Hello everybody.
This week I'm gonna be introducing you
to data types in remote sensing
and going over some basics of image visualization.
Specifically, our goals for the week are to be able
to define what vector and raster datasets are,
and also to understand the difference
between true color and false color images.
You'll also learn how you might make true color
and false color images and how you interpret them.
Just as a review, last week we talked about
what tools humans use to remotely sense our world.
We reviewed different kinds of resolution
including spatial resolution, temporal resolution,
radiometric resolution, and spectral resolution.
And I also describe the difference between active
and passive sensors.
So remember that passive sensors use the sun's energy
to measure electromagnetic energy or waves,
and active sensors use artificial energy
like laser pulses.
In terms of passive sensors,
I discussed the nine different Landsat satellite missions,
how commonly used they are,
and I also discussed how they differ
in terms of those four resolutions.
And then I also mentioned the modus
and Sentinel-2 satellites,
which are passive sensors as well.
And then finally, I talked about one active sensor method,
that's called LiDAR.
And we can use LiDAR to measure the height of buildings
or vegetation, and it can also be used to detect items
that are underwater or even under forest canopies.
So now let's talk about different kinds of geospatial data
that we use in remote sensing and GIS.
And this is probably review for those of us
who have taken GIS classes before.
One kind of spatial data
that we use a lot is called vector data.
Vector data can be points, lines or polygons,
and they represent real world features.
For example, a point can represent the capital
of a state or a point could represent a tree.
A point is a single vertex corresponding
to a latitude and a longitude.
For example, a line
is two vertices, which are two points
that are connected via this line.
Lines can represent streets, they can represent a distance
between two trees.
And then we have polygons, which are made up
of at least four vertices, and they form an enclosed area.
So polygons could represent
political boundaries like a state.
They could represent the area of a tree canopy,
they could represent a house.
And vector features can also have attribute data
that describes it as well.
So if you have 100 points, you could connect data
to those points to say some of these points
represent trees of this species,
some of them represent trees of this species,
et cetera, et cetera.
In terms of vector maps,
so some maps are completely made up of vector data.
So, for example, topographic maps, which show
how elevated land is, those are made up of vector data.
Political maps, so for example,
a political map would be like a map of the world
that shows country borders
or I've shown here a map of the United States that shows
polygons for each state.
And then also you can see point data
representing state capitals.
That's vector data, street maps, that's also vector data.
The other main kind of data in remote sensing and GIS
is called raster data.
Raster organize information into cells or pixels.
Pixels are square like,
and they're uniform in size throughout the image.
And they're also organized into rows
and columns that form a grid.
Remember that the size of the pixels basically is the same
as spatial resolution,
which we talked about last week.
In raster data, each pixel contains a value
that represents some information.
The information that that raster cell contains
could be continuous data, meaning that it's numerical
and it represents how much of a certain variable is present.
An example of this would be
if a raster represented the percent snow coverage
on the landscape or temperature or elevation.
Reflectance values are also continuous data.
So a raster can describe how much reflectance
in the red band there is on a landscape.
Raster data can also be categorical,
meaning that the numbers don't represent
how much of something there is,
but rather they represent certain classes.
So a land cover map is an example
of a categorical raster map.
Even though the raster may be assigned
numbers like 1, 2, 3, 4, those numbers don't represent
the actual numbers themselves
or how much there is of something.
But they represent classes like one may represent water,
two may represent forest land,
three may represent urban land,
and four may represent wetlands.
Here are three examples of raster maps that you could make.
So the first is a surface map.
And this map is interesting because it is a raster map,
but there's also some vector data overlaid on top of it.
So there's some vector line data,
but the surface map underneath is a,
contains continuous data.
So it contains reflectance values
and it's a true color map.
So it contains, we'll talk about this in a few slides,
but it contains information from the red, green,
and blue bands.
And then thematic maps can be continuous or categorical.
So this middle thematic map represents elevation.
And so the pixels represent
how much elevation there is in each pixels
and they're colored accordingly.
And then finally, the map on the right is a thematic map.
It can also be called an attribute map that is categorical.
So the pixels represent certain classes.
For example, the brown pixels represent agriculture.
The yellow pixels represent bare ground, et cetera.
In this class, you will learn how to make
all three of these maps.
Let's go back and talk in more detail
about spatial resolution.
So remember from last time
that high spatial resolution data has small pixel sizes
and low spatial resolution data has large pixel sizes.
I also talked about the spatial resolutions
of some common satellite sensors.
For example, Sentinel-2
has a spatial resolution of 10 meters.
Most of the Landsat sensors have a spatial resolution
of 30 meters, and MODIS has a spatial resolution
of 250 meters to 1,000 meters.
These resolutions reflect the capabilities of the sensors,
but they also reflect the different intentions
of each of these satellites.
For example, many researchers using MODIS data
are doing regional and global analyses.
So I'm bringing this up
because I wanted to talk about the trade-offs
between resolution or grain and extent.
So spatial grain is kind of another way
of talking about spatial resolution.
So it's the smallest spatial resolution of the data.
It's kind of like a pixel size.
And then extent is the overall size of the landscape
or area that you are observing or measuring.
And again, there's a trade off between grain
and extent that scientists or researchers
or sensor engineers have to consider.
So for example, satellite sensors,
the extent is often regional or global
depending on the orbit path of the satellite.
But for drones, the extent might be much smaller,
such as like one crop field.
Therefore, for satellite sensors,
an extremely high resolution, spatial resolution,
may be 10 meters.
And to get a higher spatial resolution than that
would be extremely costly and computationally difficult.
But on the other hand, drones which have a smaller extent,
they could have a grain size of just one centimeter.
So therefore, what is considered high resolution
is all relative 'cause you have to consider
both the extent and the grain.
This is something that you should also keep in mind
when you're choosing ROIs for your projects.
ROIs, regions of interests.
If you choose too large of an extent,
you may need to use lower resolution imagery.
And conversely, if you choose a super small ROI,
you may wanna make sure that your imagery
has a small enough grain size
that you can actually make out features on the landscape.
Going back to raster images,
it's important to note that rasters
can be a single layer or multi-layered.
These layers are called bands,
and as we discussed last week,
each band from a satellite sensor depicts reflectance values
from a specific range in the electromagnetic spectrum.
So for example, from the near infrared range
or from the blue range.
And you can think of multi-band images
as three dimensional cubes or like stacks of multiple grids.
Individual bands are usually displayed in gray scale.
They could also be shown in pseudo color.
But in order to get an image that has different colors,
we combine three bands
and we visualize them with three colors,
red, green, and blue.
So red, green and blue are the three primary colors
and they can be combined to make any color.
As an example, when you're watching TV,
you see 100 different colors, right?
But actually the TV can only display three different colors,
red, green, and blue.
But it displays them together in different combinations
and with different intensities.
And that creates all of the colors that we see.
Color compositing in remote sensing works the same way.
We take three bands and we assign them
to the colors red, green, and blue.
And the reflectance values in each pixel
in those three bands dictates
how much of each of those colors to display.
A true color composite, also called a natural composite,
is an image that has the red, green and blue bands
assigned to the red, green, and blue channels
on the computer.
Therefore, this composite looks like
what we would observe naturally with our human eyes
where vegetation looks green
and water looks dark, maybe a little bit blue,
bare ground might look brown.
And then buildings look white.
Many people prefer true color composites
because they look natural.
But it's important to note that there's a lot
of subtle differences in the landscape that are hard
to tell apart with true color composites.
I also want you to be aware
that a band numbers change between satellites.
So for example, some satellites, red, green, and blue bands
are labeled B3, B2 and B1,
whereas in other satellites they're labeled B4, B3, B2.
This is just something that you have to look up
before you visualize your data.
False color composites are when we create color composites
not assigning the red, green and blue bands
to the red, green, and blue channels on a computer.
I've shown here a table with a bunch of different examples
of different color composites
that we can make using the bands from Landsat 8.
The first row shows the natural color composite
that we just talked about, but underneath you can see
many others with some of them labeled with
which kinds of features they may help distinguish.
And I've shown visualizations of two of these.
The color infrared, false color composite,
and then the short wave infrared color composite.
False color composites allow us to visualize wavelengths
that the human eye can't see
like near infrared or short wave.
And using these bands can help us
increase the spectral separation of objects on a landscape,
and that helps us better interpret the data.
Finally, I wanted to give a super concrete example
about how these colors or bands can be combined
to make other colors.
So in this example, I've shown bands
and reflectance values.
This would be for one pixel.
So for example, in this one pixel with four bands,
the blue band pixel values 190,
the green band pixel values 50,
the red band pixel values 45,
and the near infrared band pixel value is 150.
So if we assign the near infrared band
to the color red on a computer, the red band to green,
and the green band to blue,
what color do you think it would show up as?
So we can look at the reflectance values,
which are also called DNs, which stands for digital numbers.
That's just sort of the raw units of reflectance values.
But you can see that near infrared has the highest number.
And so we can take that to mean
that that pixel will mostly show up as red.
And just to demonstrate that even further,
I used a website where you can put in numbers for red,
green, and blue and it'll show you exactly
what color shows up.
So I've done that with the values,
and you can see indeed that the color
that is created is red.