Welcome to the
EROS User Experience webinar series, where we talk to staff at EROS
to learn more about the data, tools and services coming out of the USGS
Earth Resources Observation and Science, or EROS Center.
Today's webinar is entitled
Simplifying Geospatial Data Workflows with NASA AppEEARS.
I'm your host, Danielle Golon, the remote sensing user services
lead here at EROS in Sioux Falls, South Dakota.
The time is currently 12 p.m. central.
So let's go ahead and get started.
First, a few logistics to ensure the best audio experience.
All participants have been muted.
If you have any questions or comments during the webinar, please
use the Q&A feature which can be found under the more settings.
If you do not see it currently or enter them into the chat
and we'll address them at the end of the webinar.
If you cannot use the Q&A feature or the chat
based on your organization settings, please feel free to email
in your questions or feedback to LPDAAC@usgs.gov.
That's LPDAAC@usgs.gov and we will answer them there.
Today's webinar is being recorded.
The recording will be available at a later date on the LPDAAC center
page of the NASA Earth Data website, as well as on the USGS
trainings YouTube channel and the USGS Media Gallery.
At the start and end of the webinar, we will have a few polling questions.
These polls are optional,
but your answers can help us to create a better user experience in the future.
The polling questions are available via the polls feature in teams or in the teams
chat for our audience members who are not able to use
that team's polling feature due to their organization settings.
If the polls feature does not work for you,
please feel free to respond to the polling questions in the chat instead.
The questions are the same, so just to either use the post feature
or the chat, whichever option works best for you.
You should be able to see
the first set of polling questions now, so please feel free to fill those out
at your leisure and we'll leave those open during the entire webinar.
Today's webinar will consist of a presentation, several
live demonstrations, and then a question and answer session.
Today's speaker is Nathan Roberts, a contractor and data scientist
for the Land Processes Distributed Active Archive Center,
or LP deck, located here at Eros.
Nathan works on developing workflows and teaching users
how to leverage the application for extracting and exploring analysis
ready samples, or appears in their remote sensing work.
Nathan's presentation will provide an overview on NASA appears,
as well as a demonstration of the appears application
and more information about the appears API.
Once Nathan has finished his presentation, we will then transition over
to an optional set of final polling questions,
and then we will move on to the Q&A portion of the webinar.
We have several staff members from Aras on the line from User Services
and the Lpac here at EROS to help answer any questions you may have.
After Nathan has finished his presentation.
So again, please feel free to ask your questions or feedback
throughout the webinar using that Q&A feature in the teams
or in the webinar chat.
We'll try to answer all the questions within the time allotted,
but if we're not able to address your question during the final
Q&A portion, we will follow up with you offline.
And if there's a future webinar topic
you would like us to cover, please feel free to suggest that as well.
With that, it's my pleasure to introduce today's speaker, Nathan Roberts.
Take it away, Nathan.
Thanks, Danielle, for the introduction.
Hello, everyone. My name is Nathan Roberts.
I am a data scientist here at the LP DAC,
and today I'm going to present a webinar on one of our tools,
which is called the application for Extracting
and Exploring Analysis Ready Samples or Peers.
And more specifically, I'm going to show you
how appears can help you simplify your existing or perhaps
future remote sensing spatial analysis data workflows.
All right, so before we begin, just a brief outline of today's webinar.
First I'm going to talk a little bit about the LPDAAC.
The Land Processes Distributive Active Archive Center provides
some background on the year tool, and specifically what
high level features the tool has to help you
with those complex geospatial data workflows and how it can help you
simplify those and to further illustrate
that we have some demos on the
to vehicles appears has for allowing you
to do those spatial data analysis and also some additional add
on features appears as to further enhance a user experience.
And then at the end of the talk,
I'll save some time for some questions and answers.
All right.
With the outline out of the way, we'll jump into what exactly is the LP DAC?
The land process is distributive, active Archive Center.
We are one of several discipline specific
NASA data centers, and in particular, the LP.
DAC is responsible for ingesting or collecting spaceborne
remote sensing data involving land processes, archiving
and processing it, and delivering that to our wide ranging user community.
The LP DAC is part of NASA's Earth
Science Data and Information System Branch, or ESD, is most importantly,
all of this data is available to anyone at no cost.
The LP DAC is a long standing partnership
dating back to the early 1990s between the USGA, the U.S.
Geological Survey, and NASA, and we are located
at the USGS Eros and Sioux Falls, South Dakota.
So just at a high level, what exactly does the LP DAC do?
The LP DAC has two main areas of responsibility.
First, we ingest again retrieve spaceborne remote sensing
data involving land remote sensing from a variety of missions.
The second responsibility is to distribute all of this data to a multi disciplinary
user community or you individuals perhaps listening on today's webinar.
We also help support and develop tools
to help users consume and use that data.
So what kind of data collections does the LP DAC currently serve?
We have around, as of today, around 700 different data products
that relate to land remote sensing, again all available to anyone at no cost.
These include products from the Aster Motus, VRS, Eco Stress,
emit, Jedi HLS, which is the harmonized Landsat Sentinel product.
NASA measures, which is a NASA funded research
arm of individuals who conduct NASA funded research.
We archive and distribute their data.
We also have community members from outside can submit
a proposal to have us archive and distribute their data as well.
All of these 700 different data products are all available within the NASA
Earth Data Cloud, which is Amazon AWS style cloud environment.
So now that we've got that out of the way,
I just wanted to highlight some challenges in working with remote sensing data.
So we'll think about this as we move into what appears
can do to simplify and negate some of these challenges.
First and foremost, remote sensing data often involves large data volumes.
Thinking back to our 700 products, if you specifically look
at the Aster mission, we have 25 years worth of data,
around 4 million or so unique imaging opportunities.
So big time series involve handling big data.
Often these data are in different formats such as Geo, Tiff, netCDF, and Hdf5.
And for an each data set from each mission is unique.
So not one mission.
All the data is different and unique in the era of stacking data from different
missions, that can be difficult and also relate to the last bullet.
The time intensive pre-processing needed to get all of this data into a state
where you can perform analysis, prepping all this raw data for analysis can be time
consuming and resource intensive, and that's where a Pearce can help.
So now that we went through that, let's look at a specific use case.
So we'll think about use case and how appears can help us with this problem.
So this use case would be we're going to look at examining
vegetation indices time series using a Terra motus product.
We're going to incorporate those vegetation indices
with a land surface temperature LST product.
And the normalized difference vegetation index from those vegetation indices
for a time period of April 1st through September 30th of 2018 through 2024.
And we're going to do that by extracting the values
of those variables from specific list of latitude and longitude points.
And to accomplish this, we're going to use the terra motus
vegetation indices 16 de the mod 1380
and the terra motus land surface temperature and emissivity daily product.
The model 1181.
And now we'll get into appears
and how that will start introducing what appears can do for you.
So first and foremost appears as a data volume reduction machine.
Over its life, we have found
that appears as allowed users to reduce the volume of products
they need to retrieve to perform analysis by more than 99%.
When compared to the raw source files, a Pearce can help you facilitate
data prep by doing some of that pre-processing
for you and handling different types of data
set formats appears ensures traceability and reproducibility.
If we provide tools that I'll show during the demo portion that you can share
amongst your team or yourselves to resubmit the exact same sample
and get the same results appears enhanced this operability
across a diverse amount of data sets and the era of integrating
various instruments data products from different missions appears allows you
to better interoperate from different missions.
We are always soliciting feedback on appears,
and we try to adapt it to provide a better user experience.
And the appears tool is available at that link provided.
If you kind of wanted to follow along during the demo portion.
So what exactly is appears at a high level?
Appears is designed to offer a simple and efficient way
to perform data access and data transformations.
And to do that, what appears allows users to do
is to subset data spatially by either a geometer tree,
feature polygon, or point sampling like latitude and longitude.
It also allows you to subset temporally
for a specific date range and also by layer or variable.
And we do all of this through two different what we call
task types a point sample for the latitude and longitude
value extraction, and area samples for the side of the appears tool
that relies on a user defined geometry, such as a polygon area.
Samples are delivered to the user as a clipped mosaic to their geometry.
So if your data products are in the same projection on the same day,
peers will mosaic and clip those to a geometry that the user has provided.
You can customize your outputs with the peers,
meaning you can select from a list of different projections.
You can also get the data and the native or the raw
input file projection if you choose.
You can also select between different formats such as Cloud
Optimized Geo, Tiff or cog or netCDF.
Once the appears process has run, we provide all of the decoded quality
information associated with the product, regardless
if the user asked for it or not, and we aggregate the quality
and the layers or variables ordered with some different
summary statistics and plots, which I will show during the demo portion.
Again, this is designed to provide reproducible results
and we provide different ways that users can really look in, explore their data
through our interactive charts and visuals before even downloading anything.
So what kind of data can appears?
Process appears enables access to a variety of geospatial data
from a number of federal archives, including the LP, DAC, NASA,
snow and Ice DAC, the Oak Ridge National Laboratory DAC,
the National Park Service, and the USGS Arrow Center.
So there are two main vehicles that users can work with appears
first through a graphical user interface or gooey, and also an API
application programing interface for those users who prefer more
of a programing type experience.
Back to our use case.
On our vegetation indices, we can look at how
appears can help us reduce that data volume.
For that request spatially, temporally and by variable.
So if we were to work with that large time series for those two products
across our time series from 2018 through 2024,
that would involve 1176 files that we would have to retrieve,
and we would have to store 2.8GB
in raw data volume before we even started processing.
And those Motus version 6.1 products, the land surface
temperature and Vegetation indices products are currently distributed
in a PDF four format, which may cause difficulty.
So we crosswalk that to what appears can do.
We can scrub that 1176 files down to two files.
We can reduce that 2.8GB data volume to four megabytes of data volume,
and we can convert that HDF four format
file to a CSV format.
And we can do all of this in about two hours of processing time.
So in two hours we have outputs we can further feed into different models
or gain information from the appears produced outputs.
And now I'm going to switch gears a little bit here from the slides.
And we're going to take a look at a couple of demos.
The first demo will walk through the appears home page
and documentation, and then I will walk through submitting
a point sample based on the use case I outlined earlier, and also an area
sample based on, eco stress, Landsat,
USAR, rd, type of area sample analysis.
I'm going to exit the slides and we'll bring up the tool.
So again if you want to follow along there's the link up above.
So this is the appears home page.
And we're going to focus right now on the documentation for the tool.
Importantly before you can use the A tool, much like
if you were downloading data traditionally through Earth Data Search client,
you do need to sign up for a NASA Earth data account.
Sometimes referred to as a Urs account.
That same account will allow you to interact with appears.
So I will start here under the help menu and we'll take explore
the documentation. And
there are
two different vehicles for acquiring a peer samples,
the first being a point sample and then second being an area sample.
And the documentation here will provide you information
on how to submit those extractions, how to view your results
once they're processed,
how to interact with your results, and finally, how to download your results.
Importantly, at the appears, outputs are stored for 30 days,
so after 30 days those orders are removed
and if you needed to resubmit that, you can do so.
I will demonstrate how you can do that should your order expire.
So the next point here we're going to work with our point sample.
And before we do that we do need to log in.
So I will log in.
And then we're going to start with our point sample.
So we go here to the extract menu click on point.
And now this is our point sample based menu.
So looking at this from left to right
the start A new sample is
where we would go to start a new sample request.
If we have samples that we've already processed
that we want to either copy to resubmit or copy
to add or subtract different things, we can do that.
All the completed requests are listed on this column under copy a previous request.
We can click on that and it'll copy exactly what we had submitted
before the last area over here.
Upload a request file
allows you when your order is complete, you're delivered
a Json file containing details of your request.
If you save that from a request,
you can upload that to this.
Upload a request file box and appear as will resubmit
the exact same request as you had previously.
So I'm going to
start over here on extract a New Point sample.
First thing we need to do here is to give our request a name.
So I'm just going to call this and dvi lst for land surface
temperature and normalize difference vegetation index respectively.
And I'm going to call it demo.
All right.
So and we're working with the point samples in this case.
So recall that that is a list of latitude and longitude that you give to the tool
and appear as we'll retrieve the pixel values for those points.
There are two different ways to give a point.
Sample geometry to the tool.
You can scroll down here and look at the map and click
and add some points like this.
And your points are then reflected up here in this box.
However, I'm going to do the second option
which is to upload a CSV containing my points.
In addition to the points, the CSV file contain up to four columns.
Those columns have to be latitude and longitude are required
and have to be named as they are outlined here in bullets three and four.
You can also add an I.D.
column, which is a uniquely identifies your point
in some unique way, such as like an example plot,
something unique to that point.
You can also provide a category,
which is a label that is common to a group of points,
and that appears can use that to aggregate statistics based on a category.
So for example, you could work with land cover types would be a good one.
For bullet number two, I'm going to add my points.
And now we can see that my list here has been populated
by my 15 different points and the CSV
was structured such that plot is our ID.
Our land cover type is our category.
The column here is our X value, our latitude, second value longitude.
Also, we can look down here at our map and we can see
all of our points have now been rendered to the map.
So I am working in southwest Minnesota
and we're looking at vegetation.
So in that case, maybe
we don't so much need to know about ndVi,
since that's a measurement of healthy vegetation over the winter months.
So in addition to being able to subset temporally by a specific date range,
a Pearce can also further subset temporally by doing like a seasonality
type of temporal subsetting, which is what I'm going to do here.
I'm going to start with a just a basic kind of growing season type
scenario of April 4th through the end
of September, September 30th.
And then I can specify the years
I want to work with by using my slider bar
to locate 2018 to start,
and then we move this slider bar to the left
to locate our ending year 2020.
For the last year, we have a full range of data for these months.
And our next
step here is to select our product.
So our product for the first product is the model
11 a.
A one
the Terra Motus land surface temperature
emissivity 1000 meter daily product.
Once we've selected our product,
we get a list of the variables or layers that are within that product.
So we have 12 different layers or variables within this product.
What I am looking for is the last night.
Any time you see this little eye
button, it appears if you click on it, it'll give you some basic information.
In this case, what we're seeing here are the description of the layer
or variable in question.
So things like units this is temperature.
So we're working in Kelvin a scale factor.
The quality layer etc.
all available here.
So once I've located my.
My layer
I click it to move it over to the selected layers.
So what I have done now is I have subset
this product by the variable or layer.
I have went from 12 layers and I'm only going to process one layer.
Our next product
to select is our vegetation indices product.
So I simply click the X right here to remove that
and then look for the vegetation product.
And we're going to be working with the mod 13 A2 vegetation
indices 16 de 1000 meter product.
And I am going to specifically look at the ndVi,
the normalized difference vegetation index.
So once again I have reduced my layers from 12 to just two.
So with that selected we can click submit
and our order will go through.
You'll notice that I got a nice green banner up here.
That's a reflection of a successful submission.
If there was a red notice up here
there would be something that we would need to correct.
The last thing for submitting is
you're allowed to submit up to 1000 points for appears to process.
So we will go retrieve one of our layers here.
Our request we go to this explore menu and that is listed out here.
You can see our order that we just submitted here
now is queued for processing.
So that's waiting to be for appears to pick it up to process it.
The layer button
detail here will give you information on what you've submitted.
And I am going to
pull up an order that I have already submitted
so we don't have to wait to process.
So when you view your order, this is the screen that you will get
under sites.
We can look at our 15 different points.
So if I click on this one for example,
my map will switch and my graphics down here will switch.
For Motus we have the ability to show only the very good quality.
So if we click the eye here, there will be a description
of the quality information for our model 11 A1 land surface temperature product.
So zero is considered to be good quality.
So we're going to only tell it to show us good quality.
So this is simply a layer comparison.
So we are looking at our land surface temperature night
over our entire seasonality time series.
So if I add some lines will now
see that here.
This graph might look kind of busy.
So if we are interested we can actually filter this out just by year.
So now we're only looking at art time series from April 1st, 2018
through September 30th of 2018.
Now we'll compare layers.
We're going to use this to see if we can infer
any type of relationship between the land surface, night
temperature and the normalized difference vegetation index.
And once again, you can select whatever point you wish
will randomly select this one.
Number 11 for park and for quality.
I only wanted to work with the good quality again and I'll add some lines.
So one thing I would point out
is that this graph has a lot less points on it than we saw
with the land surface temperature, and that is because the land
surface temperature is a daily product produced every day.
The normalized difference vegetation index product is produced every 16 days,
so appears will only plot on this view
instances where you have acquisitions from the same day.
So our 16 day ndVi
product here, there's less temporal depth with the 16 day.
So that's why we see less points in this view.
Now we'll look at our categorical overview
and let's work with our land surface temperature.
So in this view we have an aggregation of all 15 of our points.
Our first box plot here is for quality.
So if we click on this I we are given information
on what the quality is, what that means for these products.
So if we want to work with only the good quality
we can click on this box plot.
And now our box plots our aggregations for our three categories.
The box plot ranges reflect
only those instances of good quality.
Our box plots, also the number of observations
or pixels in the plot are only reflective
of good quality.
And we can look further down here under sites.
And that is the same.
15 site box plot ranges have also been reset
to only show instances of good quality,
but we can go even further in this view.
If we wanted to take a look at just Agriculture ground, we can click that now.
If we scroll up, we can see our total number of observations
that are reflective of good quality but within our agriculture ground category.
So all our points that are classified by it as agriculture ground.
Now we show that information.
Similarly, if we scroll down to our plots because we've selected
the agriculture ground category, we only show aggregated statistics
across our sites for that category.
So one way that you can use the interactive visuals
here to further to get information out of this analysis.
And when we're done, if we want to revert
back to the default view, we simply click reset.
And now we see all of the information again for our point sample.
Okay.
So we're going to switch gears out of joint sampling
and look at an area sample.
So I've went up here to extract and clicked on area.
And we have the same three options.
We can start a new request.
We can copy a request or we can retrieve our Json
file from our order and submit that back here.
So we'll start here with an area sample.
Much like the point we need to give it a name
and we're going to work with Eco stress and Landsat USADA.
So we'll call that.
Just give this a nice title.
And over here like the point
sample, you can either
draw your own geometry so I can draw a nice box.
Or we can provide a peers a geometry
and either a shapefile or a GeoJSON format.
So I'm going to reload the page and I'm going
to use the GeoJSON option.
So we'll give this a title again.
And we'll browse
to the location of our GeoJSON
which is Yosemite National Park in California.
And we'll wait for a year to digest that information.
And there we go.
So here's our GeoJSON geometry has been rendered.
Now on the map.
It did simplify it a little bit.
And there's a limitation on points, but I think that will be just fine.
And we'll start with our specifying our date range.
So in this instance we're going to start with January 1st of 2024.
And we're going to end with December
31st of 2024.
And we're going to do a straight time series.
We're not going to use the recurring,
but you can use the recurring feature in an area sample as well.
So I'm going to search for our layers.
So I'm going to work with the eco stress swath
land surface temperature product.
So you'll notice here
we have 17 layers within this file.
And I am interested in the LST
or land surface temperature variable.
So I'll move that across.
And next I'm going to look
for our Landsat USAR
with our temporal our start date being 2024,
we will add both the Landsat eight and nine,
the Landsat USAR D collections
and appears are broken out by mission.
So we want to look for Landsat eight.
And we'll note all of the Kua information.
All of this will be processed regardless if we order it or not.
So I'm going to focus on band five,
the near-infrared band and the Red band.
Band four
with the idea that maybe I want to calculate my normalized
difference vegetation index, or ndVi based on the Landsat bands.
In this case.
And then I will search
for the Landsat nine RDA
surface reflectance,
and we will do the same.
We'll select band five, the near-infrared and band
for the red band.
And I'm going to ask for a geo tiff.
And in this case we can specify a projection.
So I'm going to go with a simple geographic
wgs84 projection for this.
And we will click submit.
And we'll move up to the top.
And we'll get that green banner to let us know that our order
has been successfully submitted.
Now we'll look at the explore tab.
I have one of these already processed for us to look at.
So if we scroll down we can look at our areas sample.
So here is what our results of our area sample.
If we're looking at starting over here
under feature you can submit more than one geometry
at a time to appears I only submitted one area sample, but if you had submitted
multiple geometries those would be listed out here.
You can also click the eye button here
and look at basic information from your geometry.
And we can
start by exploring our layers of our area sample.
We'll start with the eco stress.
So if we look at our chart here, we have a lot of observations.
The Landsat would be every 16 days.
Eco stress is Icpsr or National Space Station dependent.
So we have a lot more observations.
So our box plot here may look kind of cluttered, but that's
okay because appears can help us sample that down.
If I click the magnifying glass to select my year,
I can use my slider to select,
let's I need to make a range of months.
So why don't we start with like somewhere around June 1st
through the end of July, and now my box plot
will update showing only those months
within this range from June through July.
Also, our quality information has also updated
to show the up quality information for observations on those dates.
So we'll keep looking down here.
What I'm looking for for my use case here is a clear observation.
So we'll using the eco stress quality.
We can see that on June 19th of 2024.
The observation is clear in the cloud mask.
So little cloud contamination.
So that would be a good one to look at.
So appears can help you narrow down on.
If you're interested in cloud free observations, you can learn
that information before you spend time downloading the outputs.
So another kind of bonus feature with the peers.
If you are hovering on any of these little bars,
you get pop up showing what is going on.
Like for example, on this June 3rd observation, we have a cloudy observation,
so maybe we would want to skip that when we go to download our outputs test
and not waste time downloading a cloudy observation
and the QA stats much the same.
You can hover over these different colors in the bar chart to learn
quality information about these different observation points.
I am going to reset my view so I get the whole time series.
While we take a peek at the Landsat eight,
we'll look at the band five, the near Infrared.
So if I want to see
what's going on in June here,
we'll look for June.
Oh look at that.
We have a June 19th observation so appears can help.
You also look for instances where instruments made
a concurrent or coincident acquisition on the same day.
Like if you want land surface temperature from eco stress for June 19th.
Now we know we have a Landsat observation also from the same day.
So that can be helpful with different research questions.
The quality information that's contained in the collection
to Landsat USAR surface reflectance is also provided.
So if we look at June 19th, we can look through here
and see this different quality information by hovering over
our chart.
We've got that.
The pixel QA contains cloud quality information on Landsat observations.
So that would be a useful one to investigate.
And also we provide the radiometric saturation band as well.
And the aerosol QA all
part of the Landsat surface reflectance collection.
So we'll explore what it's like to download one of these samples.
So I'm going to click this download button and I get a bunch of files.
And similarly to the point
we provide you with some basic metadata files for every order.
This file could be important.
This is a text file
containing all the direct URLs to the input granules for your order.
The read me provides information on different caveats for all data sets
or products that we serve, and appears so that would be a important one.
This is the Json file I referenced.
We provide that with both area and point samples, allowing you to reproduce
the same order if your order expires, or you would like to give that file
to someone on your team as an example to submit, they can do that.
All the aggregated statistics that we were kind of looking
through on the last screen, we provide you the raw csvs
for those in the area sample, so you can look at those offline.
If you choose.
We're going to keep scrolling
down here and get our list of appears processed outputs.
So this is our area sample.
We have clipped mosaics for our eco stress swath land surface temperature
and our near-infrared and red bands from the Landsat eight and nine surface
reflectance products.
All of that has been processed.
So if we keep scrolling down here, we'll see.
We'll get to the bottom.
And while we have 1101 total outputs that we would need to download,
but since we utilized our summary statistics,
we know that on June 19th,
we had a clear survey station which corresponds to day of the year
171 for 2024.
So we can simply just search for day 171.
And now we can filter our available downloads just for that day of the year.
So I went from more than 1100 outputs to download to just 21.
So something that
can be helpful in saving time downloading things.
So I'm going to click this off.
Let's say we wanted to download all of these outputs.
But we can do that by clicking on this checkbox.
And we can go over here to the download menu.
There are two different ways you can download appears outputs.
You can download the files which allows you to download
multiple files in browser, or you can save a file list
which gives you the URLs to the outputs in S3 that appears produced.
And toward the end of the talk, I'll show you.
We have a repo where we have documentation on how you can programmatically retrieve
those outputs.
And now I'm going to flip
back to my slides here.
And just going to highlight on.
Since we went over how you submit and view your orders, it appears some additional
add on tools that we offer users with appears, which include our appears
APIs for those that want to interact with the tool programmatically.
And we'll look at that.
And also a QGis plugin,
which has been developed as an add on for the QGis software package.
That plugin allows you to stream or just render your outputs in QGis
without the need to go through the downloading of anything.
Provided you have submitted your order as a geo tiff
or a format, you will be able to utilize that QGis plugin.
I'm going to go back to help.
And we'll explore the
appears API documentation.
Appears documentation
provides some basic helper scripts documentation on the different
API methods to interact with the appears API.
So for example, if I look at tasking
I can look at let's look at submit a task.
So right now I'm looking at
just a boilerplate example, the required fields
and how you would submit a task request using curl from command line.
Or you can click on Python and get a Python example
of all of those using the request library.
Or if you're an AR user, you can click on R
and get some help or code snippets on using our.
So just how you can take your peers experience
to the next level and use the API, the next kind of add on
we'll take a look at, here is our QGis plug in.
So I've already got it set up here. So
the documentation on how to install the QGis
plug in is available on its repo to start working with it.
So if I've got a bunch of different tasks here,
but I'm going to focus on
the one that we submitted during that area, sample part of the demo.
And so if I click on select task I'll get a list of outputs
related to that task.
And what I am looking for here is our land
surface temperature from day 171
or June 19th of 2024.
Oh there it is right there.
So we'll simply click Load File and we will wait for QGis to retrieve that.
So again what this is doing
is it's streaming that output that we looked at a few minutes ago.
It's retrieving it from S3 from the cloud
and rendering that output here in QGis, where you can use
all the QGis features to make layouts, do analysis, etc..
Back to the slides and we have a GitHub
resources available at the link provided right here.
Those resources we have, if you're wanting to get started
with the appears API and need some further helper scripts,
we do offer those how to submit tasks for RDM endpoint samples.
Jupyter notebooks, and Python format.
There's also those same tutorials in R
if you're an R user, and also the last helper script.
Here's how to bulk download appears.
Outputs using command line.
And that's where you can feed in that list of URLs from your outputs
into that command line tool to retrieve your outputs programmatically.
And we are always interested if anyone has any contributions
or pointers or feedback for any of our helper
scripts, and can, feel free to leave those on the repo.
And if you would like to get in touch
with us, our website Earth data.nasa.gov centers lp DAC.
You can email us lp dac@usgs.gov or we also have the earth
data forum where you can ask us questions or get help regarding appears.
And I think that concludes my presentation here and we'll switch
now I'll be happy to take any questions.
Excellent.
Thank you for a great presentation and for showing us those really cool demos.
Nathan, for those listening in, I've added some final links
to the chat which include more information about appears,
as well as information about how to sign up to be notified
for future webinars, and a link to watch some of our previously recorded webinars,
which this webinar was recorded, and it will be available at that link
at a later date.
At this time, we have a couple optional final polling questions
for the webinar, so I'll go ahead and publish those.
There we go.
And I will add those in the chat in a minute.
But if you'd like to go on and start filling out those
if you're interested in answering again, those are optional, but they help us
create a better data experience in the future.
So you should now see those polling questions
and do remember the same set of questions as the ones that I'll be posting in chat,
so please only answer them
once, either in the teams feature in the polls or via chat.
Whichever method works best for you and your organization settings.
And if you have any final questions
or feedback about appears, perhaps any data or tutorial ideas
you'd like to see in the future, please add those to,
you know, the Q&A feature in teams or in the chat now as well.
With that, we'll give you a few moments to answer those polling questions
and to submit any final questions or feedbacks you have for Nathan.
I have now posted the polling questions in the chat.
All right.
It looks like we have some polling question answers rolling in.
So thank you to everyone who's answering those.
Please feel free to continue to answer those.
If you're still working on them.
But for now, we'll transition over to the Q&A session from the audience.
I didn't see any questions in the chat, but I do know
we've had a couple of common questions that users ask,
so I thought it might be nice to kind of cover that.
Nathan.
So, for example, I think you covered this in the webinar, but just to reiterate,
how many points can you submit for the point sample request, right
appears will allow you to submit 1000 points in a point sample.
And if you go over that, you'll see a error message in the red
banner that I referenced earlier during the webinar with the information.
Similarly with area samples, if you submit too large of a geometry
that will be flagged as well in that location, it's generally what we consider
to be best practice to submit targeted smaller geometries with the peers
so the data can be processed and returned to the user faster.
Thank you.
Another common question that we get is if users have feedback about peers.
And I think you also touched on this,
but what's the best way for them to submit that feedback?
I'm sure you can always email us helpdesk@usgs.gov.
Or we also have the Earth Data Forum where we can take feedback. Yes.
And then it appears there is also in the top right corner
that feedback button that also will submit the feedback directly to the team.
So if you're working directly in a peers, you can always use that button as well
and submit any screenshots that kind of things. There as well.
All right.
Any other questions in the chat or check in the Q&A feature?
It seems like that is all the questions that we have for today.
Don't forget if you have any questions later on on appears or any data, tools
or services the USGS Aeros provides, you can always email our team at Custer.
About usgs.gov.
I'd like to once again thank our speaker, Nathan for his time
and great presentation, and I'd like to thank you all for joining us today.
We hope to see you again in a future webinar.
If you would like to be notified of one, that next webinar will occur,
please sign up for the EROS User Group by emailing custer@usgs.gov.
And again, thank you all for taking time to join us today.
We hope you have a wonderful rest of the day, and we'll stick around
for a few more minutes, just in case anyone else has any questions or feedback.
See a lot of people
giving thumbs up and applauses in the chat so thank you all for those.
And yes, very great job, Nathan.
All right we will.
I see we have a hand raised.
Unfortunately I'm not able to unmute you, Alan,
but if you want to ask your question in chat, we'll be happy to answer it there.
The question is, will tomorrow's session have new material?
Sorry, I can we maybe expand on the question tomorrow?
Session? Yes.
Today is the only appears webinar we have going on.
I know the Opac
team has another webinar next week that everyone could join, but it's not on.
Appears so Alan, if you want to clarify in chat,
we'd be happy to help figure out which session
you are attending, and I think it will be new materials
and of us on the call will be presenting there
and I'll post a plug in for the webinar that I was talking about
then will be happening on July 21st.
The 22nd, I'll put that in the chat.
It's fundamentals to use hyperspectral and thermal NASA data observations.
So if anyone's interested in attending that as well, I'll post that one.
And that one does have two buttons for registration
since it's a two day activity.
But that's next week.
And for those still on the call,
we're just hanging out in case there's any other questions that come on.
And to just capture some last little feedback.
So feel free to hop off and we will catch you next time.
We definitely have a webinar that we are planning in the early fall,
so we'll look forward to that one.
Perfect.
Glad to hear it.
Alan and Bob, I see your response to the polling question.
Presentations ICESat-2 data and applications in forestry.
I'll pass that along to the ICESat-2 team that we work with.
Typically.
I don't believe that's something we would present on from the USGS areas.
And but I'll talk to a couple people and see if maybe we could do a joint
webinar or if they have any opportunities coming up for you as well.
All right.
Not seeing any new questions coming in through the chat.
And I've got 1:00 so we will let everyone go.
Thank you again for joining us.
Hope you have a great rest of your day.