- [Instructor] Hi, welcome to week two
of Remote Sensing Foundations.
This week we will talk about sensors on the ground
and in the sky.
At the end of this week
I hope you'll be able to describe some types
of remote sensing imagery
and how they differ in spatial and temporal dimensions,
access and understand how to query
the Earth Engine catalog of data,
including, finding an understanding about metadata
for a data set of interest,
and also know how to draw points and polygons
to map a place of interest in Google Earth Engine.
This semester we have the unique opportunity
to use a working draft of a new Google Earth Engine textbook
called "Cloud-Based Remote Sensing
with Google Earth Engine."
Because I am an author of a chapter in this book
the editors of the book have given us permission
to test drive the chapters and tutorials.
I will be assigning some readings from here
and I would like you to follow along
with the code tutorials,
especially as we get underway in using Google Earth Engine.
Please don't be intimidated by the amount of reading
that it appears that I am assigning.
I can assure you that it's not that much text.
Really what takes up a lot of space on all these pages
are the tutorials themselves
because they are shown in big blocks of visuals
and include the code that you will be entering
in your code editor in Google Earth Engine.
This week, we will be taking a look at
F1 Programming and Remote Sensing Basics,
and specifically at chapter F1.0,
JavaScript and the Earth Engine API,
and also F1.1, Exploring Images.
I'm confident that the amount of reading
that has been assigned in this week will not take too long
and hopefully you will gain a good understanding
of how to work with Google Earth Engine Code Editor
and how to go about searching
through the catalog of images that Earth Engine has.
By way of a little review,
last week we touched briefly on particle energy
and how it can be organized
and understood in terms of the electromagnetic spectrum,
which is the entire distribution
of electromagnetic radiation according to the wide range
of frequencies, wavelengths, and photon energies.
The top left figure helps us understand that what we measure
and with remote sensing is energy
and how energy transmits from a surface.
We understand that the main source of radiation
that is incident on our planet comes from the sun,
which emits a broad spectrum of electromagnetic energy
from long wave microwave to short wave ultraviolet.
How we measure the sun's radiation depends
on what sensor we use
and on how the radiative energy behaves
as it moves through our atmosphere
to the surface of interest and back to the sensor.
All the types of radiation, or energy, coming from the sun
composed of what is in the visible range of light
and outside of what the human eye can see
has the potential to be absorbed,
scattered, reflected, transmitted,
and also emitted
as it travels from the source to the sensor.
Satellites measure what travels back
from the source to the sensor,
and these images must then be processed with algorithms
that account for the atmospheric conditions.
We can use the spectral signatures
of how different surfaces absorbed
and reflect different wavelengths of energy to interpret
what we are seeing on the ground from space.
Finally, the type of sensor that we use to determine
what, when and how we detect spectral signatures
on the Earth's surface.
Active sensors use an artificial source of energy.
The satellite itself can send a pulse of energy
which can interact with a target surface.
Essentially humans can control the nature, wavelength,
power, and duration of the source energy.
An example of an active sensor type is called LIDAR,
or Light Detection and Ranging.
These sensors send out a pulse of laser light
and record the reflectance back to the instrument.
We can then use data to construct height profiles
of forest canopy, ice and other surface features.
Passive sensors on the other hand,
record the natural energy that is reflected
or emitted from the Earth's surface.
Landsat is a well known example of a passive sensor
that has mapped the Earth's surface for over 40 years.
The downside of passive sensors
is that they typically only measure
during the daytime and in good weather.
Remote sensing is a tool that is particularly helpful
to characterize the Earth's biophysical environment,
including variables related to habitat, climate change,
and human modification of VR systems,
as well as vegetation, composition, function,
phenology, physiology, and morphology.
These observations require integration
with the biological understanding gained
from the vast advances in biological methods and tools
for observing the many dimensions
of life above and below ground.
Here, we can see an example
remote sensing data layer shown at a spatial grain
of one kilometer and extent of 40 kilometers,
from left to right on the top left side of the figure.
The environment, function, phonology,
physiology and morphology.
We can also think of the temporal variations
as shown across the top right,
from hourly and daily to seasonal, annual and decadal.
Finally, I wanted to show you this interesting figure
from Levizzani and Cattani from 2019.
This figure depicts many
but perhaps not even all of the tools
that we humans utilize to remotely sense our world.
Though we will not delve into each
and every one of these tools,
I wanted to put this here
to show you how many remote sensing data tools
we use on a daily basis without much deep thought
about how they are measured, what they measure
and how it can be used in conjunction
with other data sets,
either directly measured
or derived to gain a deeper understanding
of the world in which we live.
I wanted to put these last two figures in here
to hopefully spark your interest
and perhaps plant a seed in your thinking
about what you might wanna do your semester project on
because literally the sky, or technically,
beyond the sky, is the limit.
There are many types of sensors out there.
Here is one example of a satellite series of sensors.
Beginning in the mid 1960s,
the US began an effort to develop
and launch the first civilian earth observation satellite.
This is when the Earth Resources Technology Satellite,
or Landsat, was born.
There have been eight Landsat missions to date
with the first beginning in 1972
with the launch of Landsat 1.
Currently Landsat 7 and 8 are still operating.
Landsat 7 was launched in 1999 and 8 was launched in 2013.
There's also a Landsat 9 mission planned
for launch later this year or early next year.
The Landsat mission example provides us
with a good opportunity to talk about
a number of different sensors.
Landsat missions have different sensors
which measure in slightly different regions
of the electromagnetic spectrum.
Do you remember from last week
whether the Landsat satellites contain active
or passive sensors?
Okay, so Landsats 1, 2, and 3
had a multi-spectral scanner aboard.
If you remember from last week
a multi spectral scanner is a passive sensor
that distinguishes and measures a range of spectral bands.
Landsats 4 and 5 contain a thematic mapper.
This is a related sensor type
but includes more spectral bands,
including the NIR infrared and the short wave infrared,
also known as SWIR.
Landsat 7 senses an additional band.
So it was named ETM+, or the Enhanced Thematic Mapper Plus.
Landsat 8 has ETM+
but also contains the addition of a Thermal Infrared Sensor.
It measures 11 bands overall.
If you look at the figure at the bottom right,
you can get a sense of where each Landsat measures
across the spectrum of wavelengths.
In order to understand
what different satellite sensors can measure
we must understand what resolution they operate at.
There are a number of different resolutions
that we must consider.
First, the spatial resolution of a sensor
is essentially the size of a pixel
when that sensor's data is mapped.
It is a size of the smallest area
that can be separately recorded as an entity on an image.
It is also the distance between patterns
or objects in an image
that can be separated from each other.
Spatial resolution is often expressed in meters
but can be expressed in finer
or coarser measurements as well.
At the bottom right
the resolution of a Landsat image is around 30 meters.
At 30 meter resolution, two adjacent objects,
30 meters long or wide can be separated or distinguished
by the human eye.
On the bottom left, we see a coarser MODIS image
of the same region of interest.
Here the pixel size is 250 meters.
And so it's harder to distinguish
individual building structures.
Temporal resolution is also known as the revisit period
or the length of time for a satellite
to complete an entire orbit cycle.
It's basically the time it takes for a satellite to return
to approximately the same location in space.
For instance, for Landsat, this is a 16 day period.
Revisit periods are dependent
on the orbital speed of the satellite,
as well as the type of orbit
and what the global coverage of the instrument is.
Some sensors, like currently
the ones aboard the International Space Station
for instance,
do not measure the entire planet
because of the orbit path
that the International Space Station takes.
On the triptic at the bottom of the slide,
you can see a number of different Landsat images
that were recorded in 1973, 2000 and 2006.
This is from a study
that's showing the urbanization of a desert city.
Spectral resolution indicates the sensitivity of a sensor
to distinguish a range of frequencies.
It is the ability of a sensor to define wavelength intervals
which corresponds to the number of spectral bands
which are measured.
For instance, the bottom right
are some examples of spectral resolutions.
What is called the panchromatic measures one band
and is typically displayed in black and white.
Multispectral, which measures four or greater bands,
and typically includes the visible in the infrared spectrum.
And finally, there's the hyperspectral,
which measures hundreds of bands
across a broader wavelength spectrum.
Okay, so with this knowledge we can move forward
and delve a little bit deeper into Google Earth Engine.
Today, I'd like to show you about how to search for images
and also how to draw a shape around a region of interest.
Here I'm just showing a screenshot
of the opening page of Google Earth Engine
when you sign it to your account.
First, let's learn how to access the Earth Engine catalog.
When you open Google Earth Engine,
you'll see that there is a search box
at the top near the Earth Engine icon.
If you click on that box,
a dropdown will come up that shows Browse data catalog,
also Suggest a new data set.
If you click on Browse data catalog,
it'll take you to a webpage that shows the entirety
of the Earth Engine catalog,
indexed and searchable there as well.
You can also suggest a new data set.
So if you're searching for a data set
and you can't find it,
and you really wanna work with it in Google Earth Engine,
you can also put in an inquiry
to please upload that data set as well.
Another thing I wanna point out here
is if you look to the bottom right you'll see two boxes,
one for map and one for satellite.
Right now I have map clicked.
On the next page I'll show you what it looks like
if you click the satellite instead.
Okay, so if I wanna search for Landsat image for instance,
I can just type the word Landsat up in the search box.
And by the way, you can see
that this is what the satellite option looks like.
It is a Google Earth Engine satellite image that is,
I believe Landsat, and it pretty much at this point
looks the same as if you were to open Google Earth as well.
So back to Landsat.
I typed the word Landsat in
and what comes up are place names, rosters, and tables,
all that contain the word Landsat.
Since I'm interested in the Landsat satellite image,
I'm gonna be most interested in those roster data sets.
So if I click enter what comes up is a popup
that shows all of these different Landsat options.
For purposes of this example,
I'm gonna go ahead and click on
the USGS Landsat 7 Surface Reflectance Tier 2 data set.
When I do that, another popup comes up
and what I see this time
is a description of the Landsat 7 data set
as it exists in Google Earth Engine.
If you can see right below the icon on the left side
under data availability,
this image is available from the date 5/28, 1999,
May 28th, 1999.
If there was an end date for this data set
then there would be an end date up there as well.
But as we know, Landsat 7 is still an operation
so there is no end date.
And the data set gets uploaded
as it is processed by Landsat and the folks at USGS.
You can also see that there's a collection snippet
with a little copy icon there.
If you wanted to copy and paste
this dataset directly into your script,
you could do that as well
but we'll talk about how to do that more later.
Up at the top, you can see four tabs.
Description, Bands, Image Properties and Terms of Use.
These are all scrollable as well.
So with Description, you can read all about what the image
or what the Landsat 7 sensor records
and how long it's been up and why it was put in place.
Also data provider notes.
If you then click on Bands,
you'll get a list of the bands,
what their description of each band is
and also what wavelength range they capture.
This is very important for understanding
how to work with these data sets.
So we should always come
and look at what bands are available
because sometimes when you are working
with the data set itself in Google Earth Engine
you'll be calling out the bands by name.
So you'll need to know what they're named
and also what their description is to understand.
You can also scroll down.
So Landsat 7 has far more than seven bands.
Then if we look at Image Properties
we get further descriptions
of some of the properties of the image that we see
and how it was collected.
Finally, I just wanna point out that there's a
tab open button at the top right there.
So if you didn't find the information
that you were looking for with this pop up,
you could also click that
and it would take you directly
to the Earth Engine data catalog page
for USGS Landsat 7 Surface Reflections Tier 2 data.
And again, you can see that the description, bands,
image properties, terms of use,
and some of the other information
are all also on this webpage as well.
But back to our popup.
If you click Import,
then what you'll have happen is
the entire image collection will be imported
into your script.
You can also rename this image collection
and then below you can start to work within it
by coding different queries
or filtering the data set as you wish.
Okay, so of course
Landsat is not the only thing that you can search for
in the Google Earth Engine catalog.
And in fact, you don't also have to know the name
of a satellite that you're interested in working with.
Sometimes we wanna look at data sets or place names.
Here's just another example
where I search for the name human
because I wanted to see what types of human
interactive data sets were out there
in the world of Google Earth Engine.
So I came up with a global human modification data set.
Once again if I click on that
I'll get a description of what it is I'm seeing.
So a lot of these data sets have been
calculated from satellite imagery
and processed by other people.
These data sets were developed
and then they were uploaded
and then shared freely on Google Earth Engine.
And so now we can download them
and use them ourselves if we want.
So here's a description of what this
global human modification data set is.
Also what the bands are.
So here's an example of a data set
that only contains one band
and it tells us what the resolution of the data set is,
which is a thousand meters.
And then finally, it also will tell you how to cite it.
So if you're ever working with a data set
that somebody else produced
and you're furthering that data set
then you want to publish it.
This is where you're gonna find that information
on how to cite what you worked with.
Okay, so now let's learn how to draw
boxes and polygons around regions of interest.
Here I've gone on my script page
and I've used the scroll features on the map
to be able to navigate around the globe.
And I've taken myself to an area
in the east coast of Madagascar that I know well.
It's actually where I did my PhD research
and I loved working in the forest there.
So I'm gonna draw a region of interest
right around the area that I did my PhD research in.
One of the things that you'll notice from this map,
which is Landsat based,
is that there are dark green areas and light green areas.
Dark green areas here actually indicate primary rainforest.
Whereas the lighter green areas are the secondary forest
or other types of vegetation
but I'm interested in looking at a primary rainforest.
And so I'm going to first go over
and click on the geometry icons
down at the upper right of the map.
If I click on that box,
what will pop up will be a geometry pop up
that now I have the capability to draw a box,
just by clicking and dragging my mouse directly on the map.
So I've drawn a box around an area.
This is called Betampona.
I've drawn a box around Betampona
because I want to perhaps look at a stack of Landsat images
or maybe perhaps look at change around this particular area.
So I'm only gonna be doing that around that area.
If I wanted to draw a polygon, though, for instance,
maybe I wanna look at the area around a city that's nearby,
the city of Toamasina.
If I wanted to look at something maybe,
urban modification or human modification around this area,
maybe I would draw myself a different type of geometry.
So I'm gonna draw a polygon.
So I click on that polygon
and what will pop up will be the same scroll feature
and option that I had with the box.
But now when I click once,
a little circle will pop up and then I can drag
and drop lines as if I'm drawing a shape.
So I dragged and dropped the lines
and drew them all over Toamasina.
Finally, another way to draw geometry
or to point out a place on a map
is to click on the point,
the place pointer.
If I click on that, I can drop one anywhere.
So I think these features,
this is similar to what you would see
if you were working in Google Earth.
Here, I've dropped it on Mahavelona,
which is a well known vacation spot
in the east coast of Madagascar.
So those are three different ways
that you can indicate a region of interest on a map
and you can actually go back
and rename those areas just by clicking once on the imports,
under the imports, you can rename it.
And when I renamed it
I just called it red box, green polygon and blue point.
It'll automatically change below.
You can also click those check boxes
on and off on that map
so that you can see those boxes actually show up
or not show up,
which can come in handy if you're drawing maps
and you're trying to check your work as you go.
Sometimes you might not want those on
but you wanna know where they are
when you're trying to work with imagery.
Okay, so now we have searched around
in the Google Earth Engine catalog a little bit
and we've also learned how to draw polygons,
boxes and points for a region of interest in the catalog.
I definitely encourage you all to play around a little bit
in these blank scripts
and kind of get to know the features
that are available to you.
Also, I highly suggest that you mess around with
the Earth Engine catalog.
Just put in some different words to search.
See if you can come up with some data sets
and just kind of see what's out there
because that will be handy for you for the future.
Finally, for week two's assignment,
I'd like you to read the first 34 pages
and follow the tutorials
in that "Cloud-Based Remote Sensing
with Google Earth Engine" book.
I promise that it's not a lot of reading.
It is a lot of helpful tutorials
and helpful JavaScript details that will be really important
for your future ability to be able to accomplish
what you need to do in Google Earth Engine.
Please do complete the knowledge check as well.
And finally, I'd like you to draw an ROI
and talk about it on YellowDig.
All right, well until next week,
have a great one everyone, thanks.