker 2: So these are some of the key layers. There's an

Speaker 2: unwrapped phase layer that's the actual unwrapped phase. It's at

Speaker 2: eighty meter resolution. Looking down to the below that we

Speaker 2: have the interfergram coherence. That's also for the wrap unwrapped

Speaker 2: phase that's also eighty meters. Then there's a wrapt phase.

Speaker 2: They actually include the wrapped into parrogram within the gu

Speaker 2: NW file at a much higher resolution. This is a

Speaker 2: twenty meters pixel spacing, and the wrapped into paragram then

Speaker 2: has these individual color contours that are the all the

Speaker 2: phases between minus pie and pie that repeats across the scene.

Speaker 2: And another key layer is the anospheric phase. In this

Speaker 2: particular slide we've taken the the anospheric phase is normally unwrapped.

Speaker 2: It comes as an unwrapped phase at eighty meters, but

Speaker 2: it's been re wrapped here just for a better comparison

Speaker 2: to the wrapt phase, which shows that almost all of

Speaker 2: these fringes in the in the wrapped phase interferogram are

Speaker 2: due to the honosphere. And that's one of the key

Speaker 2: things about SAR in defrometry. With the L band radar instrument.

Speaker 2: L band as a twenty four centimeter wavelength and it's

Speaker 2: much more sensitive to the anospheric effects than shorter radar wavelengths,

Speaker 2: So the anospheric phase is a key thing that we're

Speaker 2: going to need to subtract for most of the types

Speaker 2: of analysis that we do. The other two panels here

Speaker 2: show solid earth tide, which is the people are probably familiar,

Speaker 2: I'm sure familiar with the tide of the oceans, But

Speaker 2: it turns out that the gravitational attraction of the Moon

Speaker 2: actually also causes the solid earth to flex somewhat and

Speaker 2: that can have a small but a measurable effect on

Speaker 2: the interferogram. So we include a calculation of that in

Speaker 2: case you want to remove this small but easily determined effect.

Speaker 2: And then another layer is the tropospheric phase. So the

Speaker 2: homosphere is a part of the atmosphere that's very high

Speaker 2: above the earth over basically over one hundred kilometers, and

Speaker 2: the tropospheric phase is the lower fifteen kilometers where we

Speaker 2: all live and where most of the water vapor is,

Speaker 2: and that water vapor actually causes an effect on the

Speaker 2: propagation of the radar waves. So we also include an

Speaker 2: estimate of what the tropospheric phase is from a weather

Speaker 2: model provided by the European Center for Medium Range Weather Forecast.

Speaker 2: So as written down here, the unwrapped phase and coherence

Speaker 2: and the anospheric phase are stored at eighty by eighty

Speaker 2: meter pixels. There's there's a water and subswath mask also

Speaker 2: at eighty meters. There's the twenty by twenty meter pixels

Speaker 2: of the wrap phase, and then there's a there's also

Speaker 2: a wrapped phase coherence layer that also has that twenty

Speaker 2: meter pixel size, much much higher resolution than the wrapped

Speaker 2: into unwrapped coherence, and then there's these the tropospheric delay

Speaker 2: and solidar died are actually stored as three DQ's which

Speaker 2: is a little bit complicated to explain and more advanced topic.

Speaker 2: And the range Doppler into program and range Doppler unwrapped

Speaker 2: into program are only saved and stored in the in

Speaker 2: the archive for over criosphere some areas of Greenland, Antarctica,

Speaker 2: and other major ice sheets. So this is sort of

Speaker 2: a review from the session two from the ASF folks

Speaker 2: about how NYS data are stored in the NASA Earth

Speaker 2: Data System. The Earth Data System is hosted in the

Speaker 2: Amazon Web Services cloud. Earth data can be searched directly

Speaker 2: from the Earth data search tool at this URL find

Speaker 2: that not as easy to use as the more specialized

Speaker 2: search tool at the Alaska Satellite Facility ASF that's specifically

Speaker 2: designed for searching for SAR data including NYSAR, So I'll

Speaker 2: be showing how to use that. Heidi also showed and

Speaker 2: that her presentation, but I'll be showing it just for

Speaker 2: specifically for GNW data. So you can download either from

Speaker 2: either one of these user interfaces, and it's the same data,

Speaker 2: it's just the user interface is different. The ASF guide

Speaker 2: to nice Data download usage and Tutorials is excellent and

Speaker 2: I highly recommend going to the nicsartdocs dot ASF dot

Speaker 2: Alaska dot edu page to find out more information about

Speaker 2: how to do the downloads and various usage, and they

Speaker 2: have some tutorials there. At the time that we're that

Speaker 2: I created this presentation in June twenty twenty six, we

Speaker 2: only have a limited set of uncalibrated data that were

Speaker 2: released publicly in February, and that's what I'm going to

Speaker 2: be showing today because that's what's available now. The planned

Speaker 2: release of the calibrated data is in July, and this

Speaker 2: part of the af nice art docs page, the updated

Speaker 2: release timeline, which they update frequently, so you can see

Speaker 2: what most recent timeline is for the big calibrated data

Speaker 2: release in July. So I'm going to show here how

Speaker 2: to search for the GEOMW. Do you go to unwrapped

Speaker 2: into programs through the ASF search tool. Usually if you

Speaker 2: go to the search tool, it comes up with the

Speaker 2: data sets set to NISAR automatic as default, but if

Speaker 2: it's not, then you have to choose NISAR. Draw a

Speaker 2: box around your area of interest. This is an area

Speaker 2: in Ethiopia where the volcanic eruption happened in November. I

Speaker 2: just drew a box around the two volcanoes, and there's

Speaker 2: a work Ali volcano and the adjacent Highly Gooby. The

Speaker 2: eruption actually came out of Yily Gooby, but there was

Speaker 2: additional depth information underneath Workdale because the magma drained out

Speaker 2: of Urdali and went into the Hailey Gooby volcano. So

Speaker 2: once you've selected your box, then you can add a

Speaker 2: search filter with the filter button at the top of

Speaker 2: the page and come down here choose science product G

Speaker 2: and W two only get the j code unwrapped into programs.

Speaker 2: You can also choose a date range if you only

Speaker 2: want to see certain dates here, and this is the

Speaker 2: result of that search for the GNW files over the Hailey,

Speaker 2: Goobie and Erdale area in Ethiopia. One of the key things,

Speaker 2: of course, we're talking about interferograms. So the interferograms have

Speaker 2: two dates of the before date and the after date.

Speaker 2: In the list here, the interms are always listed by

Speaker 2: the first date of the pair. And because we're trying

Speaker 2: to see this eruption that happened on November twenty third,

Speaker 2: we choose this interfergram that has the first date on

Speaker 2: November twenty second, and then which then has a second

Speaker 2: date twelve days later on the December fourth, and so

Speaker 2: that's on the left here you choose the specific scene,

Speaker 2: and then in the middle here it shows details about

Speaker 2: that scene. Is a very complicated naming convention here, but

Speaker 2: the ASF interface shows the key piece of information down

Speaker 2: here extracted out of that name, the start time of

Speaker 2: the first date, stop time of the second date, the

Speaker 2: track number, the frame number. It's an ass sending track.

Speaker 2: This frame is a full frame. The interferogram polarization is

Speaker 2: HH that's horizontal transmitting, horizontal receive and the range bandwidth

Speaker 2: which determines the resolution is twenty mega hurts. And then

Speaker 2: there's this CRID that's the composite release ID that tells

Speaker 2: you the specific version of the software that was used

Speaker 2: to create that product. This is from the preliminary release

Speaker 2: of February and it's CRID x five zero ten. The

Speaker 2: release in July is going to be P zero five

Speaker 2: zero twenty two, and that's that's how you know the

Speaker 2: difference between what processing version. So then Oliver on the

Speaker 2: right side, you get the list of the actual products

Speaker 2: for that scene. There's actually a list of products. Probably

Speaker 2: height he covered this. The top one is the level

Speaker 2: two q n w HDF five file. That's a large

Speaker 2: two gigabyte file, but there's also other files here. You

Speaker 2: press this little download button to just download. If you

Speaker 2: have not logged into earth Data in this session, then

Speaker 2: you may have to log into earth Data again. But

Speaker 2: there's also some other items here that are useful to download.

Speaker 2: There's a browse image that's the image that actually shows

Speaker 2: in this lower part of this center panel. If you

Speaker 2: scroll down to overview of the phase of the whole scene,

Speaker 2: there's a QA report that has a number of images

Speaker 2: of all the different layers and those two can be

Speaker 2: used to make sure that the scene covers the area

Speaker 2: you want to cover without having to download that big

Speaker 2: two gigabyte file. And then it's not visible here because

Speaker 2: I haven't scrolled down in the slide. There's another file

Speaker 2: called the footprint KML. By combining that footprint KML file

Speaker 2: with the PNG, then you can visualize it in Google Earth.

Speaker 2: This is just an image taken from one of the

Speaker 2: pages of the QA or Quality Assurance Report. These reports

Speaker 2: are available for all nice level one to level three products.

Speaker 2: First page it's basic metadata data about the acquisitions, and

Speaker 2: then the other pages have these types of maps and

Speaker 2: or graphs of histograms. On the left here is the

Speaker 2: full unwrapped phase. You can see it goes from minus

Speaker 2: ten to plus thirty radians. That's much more than the

Speaker 2: minus pie to pie. On the right. Here, what they've

Speaker 2: done is taken that unwrapped phase and rewrapped it to

Speaker 2: the range between zero and seven pie. This is what

Speaker 2: the data and the February release drawing uh the new

Speaker 2: The new data that's being going to be processed and

Speaker 2: available in July will have a wrap at the rewrapping

Speaker 2: it zero to two pie, which is a little bit

Speaker 2: different and there's there's similar pages for all the other layers.

Speaker 2: So this is what the that browse product looks like.

Speaker 2: If you take that PNG image and and the KML

Speaker 2: and put them in the same directory and double click

Speaker 2: on the k mL file, it should be able to

Speaker 2: load into Google Earth if you have Google Earth on

Speaker 2: your computer, or you can also load it into Google Maps.

Speaker 2: It's a very convenient way to see the coverage of

Speaker 2: the scene. The Hayley Gooby area is this corner near

Speaker 2: the edge of the scene. As I mentioned before, this

Speaker 2: is the seven pile wrapped phase and the final products

Speaker 2: will have a two pie rewrap of the face which

Speaker 2: will make it look considerably different with more fringes. So

Speaker 2: now I'm going to talk about some of the uses

Speaker 2: of the NISAR INSUR data using some examples from previous satellites,

Speaker 2: since we don't yet have a lot of good examples

Speaker 2: from NISAR. This is a map that was made more

Speaker 2: than twenty years ago of volcanoes in the central Andes

Speaker 2: of South America, and Matt Pritchard, who was then a

Speaker 2: student but is now professor at Cornell, made this map

Speaker 2: and found that there were significant InSAR signals over many

Speaker 2: of these volcanoes in the central Andes. Some of these

Speaker 2: signals were over volcanoes that had known recent eruptions, but

Speaker 2: other of these volcanoes did not have any known eruptions

Speaker 2: and weren't even known to be active. But the InSAR

Speaker 2: here then showed that these were having some amount of

Speaker 2: displacement and deformation at depth and had to be considered active.

Speaker 2: This is a more extreme event that happened also an

Speaker 2: Ethiopia and in a part called the Assault rift. There

Speaker 2: was a huge injection of magma into a shallow fissure

Speaker 2: that opened up at the two sides of the fissure

Speaker 2: spread apart by more than five meters. These are people

Speaker 2: along the edge of the fissure, but for scale we

Speaker 2: can see that this wide fissure is basically the line

Speaker 2: here the middle of this interfer man and all these

Speaker 2: fringes show that show the displacement of the ground surface

Speaker 2: away from this dike that wasn't intruded into the crust

Speaker 2: here in East Africa, and this happened in two thousand

Speaker 2: and five. This is an example of an earthquake that

Speaker 2: we that I studied with some other people that happened

Speaker 2: in twenty fifteen.

Speaker 1: It was.

Speaker 2: Mage Tu seven point eight that happened has started at

Speaker 2: the location of this star here in an area near

Speaker 2: a town called Gorka in Nepal, but it all then

Speaker 2: propagated far to the east, past Katmandu and into more

Speaker 2: almost too eastern Nepal, a distance of over two hundred kilometers.

Speaker 2: And so this map here is made from ALOS two,

Speaker 2: the Japanese as SAR satellite called ALOS two, which is

Speaker 2: also an Alban system. It was launched in twenty fourteen

Speaker 2: and it's now being being moved to retirement because it's

Speaker 2: been replaced by a new satellite of Jackson's called ALOS four.

Speaker 2: But in twenty fifteen it had just started operations and

Speaker 2: it got this excellent image of the Gorka earthquake. We

Speaker 2: can show here the line of sight direction. As I

Speaker 2: mentioned earlier, what we measure with InSAR is the displacement

Speaker 2: in the range direction. That's also normally called the line

Speaker 2: of sight between the satellite and the ground. So a

Speaker 2: lot of times they'll see this LOS or line of

Speaker 2: sight arrow showing the horizontal projection of that line of sight.

Speaker 2: But that line of sight also has a vertical dimension,

Speaker 2: which is because the satellite's looking down at the ground,

Speaker 2: and the ins are only measures displacements that are parallel

Speaker 2: to that line of sight direction. And for this specific

Speaker 2: earthquake we have we have these GPS stations that were

Speaker 2: being operated in Nepal. The triangle here shows the location

Speaker 2: of the station and the arrow shows what direction that

Speaker 2: GPS measured the ground displacement, and you can see that

Speaker 2: that ground displacement is almost perfectly perpendicular to the line

Speaker 2: of sight, and that means that we're basically not able

Speaker 2: to measure that horizontal motion because it's it's perpendicular to

Speaker 2: the line of sight and the horizontal component of the

Speaker 2: line of sight, and what we do can measure is

Speaker 2: the vertical component uh this earthquake. It was a thrust earthquake,

Speaker 2: and that means that the area of the Humalias was

Speaker 2: thrust to the south and part of that resulted in

Speaker 2: uplift in the in this area of red, including the

Speaker 2: city of Katmandu, and the area further north in the

Speaker 2: high Himalias was actually dropped down as much as as

Speaker 2: one meter h There was a it was one little

Speaker 2: extra weird thing here that that's kind of an extra complication,

Speaker 2: but we're only seeing the vertical component of this this

Speaker 2: particular earthquake. In other cases, the radar line of sight here.

Speaker 2: Usually if you see a symbol like this, the longer

Speaker 2: area is the direction of the satellite was moving, and

Speaker 2: the smaller arrow is the radiar line of sight direction.

Speaker 2: In this case, the line of sight is west and down.

Speaker 2: But we're and we're looking at displacement along this central

Speaker 2: part of the San Andreas Fault in California, and this

Speaker 2: particular part of the San Andreas Fault moves continuously at

Speaker 2: almost the full rate, so we can see after a

Speaker 2: few years this large area of discontinuous motion across the fault,

Speaker 2: showing that the fault is creeping or or moving a seismically,

Speaker 2: and that this particular section between Parkfield and San Juan Bautista,

Speaker 2: the central San Andreas, is this creeping or a seismic

Speaker 2: slip that we believe means that while it may have

Speaker 2: some small earthquakes, may be up to magnitude four, it's

Speaker 2: not going to participate in large earthquakes. And it acts

Speaker 2: to separate the northern part of the San Andres to

Speaker 2: the north of west, which is the section that ruptured

Speaker 2: in nineteen oh six from the southern part of the

Speaker 2: San Andreas fault from parksfell to the south that ruptured

Speaker 2: in last ruptured in eighteen fifty seven. Another thing that

Speaker 2: we can use the InSAR for is looking at surface changes.

Speaker 2: As I mentioned earlier, coherence or correlation measures then change

Speaker 2: the noise in the radar between the two dates, and

Speaker 2: that can be affected by both vegetation or major changes

Speaker 2: in the surface. This is a paper that I wrote

Speaker 2: twenty five years ago, so of course at that time

Speaker 2: I used the word interferometric correlation, which is the same

Speaker 2: as what we now call coherence. And in this particular case,

Speaker 2: there was this horrible, devastating earthquake in the city of

Speaker 2: Bam in Iran at six point five in December two

Speaker 2: thousand and three. This was just after the ENSAT satellite

Speaker 2: started operations, so we were very we were able to

Speaker 2: get this Envy Sat into pherogram and one of the

Speaker 2: things we noticed when we first made the interferogram is

Speaker 2: that the correlation or coherence had this line extending south

Speaker 2: from the city. The geology there in Iran. We're trying

Speaker 2: to find where the fault rupture might have been. We

Speaker 2: sent them this map and they were able to go

Speaker 2: to the field and this was basically a desert area

Speaker 2: that nobody ever went to and find the surface ructures

Speaker 2: marked by these circles at the locations where there was

Speaker 2: this decrease in the coherence in the interferogram. And then

Speaker 2: we took this a step further and looked at the

Speaker 2: change in coherence if I go back this particular the

Speaker 2: city of Bam, it was mostly it is the main

Speaker 2: population center and largely houses, with some vegetation between some

Speaker 2: of the houses. But then there was an adjacent town

Speaker 2: over here called Barravont, and this is almost entirely date palm,

Speaker 2: so it's vegetation and this low coherence over Barravot is

Speaker 2: due to vegetation, whereas a lot of this low coherence

Speaker 2: over Bam is due to actual destruction of the buildings.

Speaker 2: And so by taking the interferogram that includes the earthquake

Speaker 2: the co seismic correlation or coherence, and subtracting the coherence

Speaker 2: of another interfrogram that we made for a pair before

Speaker 2: the earthquake, then that subtracts out the effects of the

Speaker 2: of the vegetation. So Barravat is now approximately zero, and

Speaker 2: these red areas in the city of BAM show the

Speaker 2: extreme destruction of large parts of the city and the

Speaker 2: fault ruptures going south are very clear in this red color.

Speaker 2: And we also found that there was an additional fault

Speaker 2: rupture north of the city. So this is what we

Speaker 2: call coherent coherent change detection or correlation change, and that's

Speaker 2: another use of interfometry is to actually use the coherence

Speaker 2: as a measurement of service change and possibly damage to buildings.

Speaker 2: The fault rupture goes down here to the south and

Speaker 2: it ruptured towards the city, which really focused the energy

Speaker 2: into the city and cause of this horrible devastation. I

Speaker 2: think over a quarter of the population was killed in

Speaker 2: this unfortunate earthquake. So landslide motion is another application of

Speaker 2: SARI indefrometry. This particular interferred interferometric SORROW map was made

Speaker 2: with a airborne radar system that now so owned called

Speaker 2: it's called UAVSR, but it actually flies on pilot as

Speaker 2: Goldstream airplanes, and that radar was built at JPL and

Speaker 2: it flies on the piloted airplanes out of NASA aims

Speaker 2: that also uses L band like NISAR, so this is

Speaker 2: sort of a preview of what NISAR is showing but

Speaker 2: using the airborne system. The airborne system, because it's closer

Speaker 2: to the ground, can have much higher resolution, so it's

Speaker 2: not quite the same, but it gives an idea of

Speaker 2: how we can measure these very high displacements. This particular

Speaker 2: landslide as a central section here that moves over two

Speaker 2: centimeters every day, and I'll be showing another example a

Speaker 2: little bit later in Los Angeles moving almost this fast.

Speaker 2: So one of the things to think about is what

Speaker 2: are the limitations on in solar measurements. So we observe

Speaker 2: a phase here the FI observed, and it's actually the

Speaker 2: sum of all these other effects. There's the line of

Speaker 2: sight displacement, which is what we want to measure. As

Speaker 2: I mentioned earlier, there's some amount of phase that comes

Speaker 2: from the troposphere, there's some amount of phase that comes

Speaker 2: from the on sphere. There can be some phase that's

Speaker 2: due to inaccuracies in the DM that you're using for

Speaker 2: the processing. And then there's this noise term which is

Speaker 2: basically that decorrelation. And so the these phases are added together.

Speaker 2: I mean, the noise is random, so we can't really

Speaker 2: we can't subtract it out the homosphere. We have an

Speaker 2: homospheric correction layer in NISAR and atropospheric correction layer in NSAR,

Speaker 2: so we can subtract out both of these effects and

Speaker 2: get back the line of sight displacements that we want

Speaker 2: to measure. The coherence effects are multiplicative, They're not like

Speaker 2: the phase effects that are additive, and that means that

Speaker 2: any one of these effects, if any one of these

Speaker 2: different effects that are low, then the resulting multiplier multiplied

Speaker 2: coherence is also low. It also means that we can't

Speaker 2: just subtract out one effect because they're multiplied together and

Speaker 2: not added. And in this particular separation, I've separated out

Speaker 2: the volumetric coherence due to trees. There's also some amount

Speaker 2: of effect of loss of coherence due to steep slopes.

Speaker 2: There's a a coherence change that's gradual like plants growing

Speaker 2: or that's that's one of the typical ones ah. And

Speaker 2: then there's a correlation loss due to sudden changes like

Speaker 2: the landslide disruption or the disruption of an earthquake. So

Speaker 2: these are the the key components of the coherence. So

Speaker 2: now I'm going to be talking about two case studies

Speaker 2: for how applying some nice are insert data to volcanoes

Speaker 2: because I want to have this accessible to everybody right away.

Speaker 2: I'm going to be using data that was released in

Speaker 2: that February release that's already at the ASF and Earth

Speaker 2: Data Data repositories. During the time of the that's included

Speaker 2: in that February release, there were no earthquakes with significant

Speaker 2: InSAR signals, so I'm gonna not gonna be able to

Speaker 2: show you an earthquake example. So we're going to be

Speaker 2: looking at this volcanic eruption in Ethiopia that's part of

Speaker 2: the East African rift system. As I mentioned earlier, there's

Speaker 2: these two volcanoes next to each other. The Ali volcano

Speaker 2: has been very active for decades and has had a

Speaker 2: lot of eruptions, and then adjacent volcano called Haley Goofy

Speaker 2: had not been active and in historic time, and there

Speaker 2: are suggestions that it hadn't been active for as much

Speaker 2: as five thousand years, although obviously we don't have five

Speaker 2: thousand years of recorded history. It's part of the East

Speaker 2: African rift system where the eastern part of the African

Speaker 2: Plate is being as pulling apart. That gives this extensional

Speaker 2: stress to the to the rocks there and this Hailey

Speaker 2: Goobi volcano erupted suddenly on the twenty third of November

Speaker 2: without people really expecting it. What we can see in

Speaker 2: the INDO program is that in fact, the magma drained

Speaker 2: out of the art Ali volcano and the Orte Ali

Speaker 2: volcano actually had a dyke that contracted, and that magma

Speaker 2: then moved into Haley Gubi and erupted into in a

Speaker 2: quite explosive eruption that spread ash over a large area

Speaker 2: and it even sent to ash into the stratosphere. So

Speaker 2: there were ash clouds that went they blew far to

Speaker 2: the east after this eruption. So first I'm going to

Speaker 2: show you some slides with wrapped into programs that were

Speaker 2: generated by Matt Pritchard who's at Cornell. As I mentioned,

Speaker 2: he's still working at volcanoes since he's since he's been

Speaker 2: a grad student, and he made these slides. These maps

Speaker 2: are just for a small subset of the full nice

Speaker 2: our frame. So Matt went back and looked at there

Speaker 2: was one nice or Into perogram for the twelve days

Speaker 2: before the eruption, and in fact we can see this

Speaker 2: is a wrapped into program. So the the faces are

Speaker 2: mine pie to pie, which we can approximately convert to

Speaker 2: surface displacements between zero and in twelve centimeters. There was

Speaker 2: a small amount of displacement here in the haley Goobi volcano.

Speaker 2: Probably the magma was starting to move into the volcano

Speaker 2: before November twenty second, and there was some indication that

Speaker 2: this dyke underneath the earth Ali volcano had started to

Speaker 2: close or contract, so a little bit before the eruption,

Speaker 2: there was this precursor, but nobody had seen this because

Speaker 2: they hadn't bought to look at it. And then the

Speaker 2: next interferogram is the interferogram that includes the eruption. As

Speaker 2: I mentioned the haley Goobi eruption, he sent a huge

Speaker 2: amount of volcanic ash out and it h ash and

Speaker 2: other larger rocks are are called tephra by the by

Speaker 2: the volcanologists, and that tefra has covered up the land

Speaker 2: surface and and made it completely different from the two

Speaker 2: radar images. And that's why we see this area of

Speaker 2: low coherence or noise in the wrapped into program. And

Speaker 2: there's a slight hint of some possible deformation just to

Speaker 2: the west of the main Heligov volcanic peak that is

Speaker 2: still being studied. And then this is the signal over

Speaker 2: there or to Ali volcano. And what we see here

Speaker 2: this is actually uh sort of the opposite of that

Speaker 2: dike intrusion that I showed from the the one in

Speaker 2: the assault rift. In this case, the two sides of

Speaker 2: the of the dyke moved together because the magma actually

Speaker 2: drained out of the dyke during this and moved into

Speaker 2: Haley goofy.

Speaker 1: And uh.

Speaker 2: So this is the wrapped into program I'm showing these

Speaker 2: uh here because the demonstration I'm going to show is

Speaker 2: with the q g I S program and the q

Speaker 2: g I S program cannot show the wrapped into programs.

Speaker 2: And then this is the the radar track angle. It's

Speaker 2: an assending track with the radar looking to the left

Speaker 2: for the west to the left. So you've probably heard

Speaker 2: in the in the previous session about how nice are

Speaker 2: are stored in this HDF five format the HDF five

Speaker 2: files or a version of what's called net CDF. The

Speaker 2: r g I S package can read the nice r

Speaker 2: HDF five files directly, but we're I'm going to be

Speaker 2: using g DOLL and q g I S. The QGIS

Speaker 2: is an open source and free and open g I

Speaker 2: S program. It's available for a wide variety of platforms,

Speaker 2: whereas RGS only runs under Windows and the present version

Speaker 2: of g doll needs to be told that this this

Speaker 2: HDF five file should be read with the net CDF

Speaker 2: driver in g DOLL, and because QGS uses g DOLL,

Speaker 2: we also need to do that for opening it in QGIS.

Speaker 2: So there's two options. We can actually renamed the file,

Speaker 2: take the dot h five file that we download from

Speaker 2: a s F or data and change it to b

Speaker 2: dot NC for net CDF. That's the preferred option. You

Speaker 2: can also add this net CDF colon to the front

Speaker 2: of the file name, and that allows you to open

Speaker 2: it when you're doing opening the file and q g

Speaker 2: I S, but that actually doesn't work very well in

Speaker 2: QGIS for doing additional processing steps, so I recommend if

Speaker 2: you're can open in q g A S to use

Speaker 2: this renaming option. But this net CDF colon can be

Speaker 2: used for doing other g doll operations that you can

Speaker 2: do from the command line or other ways. And as

Speaker 2: I mentioned earlier, the QGIS cannot display the wrapped into

Speaker 2: program layers in the gun W file because those are

Speaker 2: complex numbers and QGS doesn't know what to do with

Speaker 2: complex numbers. I'm going to stop here and.

Speaker 1: Go to q g I S.

Speaker 2: Okay, so I've already I pressed the load raster data

Speaker 2: set button. The load data set, I've selected raster here.

Speaker 2: I'm going to go to the directory. You can see

Speaker 2: here is my g O n W file. I've renamed

Speaker 2: it as dot NC, so now I can open it

Speaker 2: directly in QGIS. When I press AD here, it shows

Speaker 2: me all the possible layers that are inside this g

Speaker 2: on W file. I don't want to load all those files,

Speaker 2: all those layer I'm just going to load the coherence

Speaker 2: magnitude ononospheric phase screen ionospheric phase screen on for certainty,

Speaker 2: the unwrapped phase the mask, and the wrapped into pherogram

Speaker 2: coherence magnitude that has that twenty resolution. So this is

Speaker 2: the it opens this. It actually loads it into QGI

Speaker 2: S as a as a group of layers, all those

Speaker 2: layers from the same interferogram. I'll just turn off all

Speaker 2: the layers, but the coherence first so we can look

Speaker 2: at the coherence. Okay, So I've turned off all the

Speaker 2: layers except for the the wrapped in, the unwrapped into

Speaker 2: pherroogram coherence, and now I'm going to zoom to that layer.

Speaker 2: This is the coherence for the whole scene. You can

Speaker 2: see this is a desert area, so that coherence is

Speaker 2: very high. Almost everywhere coherence is a normalized number. It

Speaker 2: goes basically from zero to one. And there's large areas

Speaker 2: here which are very close to one. In this dry

Speaker 2: area with very little vegetation, there's this basically zero coherence

Speaker 2: in the top right corner. That's the ocean of the

Speaker 2: Red Sea adjacent to the land.

Speaker 1: Here.

Speaker 2: Water always has basically zero coherence because the surfaces is

Speaker 2: completely different twelve days apart or even an hour apart.

Speaker 2: And there's another water body here I'm not sure what

Speaker 2: the name of that lake is. And then there's some

Speaker 2: areas here that have sort of intermediate coherence values, and

Speaker 2: those are areas that probably have some loose sand and

Speaker 2: and and sand is one of the things that can

Speaker 2: cause there's a large amount of sand at the surface

Speaker 2: can cause low coherence just because it absorbs a fair

Speaker 2: amount of the radar and reduces the amount of signal

Speaker 2: sent back. But also in a lot of desert areas,

Speaker 2: the sand can move around and there will also cause

Speaker 2: lower coherence. And then we can see this sort of

Speaker 2: spot up here, and that's the area of the Hailey Gooby.

Speaker 2: This big blob here is the low coherence of the

Speaker 2: Hailey Gooby eruption and ash flows. And then there's an

Speaker 2: area of the Earth ali that where the displacements are

Speaker 2: so large that the coherence it gets lost. One of

Speaker 2: the rules of InSAR is if the displacement within the

Speaker 2: radar pixel size is more than half of the radar

Speaker 2: wavelength or with with nicear that's twelve centimeters. So if

Speaker 2: you have twelve centimeters within the eight meter pixel of

Speaker 2: our inter program, then you'll lose coherence and not be

Speaker 2: able to make a measurement. This is the unwrapped phase.

Speaker 2: One of the things we offer will I almost I

Speaker 2: always do when I load the unwrapped phase is go

Speaker 2: into the band rendering and change it from a gray

Speaker 2: scale to a pseudocolor I usually use this color ramp

Speaker 2: from red through white through blue, and that gives this.

Speaker 2: Now we can see clearly that the two sides of

Speaker 2: the dyke in order Holly moved in opposite directions. We

Speaker 2: can also see that the area of Hailey Gooby is

Speaker 2: basically pure noise, and we can go it back into

Speaker 2: that and change the color scale. This is still in radiance.

Speaker 2: It's unwrapped phase minus twenty two plus twenty radians. The

Speaker 2: displacements are larger on the east side of the dike

Speaker 2: than they are on the west side of the dike,

Speaker 2: and that's because we're measuring these displacements in the radar

Speaker 2: line of sight. The line of sight of this track

Speaker 2: is down into the west from the satellite to the ground.

Speaker 2: The blue means it moved to the away from the radar,

Speaker 2: and red means moved towards the radar, and that means

Speaker 2: that the two sides moved towards each other. And that's

Speaker 2: because this dike is is contracting. Because the radar moved up,

Speaker 2: the land moved out, and because so on the on

Speaker 2: the east side here the ground has moved away from

Speaker 2: the radar both horizontally towards the dike, but it also

Speaker 2: moved down as part.

Speaker 1: Of the.

Speaker 2: The displacement due to that withdrawal of the magma. And

Speaker 2: because the westward and the down motions are both in

Speaker 2: the same direction relative to this radar line of sight,

Speaker 2: those two are adding together and we get a larger

Speaker 2: signal on the on the east side here, and on

Speaker 2: the west side, the ground motion is to the east,

Speaker 2: but the ground all moved down, and that means that

Speaker 2: the horlental motion is towards the satellite, but the vertical

Speaker 2: motion is away from the satellite, and they tended to

Speaker 2: partially cancel out. And that's why the displacements on the

Speaker 2: west side are quite a bit smaller than the east side.

Speaker 2: So that's the interpretation of this dike. A dyke collapse

Speaker 2: or or contraction. Earthquakes have a very similar type of

Speaker 2: pattern you'll have. You're always measuring this combination of the

Speaker 2: horizontal and vertical displacements, so you'll see a similar You

Speaker 2: have to do this similar type of analysis of what

Speaker 2: displacements mean in the radar line of sight, and by

Speaker 2: getting a second radar line of sight, say this is

Speaker 2: the ascending track where the satellite was moving north. There

Speaker 2: was also a descending track over this area where the

Speaker 2: radar satellite was moving south. So that would then have

Speaker 2: the satellite looking to the east, and that would show

Speaker 2: an opposite pattern here, which would tell us that this

Speaker 2: has a large horizontal component. That descending track was not

Speaker 2: included in the February release, so I'm not going to

Speaker 2: show it to you here, but now I'm gonna zoom

Speaker 2: out to the whole frame. This is the whole frame,

Speaker 2: and so the displacements there or to ali are quite large.

Speaker 2: But if we go back and change this color scale

Speaker 2: to be more residsentative of the whole scene, then we

Speaker 2: can see this is from minus two to plus four radians.

Speaker 2: We can see that there's this large kind of gradient

Speaker 2: of the displacements across the scene, and that's largely due

Speaker 2: to the honosphere. Nice are assending track scenes are acquired

Speaker 2: at approximately six am local time, and that is when

Speaker 2: the atmosphere is less active. So generally the ascending tracks

Speaker 2: have less ionospheric effects, and the descending tracks are required

Speaker 2: at six pm local time, and that is a time

Speaker 2: when the answer is much more active. So you'll see

Speaker 2: in many places that there is a descending track into

Speaker 2: program that has many more ionospheric effects or fringes than

Speaker 2: the ascending track for even the same location. And it

Speaker 2: also depends on latitude. The atmosphere is much stronger near

Speaker 2: the equator. This is quite near the equator in Ethiopia,

Speaker 2: and they're less at middle latitudes, and then they're very

Speaker 2: strong again near the pole, so looking at Alaska or

Speaker 2: other high latitude areas, you'll see that the homosphere is

Speaker 2: even more extreme. So we're gonna just take a quick

Speaker 2: look here at the honospheric phase screen.

Speaker 1: This is the.

Speaker 2: And one of the tricks I can use in is well,

Speaker 2: I'll use the same uh pseudo color. We're gonna sent

Speaker 2: this to two percent count and we can see now

Speaker 2: that the amospheric estimates are generally our our are generally

Speaker 2: uh close to zero for this scene, but if it

Speaker 2: was a descending track scene, we'd see a much stronger

Speaker 2: So this particular scene is not a good example to

Speaker 2: show for the ionospheric phase screen calculation. This was processed

Speaker 2: with the older version of the nice R software and

Speaker 2: it has these blue areas are places that had that

Speaker 2: low coherence, and the atmospheric estimate is with that old

Speaker 2: software was it ends up with bad values in those

Speaker 2: areas of low coherence. That's been fixed in later versions

Speaker 2: of the software, and we'll see that the amospheric estimates

Speaker 2: for more recent that are going to be released next

Speaker 2: month or in July, we'll have much higher quality and

Speaker 2: not have these kind of weird patterns where there's low coherence.

Speaker 2: One other thing I just wanted to give a quick

Speaker 2: look at is the honest spirit. I mean the wrapped

Speaker 2: into program coherence. A turn off these phase layers. So

Speaker 2: this we go back to Haley goofy. Here the m.

Speaker 1: This is the.

Speaker 2: This is the coherence layer from the wrapped into paragram

Speaker 2: at eighty meter pixels. And if we turn that off,

Speaker 2: we can see the the coherence layer from the the

Speaker 2: wrapped into pharogram. And this has twenty meter pixels and

Speaker 2: shows you much greater detail. There must be some kind

Speaker 2: of of a tongue here of maybe a valley where

Speaker 2: the ash has moved down the valley here, so we

Speaker 2: get more much more detail in the coherence of the

Speaker 2: wrapped into program layer. That so the QJAS can display

Speaker 2: the coherence from the wrapped inter program, but it can't

Speaker 2: display the phase from the wrapped into program. So that's

Speaker 2: why I showed you the slides. So that's our example

Speaker 2: of a volcanic eruption and with similar earthquakes. And now

Speaker 2: I'm gonna go back and show you landslide example. So

Speaker 2: landslides are a major hazard in many places. We have

Speaker 2: a lot of landslides in California and They're also important

Speaker 2: process in any mountainous areas, but they can occur even

Speaker 2: in relatively mild hilly areas in a wide variety of locations.

Speaker 2: The amount of impact of the landslides depends on their

Speaker 2: specific location, and we're going to be getting this nice

Speaker 2: our data to be able to map landslides worldwide. I'm

Speaker 2: going to be talking today about a specific landslide in

Speaker 2: an area called Palas Verdes. This is a city, a

Speaker 2: city in Palos Verdes called Rancho Palos Verdes, where a

Speaker 2: landslide accelerated in twenty twenty three and went right through

Speaker 2: the edge of the landslide went right through this neighborhood,

Speaker 2: including this house right here. This is the vertical view.

Speaker 2: You see this landslide edge here that went right through

Speaker 2: the middle of this house and completely tore it apart

Speaker 2: in twenty twenty three. This house has now been was

Speaker 2: red tagged. That's the US equivalent of condemning the house

Speaker 2: is saying it's no longer useful for human occupation, and

Speaker 2: it's now been torn down. But these other houses nearby,

Speaker 2: a lot of them had only what we call yellow tag,

Speaker 2: which means that the the owners could actually still go

Speaker 2: into them. Some of them they've been trying to put

Speaker 2: some like supports underneath the underneath the house to try

Speaker 2: to keep it from from sliding down the hill more uh,

Speaker 2: and that's a that's an ongoing situation here.

Speaker 1: Ah.

Speaker 2: These landslides are as I mentioned in Palas Verdes. Pallas

Speaker 2: Verdes is this this circle is in the wrong place,

Speaker 2: it gets shifted a little bit. Pallas Verda is this

Speaker 2: funny little peninsula that sticks out from the coast just

Speaker 2: south of Los Angeles. Downtown Los Angeles is up here.

Speaker 2: These two black lines here are the low coherence of

Speaker 2: the lax runways. These views here are a combination of

Speaker 2: the wrapped phase for these pairs and the and the coherence.

Speaker 2: That's one of the tricks that I often use is

Speaker 2: actually combining the coherence with the phase. I'll show you

Speaker 2: that in a minute. Palos Verius Peninsula is southwest of

Speaker 2: the main part of Los Angeles, and the southern west

Speaker 2: coast of the Palas Verdes has this large landslide complex.

Speaker 2: Some parts of this landslide complex have been moving for

Speaker 2: about sixty years since actually the nineteen fifties, almost sixty

Speaker 2: five years, and that part was known to be has

Speaker 2: been moving for a long time, but we had very

Speaker 2: heavy rainfall in twenty twenty three and again in twenty

Speaker 2: twenty four, and that caused both those older parts of

Speaker 2: the landslide and other parts of the landslide to accelerate

Speaker 2: drastically and caused the damage that you saw on that

Speaker 2: previous slide. At one point it was moving at the

Speaker 2: rate of around five centimeters per day, even faster than

Speaker 2: that Slumgullion landslide that I showed earlier. It's based in Colorado,

Speaker 2: so these are wrapped into program results from the on

Speaker 2: the left here the descending track and on the right

Speaker 2: the ascending track for an interval in between November and

Speaker 2: twelve days November twenty third December fifth. Turns out that

Speaker 2: just one day later the ascending track November twenty fourth

Speaker 2: and December sixth, and you can see these two maps

Speaker 2: look very different, and that's again because we're measuring the

Speaker 2: surface displacements in this radar line of sight direction. The

Speaker 2: descending track, the line of sight is to the east

Speaker 2: slightly south, and on the ascending track gets to the

Speaker 2: west and slightly south. So the horizontal motions in these

Speaker 2: two look directions are going to have opposite signs, and

Speaker 2: the vertical motions have the same sign If we look

Speaker 2: at the said this area and the upper left, it's

Speaker 2: blue in the assending track, descending track and in the

Speaker 2: assending track, and that's because that area is moving downward.

Speaker 2: This is sort of the top of the landslide where

Speaker 2: the land is moving down and then out and other

Speaker 2: areas here the two the two measurements are in opposite directions,

Speaker 2: and that's because it's mostly we're measuring the horizontal motion.

Speaker 2: But again we also have to remember that the big

Speaker 2: motion of this landslide is actually almost due south, and

Speaker 2: that's in the direction that's roughly perpendicular to the right

Speaker 2: ero line of sight. So we're not at we're not

Speaker 2: seeing the big motion of the landslide southward, but we're

Speaker 2: seeing this vertical motion along the edges, and some parts

Speaker 2: of the landslide are moving west and some parts are

Speaker 2: moving to the east. And this is just the same.

Speaker 2: You'll notice on this that it's actually hard to tell

Speaker 2: which is positive and which is negative, and that's one

Speaker 2: of the difficulties of interpreting wrapped phase. And this is

Speaker 2: the same to inter proagrams with the unwrapped phase. Of

Speaker 2: course it's eighty meter pixels instead of twenty meters, so

Speaker 2: we have a course of resolution, but we can see

Speaker 2: which parts are positive and which parts are negative. Again,

Speaker 2: we have this upper part in the upper left corner

Speaker 2: which is red or positive away from the satellite in

Speaker 2: both ascending and descending, and that's because it's moving down.

Speaker 2: And then there's this section in the southwest where it's

Speaker 2: positive on the ascending track because it's moving towards the

Speaker 2: satellite to the west, and it's negative on the descending

Speaker 2: track because it's moving to the west, which is away

Speaker 2: from the satellite on the descending truck. Due to the

Speaker 2: way these particular tracks were required, they ended up excluding

Speaker 2: these particular scenes from the February release. So I'm only

Speaker 2: going to show you the ascending track data which is

Speaker 2: in the February release. And now we're going to go

Speaker 2: back to QGIS and open the Los Angeles into program.

Speaker 2: I already I did the same process of downloading the

Speaker 2: g n W from the ascending track for the area

Speaker 2: of Los Angeles. UH. And this is this is after

Speaker 2: we'reasking So this is the unwrapped phase.

Speaker 1: UH.

Speaker 2: Again, as we the ocean next to the p peninsula

Speaker 2: is is water, so that the unwrapped phase is a

Speaker 2: whole noise. So one of the one of the ways

Speaker 2: to to hide that unwrapped that the noise in the

Speaker 2: ocean is to go to this coherence map. This is

Speaker 2: the coherence map from the unwrapped phase, and make this

Speaker 2: particular coherence map set to multiply instead of normal. And

Speaker 2: now if we put the coherence map on with I

Speaker 2: only turn off this Google satellite with the unwrapped phase.

Speaker 2: Now we get this this view where the the low

Speaker 2: coherence of the ocean is is black and therefore easier

Speaker 2: to focus on the on the land on the land

Speaker 2: park where the measurements are good. So this is the

Speaker 2: the assending track. So again this has that area to

Speaker 2: the at the north part of the landslide that's uh

Speaker 2: moving away down and to the west slightly, and this

Speaker 2: lower part where it's also moving to the west. One

Speaker 2: of the other things I can show you with this

Speaker 2: inter program is the whole scene. If we look at

Speaker 2: the whole scene and change the stretch of the interferogram

Speaker 2: on this pair, we can see that there's a much

Speaker 2: stronger ramp from north to south and that's due to

Speaker 2: the anospheric effect on this pair. And if we go

Speaker 2: to the anospheric face screen layer, this is the face

Speaker 2: screen layer we turn offic coherence. We'll set back to

Speaker 2: be a pseudocolor, and let's turn this coherence back on.

Speaker 2: So now we can see basically the same pattern of

Speaker 2: gradient across the scene in the amospheric phase screen. So

Speaker 2: by subtracting this ionospheric phase screen from the unwrapped phase,

Speaker 2: then we get a corrected into fairground that we can

Speaker 2: use to look at a larger area of the scene.

Speaker 2: So one of the other things we can see in

Speaker 2: this overview of the whole scene are these little patches

Speaker 2: of low coherence. These are in the mountains, the San

Speaker 2: Gabriel Mountains that are just north of Los Angeles, and

Speaker 2: these are actually areas of snow. This was a pair

Speaker 2: that's in the winter, and these higher elevations and the

Speaker 2: San Gabriel Mountains are covered with snow and that's what's

Speaker 2: causing this low coherence in the high parts of the land.

Speaker 2: And further to the north is the actually the central

Speaker 2: Valley of California, and there's little patches of of This

Speaker 2: is a major agricultural region. And there's areas of low

Speaker 2: coherence here which are in some cases places where the

Speaker 2: farmers have plowed their fields. If a farmer plows his fields,

Speaker 2: he completely changes the surface and that causes low coherence,

Speaker 2: and that can also give you a loss of coherence.

Speaker 2: And this big sort of triangular area is the Mohave Desert,

Speaker 2: which has a lot of sand, so that also has

Speaker 2: generally a lower coherence than the more rock areas around it.

Speaker 2: There's this funny blob of a weird phase here that's

Speaker 2: actually caused by radio frequency interference that it's present. There's

Speaker 2: actually a Federal Aviation Administration FAA radar system there and

Speaker 2: it causes interference with the nice our data that's present

Speaker 2: in these older scenes. But we've now implemented a filter

Speaker 2: that removes this interference and you won't see that in

Speaker 2: the release data that's being released in July. And that's

Speaker 2: an overview of how we can work with the actual

Speaker 2: data of the GMW files in a GIS. And I

Speaker 2: think that's a good place to end up here.

Speaker 1: Thank you, thank you very much doctor Fielding for that

Speaker 1: great presentation and demonstration. Now I will do a summary

Speaker 1: of today's session as well. As the previous two sessions,

Speaker 1: so this is a summary of today's session. InSAR measures

Speaker 1: distance from the satellite to the ground with high precision

Speaker 1: by using the base of the reflected radar signals. The

Speaker 1: coherence of the insurface is a measure of surface or

Speaker 1: surface cover stability at the radar wavelength scale and phase cycles.

Speaker 1: In a repeat passenger paragram show change in distance to

Speaker 1: the ground by half the radar wavelength that's twelve centimeters

Speaker 1: for NISAR. The NISAR InSAR products enable user analysis of

Speaker 1: interferograms with few additional steps, and the nicer insert measurements

Speaker 1: of surface motion are useful for a variety of geological processes,

Speaker 1: some hydrological processes, dynamics of glaciers, and other effects that

Speaker 1: displace the surface or large structures. The nicer gun W

Speaker 1: interferometric product is geo geocoded and is the best suited

Speaker 1: product for users that are new to InSAR. And here's

Speaker 1: the summary for the second session. NICs OUR data are

Speaker 1: freely and openly available. The L band data can be

Speaker 1: accessed through the Alaska Satellite Facility, while the S band

Speaker 1: data along with coincident L band data can or are

Speaker 1: available through isroe's bounity platform. L BAND provides near global

Speaker 1: coverage every twelve days, whereas s band observations are acquired

Speaker 1: primarily over India and selected science and calibration validation sites

Speaker 1: outside India. NICs OUR data are distributed in HDF five formats.

Speaker 1: The level two NISAR products are geo coded, allowing them

Speaker 1: to be readily overlaid with other geospatial data sets Jupiter notebooks.

Speaker 1: These are open source notebooks that contain algorithms for the

Speaker 1: missions different science disciplines, and these notebooks are publicly available.

Speaker 1: The only Global three product generated by NISAR is sol moisture,

Speaker 1: which has a spatial resolution of two hundred meters. The

Speaker 1: GCOV product is the geocoded geometrically terrain corrected backscattered data

Speaker 1: in gamma knot and it's the most suited for those

Speaker 1: new to radar and wanting to do ecosystem type studies,

Speaker 1: so for example deforestation, biomass, agriculture, flooding, etc. The GUNW

Speaker 1: product is a geocoded wrapped and unwrapped interferogram and is

Speaker 1: it is the best suited data set for those new

Speaker 1: to instar and wanting to do surface deformation or movement

Speaker 1: type studies, and finally, NYSAR data can either be downloaded

Speaker 1: for local analysis or accessed and processed in the cloud,

Speaker 1: depending on your workflow and this is the summary from

Speaker 1: session one. NYSAR is designed to monitor continuous Earth surface

Speaker 1: changes from the odor of centimeter to meter scale globally

Speaker 1: day and night and through almost all weather cloud conditions.

Speaker 1: The key focus processes include tectonic deformation, I sheet motion,

Speaker 1: ecosystem change, and agricultural monitoring. NISAR has an L band

Speaker 1: sensor at twenty four centimeter wavelength and an S band

Speaker 1: sensor operating at nine point four centimeter wavelength. The satellite

Speaker 1: has an exact twelve day repeat cycle and an imaging

Speaker 1: swath of about two hundred and forty kilometers. The spatial

Speaker 1: resolutions vary between three and thirty meters depending on the

Speaker 1: radar acquisition mode. Polarization acquisitions vary depending on the acquisition mode.

Speaker 1: The core science disciplines cover the cryosphere, ecosystems, and solid earth,

Speaker 1: and NYSAR can also address different applications and support disaster

Speaker 1: and hazard management. Data processing levels span from level zero,

Speaker 1: which is the raw data, to level three, which is

Speaker 1: a geocoded derived product. With soil moisture as mentioned already

Speaker 1: being the only Level three global product being generated. Level

Speaker 1: one and Level two products contain information about amplitude, phase,

Speaker 1: interferometric products, coherence and pixel offsets. The Level one are

Speaker 1: in radar coordinates, range Doppler and Level two products are

Speaker 1: geocoded and analysis ready. In July twenty twenty six, the

Speaker 1: fully calibrated forward processed data will be released with the

Speaker 1: data availability latency of thirty six to seventy two hours.

Speaker 1: As a reminder, there is a homework assignment. You can

Speaker 1: access the homework through the training web page. As of today,

Speaker 1: the answers must be submitted via the Google forms and

Speaker 1: the do date is August six. A certificate of completion

Speaker 1: will be provided to those that attend all three or

Speaker 1: attended all three live webinars. The attendance is recorded automatically

Speaker 1: and complete the homework assignment by the deadline. The certificate

Speaker 1: of completion will be emailed approximately two months after today.

Speaker 1: I like to thank all of our guest instructors for

Speaker 1: this training series, doctor Franz Meyer from the University of Alaska,

Speaker 1: Fairbanks and the Alaska se Facility. Hidi Christiansen from the

Speaker 1: Alaska Satellite Facility as well, and Upama Sharma from the

Speaker 1: National Remote Sensing Center from Israel, the Indian Space Research Organization,

Speaker 1: and finally doctor Eric Fielding from JPL. And of course,

Speaker 1: if you have any questions about the material that was

Speaker 1: presented today, please don't hesitate to contact doctor Eric Fielding

Speaker 1: through the email that you see here, so please share

Speaker 1: your thoughts. Before I close, I just wanted to remind

Speaker 1: you that within a day or two you'll be receiving

Speaker 1: an imitation to complete a short online survey and this

Speaker 1: feedback will help us improve the RSET program. The participation

Speaker 1: is optional and all responses are confidential, but the survey

Speaker 1: data is really critical to help us understand how to

Speaker 1: better meet your needs. So we want to help you

Speaker 1: use your observation data more effectively. But we need your feedback,

Speaker 1: so please share your thoughts. And with that, we've reached

Speaker 1: the end of today's session, session number three and the

Speaker 1: end of this training series. So I'd like to thank

Speaker 1: doctor Eric Fielding for the great presentation today, and we

Speaker 1: will now begin our question and answer session. Great and yes,

Speaker 1: thank you for all of the great questions that have

Speaker 1: been coming in. We've been compiling those and we'll be

Speaker 1: sharing that document here on screen. But just to remind

Speaker 1: everyone that please there are three sessions as part of

Speaker 1: this training, and please feel free to contact either me

Speaker 1: or any of our guest speakers whose emails we posted

Speaker 1: for each session and as a heads up. So this

Speaker 1: is an introduction to nice our training, and we are

Speaker 1: planning several trainings that use nice our data throughout the

Speaker 1: next year, so please keep tuned into what's coming next.

Speaker 1: All right, So we do have doctor Fielding is here.

Speaker 1: He has been answering the questions that have been coming

Speaker 1: in and we'll try to get through as many questions

Speaker 1: as we have time. If we don't get through all

Speaker 1: of the questions, we will be posting this document on

Speaker 1: the training web page with all of the questions answered. Okay,

Speaker 1: so let's get started. Question number one, I need to

Speaker 1: know if it's possible to measure the height of a crop,

Speaker 1: for example, corn or soybeans using radar technology and what

Speaker 1: teristics should the radar have to achieve a result with

Speaker 1: an acceptable margin of error? Go ahead, Doctor Phey.

Speaker 2: Nice InSAR is not particularly sensitive to crop height because

Speaker 2: it's l band and especially for you know, relatively small crops.

Speaker 2: Like corn or soybeans. The radar is going to be

Speaker 2: going through and mostly bouncing off the ground. If you

Speaker 2: want to measure crop height, you probably want to use

Speaker 2: radar with a shorter radar wavelength like expand, where the

Speaker 2: crop height would be more have a bigger effect on

Speaker 2: the interfraometric phase. In addition, to really get a good

Speaker 2: measurement of height of crops, you would want to have

Speaker 2: different a larger baseline. Niceare is designed to fly with

Speaker 2: the satellite keeping the orbit small and all the baselines small,

Speaker 2: so the effects of height vegetation height are going to

Speaker 2: be less for trying to with the in differometric effects.

Speaker 1: Great, thank you. Question number two, I've been trying to

Speaker 1: download a gcov nice our file from ASF Vertex, but

Speaker 1: each time the download complete, it starts restarts instead of finishing.

Speaker 1: So what could this be? And the answer to that is,

Speaker 1: we have forwarded this question to the Alaska Satellite Facility

Speaker 1: team and we will post a response to this here.

Speaker 1: It could be the server you're using, but we will

Speaker 1: be responding to that. Sorry, the browser that you're using.

Speaker 1: All right, So the next question number three, can ins

Speaker 1: our time series be used to detect glacier surface deformation

Speaker 1: or glacier velocity changes, and are there any planned applications

Speaker 1: for monitoring glacier instability or glacier lake outburst flood hazards.

Speaker 1: Go ahead, doctor Feeling.

Speaker 2: Yes, the ins OUR time series and it can be

Speaker 2: used to measure a glacier surface velocities. That is one

Speaker 2: of the main applications of NSAAR. I'm on the solid

Speaker 2: Earth part of the science team, but we have several

Speaker 2: people in the Chrisphere part of the science team that

Speaker 2: are using or INSUR data to measure glacier velocities both

Speaker 2: in mountain glaciers and in the large ice sheets the

Speaker 2: the the Chriosphere people also use pixel offset tracking to

Speaker 2: measure the glacier velocities where the velocities are larger, and

Speaker 2: one of the examples of is this it's live project

Speaker 2: that's that's highlighted here that uses pixel offset tracking on

Speaker 2: the radar amplitude images both from NICE with from NICs

Speaker 2: are Sentinel one landst Sentinel two to get a complete

Speaker 2: picture of what the velocities are for for the faster

Speaker 2: moving glaciers, but the InSAR is good for the parts

Speaker 2: of the glaciers that are moving more slowly, so it's

Speaker 2: really depending on how fast the glaciers are moving which

Speaker 2: technique you need to use. InSAR can be used to

Speaker 2: see if the glacier is speeding up or slowing down

Speaker 2: with time, and that can be possibly helpful for looking

Speaker 2: at glacier lake outburst flood hazards.

Speaker 1: Great, thank you, Let's go on to then question number four.

Speaker 1: Has NASA evaluated integrating instar nic insert deformation measurements with

Speaker 1: machine learning models to predict landslides or glacier related hazards

Speaker 1: and high mountain environments And what are the main challenges?

Speaker 2: Well, we haven't. We don't yet have enough n INSER

Speaker 2: data to do machine learning analysis, but scientists have done

Speaker 2: machine learning analysis with INSER data from other satellites. One

Speaker 2: of the main challenges of applying machine learning models is

Speaker 2: that many existing machine learning models use either the time

Speaker 2: series of each pixel or the spatial distribution of pixel values,

Speaker 2: which are displacements for InSAR from a single image. The

Speaker 2: machine learning needs to consider both the spatial distribution of

Speaker 2: displacements and the time history at the same time, and

Speaker 2: that may require more advanced machine learning models to be

Speaker 2: able to handle that type of really three dimensional information

Speaker 2: of both two dimensions of spatial distribution and one dimension

Speaker 2: of time history.

Speaker 1: Great question number five, how do we map urban flooding?

Speaker 1: And I'll take that one because for that you need

Speaker 1: the gek of the amplitude data. And in fact, yeah,

Speaker 1: INSAAR is not useful for mapping surface water extent, whether

Speaker 1: it's open water or inundated vegetation. So the plan in

Speaker 1: terms of our set trainings is for actually the next

Speaker 1: training to be focused on mapping floods. So do keep tuned.

Speaker 1: The next question number six, what are some other phase

Speaker 1: on wrapping algorithms other than SNAFFU.

Speaker 2: So the standard algorithm that's used by ICE the for

Speaker 2: the nicear processing is SNAFU, but there are the ICE

Speaker 2: three software package that nice Mission uses for processing does

Speaker 2: have other unwrapping algorithms included in the software. So it

Speaker 2: is possible if you want to do your own processing

Speaker 2: with ICE three to use those other options. And there's

Speaker 2: also commercial packages such as Gamma that can process nice

Speaker 2: our data with their own phase unwrapping methods. There are

Speaker 2: a few examples here. There is called Spurt and Whirlwind.

Speaker 2: The nicear processing system may be moving to one of

Speaker 2: these other packages in the future, but at the moment,

Speaker 2: the present software is using SNAFU as a standard for

Speaker 2: the standard product. The Opera project uses a different phase

Speaker 2: on wrapping, and they will be producing displacement products from

Speaker 2: NYSAR for North America within a year or so.

Speaker 1: Great, thank you. Let's move on then to the next

Speaker 1: question number seven. How does SORROW and instart differ relating

Speaker 1: information about forest there canopy size and chlorophyll content? And

Speaker 1: how does one try to correlate water resources in a

Speaker 1: nearby environment with inundated or non inundated areas. Okay, so

Speaker 1: this is a this question has multiple parts. I'll answer

Speaker 1: part of it in that chlorophyll content. For that you

Speaker 1: need to use optical right, because radar is not going

Speaker 1: to be sensitive to the chemical properties of vegetation. However,

Speaker 1: as posted here, there have been attempts to estimate ndv

Speaker 1: I from SAR with deep learning techniques, and there's a

Speaker 1: link to that paper here. But uh, go ahead, doctor Fielding,

Speaker 1: you want to respond to the rest of us.

Speaker 2: Yeah, the in sorrow is not going to tell you

Speaker 2: anything about the chlorophyll content. It's only seeing the physical

Speaker 2: structure of the forests, so it can have some ah wait,

Speaker 2: some some effect due to the volumetric scattering in forests,

Speaker 2: so that's somehow related to the biomass.

Speaker 1: But the.

Speaker 2: The nice R biomass estimates are made from the optical

Speaker 2: the amplitude images the g coves, so the standard nice

Speaker 2: R biomass products are not using insert.

Speaker 1: Great. Then let's move on to the next question, which

Speaker 1: is question number eight. How effectively does nicer L band

Speaker 1: retain coherence over Korea's forested and mountainous terrain.

Speaker 2: I think one of the a SF people has did

Speaker 2: this search and pasted in the search url here so

Speaker 2: you can go and look at the There are a

Speaker 2: few scenes from the February release already online that you

Speaker 2: can look at the coherence layer and see what the

Speaker 2: what the coherence is for those areas. And in fact,

Speaker 2: we have looked at a number of forested areas around

Speaker 2: the world and the coherence is much much better than

Speaker 2: with any seed band radar, especially because of the twelve

Speaker 2: day and repeat time and high spatial resolution of NISAR.

Speaker 1: Wonderful. Okay, question number nine, will NISAR products be available

Speaker 1: in Indonesia?

Speaker 2: Yes, the global forward processing that's going to be released

Speaker 2: later this month or expected to be released later this month,

Speaker 2: will include global coverage, so it will include Indonesia and

Speaker 2: the whole rest of the land area of the Earth.

Speaker 1: Great question number ten. My intention is to learn how

Speaker 1: NYSAR can be applied for landslide early warning in extreme

Speaker 1: weather events.

Speaker 2: So NISAR is not going to be useful for early

Speaker 2: warning of landslides caused by extreme weather events. The InSAR

Speaker 2: coherence and amplitude images can be used to map the

Speaker 2: extensive landslides after they have occurred, but NISAR does not

Speaker 2: have any way to provide real time precipitation estimates that

Speaker 2: would be necessary for early warning of landslides during an event.

Speaker 2: NISAR does have this soil moisture estimates that we haven't

Speaker 2: really talked about so much in these trainings, But there's

Speaker 2: the two hundred meter so moisture estimates and that can

Speaker 2: measure what the swarm oister is before the event, and

Speaker 2: that could be confined with some receptation estimates to better

Speaker 2: predict where landslides might occur during the extreme weather.

Speaker 1: Good. Okay, the next question number and by the way,

Speaker 1: before I move on, Yes, in terms of future trainings,

Speaker 1: we will be doing one on the soul moisture product

Speaker 1: as well, sometime within the next year and a half.

Speaker 1: All right, The next question is number eleven. What are

Speaker 1: the recommended best practices or threshold values for using the

Speaker 1: coherent band to mask out severe phase noise.

Speaker 2: The exact relationship between the coherence measurement and the numbers

Speaker 2: and phase noise depends on the number of looks used

Speaker 2: in the in the processing and the way that coherence

Speaker 2: is estimated with nice our data. Typically, areas with coherence

Speaker 2: less than zero point two is completely decorrelated, so that

Speaker 2: would be like water surfaces, And in many cases coherence

Speaker 2: less than zero point four is getting to be too

Speaker 2: noisy for accurate phase unwrapping and displacement measurements, So that's

Speaker 2: typically the threshold that I would use for masking out

Speaker 2: unreliable phase and displacement measurements.

Speaker 1: Good along those lines, on landslides, number twelve, how effective

Speaker 1: is ins are for rapidly moving landslides where coherence is

Speaker 1: quickly lost and all there are there alternative processing strategies

Speaker 1: for such e buns.

Speaker 2: So the L band radar wavelength of NISAR is able

Speaker 2: to keep coherence for landslides that are moving up to

Speaker 2: one centimeter per day. Roughly, the Palas Verdes landslide example

Speaker 2: that I showed you is presently moving around to centimeter

Speaker 2: per day and still keeps coherence. If it's moving faster

Speaker 2: than that, then you need to use the pixel offset measurements.

Speaker 2: The standard NISAR processing to the geo coded offset products

Speaker 2: is only done in chrisphare areas, so you would have

Speaker 2: to do your own pixel offset measurements with the nice

Speaker 2: ART data to measure the faster land slides moving faster

Speaker 2: than one centimeter per day, but one sentiment per day

Speaker 2: is really unusual for landslides.

Speaker 1: Okay, great, The next one, what are the opportunities Question

Speaker 1: number thirteen the opportunities and limitations of using nice our

Speaker 1: InSAR for long term wetland hydrology monitoring.

Speaker 2: So nice ARE wetland extent is typically is measured with

Speaker 2: the nice are amplitude images the g codes, not with

Speaker 2: the InSAR, but InSAR data has been used to measure

Speaker 2: the surface in and flooded vegetation because with the double

Speaker 2: bounce of the radar you can often get coherent reflection returns,

Speaker 2: and the phase of that return will depend on the

Speaker 2: height of the water. In flooded wetlands so that's a

Speaker 2: technique that's been used in a few places.

Speaker 3: In most cases, if the if there's no if it's

Speaker 3: not full of vegetation, then the coherence will be zero

Speaker 3: and you can't use InSAR phase.

Speaker 1: Okay, the next question number fourteen, Can we use nicsor

Speaker 1: for infrastructure monitoring like airways, I guess airports, airports?

Speaker 2: Yes. Yes. InSAR and NICE are in general that can

Speaker 2: be used for monitoring dissurfaced displacements of a wide variety

Speaker 2: of infrastructure things levees, dams, roads, bridges, pipelines, airport run airports,

Speaker 2: and other other features. And that's so it's very useful

Speaker 2: for from monitoring infrastructure. The lband radar wavelength is going

Speaker 2: to give you better coherence in places where there's vegetation.

Speaker 2: The lband radar wavelength actually does not work so well

Speaker 2: for the extremely smooth surfaces in most airports. And you

Speaker 2: may have seen in that in my example of the

Speaker 2: Los Angeles area that the areas of the of the

Speaker 2: runways of LAX were very dark and incoherent. And that's

Speaker 2: because the smooth surface of the radar of the runways

Speaker 2: and the adjacent to closely cut grass does not reflect

Speaker 2: the radar back, so there's very low signal and we

Speaker 2: can't really get a good InSAR measurement. So you probably

Speaker 2: for for monitoring airports, you might want to use a

Speaker 2: shorter radar wave length.

Speaker 1: Great. The next question the question number fifteen. With nice

Speaker 1: or dual pool, how should the effects of the ionosphere

Speaker 1: be taken into account? For instance, how should we address

Speaker 1: errors arising from channel mixing caused by the by Faraday rotation? Yeah.

Speaker 2: Since most of the nice OUR data is only collected

Speaker 2: with two polarizations, it's very difficult to correct the Faraday rotation.

Speaker 2: You wouldn't be able to do that directly from the data.

Speaker 2: But because nice Our acquisition times are at six am

Speaker 2: and six pm local time, the Faraday rotation is probably

Speaker 2: going to be small in most cases.

Speaker 1: Okay, question, Oh, go ahead? Was there anything else? No? Okay?

Speaker 1: The next question number sixteen. The nice are products have

Speaker 1: been discussed in previous sessions. Is there a document available

Speaker 1: for S band or dual wavelength L and S band products?

Speaker 2: Yeah, so there's a link here to the ROW Data

Speaker 2: product specification for the S band data products. But the

Speaker 2: S band data products have the same format and layers

Speaker 2: as the L band products including GNW. So the presentations

Speaker 2: we have here applying to the S band data as well.

Speaker 2: It's just that you have to get the S band

Speaker 2: data FROMRO and not from the NASA repositories.

Speaker 1: Great the next question, let's see question number seventeen. How

Speaker 1: does nisar's L band improve temporal of decorrelation when compared

Speaker 1: to sentinels one C band? What are the limitations of

Speaker 1: L band?

Speaker 2: So, the big advantage of L band is that it's

Speaker 2: less sensitive to vegetation. Basically, for moderate and low levels

Speaker 2: of vegetation, the radar goes all the way to the ground.

Speaker 2: For denser forests, the radar is going to be bouncing

Speaker 2: off the trunks of the trees and maybe some of

Speaker 2: the larger branches, and not the smaller leaves and small

Speaker 2: branches that move around. So the coherence is much better

Speaker 2: with L band, both over time and even just for

Speaker 2: a single short interval. In fact, in a few places

Speaker 2: where they did the special Sentinel one one day repeat

Speaker 2: last month where they had the Sentinel one A and

Speaker 2: Sentinel one C collecting dat only one day apart, the

Speaker 2: nice R L band coherence is generally better over with

Speaker 2: a twelve day nice r L band into program than

Speaker 2: with the one day seed bend inter program from Sentinel one,

Speaker 2: and of course that that was only a temporary experiment

Speaker 2: that they did during June of twenty twenty five, So

Speaker 2: the regular seed bend time interval operationally is six days

Speaker 2: when they have both the use satellites working and acquiring

Speaker 2: data like now with something on one C and something

Speaker 2: on one D.

Speaker 1: So No.

Speaker 2: One A has now been decommissioned after that short experiment

Speaker 2: in June. The main limitation of L band is the

Speaker 2: amospheric effects are stronger, so you need to do the

Speaker 2: anospheric phase correction. But Naysaur has the extra L band

Speaker 2: frequency band. We didn't really talk about that in detail,

Speaker 2: but it gives us a much more accurate estimate of

Speaker 2: the amospheric phase screen that you can use to correct

Speaker 2: the amospheric effects.

Speaker 1: Okay, great, the next question number eighteen, what is the

Speaker 1: strategy for resembling eighty meters unwrapped phase from wrapped phase

Speaker 1: at twenty meters.

Speaker 2: The in the production process of the twenty meter interferrogram

Speaker 2: is then multilook to about eighty meter pixels before doing

Speaker 2: the phase unwrapping with the snap food program.

Speaker 1: Yeah, Okay, so we have a couple of minutes left,

Speaker 1: so let's just I'm gonna select a couple of questions

Speaker 1: down the list. So let's go to question number twenty one.

Speaker 1: How does the selection of a stable reference point differ

Speaker 1: between earthquake earthquake related crustal deformation analysis and landslide displacement monitoring?

Speaker 1: Considering their distinct spatial scales, deformation mechanisms, and temporal characteristics.

Speaker 1: Choosing a stable reference point is very important for deformation.

Speaker 2: Yes, of course, the reference point is very important. For landslides.

Speaker 2: We typically choose a reference point very close to the

Speaker 2: landslide because the landslide only affects a small area, so

Speaker 2: there's usually an area nearby. It's stable that we can

Speaker 2: choose the reference point for earthquakes or deformation. For volcanic eruptions,

Speaker 2: the displacements extend over a much larger area, so then

Speaker 2: the reference point needs to be chosen far away enough

Speaker 2: that you can assume that the deformation is near zero.

Speaker 1: Great, then let's go to question number twenty three. In

Speaker 1: the last nice AR data release in February, there was

Speaker 1: a warning about ionospheric contributions not being completely removed. Are

Speaker 1: there have there been any changes in the ionospheric and

Speaker 1: tropospheric models to remove the contribution of these errors and

Speaker 1: where can one find more details?

Speaker 2: The ICE three software is on public GitHub. You can

Speaker 2: go to the ICE three GitHub and see lists of

Speaker 2: what was changed in the different versions of the software.

Speaker 2: The version that we're using for the release this month

Speaker 2: is ICE three version five point zero two point three,

Speaker 2: which is then converted to this composite release id P

Speaker 2: zero five two three that's going to be used for

Speaker 2: the release it's going to be later this month. One

Speaker 2: of the big there's been a number of improvement improvements

Speaker 2: to the honospheric estimate. As I mentioned the one of

Speaker 2: the improvements was to mask out areas of low coherence

Speaker 2: and interpolate to get a better estimate in areas of

Speaker 2: low low coherence within a scene. Another big improvement is

Speaker 2: that the the new version has filtering of radio frequency

Speaker 2: interference that it caused errors in the amospheric estimates with

Speaker 2: software used in the February release. And there's there's a

Speaker 2: number of other improvements that have been made to the

Speaker 2: anospheric estimates. I think the tropospheric correction layers are approximately

Speaker 2: the same in the new version as in the February release,

Speaker 2: but the anospheric corrections are considerably improved. So when you

Speaker 2: get your data, you want to look at this the

Speaker 2: cr ID or composite release ID and make sure that

Speaker 2: you're getting the new data whether it's P zero five

Speaker 2: zero two three as opposed to the old February release

Speaker 2: which is crd X zero five zero one zero, which

Speaker 2: is much which is the old software.

Speaker 1: Great, Let's go then to question number twenty seven. Are

Speaker 1: the available GU and ws always only between the twelve

Speaker 1: day return pairs? Well, different temporal baseline interferograms need to

Speaker 1: be processed separately if we want them so, for example,

Speaker 1: when working with sentinel data, I've used pairs with a

Speaker 1: longer temporal baseline.

Speaker 2: Yes, the nice R g ONW use are only produced

Speaker 2: from the nearest neighbor in time pairs. If you want

Speaker 2: to get interfer grounds with different time intervals, you would

Speaker 2: need to do your own interferoground processing from either the

Speaker 2: g coded slcs or the range Doppler slcs to make

Speaker 2: your own interfer grounds for different temporal baselines.

Speaker 1: Great, and then one more question, Let's go to question

Speaker 1: number forty. Can you expand a little more in coherence.

Speaker 1: What is the difference between temporal and spatial coherence? You

Speaker 1: mentioned that coherence has multiplicative components. Are these for temporal

Speaker 1: or spatial and how do you evaluate them?

Speaker 2: Temporal coherence is UH is derived from time series analysis.

Speaker 2: It's different from the spatial coherence that we've used for

Speaker 2: many years, and the coherence layer in g O n

Speaker 2: W files is a spatial coherence. The multiplicative nature of

Speaker 2: coherence applies to both temporal and spatial coherence, so it's

Speaker 2: basically anything that makes the coherence low will make the

Speaker 2: temporal coherence low and the spatial coherence low.

Speaker 1: So the.

Speaker 2: Estimation of temporal coherence is a more advanced topic that

Speaker 2: we didn't cover here, but there's going to be other

Speaker 2: trainings in the future that talk about that.

Speaker 1: Great, and how about we do one more I always

Speaker 1: liked the Q and A sessions and if we had time,

Speaker 1: we would go through all of them. I know that

Speaker 1: we're at over our time, but let's just do one more,

Speaker 1: one more question. So how about we go to question

Speaker 1: number number forty four? So I would like to know

Speaker 1: the typical baseline separation associated with the twelve day repeat

Speaker 1: past nice are acquisitions and it's expected impact on coherence

Speaker 1: over forested areas.

Speaker 2: The nice baselines for particular baseline separation are almost always

Speaker 2: under one hundred and fifty meters and typically only on

Speaker 2: the order of fifty to one hundred meters. So this

Speaker 2: means that the the spatial baseline effects on correlation our

Speaker 2: coherence are quite a bit smaller than the temporal decorrelation

Speaker 2: effects to random motion of the forest branches, and even

Speaker 2: at lband there's some amount of temporal decorrelation.

Speaker 1: Great. Okay, So as mentioned, we have a lot of

Speaker 1: questions here. We have over fifty questions. We will be

Speaker 1: answering all of your questions in this Google doc and

Speaker 1: then we will be posting it on the training web page.

Speaker 1: So don't forget the homework. You can access it as

Speaker 1: of today. And also please if you can make sure

Speaker 1: you complete the survey. It helps us a lot for

Speaker 1: future trainings. Before I close, I'd like to thank the

Speaker 1: our set team for their incredible effort in putting this together.

Speaker 1: Block Blevins, Natasha Johnson, Griffin, Slwyn Hodson, o'doy, Jonathan O'Brien,

Speaker 1: Sue Mon team. Uh. There's a whole team, uh behind

Speaker 1: all of this Sherry Morris, and I'd like to thank

Speaker 1: our our incredible guest instructors, doctor Franz Meyer, Heidi Christensen,

Speaker 1: and Upama Sharma and today UH, doctor Eric Fielding. Before

Speaker 1: I close, i'd like to provide the opportunity for our

Speaker 1: guest instructors to give some final words. Doctor Eric Fielding,

Speaker 1: you like to say some closing words.

Speaker 2: Yes, I also thank the our set team for all

Speaker 2: the work they did to get this to make this

Speaker 2: webinar function smoothly and helped to keep the whole training

Speaker 2: system convenient for me to to do the webinar and

Speaker 2: be effective for the users.

Speaker 1: Thank you very much, doctor Fielding. It's been such a

Speaker 1: pleasure and an honor to have you here with us

Speaker 1: again teaching us about InSAR and specifically now in SAR

Speaker 1: related to NYSAR. So, before I close, I'd like to

Speaker 1: thank the help from the Alaska Satellite Facility that we

Speaker 1: had today. Two members from the a SF joined us

Speaker 1: today to support UH with questions. So I'd like to

Speaker 1: thank Alex and Zachary So Alex Lewandov's and Zachary Hopinen

Speaker 1: And yes, so this is the end of this session,

Speaker 1: but hopefully it's at this training but hopefully the beginning

Speaker 1: for you to start working with nice our data. Please,

Speaker 1: we want you to work with the data. We want

Speaker 1: to hear from the community in terms of what are

Speaker 1: the challenges, so please reach out to us, to any

Speaker 1: of the instructors if you have questions, and more will

Speaker 1: be coming. As mentioned, more trainings will be coming in

Speaker 1: the near future. So thank you to all of you,

Speaker 1: to all of the participants really for your interest and

Speaker 1: your enthusiasm for this amazing mission. And until next time,

Speaker 1: have a great day everyone. Bye bye,

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