NASA ARSET_ Overview of Global Flood Product Derived from NASA Optical observations

Speaker 1: Hello, and welcome everyone to this Applied Remote Sensing training

Speaker 1: on monitoring and predicting floods using Earth observations for planning

Speaker 1: and preparedness. My name is Amita Meta, and today's session

Speaker 1: will focus on overview of global flood product derived from

Speaker 1: NASA optical satellite observations and for that we have a

Speaker 1: guest speaker, doctor Daniel's Layback from NASA Goddet Space Flight Center.

Speaker 1: We'll start with a brief introduction to Applied Remote Sensing

Speaker 1: Training Program or our SET. Our SET is part of

Speaker 1: NASA's Earth Action Program and it provides a cost free

Speaker 1: training on remote sensing satellites, sensors, methods, and tools. Trainings

Speaker 1: are provided in a variety of thematic areas listed here agriculture, disasters,

Speaker 1: ecological conservation, health and air quality, water resources, and wildlent fires,

Speaker 1: and they are tailored to audiences with a variety of

Speaker 1: experienced levels. Our SET trainings are online and there are

Speaker 1: also in person trainings offered. They are live and instructor led,

Speaker 1: or there are asynchronous and self based training available from

Speaker 1: our set website. Most RSET trainings are bilingual or often

Speaker 1: they are multi lingual, and most of the trainings are

Speaker 1: translated into Spanish and so materially is a labl from

Speaker 1: our website. Our set only uses open source software and

Speaker 1: data and accommodates differing levels of expertise. We'll start with

Speaker 1: this training series monitoring in predicteen floods using Earth observations

Speaker 1: for planning and preparedness. Now, floods are the most common

Speaker 1: and widespread of all weather related natural disasters. Floods they

Speaker 1: last for a few minutes, like in the case of

Speaker 1: flash floods, to weeks, and long lasting floods placing a

Speaker 1: huge burden on communities. It is very well recorded that

Speaker 1: floods caused loss of lives, displacement of communities, long term

Speaker 1: impacts on human health and well being. They damage infrastructure

Speaker 1: and economies, and destruct ecosystems. Every year, a number of

Speaker 1: floods occur all around the world. For example, these images

Speaker 1: from World Meteorlogical Organization. Major floods occurred in Asia and

Speaker 1: in the United States in twenty twenty five. Some of

Speaker 1: the countries affected were China, India, Nepal, Pakistan, and the

Speaker 1: Republic of Korea. In twenty twenty five, in United States,

Speaker 1: Texas and New Mexico, suffered flash floods and that killed

Speaker 1: more than one hundred people. And you can read more

Speaker 1: about flooding at this site provided by World Mythological Organization.

Speaker 1: And because of that, it's really important to monitor and

Speaker 1: predict flood so that there can be better preparedness and

Speaker 1: response for a flood disaster. These two figures are taken

Speaker 1: from a relatively recent paper by young Men at All

Speaker 1: and what it shows is a number of events of

Speaker 1: different types of disasters here and it's between nineteen seventy

Speaker 1: five and twenty twenty two. What you see here is

Speaker 1: that the blue bars shown here they show number of

Speaker 1: flood events and other disasters are also shown in different colors.

Speaker 1: But the main point here is that overall flood frequencies

Speaker 1: are going up as we go through this timeline, and

Speaker 1: number of flood events is the largest in all disasters.

Speaker 1: If you look at this figure it shows different types

Speaker 1: of floods. You can again see that riverine floods in

Speaker 1: light blue, they are the largest number of flooding occurring,

Speaker 1: and then flesh and pluvial floods are also relatively large.

Speaker 1: Another thing to notice here is that there is overall

Speaker 1: increase in flood frequency but the rate of change has

Speaker 1: slowed down in later part last twenty years or so,

Speaker 1: and also to see that there is intra annual variability

Speaker 1: of flood events. As we will see in this training,

Speaker 1: remote sensing observations are quite useful in monitoring floods and

Speaker 1: so we will be focusing on flood applications and impacts

Speaker 1: based on satellite observations, specifically optical reflectancies and synthetic aperture

Speaker 1: radar backscatter data from a number of satellites. They've been

Speaker 1: used for detecting floods. Also, there are weather model and

Speaker 1: hydrologic models which use remote sensing observations in the models

Speaker 1: such as precipitation, soil moisture, digital elevation, and lend cover

Speaker 1: and they can support both flood monitoring and prediction through

Speaker 1: these models and they are quite useful for early warning,

Speaker 1: for enhance safety planning, rescue and relief operations, and planning

Speaker 1: infrastructure and economic impact mitigation. There is a NASA Flood

Speaker 1: dashboard that you can look at provides information on floods

Speaker 1: and mapping flood impacts. Overall training learning objectives are that

Speaker 1: by the end of this training you will be able

Speaker 1: to identify data sets in NASA Global Flood Product, which

Speaker 1: is based on remotely sensed optical observations. Use the web

Speaker 1: tool NASA Worldview to access and visualize flooded regions from

Speaker 1: globebal flood product available from multiple satellites starting from twenty

Speaker 1: twenty one to near real time. Then identify opera dynamic

Speaker 1: surface water extent data for flood detection derived from optical

Speaker 1: and SAR observations, and access and visualize opera dynamic surface

Speaker 1: water extent data for flood events using NASA Worldview. Identify

Speaker 1: the capabilities of Geoglows River Forecast system for global stream

Speaker 1: flow prediction, and use Geogloss hydro Viewer to access globally

Speaker 1: available retrospective and predictive stream flow for selected rivers. There's

Speaker 1: a prerequisite fundamentals of remote sensing that's available from this

Speaker 1: link on our set website and you can get basic

Speaker 1: information about satellites, sensors, center characteristics and that will be

Speaker 1: useful in following some of the concepts used in this training.

Speaker 1: There will be three parts to this training today. As

Speaker 1: I mentioned, it will be about Global Flood Product. Second

Speaker 1: part will be on opera surface water extent based on

Speaker 1: SAR and optical observations, and the final session will be

Speaker 1: on geoglows. So next two sessions are next week on

Speaker 1: twenty third and twenty fifth of June. There will be

Speaker 1: one homework assignment posted on our set website on June

Speaker 1: twenty fifth, which is the last day of the training,

Speaker 1: and the homework will be due on July ninth, and

Speaker 1: a certificate of completion will be awarded to those who

Speaker 1: attend all live sessions and complete the homework assignment before

Speaker 1: the given due date. We'll start with today's session overview

Speaker 1: of Global Flood Product derived from NASA Optical Observations. So

Speaker 1: the objectives for part one are that by the end

Speaker 1: of this session you will be able to identify NASA

Speaker 1: Global flood product derived from themoodly sensed optical observations, recognize

Speaker 1: the data sources, spatial and temporal resolutions and limitations of

Speaker 1: optical based flood detection, gain experience using NASA Worldview to

Speaker 1: access and visualize global flood product in near real time,

Speaker 1: and know where to access flood product. Files are available

Speaker 1: for download. Here's the outline for today. Doctor Daniel's layback

Speaker 1: will start with examples of flood cases using Global Flood

Speaker 1: product or near real time global flood product. He will

Speaker 1: provide history and background of global flood product, then overview

Speaker 1: of nat flood products, a flood detection approach based on

Speaker 1: optical imagery from modis on Terra and Aqua satellites and

Speaker 1: weirs on NOAH twenty and twenty one satellites. Then talk

Speaker 1: about flood product EVALUEUA and case studies of flood detection.

Speaker 1: He will also demonstrate near real time global flood products

Speaker 1: access in visualization using NASA Worldview. A note about asking questions.

Speaker 1: Please put your questions in the questions box and we

Speaker 1: will address them at the end of the webinar. Feel

Speaker 1: free to enter your questions as we go. We will

Speaker 1: try to get to all the questions during the question

Speaker 1: and answer session. After the webinar, the remainder of the

Speaker 1: questions will be answered in the Question and Answered document,

Speaker 1: which will be posted on the training website about a

Speaker 1: week after the training. With that, we want to introduce

Speaker 1: our speaker for today, Doctor Daniel S. Layback. Doctor Slayback

Speaker 1: is a research scientist in Biospheric Sciences Branch at NASA

Speaker 1: or Space Flight Center. Doctor Slayback's work focuses on the

Speaker 1: application of remotely sensed imagery to study a variety of

Speaker 1: Earth system elements and building systems to generate and deliver

Speaker 1: data products. His current activities include examining the causes and

Speaker 1: impacts of land cover and land use change in high

Speaker 1: altitude Indian peatlands, development and operation of a near real

Speaker 1: time system to generate global daily flood map products, monitoring

Speaker 1: and quantifying the evolution of a new volcanic island in Tonga,

Speaker 1: and building data services for query and distribution of petabytes

Speaker 1: of high resolution imagery. Previously, he has worked on a

Speaker 1: range of projects, including Indian leisure change, studies on monarch

Speaker 1: butterfly over wintering habitat, evaluation of environmental codilates, of vertebrate

Speaker 1: diversity in the Western US, local and regional defas water

Speaker 1: station assessments, examining trends in global vegetation dynamics, and calibration

Speaker 1: of EdVance very higher solution radiometer data products. With that

Speaker 1: mean my doctor slay back, Doctor slay.

Speaker 2: Back, Okay, Well, thank you, Amita for the introduction. You know,

Speaker 2: as she said, I'm Dan Slayback. I'm a research scientist

Speaker 2: at the Goddard Spaceflight Center. I've been working on the

Speaker 2: flood product here for about the past fifteen years. So

Speaker 2: I'm excited to show you all an overview of the

Speaker 2: product and hopefully help you be able to use it

Speaker 2: more more usefully for any work you might have. So

Speaker 2: launching right in, I'm going to give just a brief

Speaker 2: overview of the talk. I'll talk about the product's history,

Speaker 2: a brief overview of what the product is, the approach,

Speaker 2: in other words, how we generate the product, recent updates,

Speaker 2: the product we had drop up the last December, the evaluation, limitations, distribution,

Speaker 2: the archive, and future directions. And then followed following that

Speaker 2: will be a short demo of showing how to access

Speaker 2: the product and look at it online. So a brief

Speaker 2: overview and history. The main features are it's a daily

Speaker 2: global two hundred and fifty meter resolution product. It's based

Speaker 2: on twice daily observations. It is neural time. This is

Speaker 2: one of the key features of this product compared to others.

Speaker 2: So within three hours of satellite overpass and usually sooner

Speaker 2: than that, we have a product out and then it

Speaker 2: updates if there's a new satellite overpass from the other satellite.

Speaker 2: We're really just detecting water. We detect non flood water

Speaker 2: and floodwater, and we categorize it as flood based on

Speaker 2: reference water layers, so it's really worth or a water

Speaker 2: detection project. The limitations, of course, this is based on

Speaker 2: optical imagery, so cloud cover is often a problem, as

Speaker 2: you can see an example in the right. Although it

Speaker 2: did capture that flood quite well, we can't see through

Speaker 2: clouds unfortunately. Flash floods and small floods, you know, floods

Speaker 2: that are not on the ground very long, are very

Speaker 2: difficult to capture, both because we may not observe them

Speaker 2: because they're not there that long, and because they're often

Speaker 2: small and spatial extent, so the two indred fifty meter

Speaker 2: IXTL size can be a limiting factor. The brief history,

Speaker 2: so this all started over twenty years ago when Bob

Speaker 2: Brackenridge at the Dart and Flood Observatory was using the

Speaker 2: what was called at that time Motus Rapid Response imagery.

Speaker 2: These were image JPEGs made from Modus observations you know

Speaker 2: within you know, shortly after after acquisition. So he was

Speaker 2: manually taking those and making flood maps out of them,

Speaker 2: which was quite helpful to be able to map flood

Speaker 2: within hours of having a satellite observation. So back in

Speaker 2: twenty ten we decided to operationalize this so that it

Speaker 2: didn't require somebody to take their time and effort to

Speaker 2: manually grab these images and apply threshold and try to

Speaker 2: map flood. So that's when this project really started. And

Speaker 2: our initial product is for those of you that have

Speaker 2: used it for some time, was the what you see

Speaker 2: in the lower right corner. There was a graphic map

Speaker 2: like this for by tend to retiles. There were also

Speaker 2: geotip products you could download, and so that carried on

Speaker 2: for about eleven years or twelve years, and then we

Speaker 2: began around twenty twenty transitioning this into Lance operational production systems.

Speaker 2: The previous system was really a PI run system based

Speaker 2: on a PI server, and it was not terribly robust,

Speaker 2: but it worked for the most part, but it was

Speaker 2: it had limitations. So we transitioned fully into Lands and

Speaker 2: the mote Apps production system that generates all the Modus products.

Speaker 2: This is much more robust, gets out the door a

Speaker 2: little bit quicker. I need to advance there, and that

Speaker 2: went into production in early twenty twenty one, and then

Speaker 2: we discontinued the old product, which we call the legacy product.

Speaker 2: At that point, about a year ago, we introduced a

Speaker 2: beer's product eventually take over from the Motus product. Is

Speaker 2: the Motus sensors are eaching end of life. So now

Speaker 2: I'm just going to show a few examples of the

Speaker 2: product and action so you have a better feeling for

Speaker 2: what it looks like and what it can do. This

Speaker 2: is a flood product from Southeast Asia. Way back in

Speaker 2: twenty eleven, major flooding, particularly Central Thailand, Central Cambodia. The

Speaker 2: Lake in the middle of floods pretty routinely. That the

Speaker 2: flooding in Central Thailand was more unusual as a significant event.

Speaker 2: So this is quite scaled out major flooding that we're

Speaker 2: able to observe. This is flooding from the Ukraine Coca

Speaker 2: Dam breach in twenty twenty three. The dam was breached

Speaker 2: about two or three in the morning on the sixth

Speaker 2: of June, and within twelve hours we had a product

Speaker 2: out the door showing this flood extent. So this shows

Speaker 2: you a couple of different things. You know, clearly we're

Speaker 2: just detecting water and calling it flood or surface water

Speaker 2: based on a separate reference water layer. You see the

Speaker 2: pixelation of the two hundred and fifty meter pixels here.

Speaker 2: So it's in particular in Kurslon City we're not detecting

Speaker 2: much flooding because it's an urban area and there's although

Speaker 2: the roads may well have been flooded, a lot of

Speaker 2: the buildings remain dry. So that is one problem limitation

Speaker 2: of the product. There's another example from the twenty twenty

Speaker 2: two Pakistan floods, major flooding in Pakistan that you all

Speaker 2: might remember. It was all over the news. This is

Speaker 2: in the Worldview app which I will demonstrate later. Again

Speaker 2: a major large regional flood. Here's a time series of

Speaker 2: an earlier flood twenty fourteen in Saying region, showing the

Speaker 2: daily product over ten eleven days. Where it is so

Speaker 2: you can actually see the flood moving downstream and slowly receding.

Speaker 2: Was the end of disciple. So again, these large floods

Speaker 2: are it's quite good at capturing these generally, Okay, So

Speaker 2: here is an overview of the approach. There's four basic steps.

Speaker 2: I'm just gonna review them quickly here and then i'll

Speaker 2: go into them in more detail on the following slide.

Speaker 2: So the first step is water detection. This is where

Speaker 2: we apply an algorithm to detect water. This is, you know,

Speaker 2: the key thing going on here water detection. Step two

Speaker 2: is what we call multi look compositing. This helps deal

Speaker 2: with some of the false positives we get from shadows

Speaker 2: and helps us, you know, sort of look around clouds

Speaker 2: as clouds move from image to image. So we combine

Speaker 2: several images the water detections from several images. The third

Speaker 2: step is terrain masking that gets rid of false positives

Speaker 2: in mountainous terrains where we have a lot of shadow

Speaker 2: from the terrain. And the fourth step is flood identification,

Speaker 2: which is simply comparing the result which we've gotten at

Speaker 2: that point to reference water mask and if the water matches,

Speaker 2: we call it surface water, and if it doesn't match,

Speaker 2: we call it flood. So I'll go into these in

Speaker 2: more detail next. Okay, So first step one water detection.

Speaker 2: So the first question is the data, the incoming data.

Speaker 2: So we have the MOTUS source imagery. MOTUS stands for

Speaker 2: the Modern Resolution Imaging Spectral Radiometer. This is an instrument

Speaker 2: that's been on two NASA satellites since the Terra satellite

Speaker 2: since nineteen ninety nine and the Aqua satellite since two

Speaker 2: thousand and two. They're both very much at end of life,

Speaker 2: so the missions will probably be ending in the next year,

Speaker 2: which is why we have this separate viers product, which

Speaker 2: I'll show you the details on the next slide. But

Speaker 2: the two products are really parallel. Everything's the same except

Speaker 2: the input data essentially and some differences from that. But

Speaker 2: everything else how they're generated is identical. So the key

Speaker 2: features of MOTUS it's two hundred and fifty meters global

Speaker 2: twice daily. With these two satellites, we can't see through clouds,

Speaker 2: so we are blocked by the cloudy planet, which it

Speaker 2: is often cloudy. On the bottom you see the data

Speaker 2: collect from ERA from the Terror satellite, and on the

Speaker 2: right from the Aqua satellite. You'll see you see these

Speaker 2: black wedges at the equator. These are swath gaps. These

Speaker 2: move from day to day so they are not always

Speaker 2: in the same place, but it can cause limitation of

Speaker 2: the data available equatorily. So that's the overview of MODUS.

Speaker 2: Here's Veers very similar. We also have this on several satellites.

Speaker 2: The ones we use are called now twenty and twenty one.

Speaker 2: They've been up for nine four years. GRED and seventy

Speaker 2: five meters resolution, so this is coarser than the Modus instrument,

Speaker 2: but also global with two satellites twice daily. They're also

Speaker 2: both in the afternoon, only about fifteen minutes apart. Like

Speaker 2: Modice it was more like three hours apart. And one

Speaker 2: of the nice features is we get rid of the

Speaker 2: equatory of swath gaps, so you don't have those areas

Speaker 2: of missing data along the equator. On the next slide,

Speaker 2: i'll show you the water detection algorithms. This is what

Speaker 2: Bob Brackenridge developed way back in the early two thousands

Speaker 2: and we've maintained it since that time. Is the key

Speaker 2: feature is the near infra red red threshold at the top.

Speaker 2: The other two conditions to sort of get rid of

Speaker 2: some edge cases, but it's really a threshold based approach.

Speaker 2: If you apply that threshold to a single input image

Speaker 2: like the Modus aquad image shown in the middle, you

Speaker 2: get simply you know, water in blue or no water

Speaker 2: in white, or no data or we didn't have any

Speaker 2: of the data in gray, which includes the swath gaps

Speaker 2: which see here, but also where the data is bad.

Speaker 2: In this case, the imagery was saturated at very bright clouds.

Speaker 2: That's much less a problem currently, but in the past

Speaker 2: you would see those issues. But we're certainly not detecting

Speaker 2: water under a bright cloud in any case, so that

Speaker 2: doesn't affect us. So on the next slide we move

Speaker 2: on to multi look compositing. So once we detected water,

Speaker 2: that's great, but we do have some issues with false

Speaker 2: positives from shadows looking like water in particular cloud shadows

Speaker 2: entering shadows. To deal with the cloud shadow false positives,

Speaker 2: we use this multi look compositing, but also allows you

Speaker 2: to collect imagery over one, two or three days because

Speaker 2: the clouds will move, they may move out of the

Speaker 2: way and you may be able to see the surface.

Speaker 2: So depending upon your timeline as a user, for how

Speaker 2: recent you need your input data to be, you might

Speaker 2: get a better product with the two or the three

Speaker 2: day products then with the one day because it may

Speaker 2: have been less valid yesterday. So the basic idea behind

Speaker 2: the multi look compositing is requiring multiple water observations for

Speaker 2: a pixel to be marked as water in the output

Speaker 2: to be carried through. So for a two day product,

Speaker 2: we in theory have four input images, two from each

Speaker 2: satellite per day, and typically the threshold although it varies

Speaker 2: a little, but it's typically half of the number of observations.

Speaker 2: So if we have four observations for a pixel, then

Speaker 2: we're going to require but two of those be detected

Speaker 2: as water in order to call the output pixel water.

Speaker 2: So applying that to this set of four images, we

Speaker 2: get that compositive water math on the right. Again, it's

Speaker 2: not telling us FUD or not, but it's telling us

Speaker 2: where we think we really most likely to have water.

Speaker 2: The next step is terrain false positive masking. So along

Speaker 2: with the cloud shadows looking like water, we have terrain

Speaker 2: shadows looking like water. We have two things we apply

Speaker 2: for this. We have computed terrain shadow masks, where we

Speaker 2: compute the topographic shadow at the midpoint of the month,

Speaker 2: so monthly for each month at the two different times

Speaker 2: for the satellites, and that gets rid of most of it,

Speaker 2: but there can still be some residual so we apply

Speaker 2: a general topographic mask based on the hand algorithm, which

Speaker 2: stands for height above nearest drainage. This essentially masks out

Speaker 2: areas where it is unlikely for footwater to accumulate based

Speaker 2: on the local photography, as there would be drainage draining

Speaker 2: it away of course, a flash flood might be there

Speaker 2: no temporarily, but we're unlikely to observe those due to

Speaker 2: the resolution and the time issues. I'll have a more

Speaker 2: detailed example of this in a few minutes. But the

Speaker 2: next step is, once we've applied the mont compositing and

Speaker 2: the masking, we compare the detected water to a reference

Speaker 2: water layer, and if it matches the reference water layer,

Speaker 2: we call it surface water, and if it doesn't match,

Speaker 2: we call it flood. And so we just classify the

Speaker 2: output very simply on that way. The reference water layer

Speaker 2: is based on a separate Modus product, a totally different

Speaker 2: algorithm than ours. It's a yearly product in the mod

Speaker 2: forty four W, so it's a very conservative approach and

Speaker 2: that generates the out. But now, as I mentioned, we

Speaker 2: have saw on some other slides, there's also a recurring

Speaker 2: flood layer. This was a new feature we added last December.

Speaker 2: It's the same idea. We're detecting water all the same,

Speaker 2: but we're comparing it now both to a yearly reference

Speaker 2: water layer and to a monthly recurring flood mask. And

Speaker 2: that recurring flood mask is based on analysis of our

Speaker 2: twenty two year product archive and any areas that are

Speaker 2: regularly recurring in October, for example. In this example, we're

Speaker 2: going to classify the output differently as recurring flood So

Speaker 2: this helps the user identify when the flooding, which may

Speaker 2: be extensive as you see here, is actually rather routine,

Speaker 2: or when on the western side of toy Saffy Can

Speaker 2: Cambodia here where it was actually not routine but also

Speaker 2: fairly extensive. So some details. I'm going to run through

Speaker 2: some detailed examples of what I just showed you, just

Speaker 2: to make a little more concrete. And again, the key

Speaker 2: issue we're dealing with is false positive masking. Shadows look

Speaker 2: like water. Cloud shadows look like water. Experience shadows look

Speaker 2: like water. We only have two bands at the highest

Speaker 2: resolution at two hundred and fifty meter resolution of three

Speaker 2: seventy five, and the beers in the red and the infrared,

Speaker 2: and water is very difficult to discriminate from shadow with

Speaker 2: just those two bands, so this is sort of the

Speaker 2: main limitation of using those bands. But we want the

Speaker 2: high resolution, so we use those so train shadows mostly

Speaker 2: in mountains in the winter. Our first cut approach is

Speaker 2: applying to computed terrain shadow masks and then the second

Speaker 2: cloud is to hand. I'll show that in a minute

Speaker 2: cloud shadows. The main approach is really just the multi

Speaker 2: look composity. The one day composite does not typically have

Speaker 2: a threshold greater than one, so any water detection will

Speaker 2: go into the one day product. And if you have clouds,

Speaker 2: you may likely have a lot of false posits, or

Speaker 2: you may not every cloud shadow gets detected as water,

Speaker 2: but it can be, it can be, it can be problematic.

Speaker 2: The two in the three day much much less, but

Speaker 2: I'll show you that as well. And so the key

Speaker 2: to all of this is looking at the source imagery

Speaker 2: and the world future, as I'll show in the demo,

Speaker 2: is a great way to be able to do this easily.

Speaker 2: So here's a detailed example. Looking here, we have a

Speaker 2: Modus aqua image from the Alps in winter. This is

Speaker 2: a bit of a mess. This is not ideal, certainly,

Speaker 2: but it shows some of the problems and how we're

Speaker 2: dealing with them. So we have clouds in white or pinkish.

Speaker 2: This is a false color composite out of symmetry. Snow

Speaker 2: is in Cyan, very bright. There might be some water here.

Speaker 2: It's sort of hard to see. Like Geneva is covered

Speaker 2: with clouds mostly. So if we apply our water detection

Speaker 2: algorithm to simply that single image, this is the result.

Speaker 2: So all the yellow is the detected water, and so

Speaker 2: there's some real water parts of like Geneva are detected.

Speaker 2: There's a lot of not real water terrain shadows next

Speaker 2: to the mountains, and there's a lot of cloud shadow

Speaker 2: next to the clouds that is not real. So this

Speaker 2: is you know, essentially useless at this level. You would

Speaker 2: have no idea what's real or not. If we composite

Speaker 2: this over two days, we get rid of a lot

Speaker 2: of the cloud shadows, allse positives if I fot back

Speaker 2: or you kind of see that there's some that are persistent.

Speaker 2: So it doesn't solve all of those with a two

Speaker 2: day The three day would do better, but it's doing

Speaker 2: a pretty good job, but it's doing really nothing for

Speaker 2: the terrain shadows, which if anything, only grow. But we

Speaker 2: get with two days of data, we're getting, you know,

Speaker 2: pretty good coverage of Lake Geneva. Not everything, but here's

Speaker 2: just the terrain to show you, you know, how how

Speaker 2: much terrain is involved in the Alps in Italy, France

Speaker 2: and Switzerland. Here is the terrain shadows computed from February

Speaker 2: morning and thirty in the morning, and then from one

Speaker 2: thirty in the afternoon to the sweep be relevant to aqua,

Speaker 2: the super be relevant to the Terra space craft observations.

Speaker 2: So you see they shift over time. If here's again

Speaker 2: the detective water, the composited two day detections water detections.

Speaker 2: If we apply those two terrain shadows, it does that,

Speaker 2: so we get rid of back and forth. We get

Speaker 2: rid of a lot of it, but there's still some

Speaker 2: residual terrain shadow along the mountain bridges. So here is

Speaker 2: the height the hand mask. This is the computed height

Speaker 2: above nearest drainage. It looks again like ghography more or less.

Speaker 2: Here's we threshold that by thirty meters. This was imparibly

Speaker 2: determined and that seems to generally work well all over

Speaker 2: the Earth. And once we apply that we it gets

Speaker 2: for most of that remaining noise. So most of the

Speaker 2: yellow here is what we is probably real water either

Speaker 2: you know, small lakes. I don't know that there was

Speaker 2: actually any flooding in this example, but any floodwater would

Speaker 2: also be appearing here. So we've cleaned it up quite

Speaker 2: substantially with this approach the multi look compositing for cloud

Speaker 2: shadow masking or to deal with cloud shadows. Again, we

Speaker 2: sum up all the water detections over the observation period,

Speaker 2: and so for every daily product, there's a one day composite,

Speaker 2: there's a two day, there's a three day. So you

Speaker 2: can compare those to one another and see which is

Speaker 2: best for the particular events an area of interest. The

Speaker 2: one day again only requires one water detection, so it

Speaker 2: will probably contain cloud shadow false positive if there are clouds.

Speaker 2: If there are not clouds, it is probably the best

Speaker 2: product to use because you are seeing today's data or

Speaker 2: the day of the product data. The two day is

Speaker 2: a good comprom a promise between one day at three day.

Speaker 2: You require two water observations, so that gets rid of

Speaker 2: a lot of the noise, but not everything. The three

Speaker 2: day really has a very few false positives, but it's

Speaker 2: extended over three day look back periods, so it's not

Speaker 2: going to it may not be as current. Okay, So

Speaker 2: you know this raises the question, of course, is the

Speaker 2: main sort of user complication of the product is which

Speaker 2: flood product should you use? And it really depends on

Speaker 2: the cloud conditions and your tolerance for false positives or

Speaker 2: false negatives. If you're really looking at an emergency situation,

Speaker 2: you only want the most up to date. You probably

Speaker 2: want to look at the one day, but you need

Speaker 2: to verify that you don't have cloud issues with that

Speaker 2: a good balance, it's the two day. The best approach

Speaker 2: is really to check the imagery, and I'll be showing

Speaker 2: you that in a minute. Here's an example that This

Speaker 2: is showing our not yet public our flood viewer. It's

Speaker 2: similar to Worldview, but it has the one day product

Speaker 2: available in it, which is why I'm using it here.

Speaker 2: But regardless, it's showing the flood products and you can

Speaker 2: look at the source imagery as well. So this was

Speaker 2: for a flood reported in southern Poland in September of

Speaker 2: twenty twenty four. If you look at the raw product,

Speaker 2: the pink here is the flood. There's a lot of

Speaker 2: flood showing up, so you might think, wow, you know,

Speaker 2: this is a significant event. Seems to follow linear type patterns,

Speaker 2: so those might be rivers, so this might be good,

Speaker 2: but you really should be looking at the imagery. So

Speaker 2: in this side we've clipped on the Modus Terra background

Speaker 2: image and impulse color and there's not much cloud. There's

Speaker 2: that cloud in the top north. But you might think, okay,

Speaker 2: this is pretty good, but you should keep looking. This

Speaker 2: is the one day composite. You never know what you're

Speaker 2: going to get. If you look at the Modus aqua image,

Speaker 2: we see a lot of cloud, and suspiciously, we see

Speaker 2: a lot of the flood falling directly in the cloud shadow.

Speaker 2: Not all of it, but some of it. So now

Speaker 2: you know you have something you need to be paying

Speaker 2: attention to. Here's the two days, so I will get

Speaker 2: rid of most of that flood falling in the in

Speaker 2: the cloud shadows. There might still be some, It's hard

Speaker 2: to know for sure, but this is one of the images.

Speaker 2: This is the aqua image again. But again you would

Speaker 2: want to look really at four images for the two

Speaker 2: day product, today's ra An Aqua and yesterday's Terara Naqua.

Speaker 2: The same thing applies for the beer's product. You'll just

Speaker 2: be looking. You know, it's twenty and twenty one. Just

Speaker 2: terror here's a three day composite for the same event.

Speaker 2: So now we really we drop off the amount of

Speaker 2: flood being reported quite substantially, and you know, maybe too

Speaker 2: much because you can you can barely see in some

Speaker 2: of those areas that probably more dark floodwater than it's

Speaker 2: being reported and red And if you again look at

Speaker 2: the imagery you'll find it two days ago. So the

Speaker 2: initial day of the three day composite, it was totally cloudy.

Speaker 2: So you're not getting any additional water observations by expanding

Speaker 2: to a three day but it is raising the threshold

Speaker 2: that of the number of water protections that had to occur.

Speaker 2: So this is a case where the three days certainly

Speaker 2: very conservative vestiment of flooding, but probably you know, it's

Speaker 2: missing some Okay, So moving on some recent updates with

Speaker 2: the product. Again, as I mentioned the recurring flood. This

Speaker 2: we introduced last December with released one point one of

Speaker 2: the product. The objective was to distinguish recurring flood from

Speaker 2: anomalous unusual flooding events, which in previous products for the

Speaker 2: last fifteen years, any unusual water was reported as flood

Speaker 2: and certainly it may well have been flood, but it

Speaker 2: may have been you know, un not as some flood

Speaker 2: the recurring annual seasonal flooding. So we to do this,

Speaker 2: we had we reprocessed the full twenty two year modus

Speaker 2: flood product archive or that doing that allowed us to

Speaker 2: generate this, and the temporal framework is using a rolling

Speaker 2: a three month window. We only require that three we

Speaker 2: detect flood within for three days within the window, but

Speaker 2: we require that happens within the third of the years,

Speaker 2: so seven of the twenty two years. So that seemed

Speaker 2: to be the best approach to sort of do this

Speaker 2: somewhat conservatively, but not too conservatively. But again, any user

Speaker 2: could take the final product and reclassify it with their

Speaker 2: own water masks. These are just data values in the

Speaker 2: in the flood product, so really simple gis operation to

Speaker 2: reclassify it if you have an improved annual or recurring

Speaker 2: flood masks for your region or zone of interest. And

Speaker 2: in this example for the Mississipi flooding, and you could

Speaker 2: see on the left or you'd see on the right,

Speaker 2: a lot of it was actually unusual flooding. That's what

Speaker 2: would have been read. Here's some examples. Here's northern California.

Speaker 2: This often floods in the winter, This northern Sacramento Valley.

Speaker 2: The previously we were constantly reporting lots of flood in

Speaker 2: northern California, and these areas were underwater, but they are

Speaker 2: all than underwater in the winter. So the new product

Speaker 2: is showing, you know, most of it's reported is recurring flood,

Speaker 2: not flood. There's the I think the same Mississippi example. Again,

Speaker 2: most of this was actually unusual flooding. There were some

Speaker 2: areas of recurring flood, but a lot of it was

Speaker 2: fairly unusual, so that was significant events. The severe product

Speaker 2: doesn't really look different than the modus. And again this

Speaker 2: is the one day here because there were no clouds

Speaker 2: or a few clouds that were complicating matters in this

Speaker 2: Punjab in September last September. Again mostly this was not

Speaker 2: recurring that dairy does flood quite regularly, but this level

Speaker 2: in this region, this was for September, this was unusual.

Speaker 2: And this is actually a custom composite generated from one

Speaker 2: week of data. So this is you know, things you

Speaker 2: can do with the raw data. You don't like the

Speaker 2: one or two or three day You could make a

Speaker 2: week long composite. It just added up in the gis

Speaker 2: essentially evaluation, so we uh with the initial the legacy product.

Speaker 2: So fifteen or so years ago we did an evaluation

Speaker 2: procedure to determine are we detecting you know, how often

Speaker 2: are we detecting flood? Is this working? Or you know

Speaker 2: what areas are problematic? We see differences between detecting flood

Speaker 2: and non flood water. The main caveat with the evaluation

Speaker 2: we did was that that we don't have vigorous ground

Speaker 2: true data sets for a global product of flooding that

Speaker 2: is very expensive and collect and it's always going to

Speaker 2: be biased towards accessible locations versus other areas. So we

Speaker 2: did a very fairly simple qualitative assessment using typically the

Speaker 2: Motus imagery itself or Lance adam injury that was available

Speaker 2: on the same day to determine, you know, is this

Speaker 2: really water and flood or not. So we picked fifty events,

Speaker 2: fifty flood events from the Dartmouth Flood Observatory Master List

Speaker 2: with global distribution, different line covers, et cetera, and fifty

Speaker 2: permanent water sites so not flood but just lakes rivers

Speaker 2: to evaluate whether we're getting those as well. And again

Speaker 2: this was connected for the legacy product, but the core

Speaker 2: algorithm here has not changed, so it's it's and we've

Speaker 2: looked at this for the the Lance product that was

Speaker 2: introduced five years ago. And there were no significant differences.

Speaker 2: So here are the results. So for the flood detection

Speaker 2: sites which are shown on the top and the permanent

Speaker 2: water sits in the bottom, you know, roughly a third

Speaker 2: for both set of sites it was too cloudy, so

Speaker 2: we don't count those. It was too cloudy to really

Speaker 2: tournament anything, so we don't know. But of the two

Speaker 2: thirds where it was accessible for the flood, you know,

Speaker 2: the rating the qualitative raiding given was good or better

Speaker 2: for two thirds of the case of sixty six percent

Speaker 2: and eighty five percent for the permanent water, so we know.

Speaker 2: You know, there are certain flood events that it does

Speaker 2: not work well on. These are typically small, limited in

Speaker 2: extent or limited in time, and which is obviously not

Speaker 2: a problem with permanent water, so it performs better with

Speaker 2: both sites. Here's some examples from that. So here's flooding

Speaker 2: in Buzzan, Herzegovina in twenty fourteen. Lance At eight. Image

Speaker 2: shows you quite clearly the flooding and our product is

Speaker 2: picking that up quite well. There's another example from kim

Speaker 2: Talk to the Mississippi River area. Again, you can see

Speaker 2: this in Motus itself. You don't even need lance at

Speaker 2: it's obviously flooding, and so our product is s working

Speaker 2: while here's an example where it didn't work well. Volcanic

Speaker 2: rock is often very dark and therefore like shadows often

Speaker 2: detected in some places detected as water. So we were

Speaker 2: initially routinely reporting flooding on the center of Hawaii Island,

Speaker 2: which is, you know, meta physically, biophysically impossible to have

Speaker 2: a lake on the top of the mountain like this.

Speaker 2: So we at the time we fixed this by editing

Speaker 2: the reference water map. But the hand mask entirely solves

Speaker 2: this problem. But this is this You will still see

Speaker 2: this in other areas where the hand mask is not helping,

Speaker 2: the volcanic dark areas, So there are some issues. Some

Speaker 2: areas of issues limitations of the product. Again, the clouds,

Speaker 2: as I over and over false negatives. We can't see

Speaker 2: through the clouds, so if it's persistently cloudy over an event,

Speaker 2: we're not gonna be able to see it false positives.

Speaker 2: Those cloud shadows detected. This swater Thames on the right

Speaker 2: shows both. So there's probably some flooding on the right

Speaker 2: part of the image under the clouds that we're not seeing.

Speaker 2: There's definitely flooding on the left that we are capturing

Speaker 2: and on the edge we may not be able to

Speaker 2: make it out, but there's false positives in the shadows

Speaker 2: of those storiated clouds on the edge. Two hundred and

Speaker 2: fifty meter resolution, we're not going to pick up small

Speaker 2: spatial extent flooding or flooding that's very rapid and not

Speaker 2: there when we observe it. Even though the two we

Speaker 2: have twice observations daily, they're at most three hours apart

Speaker 2: with notice, so there's twenty one hours when we're not

Speaker 2: having any observations. Land cover limitations under canopy, we cannot

Speaker 2: see the ground, so we're not seeing floodwater. In urban

Speaker 2: areas there's typically too many dry buildings, you know, even

Speaker 2: if the road, if the streets are flooded, and typically

Speaker 2: not picking that out. And changes in recent surface water extent,

Speaker 2: there's a tremendous number of new reservoirs being built all

Speaker 2: over the world, which you look at this product in detail,

Speaker 2: you start to realize and those will be reported as

Speaker 2: flood for a couple of years until they make it

Speaker 2: into the reference water layer, and then they'll be reverted

Speaker 2: to be to be reported as surface water. It changes

Speaker 2: in river course which you've been see in the tropics

Speaker 2: see some issues there. So but again, if you have

Speaker 2: a better reference water layer, you could certainly be classify

Speaker 2: the product accordingly for a local area. Here's an example

Speaker 2: that shows a bunch of these limitations in the same

Speaker 2: area and the same or two different products of the

Speaker 2: one day product on the left and the two day

Speaker 2: product and the right to have cloud obscuration. Of course

Speaker 2: you're not seeing things on the bottom and the right

Speaker 2: on the two day product. You know you can see

Speaker 2: the cloud and it's causing us not to report flood

Speaker 2: that we actually do report in the one day product.

Speaker 2: In the left, clouds shett for false positives are much

Speaker 2: more extensive in that circle area and the center left

Speaker 2: on the one day product than they are in the right.

Speaker 2: It's probably a few still on the right, but it's

Speaker 2: it's it's cleaned up urban areas non pen is see

Speaker 2: very little flooding there. Maybe they have really good drainage,

Speaker 2: but also probably just because it's an urban area, we

Speaker 2: are not able to capture it. So again, looking at

Speaker 2: different products, looking into source symmetry can tell you a

Speaker 2: lot about which product is the most relevant for your

Speaker 2: particular area of concern. So the archive we have now

Speaker 2: much effort and many requests released an archive of the product.

Speaker 2: We reprocessed the product back through the start of Terra,

Speaker 2: so there's twenty three full years where you have both

Speaker 2: Terra and Akala from twenty and three to twenty twenty five.

Speaker 2: Initial two years just have Terra. So the product still works,

Speaker 2: but it is you're you know, you're capturing less flood

Speaker 2: because you have half as many observations. We're working on

Speaker 2: evaluating that to provide more information about that, but it

Speaker 2: can be used. It's just you'll see a continuity between

Speaker 2: them ERA plus Aqua versus the Terra only ERA. The

Speaker 2: main difference in the processing was the reprocessing used final

Speaker 2: geolocation and final surface reflectance, so the neural time products

Speaker 2: take some shortcuts to get those products out the door

Speaker 2: quickly within three hours, and some of those are in

Speaker 2: the geolocation and some of them are in the calibration.

Speaker 2: So when we reprocessed, we're able to use all the

Speaker 2: final data for those. So this fixes minor problems you

Speaker 2: would see occasionally you would see a geolocation error causing

Speaker 2: just to report flooding in the river as if it

Speaker 2: had moved or the coastline. So those things just pear

Speaker 2: from the archive, which is nice that they were not common,

Speaker 2: but you can find them here and there occasionally. For

Speaker 2: twenty twenty six onwards, only the Modus NRT products are

Speaker 2: being archived. Currently none of the Beers products are being archived,

Speaker 2: but we're hoping to implement archiving for the NRT veers

Speaker 2: as well. Here's an example of you know what you

Speaker 2: can do with the archive. This is the recurring flood

Speaker 2: masks for this area in Cambodia to South Lake. This

Speaker 2: is a great example because the zum De scale you

Speaker 2: can see mass of flooding just wise in so many examples,

Speaker 2: but you know, from January to December you can see

Speaker 2: where you know, the areas that are typically or frequently

Speaker 2: being flooded. So if you if you detected large scale

Speaker 2: fooding in May, it's all unusual, and if you detect

Speaker 2: that same funding in November, it's probably mostly recurring flood

Speaker 2: Here's a separate example looking at the product archive for

Speaker 2: Lake Chad in Northern Cameroon, Nigeria and nichere and Chad.

Speaker 2: Just adding up the total number of daily water observations

Speaker 2: on the left for thirteen year period. This lake has

Speaker 2: been slowly going away over over time. But as you

Speaker 2: can see, the southern basin is pretty solid, the northern

Speaker 2: basin much less so, and the upper right. It's just

Speaker 2: looking at the simple number of years the water is detected.

Speaker 2: So this, you know, let's you look at some interesting

Speaker 2: things about water surface water change. Another example, the same

Speaker 2: idea for the Indus River flooding. Both of these examples

Speaker 2: are ignoring. These are just looking at water detections, so

Speaker 2: ignoring if it's classified as flood or surface water that

Speaker 2: you could certainly look at that separately. Distribution, so product distribution.

Speaker 2: The core product is generated in these ten by ten

Speaker 2: degree data files. There's two hundred and eighty seven of

Speaker 2: those spread across the Earth. Here's a map of some

Speaker 2: of them. The full map is on the homepage and

Speaker 2: then the user guide. And the core product is getting

Speaker 2: one HDF file pertile per day, and that includes all

Speaker 2: of the flood composits that one into two to three day.

Speaker 2: It also includes other ancillary layer layers with the number

Speaker 2: of water counts, some other things they go into generating

Speaker 2: the composites. If you wanted to create a custom composite,

Speaker 2: some of those layers might be of interest, or if

Speaker 2: you don't want to mess around with any of the

Speaker 2: filtering we've done, you could look at the water counts

Speaker 2: and work with those directly instead of the multi day

Speaker 2: compositing and all that stuff and the train masking the

Speaker 2: HDF files, you can fairly simply extract juts from those

Speaker 2: using GDL tools. There's an example and the user guide.

Speaker 2: We also do distribute geotip files for the flood layers

Speaker 2: within those files for the NRT products. And the flood

Speaker 2: layer is a data layer, is not an RGB layer

Speaker 2: that you've been seen in all the examples. Is simply

Speaker 2: has pixel values at zero, one, two, three into fifty five.

Speaker 2: So you throw those into a GIS and you'll have

Speaker 2: to assign colors to them to make it more intelligible.

Speaker 2: So for NRT downloads, there there are LANDS NRT download sites.

Speaker 2: These only retain data for about one week. The NRT

Speaker 2: neural time products from LANDS are only intended to be

Speaker 2: available for a short period of time because the goal

Speaker 2: is to get the nut led quickly and then for

Speaker 2: a longer older data you would you would look at

Speaker 2: the regular product, the science alreday products for downloading. You

Speaker 2: need a fore your data account. There's some browse instructions

Speaker 2: here which I will show in the demo later, so

Speaker 2: I want to belabor it here. You know the key

Speaker 2: things you need to know the collection which is six

Speaker 2: months for Motus, fifty two hundred for years, and they

Speaker 2: are distributed by day of year, so you need to

Speaker 2: know what the day of the year is, which you

Speaker 2: can google that and get a conversion from a month,

Speaker 2: month day to day of year quite easily. So we'll

Speaker 2: show this more later, so I won't talk about it

Speaker 2: too much now. The archive product downloads are from lads.

Speaker 2: This is only the Motus product of the reprocess and

Speaker 2: the narrow time from twenty twenty six forwards, similar type

Speaker 2: of approach, and I will also show this in the demo.

Speaker 2: There's also you can you the imagery in Worldview. Some

Speaker 2: of the examples I've shown you are in Worldview, like

Speaker 2: this one here. This is very convenient because you don't

Speaker 2: have to worry about the tiles. It's almost a globally

Speaker 2: you don't see the tiles, and you can compare it

Speaker 2: to lots of other thousands of other satellite image products

Speaker 2: that NASA has available, and you can do things of

Speaker 2: comparison compared one day versus another, or one product versus another.

Speaker 2: So it's very convenient. Because the lance this latest version

Speaker 2: of flood products started was again generation in twenty twenty one,

Speaker 2: those layers in Worldview only go back through twenty twenty one,

Speaker 2: so older products, older flood products will not show up

Speaker 2: in Worldview. We're hoping to rectify that at some point

Speaker 2: by processing those older products into the Gibbs imagery that

Speaker 2: Worldview accesses. Yeah, and I will show this as well

Speaker 2: in the demos, so I'm not going to stay on

Speaker 2: the slide too long now. And also when you start Worldview,

Speaker 2: you will see this welcome to Worldview page and there's

Speaker 2: an item there called assessing Floodwaters. So if you are

Speaker 2: not familiar with Worldview or not familiar with the flood product,

Speaker 2: I would recommend going through that. Other than other than that,

Speaker 2: you become the ad layers and you will get down

Speaker 2: to business of adding the flood product, and I will

Speaker 2: show that in the demos directly. Worldview also now has

Speaker 2: an events tab, and once had an events tabit it

Speaker 2: has a flood item in the events so you can

Speaker 2: filter on that. But sorry, this will show you all

Speaker 2: recently reported floods based on the gas database, and so

Speaker 2: if you've heard about a flood but weren't sure where

Speaker 2: it is, this might be a good way to find it.

Speaker 2: Many of these floods are too small for us to

Speaker 2: be detecting, like it can be quite small and still

Speaker 2: reported by g dacks. Whether we're going to have relevant

Speaker 2: imager is a question. It will also bring up other

Speaker 2: related If you put on one of these these events,

Speaker 2: that will bring up other relevant flood layers for you

Speaker 2: to look at, So that can be immediate to orient

Speaker 2: yourself towards flood events and available data. Finally, future directions.

Speaker 2: So this flood viewery which I showed in that example earlier,

Speaker 2: we're hoping to publicly release that. The main advantage is

Speaker 2: it adds the one day product we're both modus and beers,

Speaker 2: and the other advantages we're pulling in other non NASA

Speaker 2: flood products such as there's a Noah Birch Bason University

Speaker 2: flood product based on beers, and there's a radar based

Speaker 2: flood product from the Copernicus, the European Comperta system that

Speaker 2: is available in this as well. We're hoping to archive

Speaker 2: the viers neuralk time products. As I mentioned, we've also

Speaker 2: been wor being very hard on a machine learning update

Speaker 2: to water detection for Beers. This has real significant potential

Speaker 2: for improving the product. The detail of flood detection is

Speaker 2: greatly improved, and most significantly in my point of view,

Speaker 2: it rarely detects shadows as water, so all of the

Speaker 2: cloud shadow problems we have essentially disappear, and so it's

Speaker 2: so you can use the one day product without having

Speaker 2: to worry about that typically. So we're hoping to get

Speaker 2: that to roll that into an updated Beers product that

Speaker 2: is still under discussion and hopefully underway. We would also

Speaker 2: like to have a global alerts page to highlight active flooding.

Speaker 2: As you'll see when we when you look at worldview

Speaker 2: at a global scale, it's very hard to see flooding

Speaker 2: because foods usually are not that visible at this type

Speaker 2: of scale. So this would high areas of active flooding

Speaker 2: and then you can zume in and see the data directly.

Speaker 2: The main issue with this is the false positive, so

Speaker 2: making sure we're not alerting on things that are just

Speaker 2: clout shadows or and again getting the motus flood archived

Speaker 2: into Worldview, so you could look all the way back

Speaker 2: to two thousand flood events. That would be ideal. And

Speaker 2: the Sentinel three LLCI product we are hoping to expand

Speaker 2: and to look at using that product for flood detection.

Speaker 2: It has similar characteristics to Modus, but it is a

Speaker 2: morning overpass and typically places are less cloudy in the morning,

Speaker 2: so this would ideally replace you know, the morning overpass

Speaker 2: we're getting from terror provide additionally useful observations. But that's

Speaker 2: that's just you know, that's a wish SOS item you

Speaker 2: would like to move forward with. Okay, so that is

Speaker 2: the key presentation. I'm going to now move on to

Speaker 2: do show some demos using Worldview and the download sites,

Speaker 2: just to show you how to navigate through them. Okay,

Speaker 2: So here's just a brief cheat sheet for me for

Speaker 2: the demo and for h audience watching. So first I'm

Speaker 2: going to go to the product homepage just to show

Speaker 2: you a few basic things here. So there's you know,

Speaker 2: basic information about the product. Most importantly, there's a user guide,

Speaker 2: which is a PDF. I won't open it now, but

Speaker 2: I strongly encourage you to look at that. There's a

Speaker 2: summary of updates the details. There's also a summary of

Speaker 2: data access which I'm going to show you but call

Speaker 2: that info is also here there's a tile map which

Speaker 2: if you are downloading product for a specific area, you'll

Speaker 2: need to know what tile it is. This will you

Speaker 2: figure that out? This is also in the user guide,

Speaker 2: so it's there as well as here. And their ten

Speaker 2: degree you know they're on the grid, they're not weird

Speaker 2: increments of offsets from ten degrees. And finally, the FAQ

Speaker 2: section at the bottom, we have a lot of a

Speaker 2: standard set of vaqus so if you click on this

Speaker 2: you will get to this long list of questions about

Speaker 2: the products. So if you are unfamiliar with the product,

Speaker 2: or maybe even if you are, you might want to

Speaker 2: have a scan down this if you have questions, So

Speaker 2: just to introduce you to that. So now I'm going

Speaker 2: to jump into NASA Worldview, which is a great tool

Speaker 2: for looking at thousands of data products from NASA. And

Speaker 2: when you start it you will see again see this

Speaker 2: this pop introductory pop up. If you've never used Worldview,

Speaker 2: I would recommend going through this introduction to Worldview. I'll

Speaker 2: show you the basic tools, et cetera. Or the flood product.

Speaker 2: There's also assessing floodwaters tutorial, so I would recommend doing

Speaker 2: that if you if you're new to the product, I'm

Speaker 2: going to not do that and then interest of time

Speaker 2: and just show you quickly how to add the product directly.

Speaker 2: So the first thing you'll notice is when you start

Speaker 2: with View, it has the current up to date view

Speaker 2: of the Earth from Terra by default, the terror the

Speaker 2: motors sensor on Terra. You can click on Aqua several

Speaker 2: hours behind. It also has the veer sensors that we

Speaker 2: use for the flood product. So the source data is

Speaker 2: already being displayed by default in the interface. So that

Speaker 2: is great. So click on reference layers. They're not quite

Speaker 2: sure where you are then to add the flood product.

Speaker 2: We just click on add layers. You get this large

Speaker 2: menu with lots of options, but there's a flood option.

Speaker 2: Look on this. You'll see the opera products at the top,

Speaker 2: which somebody else will be talking about that for the

Speaker 2: our set training next, but for our global flood product.

Speaker 2: That here's the Beers product, there's the two and the

Speaker 2: three day players, and the Motus product the same. So

Speaker 2: I'm adding all of these in. There's some basic information

Speaker 2: on the right close this, so all of those have

Speaker 2: now been added. Probably you don't want to look at

Speaker 2: them all at the same time, so I'll just show

Speaker 2: one the two day Motus product. On the options here,

Speaker 2: I often turn off insufficient data visualization. It is basically

Speaker 2: showing you where you may have false negatives, where we're

Speaker 2: not reporting flood because it's too cloudy. But it also

Speaker 2: sort of obscures the background, so it can make it

Speaker 2: harder to see your sites of interest. So typically turn

Speaker 2: that off, but the news you would like. And again

Speaker 2: here at a global level, we don't really see a

Speaker 2: lot of flooding because it's usually not that large. Even

Speaker 2: if we go back to day however, you look closely,

Speaker 2: you will see something here in eastern Russia being largely

Speaker 2: reported in yellow, which is again recurring flood. So these

Speaker 2: are some rivers in Russia that are probably having a

Speaker 2: lot of spring snow melts still moving through them, I assume.

Speaker 2: And if we zoom right in, you can see here's

Speaker 2: the two day products. So we're looking at the Motus product.

Speaker 2: Let's look at the Modus imagery. Here's the Aqua image

Speaker 2: a little cloudy, the Terra image much less cloudy. Seems

Speaker 2: to be lining up in the river basin. If you

Speaker 2: wanted to compare, this a very nifty tool with world Views,

Speaker 2: the comparison tools. If you wanted to see if the

Speaker 2: flooding like this was happening last year, you can click

Speaker 2: on this. It's flits your screen. You have an A

Speaker 2: in a B tab is where we were on June seventh,

Speaker 2: twenty twenty six, So say and you can for each

Speaker 2: tab you can change the product. You can change the date.

Speaker 2: So let's change the date to a year ago. So

Speaker 2: let's make it twenty twenty five. You put in June

Speaker 2: seventh's just for embarrassing's sake. So a year ago the

Speaker 2: product was showing a lot less flooding than it is today.

Speaker 2: So this is a very nifty tool to compare one

Speaker 2: event versus the previous event. If you know the date

Speaker 2: for the previous event, you can of course browse dates

Speaker 2: and see what we have. You'll notice in the older

Speaker 2: a year ago data, we are not we don't have

Speaker 2: the recurring flood. That's because these imagery layers that are

Speaker 2: being displayed in Worldview were generated from the narrow time

Speaker 2: product and the year ago had not introduced that. So

Speaker 2: the other thing, but as website, let me turn off.

Speaker 2: The comparison that you can do is to try to

Speaker 2: determine if you have a good product where whether clouds

Speaker 2: are interfering At this site, I didn't see too many problems,

Speaker 2: but I'm gonna show you another area and it was

Speaker 2: undue six. Yeah, Western Australia where we have I'm recording

Speaker 2: some flund here and this is probably not It was seventh.

Speaker 2: There were just took a minute for imagery layers, so

Speaker 2: you can see here this is a very typical cloud

Speaker 2: shadow false positive problem. You see these red flood areas

Speaker 2: falling in the cloud shadows. You know, unless you knew

Speaker 2: that there was flooding in this region of interest, you

Speaker 2: would probably totally disregard this. But if you did hear

Speaker 2: there was flooding of interest, it you know there may

Speaker 2: be real flood. The two day product is showing this.

Speaker 2: If we turn that off and turn on the three

Speaker 2: day just a little more, you know, you still are

Speaker 2: getting some so's. It's not perfect, but it is definitely

Speaker 2: less than the two day But if you wanted to

Speaker 2: be sure, you should hear it looks pretty clear that

Speaker 2: a lot of this is falling in the shadows. Even

Speaker 2: check the other image, so Terra is actually looking very

Speaker 2: nice on this day. The two day product is also

Speaker 2: using data from June fifth, so let's go back a day.

Speaker 2: While fifth was not great, there's a lot of cloud

Speaker 2: and aqua, a lot of cloud shadows. So we have

Speaker 2: two days with clouds like this, where you have very

Speaker 2: bright clouds and very dark cloud shadows. This is gonna

Speaker 2: be a problem. Even in the two day product, you'll

Speaker 2: notice this herot image from yesterday, from the previous days

Speaker 2: is not nearly as problematic because it doesn't have sharp

Speaker 2: cloud shadows. So that's that's, you know, one way to

Speaker 2: look at the product and decide what you're seeing is

Speaker 2: real and useful for you or not. If you decide

Speaker 2: this is useful, you can get a link to download

Speaker 2: data from Worldview. If you quick on the data tab here,

Speaker 2: it will show you all the products that you had

Speaker 2: loaded up. And so for the flood, the two day window,

Speaker 2: this one is the main product. The second one is

Speaker 2: the geotip as it says there, So if you wanted

Speaker 2: to download the geotip the two day geotiph for this product,

Speaker 2: if I'm a it's telling you no Granules. That's because

Speaker 2: this is still twenty twenty five. If I did the

Speaker 2: comparison the other site, and we don't, the interface here

Speaker 2: is not linked to that data archived yet, so it's

Speaker 2: currently only linked to the neural Time data. So let's

Speaker 2: go forward to this year and now, if this was

Speaker 2: the product you were interested in, we're seeing five hundred

Speaker 2: grand was available. That's way too much data. We need

Speaker 2: to set an area of interest, so you can draw

Speaker 2: a box. Area available. Granules is too, so if you

Speaker 2: wanted that, you can click on here go through this

Speaker 2: opens Earth data search stick the tour for now and

Speaker 2: to use them over to Australia. You can see the tile.

Speaker 2: Let's see a preview of the data. You can see

Speaker 2: our box from the previous well few and the links

Speaker 2: to download the two data products. These are actually different dates,

Speaker 2: so day one five seven and day one five six.

Speaker 2: If you cook on the download button you will be

Speaker 2: allowed to download that year to directly. So that's how

Speaker 2: to download from Worldview, and again that only works for

Speaker 2: at the moment. That only will work for recent data,

Speaker 2: the past week's data the nuro time product. You can

Speaker 2: also just directly go to the nual Time download sites

Speaker 2: totally separate from worldview. Cook on, browse here, book on

Speaker 2: all data here. Here's where you need to know the

Speaker 2: collection number for six to one for the Motus product.

Speaker 2: It's fifty two hundred for the veers, so for Motus

Speaker 2: six one. And then you see a long list of

Speaker 2: many Motus products, most of which are my foot product.

Speaker 2: So you can use your browser search to search for

Speaker 2: what we call the short name, which is NCDWD. This

Speaker 2: is ALLID in the user bed and on the homepage,

Speaker 2: and you will see all these directories. The ones with

Speaker 2: F are geotips for specific one day, two day, three

Speaker 2: day flood layers. The one without the F is the

Speaker 2: HDF file so depending on what we WANTOK on those

Speaker 2: we'll see Even though this is neural time data, so

Speaker 2: there's no data in twenty twenty four, twenty five year,

Speaker 2: will only be data roughly the past seven to ten days,

Speaker 2: so one fifty nine will beach to day. One fifty

Speaker 2: eight would be yesterday. Look on that you'll see all

Speaker 2: the files available. It should be two hundred and eighty

Speaker 2: seven of these software one for each file. This being

Speaker 2: the tile number here, so that's the neural time download

Speaker 2: sites fairly straightforward. For the historical data, we have the

Speaker 2: LADS archived download site. So this only contains well, it

Speaker 2: contains the most reprocessed products from two five and the

Speaker 2: modus neural time products from the beginning of twenty twenty

Speaker 2: six to forward. It does not yet contain any of

Speaker 2: the BEERS products. So this is a little bit different,

Speaker 2: a little bit the same quick them find data. This

Speaker 2: loads a larger search interface, which you could certainly work with,

Speaker 2: but if you just want the simple interface that we

Speaker 2: saw before, click on online archive on the left and

Speaker 2: this opens again collection numbers for the motus we want

Speaker 2: six to one and you can search for mcd D.

Speaker 2: Here we have two directories just this one l three.

Speaker 2: These are all level fee products. This is the reprocessed

Speaker 2: historical so you click on that you see years two

Speaker 2: thousand to twenty twenty five. The NRT one is the

Speaker 2: neural time so this is only twenty twenty six from

Speaker 2: day one and forwards, so you click on any day.

Speaker 2: So again the listing all the HDF files in this space. Okay,

Speaker 2: so those are the download sites on NASA Worldview. And

Speaker 2: now I'm going to show uh how to load this

Speaker 2: imagery into qh I S. So firstly, in Worldview, let's

Speaker 2: get back to just a basic view. Uh. You know,

Speaker 2: Worldview is again not showing the raw the raw data files.

Speaker 2: It is an imagery product generated from the data files

Speaker 2: for the flood. It is showing as you see here

Speaker 2: blue for surface water, right for flood, et cetera. You

Speaker 2: can pull this directly into qg I S or r

Speaker 2: JS pro so you can work with it with your

Speaker 2: own data. And the way to do that, the way

Speaker 2: I remember how to do that is if you're in

Speaker 2: Worldview and you can become the information I have here

Speaker 2: and click on a PI access This will open. Then

Speaker 2: fell page on Gibbs Global and Global Imagery brows Services

Speaker 2: tells you all about these are the layers that are

Speaker 2: displayed in Worldview. World View is not displaying any raw data,

Speaker 2: and we want this would via JS applications. There's a

Speaker 2: sexual QGIS, RKS, et cetera. The key info here is

Speaker 2: really just the U r L for the w MS endpoint.

Speaker 2: So I'm going to copy that and then I'm going

Speaker 2: to move to HUGS to show you how to add

Speaker 2: that in. Okay, so here I've opened a new HGIS

Speaker 2: session PGS is free JS software. For those of you

Speaker 2: that I'm familiar with it, it's very very handy. And

Speaker 2: what we're going to do is add in a w

Speaker 2: MS layer for those gibbs ask the gibs service so

Speaker 2: that we can view the flood data directly in qgis

Speaker 2: with any other data you might have fig files or

Speaker 2: other energy. So the key is in the in this

Speaker 2: section on the left w S w mt S right

Speaker 2: click on that add a new connection. The only thing

Speaker 2: you really need is the r L which is what

Speaker 2: I copied from that API access page I just showed

Speaker 2: you and the name which with whatever you want massa gibs.

Speaker 2: That's what it is, and all the rest is you

Speaker 2: can be sleep it. It's free, open access. You don't

Speaker 2: need anycount And when that's added, you see this items

Speaker 2: show up become that you will see all of the many,

Speaker 2: many categories of imagery products available and gibs and you

Speaker 2: know this is what you can view in worldview. So

Speaker 2: let's add the corrected reflectance because that's always the key

Speaker 2: knowing what you're looking at. So I'm going to add

Speaker 2: the modus awquad seven to one. That's the false color

Speaker 2: and the like that for the flood product, and let's

Speaker 2: add the modus terra seven to one as well. So

Speaker 2: this again is the current state of data collected today.

Speaker 2: And then let's add the flood the flood hazard. There's

Speaker 2: the modus one day, that the modest two day. Okay,

Speaker 2: so those are all added. I'm gonna that up. You'll

Speaker 2: see them here. The one thing you cannot do here

Speaker 2: is turn different colors different category classes on or off,

Speaker 2: so you have to live with the gray insufficient data.

Speaker 2: But you can click them on on and off the hole.

Speaker 2: And here we're seeing both the injuries. So if we

Speaker 2: were finally, the other thing you need to do is

Speaker 2: turn on the time slider at the time the temporal controller,

Speaker 2: and then on here. And so this defaults I think

Speaker 2: the full range of your data. You can certainly make

Speaker 2: this go from six for example anyway, so you have

Speaker 2: less data to scroll through, and it tells you where

Speaker 2: your current date is. So just to show you another example,

Speaker 2: there's some flooding in southern Mozambique in January. It was

Speaker 2: fifth so change that directly. And so here we're just

Speaker 2: looking at the aqua image. We look at the one

Speaker 2: day flood product. It loads right up and you can

Speaker 2: see it. And then you can compare this to the

Speaker 2: two day product. The two days getting less flood, but

Speaker 2: it's getting quite a bit of it. If you were

Speaker 2: I had been looking at the one day and you

Speaker 2: were concerned about but whether you have the data or

Speaker 2: cloud shadows, it gives you. Men. Here once again we're

Speaker 2: seeing in the one day flood being reported directly in

Speaker 2: the modest shadows Terra doesn't. It's a little bit cloudy,

Speaker 2: but it's you know, these thoughts are not generating cloud

Speaker 2: shadow false positives. But if we click on the two

Speaker 2: day product off the one day, most of those are disappearing.

Speaker 2: So this again is where the two day comes in handy.

Speaker 2: But obviously if you you know, know the area of

Speaker 2: interest and what's going on, you can direct choose choose

Speaker 2: the right product after looking at the source imagery. So

Speaker 2: I think that's all I have for you here on

Speaker 2: the demo, just showing you how to get the syndic

Speaker 2: q I S and play with it initially, and of

Speaker 2: course if you have any other questions about the product

Speaker 2: or the how to use it or how to access it,

Speaker 2: the resources page and the presentation has a links for

Speaker 2: accessing help which is also all on the homepage. So

Speaker 2: ask it back to the media now, who will go

Speaker 2: over summary and continue the session. Thank you, Amida.

Speaker 1: Thank you so much Den for your excellent presentation and demonstration,

Speaker 1: especially providing information about how to visualize recent floodcases and

Speaker 1: how to download data. So again, b thank you for that.

Speaker 1: This brings us to the end of today's session and

Speaker 1: to briefly summarize what we saw. Doctor Slayback presented description

Speaker 1: of global flood product development approach. He showed that it's

Speaker 1: based on Terran Aquamotis Noah twenty and twenty one vers

Speaker 1: imagery uses red infrared and shortwave infrared reflectance to detect water.

Speaker 1: Has multilook composites available, so data from one, two and

Speaker 1: three day composites are available and based on threshold based

Speaker 1: water detection, terrain shadow and cloud shadow corrections are applied

Speaker 1: to remove false positive in water detection. Doctor Slaybek also

Speaker 1: showed how to select appropriate composite product for flood visualization

Speaker 1: or flood checking the flood based on true color imagery.

Speaker 1: If there are a lot of clouds present, then multi

Speaker 1: de composites more useful. Also, identification of recurring floods is available.

Speaker 1: It's based on twenty two years of historical flood mass data.

Speaker 1: Then we saw examples and demonstration of floodcases using Worldview

Speaker 1: recent flood places that we saw in Russia, Australia and Mozambique.

Speaker 1: Also flood data access and download using Worldview and lood

Speaker 1: Stack were demonstrated near real time and twenty three years

Speaker 1: of archive flood data from Terra and Aquar from twenty

Speaker 1: two thousand and three to twenty twenty five are available

Speaker 1: before that, so two thousand to two thousand and two

Speaker 1: data are available from Terra alone. In flood data can

Speaker 1: be downloaded as jutive from this laod's deck recent data

Speaker 1: so starting from twenty twenty one to present they are

Speaker 1: available from Worldview as we saw, and we also saw

Speaker 1: that flood data can be downloaded as jutive and analyzed

Speaker 1: in QGIS. Doctor Slabek also mentioned a few limitations of

Speaker 1: this product based on optical data, so it cannot see

Speaker 1: floods through clouds cloud shadow. Although they are removed, they

Speaker 1: can sometimes pose problem and there may be false detection

Speaker 1: of water because of that. It has medium special resolution

Speaker 1: of two hundred and fifty meters may not be adequate

Speaker 1: for resolving urban floods. It's based on twice daily observations

Speaker 1: and may miss some flash floods. Water under tree cover

Speaker 1: is not visible to satellites, and changes in surface water

Speaker 1: extents such as new reservoir changes in reserve river courses,

Speaker 1: they may not be in reference surface water mass data sets,

Speaker 1: so there may be some uncertainties because of that. We

Speaker 1: also saw future plants for flood product. There will be

Speaker 1: a release of new flood viewer in addition to the

Speaker 1: current setup in Global flood product. It will include Nova

Speaker 1: GMU and Copernicus glow Fast products. Flood products and currently,

Speaker 1: as we saw, weirs data are not archived, so they

Speaker 1: will be archived now will use machine learning. Update to

Speaker 1: water detection algorithms especially for weirs include global alerts to

Speaker 1: highlight active flooding at a global scale and include center

Speaker 1: in three old chip product. These are also optical products.

Speaker 1: So next week we will have a presentation about monitoring

Speaker 1: floods using opera surface water extent based on optical and

Speaker 1: SAR observations. Again, homework will be posted on our training

Speaker 1: website on twenty fifth of June and homework will be

Speaker 1: due on July ninth. It will be in terms of

Speaker 1: Google Forms, so you will be answering questions in Google forms.

Speaker 1: Certificate of completion will be awarded to those who attend

Speaker 1: all live webinars and complete the homework assignment by the

Speaker 1: due date. Then a certificate where email will be received

Speaker 1: in approximately two months after completion of the course. Once again,

Speaker 1: thank doctor Daniel Slayback for his time and for his

Speaker 1: very useful information and presentation about the Global Flood Product.

Speaker 1: Here is doctor Slayback's contact information if you have any

Speaker 1: additional questions, and you can always contact us at our

Speaker 1: SET if you have any questions. This is a link

Speaker 1: to our set website and our set YouTube. You'll find

Speaker 1: our recent and past training information on both these links.

Speaker 1: For questions, comments, or to share how you have applied

Speaker 1: our trainings to your work or studies, please email NASA

Speaker 1: dot R set at gmail dot com and join our

Speaker 1: mailing list to stay up to date on our latest trainings.

Speaker 1: Visit our contact page to subscribe. There's some resources and

Speaker 1: useful links based on today's presentation and here's the QR

Speaker 1: code you can access information about flood product and with

Speaker 1: that we thank you all for attending today's session and

Speaker 1: we will go to the question and answer session. Now,

Speaker 1: thank you, thank you so much. Will have our question

Speaker 1: and answer session with doctor Slayback. There are many questions,

Speaker 1: so we will address as many as we can and

Speaker 1: we will post the rest of them on our website.

Speaker 1: We'll start with the questions. There's one note. There is

Speaker 1: an exercise posted on the training web page that you

Speaker 1: can download. Exercise is not due anytime soon. It is

Speaker 1: your homework questions will be based on that, but it

Speaker 1: just allows you to explore a global flood product using worldview.

Speaker 1: So please download the exercise and work on the exercise. Okay,

Speaker 1: just a note here and thank you Jan once more

Speaker 1: for your presentation, and we'll just have a few questions here.

Speaker 1: I'll start with first questions. In the current flood detection

Speaker 1: workflow motives, envires use multil look compositing and terrain based

Speaker 1: false positive masking to improve reliability. Could similar compositing and

Speaker 1: conceptual masking approaches be extended in the future to detect

Speaker 1: other types of environmental animalies, for example, persistent reflectance changes,

Speaker 1: deby related spectral signatures or hydrologically mobilized materials. So that

Speaker 1: our observation products can support broader preparedness and environmental health

Speaker 1: assessment beyond floods, and you can unmute yourself and answer

Speaker 1: the questions, then, yeah.

Speaker 2: Thank you, Amita. Yeah, certainly you could. You could apply

Speaker 2: similar approaches to other questions. I think that really depends

Speaker 2: on what you're trying to look at and what your

Speaker 2: data sources are, and what you're what the limitations of

Speaker 2: your detection, you know, algorithm are. In our case, as

Speaker 2: I mentioned over and over, we have problems with shadows,

Speaker 2: so cloud shadows, train shadows so. And for that particular case,

Speaker 2: you know, the multi day compositing and the train masking

Speaker 2: and the hand masking or our approach to deal with it.

Speaker 2: But you know, in a more you know, if you

Speaker 2: have an algorithm that has less problems with things, you know,

Speaker 2: false positives or false detections, you hopefully don't need to

Speaker 2: absorb to those types of things.

Speaker 1: Ideally, yes, thank you. The next question is are these

Speaker 1: data available in Google or tenngin data catalog.

Speaker 2: I don't believe they are, but that is a good

Speaker 2: reminder to look into that, especially with the archive product.

Speaker 2: I suspect that would be attractive to them and relatively

Speaker 2: easy to get in since it's a fixed data set there.

Speaker 2: If you poke around there, there are some flood products

Speaker 2: that were generated using the same algorithm but a different machinery,

Speaker 2: not in the systems here. That was used to regenerate

Speaker 2: the product for historical flood events for a Nature paper

Speaker 2: that we've published in twenty twenty one, I believe, led

Speaker 2: by Brett Dulman. So that was looking at roughly nine

Speaker 2: hundred different flood events globally. So those those are in

Speaker 2: Google Earth Engine, I believe at least they were at

Speaker 2: some point, so you might see those, but it's not

Speaker 2: a global comprehensive data set.

Speaker 1: Yes. The next question is does the flood view covers

Speaker 1: all the places on the Earth and will there be

Speaker 1: options to choose and overlay composites from different products.

Speaker 2: If you mean the Blood Viewer, the new tool we're

Speaker 2: hoping to release that is global US. It's same as worldview.

Speaker 2: It shows full you know, not really Antarctica, but everything else.

Speaker 2: And yes, the part of the point of the of

Speaker 2: the flood Viewer or kind of in house viewer, is

Speaker 2: that it will bring in external flood products such as

Speaker 2: the one flood product from from the s A and

Speaker 2: the Noah, Noah of yours flood product, and so you

Speaker 2: can compare them directly with one another and see which

Speaker 2: one works best for your particular event. World we only

Speaker 2: have or mostly believe only has NASA products in it.

Speaker 1: Great question four is given that hand models successfully filter

Speaker 1: out steep ridge shadows, how does the system How does

Speaker 1: the system account for a sudden urban flesh floods where

Speaker 1: localized micro topography creates deep pool away from the natural

Speaker 1: drainage channels. Does it lead to false negatives? If yes,

Speaker 1: how can this problem be addressed and fixed in the future.

Speaker 2: Yeah, this this product is not going to work well

Speaker 2: for any kind of urban or micro topography, you know,

Speaker 2: small scale flash flooding, both because mostly because the two

Speaker 2: hundred and fifty meter pixel you're not going to in

Speaker 2: an urban area, most of that pixel is probably relatively

Speaker 2: dry rooftops or treetops and not the water on the ground.

Speaker 2: So you have the mixed mixed pixel problem. And then

Speaker 2: the timing or you know, the small smaller the flood extent,

Speaker 2: you know, flash flood constraint to a canyon or you know,

Speaker 2: canyons and urban areas between buildings, they move very quickly,

Speaker 2: so getting an observation is you know, difficult or unlikely.

Speaker 2: Perhaps you know, in the right conditions you might have

Speaker 2: a clear modus observation and that would be great, But

Speaker 2: then you have the two undred and fifty meter pencil problem.

Speaker 2: So yeah, the hand you know, hand isn't earlier problem there,

Speaker 2: but it would the hand mask would mask out many

Speaker 2: such areas because it is it's predicated on this idea

Speaker 2: that for two hundred and fifty meter pixel there's if

Speaker 2: there's sufficient drainage, that water is not going to stick

Speaker 2: around long. So if you were making a hand mask

Speaker 2: for a you know, say you had a global you know,

Speaker 2: one meter sour sensor daily that you can make a

Speaker 2: flood product from, you would well, you probably wouldn't have

Speaker 2: the same types of issues, but the hand mask would

Speaker 2: look very different at a different scale than it does

Speaker 2: forty to fifty meter.

Speaker 1: So the next question is on slight forty their difference

Speaker 1: is not significant? Correct? Also, what does composite mean in

Speaker 1: the slide showing the data for punjab in slide forty two.

Speaker 1: On slide forty D eight, how is the hand mask

Speaker 1: solving the problem?

Speaker 2: Okay, let me look slide forty was the recurrent flood example,

Speaker 2: I believe so, right, the difference there is just with

Speaker 2: the recurrent flood updates. So the detective water is the same.

Speaker 2: We're just reclassifying most of it in that Northern California

Speaker 2: example as recurring flood. But otherwise it's the same source

Speaker 2: water detection. So what was the data? And yeah, right,

Speaker 2: so that was a custom composite just generated by adding

Speaker 2: up all the one day products for one week to

Speaker 2: just bring out more clearly the flood extent for that event.

Speaker 2: Because each you know, each day, if you looked at

Speaker 2: each a you know, some days have clouds, some don't.

Speaker 2: So over same idea that we do with a three

Speaker 2: day or the two day product, but if you've extended

Speaker 2: over seven days, you can pick up you know more

Speaker 2: so that the point there was just to show that

Speaker 2: most of that flood event was actually not refurning but

Speaker 2: unusual for that time of the year. Aside forty eight

Speaker 2: Yeah on Hawaii Island. Hand solves the problem of of

Speaker 2: our product. Initially, you know, way back in the beginning

Speaker 2: reporting Central Hawaii Island as flood because it's it's that

Speaker 2: area is very steep, so there's not you're going to

Speaker 2: have water sitting on the side of Mana Loa volcano,

Speaker 2: it's gonna run off very quickly. So yeah, so hand

Speaker 2: very quickly solved that any any topography related problem. And

Speaker 2: here the problem was all that volcanic material is so

Speaker 2: dark that it gets detective as water.

Speaker 1: The next question is what does TMC mean? And it's

Speaker 1: too many clouds. Let's just read that. And next question

Speaker 1: is how do we determine the pre and post times

Speaker 1: during flood detection analysis?

Speaker 2: Yeah, I mean that is a good question. You really

Speaker 2: that depends on how you're coming at this. If you

Speaker 2: know about a flood event, ideally you have some other

Speaker 2: news type of data that's telling you that people are

Speaker 2: impacted or something's going on. If you're trying to do

Speaker 2: this simply from flood imagery or from imagery perspective, you know,

Speaker 2: if you have a daily product like cars and it's clear,

Speaker 2: you could be at least within a day, you could

Speaker 2: identify there in post flood times by when you're detecting water.

Speaker 2: But yeah, yes, the question, but yeah.

Speaker 1: It's a good question. And the thing is the in

Speaker 1: next two sessions you will see there are other tools

Speaker 1: and even a predictive model. So combining everything might be

Speaker 1: better to understand flood issues better. And question eight is

Speaker 1: if we use SAR data, would we so have cloud

Speaker 1: based question? Will it not mitigate the cloud based obstruction?

Speaker 2: Yeah, sor penetrates clouds. You do not have cloud problems,

Speaker 2: so it is phenomenal for that reason, and you will

Speaker 2: not have cloud shadows. You know, all of that goes away.

Speaker 2: It is, it is, it is great. The main limitation

Speaker 2: of stars that you don't have global daily covers and

Speaker 2: you and it's well, some of it is commercial. You

Speaker 2: have to purchase it sent on one from preparatas from

Speaker 2: the Europeans is free, but it doesn't have daily repeat.

Speaker 2: It's maybe five or six days the best. I think.

Speaker 2: It depends also there in the world. So for the

Speaker 2: right you know, if the timing works out, that can

Speaker 2: be a good solution. But if there's no stur data,

Speaker 2: then there's no star data. And the other problem with

Speaker 2: SAR is you have to typically have a free image

Speaker 2: with nope, without the floodwater present, and compare that, compare

Speaker 2: that to the post image. And there might be other

Speaker 2: ways they're doing it now, but that's the sort of

Speaker 2: a straightforward way. So you have to have an archive

Speaker 2: of dry dry line imagery for the site, which certainly

Speaker 2: probably exists for Centinel one point.

Speaker 1: Great and as proper in seted. Our set will have

Speaker 1: a nice our training starting July second. In session one

Speaker 1: is about uh star based flooding, so you may want

Speaker 1: to check that out. Next question is how does your

Speaker 1: VI's mL model behave on shadow water? Does it recognize

Speaker 1: water despite the shadow cast on it.

Speaker 2: That's a good question, and I'm not entirely sure since

Speaker 2: I'm not the developer of that model, but you could

Speaker 2: find out. I don't think it is a significant problem,

Speaker 2: but it's a good question to ask a confirm.

Speaker 1: The next question is if Moodys and Weirs can observe

Speaker 1: floods only one or two times per day under favorable conditions,

Speaker 1: how can these products support flash flood response when floodbeaks

Speaker 1: more occur may occur within one, two three hours.

Speaker 2: Yeah, similar to the earlier question, this is not going

Speaker 2: to work well for most flash blood situations, both through

Speaker 2: the timing issue and then probably the flash flood is

Speaker 2: smaller and spatial extent. Even if it's extremely dangerous, you know,

Speaker 2: working down a canyon, it may not be really that wide.

Speaker 2: So the two two hundred and fifteen meters pixel limitation

Speaker 2: as well. I mean, there's really very little ideal observational

Speaker 2: data to capture those events unless you've got you know,

Speaker 2: aircraft in the sty or something. Thanks.

Speaker 1: The next question is can these products detect urban plurial

Speaker 1: flooding or are they mainly suitable for riverine and large

Speaker 1: area flooding. What improvements are needed for dense urban area

Speaker 1: with complex training system.

Speaker 2: Yeah, urban areas are very problematic because they have a

Speaker 2: lot of dry objects rooftops, trees that are going to

Speaker 2: not be detected as water must be a very deep flood.

Speaker 2: So other than that, you know, the source of the flooding,

Speaker 2: whether privial or riverine overflow, doesn't really matter, but the

Speaker 2: urban problem is significant.

Speaker 1: The next question is can these satellite products distinguish or

Speaker 1: support analysis of compound flooding such as rainfall driven flooding

Speaker 1: combined with the river outflow, river overflow, storm search, dam release,

Speaker 1: or drainage system failure.

Speaker 2: Yeah, you know, we're just we're just detecting water here primarily,

Speaker 2: and then we're using fairly trivial methods to say if

Speaker 2: it's flood or or not flood or recurring flood, so

Speaker 2: you know the source of the flooding or the type

Speaker 2: of if there's a dam involved. That's sort of really

Speaker 2: up to the user to take the product and evaluate

Speaker 2: in more detail. Just bearing dams. As I showed in

Speaker 2: some of the slides, any new dam constructed in the

Speaker 2: past few years will probably show up as flood for

Speaker 2: a while until like it's thought up in the annual

Speaker 2: water mask, So that can be a little confusing mm hm.

Speaker 1: The next question is along the same line, which products

Speaker 1: can be used and how to look at riverine floods

Speaker 1: where small rivers are regulated with small dams or badges.

Speaker 2: Yeah, that's that's tough. It would depend on and how

Speaker 2: how you know small the flood flying surrounding the river

Speaker 2: mm hm.

Speaker 1: The next question is can satellite flood products contribute to

Speaker 1: early warning or are they mainly usually after flooding has

Speaker 1: already occurred. How can they be linked with rainfall forecasts

Speaker 1: and stream flow prediction systems such as you glows.

Speaker 2: Yeah, I think these are not going to be helpful

Speaker 2: for early one and they're just detecting water once it's

Speaker 2: on the ground. Flood models those other systems can certainly

Speaker 2: help predict when you might expect flooding based on rainfall

Speaker 2: forecasts and and serve models.

Speaker 1: So we are going to have a session on geoglows

Speaker 1: in session three and you will also see an exercise

Speaker 1: at the end where you are looking at geogloss prediction

Speaker 1: and UH one of one of the flood cases doctor

Speaker 1: Labat showed careson in in Ukraine. You can see that

Speaker 1: picking stream flow in that period and then you so

Speaker 1: if you know that, you can monitor that period and

Speaker 1: then keep looking at global food product when GEO clows

Speaker 1: up predicts stream flow, So you will have an exercise

Speaker 1: on that. And question fifteen is given the cloud comer

Speaker 1: limitations of optical observations during CEPR weather events. What are

Speaker 1: the best practices for using for fusing this product with

Speaker 1: SAR data within QIS and nearly all time emergency response?

Speaker 2: Yeah, I think you know, for a specific event where

Speaker 2: you have a you know, a person an analyst working

Speaker 2: the event, you know they should try to look at

Speaker 2: all the imagery available and see what's going to be useful.

Speaker 2: If it's you know, if they're looking world view and

Speaker 2: the notice imagery is clear, or the beers shown something

Speaker 2: and they can jump on that if they have sur data.

Speaker 2: That's typically the problem in A and A you know, yeah,

Speaker 2: an event when it when it's starting, you know, are

Speaker 2: you're actually gonna have SAR data. You might sent all

Speaker 2: one perhaps or night start perhaps, but if you have

Speaker 2: to order a new acquisition, that's going to take a

Speaker 2: day or two at least. So it's really I mean,

Speaker 2: this is sort of the problem with all of the

Speaker 2: different flood products out.

Speaker 3: There is.

Speaker 2: It's hard to know which one is going to be

Speaker 2: useful for a given event without looking at the details

Speaker 2: of that event, how cloudy is it, what's the data available, etc.

Speaker 2: So but you know, a human analyst can hopefully sort

Speaker 2: that out. An automated system that gets it's much trickier.

Speaker 2: But you know, I would just.

Speaker 3: Drop whatever you could into QGIS or look at it

Speaker 3: first in Worldview or the Flood Viewer when that's released,

Speaker 3: to give you a quick look at what's available and

Speaker 3: potentially useful.

Speaker 1: So again you will see a product next week which

Speaker 1: has both optical and SARD components that you can combine

Speaker 1: in q g S. So what what has modis thought

Speaker 1: about global flooding looking back?

Speaker 2: Yeah, that's a great question, and we're looking at that

Speaker 2: now with our with our pointy plus year r PAD.

Speaker 2: So we're hoping that the report on that in the paper.

Speaker 2: What you.

Speaker 1: Next question is, are there any methods or data sets

Speaker 1: available for floodplane mapping.

Speaker 2: Yeah, I'm not, you know, in particular familiar specifically with

Speaker 2: floodplane mapping, but I think there was a recent paper.

Speaker 2: We can look for the length and put it in

Speaker 2: the notes where they I don't know we're updating in

Speaker 2: some sort of global floodplane analysis. I believe, yeah, products

Speaker 2: like ours could be very useful for areas where flooding

Speaker 2: is routine and routinely detectable. I should say which, it's

Speaker 2: many areas, but certainly not everywhere. But yeah, otherwise, modeling,

Speaker 2: d MS all that kind of stuff, which has its

Speaker 2: own complications.

Speaker 1: Mm hmm. The next question is if optical and star

Speaker 1: based flood maps disagree, what would be the standard method

Speaker 1: for reconciling differences in the final flood product.

Speaker 2: Yeah. Again, I'm kind of repeating what I said earlier.

Speaker 2: If you have a human analyst doing this, I think

Speaker 2: they could very readily determine which one is more useful

Speaker 2: if you truly need to fuse them. That's you know,

Speaker 2: I don't know, that's that's a little harder. But if

Speaker 2: you're if you're trying to refuse on an automated basis,

Speaker 2: that's you know, we could get into trouble because one

Speaker 2: of the products might just have false positives or false

Speaker 2: negatives or you know, not really be useful. Fusing over

Speaker 2: the useful product is just going to diminish the quality

Speaker 2: of the overall output. So yeah, this is again the

Speaker 2: trick being able to look at things and see what's

Speaker 2: what's actually a good data product.

Speaker 1: M next question is how does this product differ from

Speaker 1: novah IR's flood Man.

Speaker 2: Yeah, they have a very different approach, and they have

Speaker 2: a paper out on it so you can read it

Speaker 2: in detail. I don't remember all the details offhand, but

Speaker 2: one of the differences is they don't just report binary

Speaker 2: flood that they report percent water or probability. I forget

Speaker 2: one of the two. So it's a you know, wonderful

Speaker 2: hundred or whatever it is, gradation, which can be useful

Speaker 2: in some drier areas. This seems to market up soil

Speaker 2: moisture than water, but certainly work working at.

Speaker 1: Okay, next question, and we'll take a couple of more

Speaker 1: questions and then we'll address the rest of them. Litron,

Speaker 1: how do I map flood extent when the ground stays

Speaker 1: wet the whole season? Pretending to working with Centinel sar

Speaker 1: in ge, I've also extracted flood extent from Sentinel one

Speaker 1: sar in ge and the maps came out empty. What

Speaker 1: happens when the SAR extraction is empty? Does it mean

Speaker 1: no flood or did my threshold method fail? Does the

Speaker 1: same flood principles apply?

Speaker 2: Yeah? Sorry, Flood detection is very different than what we're doing,

Speaker 2: and it can certainly be more complicated. So yeah, especially

Speaker 2: if the if the ground stays wet, I mean that

Speaker 2: is problematic even with optical I'm not sure that's our particular.

Speaker 2: I know there's a there's many different SAR algorithms for

Speaker 2: water detection and or flood detection, so they all have

Speaker 2: different strengths and weaknesses. But there's a lot of literature

Speaker 2: out there on that, and I'm not familiar with it personally.

Speaker 2: We're not doing a sour product.

Speaker 1: Yeah, yeah, So just to add a note to that.

Speaker 1: Our next session, which is going to be on twenty three,

Speaker 1: we'll talk about oper od enemy surface water extant product

Speaker 1: that has both SARA and optical components, and you may

Speaker 1: be able to ask more questions then even the nice

Speaker 1: you know, I'm going to keep moving down and take

Speaker 1: some questions later on. Next question is how much it's

Speaker 1: it's precision compared to ground reality.

Speaker 2: So the you know, the the source data is the

Speaker 2: surface reflectance product from motus or from beers, and their

Speaker 2: gelocation precision I believe is significantly less than a pixel,

Speaker 2: like maybe one hundred meters, but you could find that

Speaker 2: doctoration online for those products. Where it gets frickier is

Speaker 2: at the edge of the swath. When these sensors are

Speaker 2: acquiring data, the pixels get very blurry or extended because

Speaker 2: it's looking at an angle respect to the surface of

Speaker 2: the Earth. So those pixels and you will see this

Speaker 2: if you look in Worldview at any of the cryptic

Speaker 2: reflection status that's you check you switch between terra and

Speaker 2: A or the different viers depending on the day, it'll

Speaker 2: be sharper or blurrier. And that's because that source data

Speaker 2: is coming from closer to nater, closer straight below the

Speaker 2: satellite or towards the edge, so you have this an

Speaker 2: effect blurring effect on top of the pixel geolocation. So

Speaker 2: bottom line, the third part is that the neural time

Speaker 2: product is using preliminary relocation data and so that can

Speaker 2: be have errors. It doesn't often, but you will see

Speaker 2: sometimes a river will be shifted some distance away due

Speaker 2: to this the shortcuts they take to get the neural

Speaker 2: time data out within a few hours. That gets corrected

Speaker 2: in the final products. But for the flood product there

Speaker 2: typically isn't final later product, but for the archive that

Speaker 2: was reprocessed, all of those geolocation errors would have been

Speaker 2: fixed or where they might have occurred before. So yeah,

Speaker 2: maybe that's not a super cleayer answer, but you know,

Speaker 2: the pixels themselves are the gain location is fairly accurate

Speaker 2: for seventy five meter pixel. Great.

Speaker 1: Thank you so much for all your presentations and demonstration

Speaker 1: and answering all these questions. We have a few more questions,

Speaker 1: but right now we are already over our webinar time,

Speaker 1: so we'll address those questions later on. And we thank

Speaker 1: you all for joining to this's session and we hope

Speaker 1: to see you on twenty third for the next session

Speaker 1: of this training. And so please remember to download the

Speaker 1: exercise and work on it. It allows you to look

Speaker 1: at worldview and look at a global flood product. We

Speaker 1: want to thank our our set team here, our coordinator

Speaker 1: Natasha Johnson Griffin and our editor Maria Tito Cherry Morris,

Speaker 1: our coordinators broc Levins and Selwyn Hudson odoy Are, our

Speaker 1: learning management instructor Kevin Field and Susan Monthy, And we

Speaker 1: thank you all for attending today's session. And then once again,

Speaker 1: thank you so much for all your time and effort

Speaker 1: and contribution to this training.

Speaker 2: Problem. You're very welcome

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