[Amanda] Welcome to week five
of Remote Sensing Foundations.
This week we will learn all about
image series interpretation,
including image collections,
image compositing and change detection.
Upon completion of this module,
I hope you'll be able to define and understand
and calculate spectral indices,
including NDVI, NDWI, and EVI,
use Google Earth Engine to build and apply a mask,
classify an image using both supervised classification
and unsupervised classification and compare the methods.
We also have a few associated readings
and a three minute video that gives a clear perspective
on supervised versus unsupervised classification.
Let's get started.
Last week we talked all about image processing.
In fact, many of the concepts that we went over last week
and defined during the lecture
are the exact things that we will be doing
and making in Google Earth Engine
using the tutorials that are embedded
in the readings for this week.
So digital image processing are the steps that we take
to turn satellite data into images.
There are four basic divisions or categories
of processing that we talked about,
including restoration, enhancement,
transformation and classification.
Image restoration is often called pre-processing,
because it is mostly concerned with methods
that geometrically and radiometrically correct
the satellite sensor data before it's made into an image.
Broadly speaking, image restoration is concerned
with the correction and calibration of images
in order to achieve as faithful a representation
of the Earth's surface as possible.
Radiometric restoration deals with
the correcting of distortions
in the degree of electromagnetic energy
registered by each detector.
These can be due to atmospheric corrections
or sensor malfunction.
Geometric restoration, on the other hand,
entails correcting the distortions in an image
brought about by sensor and Earth positioning.
Some reasons why we use correction processes include
variations in the altitude, attitude
and velocity of the sensor platform,
Earth's curvature, Earth's eastward rotation,
atmospheric refraction and relief displacement.
We went over correction processes,
including using image transformation equations
in conjunction with ground control points.
Three types of re-sampling procedures that we discussed
are nearest neighbor, by linear interpolation
and by cubic interpolation,
which is also called cubic convolution.
Image enhancement is predominantly concerned
with a modification of images
to optimize their appearance to the visual system.
Visual analyses, as we went over last week,
is a key element even in digital image processing,
and the effects of these techniques can be dramatic.
Specifically we talked about contrast stretching
in two types of compositing, both spectral and temporal.
We employed compositing techniques,
or we usually employ compositing techniques
as image correction,
due to sensor issues and atmospheric interference.
Temporal composites can be a way
to backfill corrupted pixels,
but they are also commonly used
to make an image representation
or representative, sorry, of a time period
that is longer than a single snapshot in time.
For instance, if we are comparing annual changes
in the growing season length between two regions,
we would compare temporal image composites
to increase our confidence in the rate
and timing of change between regions.
Image transformation refers to the derivation
of new imagery as a result of some mathematical treatment
of the raw image bands.
So while enhancement and restoration
have methods that involve correcting sensor data
to make it a better representation
of features on the Earth's surface,
image transformation and classification are employed
as methodologies towards interpreting images
using mathematic and scientific methodologies.
Two examples of transformation that we talked about
were vegetation indices and principle components analysis,
but there are many others,
and we'll go over in more detail a little bit today
about vegetation indices.
We also talked about image classification,
which refers to
the computer-assisted interpretation of images.
We compared supervised
versus unsupervised classification methods,
where a major difference is that
the supervised classification uses training sites
to distinguish between feature classes
and unsupervised classification uses clustering methods
to group pixels with similar spectral signatures together
into information classes.
As analysts, we have a role to play
in both classification methods.
For supervised classification, this role is a priority
or done before the classification
and involves the choosing of enough training sites
and knowledge of what is on the ground.
In unsupervised classification,
our only role is to group the information classes
into feature classes after the classification has been done,
and again, with knowledge about what is on the ground.
And finally, we spoke briefly about the masking of water,
whether it be solid, liquid or gas.
This week, you will learn how to build
and apply a mask using Google Earth Engine.
Okay, so hopefully now from our readings,
you already have a sense
of what image collections are in Google Earth Engine.
In many cases, creating an image collection
is the first step in any workflow process
for creating or interpreting images.
One of the most fundamental parts of Google Earth Engine
is selecting the images you need and displaying them.
Here I've just put up a really colorful example
of a workflow where someone developed
a data product of global temperature
and then created a image collection
of the global temperature product,
and finally, used that to create a GIF
to sort of animate through time
what's happening with global temperature.
So at this point, I expect that you all
understand what an image collection is
in Google Earth Engine
and how we start with a satellite repository essentially
and we import that into our script,
and then we build an image collection from that repository.
There are numerous ways to reduce
an entire satellite repository into an image collection.
We can filter over space, we can filter over bands,
we can also filter over time
and we can filter over combinations of all of these.
So we use filtering and mathematical reduction methods
in Google Earth Engine to create an image
from an image collection.
We can also use them
to create an image collection from a repository,
many different combinations of both.
In the figure here, I'm showing-
The words reduce are used
and reduction methods are mathematical in nature,
and typically what they entail are writing functions
that resample the data set for a given statistical equation.
So mean, median, minimum, maximum
standard deviation or percentile.
That's also another way to go from
image collections to images.
That's where we take that stack of images
and then we take, say, the mean pixel,
the mean per pixel of all of the different images
that are in that stack in order to create our output image.
Last week, we talked a bit about image composites
and in the context of both spatial
and temporal compositing.
So with spatial compositing,
a lot of times we are correcting images
and we're backfilling pixels that might have
sensor issues with them or perhaps atmospheric ones.
With temporal composites,
sometimes that's the method by which we backfill,
but we also can use them to gain a better understanding
of what's going on for our given site through time.
So here I'm showing an example workflow schematic
for starting with Landsat 2 and Sentinel collections
and creating an image composite using both.
So we can also make fusions
of two different satellite projects, products, I'm sorry,
and the way in which we do this in Google Earth Engine
is by starting with these different collections,
you know, applying filters to kind of gain
the date range that we're interested in,
an area of interest, the band range.
Then we might apply a cloud mask or maybe a water mask.
We then would use a reducing function
to get from all of those different data sets
all the way down to one image composite
that's representative of the area
that we're interested in looking at
and during the time period and including the bands
that we're interested in working with.
All right, so we'll spend a few minutes now
talking about spectral indices.
And as we talked about last week,
there are a whole suite of spectral indices
that we have to use at our fingertips.
And, you know, basically what we can do
or what we do when we are calculating spectral indices
is we're taking advantage of the differences
in percent reflectance across the different wavelengths
that different features of the landscape have.
And so, as we talked about a bunch last week,
normalized difference vegetation index, or NDVI,
is the most widely used vegetation index in research.
It's mainly used to monitor and assess
the greenery from regional to global scales,
although I've also seen it applied
at sort of finer scales as well.
And it quantifies vegetation
by computing the difference
between the near infrared and the red light.
So the equation again, as showed at the bottom left,
and as we discussed last night or last week, sorry,
chlorophyll strongly absorbs visible light.
And the cellular structure of the leaves
strongly reflects near infrared light,
which is invisible to the human eyes.
So unhealthy vegetation absorbs
more of the near infrared light rather than reflecting it.
So in that way, too, you know,
we can look at correlations of near infrared changes
and compare them to the red light and measure plant health.
So the value of NDVI ranges between negative one and one.
The NDVI values near positive one, between 0.6 and 0.9,
usually indicate very dense vegetation.
The negative values, so negative one to near zero
is usually representing clouds, water and snow,
and the values near zero but positive
are usually indicating things like barren rock, sand,
sometimes snow or dirty snow.
So moderate NDVI values are often indicative,
so like between 0.2 and 0.5,
they're usually indicative of sparse vegetation
or shrubs and grasslands.
Often too, we can also use this to look at crop health.
So in that same value range,
you can also find, you know, crops.
So, as I said before, the application of NDVI
is pretty widely diversified.
It's one of the most used indexes
and it's used in precision agriculture farming
for monitoring vegetation health.
So if you're using a time series of NDVI,
you can look at crop health.
Same thing with forest monitoring, also drought monitoring.
And it's also used in the analysis of land use
and land cover changes,
or in assessments of land degradation
and finally and also in urban sprawl as well.
So secondly, a slightly more complicated vegetation index
is the enhanced vegetation index or EVI.
EVI is calculated similarly to NDVI,
but with some additional atmosphere corrections
to meet with the inaccuracies of NDVI.
So EVI is capable of eliminating
the background and atmospheric noise
as well as non-saturation.
So when you have very, very, very green vegetation,
NDVI can basically tend to saturate,
which means it gets so, so close to plus one
that it kind of makes the image not as usable
in terms of assessing change or health through time.
It's not as accurate.
So EVI is capable of eliminating
the background atmospheric noise
to try to account for some of this.
It enhances the vegetation signal
with better sensitivity in high biomass regions.
So it's often used in studies
of tropical rainforest, for instance.
So again, it's another vegetation index
that's used to quantify the greenness of vegetation,
and I've put a more complicated version
of the equation in the middle.
This week, we'll be asking you to calculate EVI
and we'll have you use a more simplified version of that.
So there's a couple of different equations that we use,
but I wanted to put this here
just to kind of differentiate as well, too,
that we can in fact calculate these indices
in a couple of different ways.
And here we're showing there's an L in the equation,
which is an adjustment for canopy background
to address like the saturation of vegetation greenness.
There's also coefficients of aerosol resistance
to kind of mitigate aerosol reflectance.
But essentially another way to calculate this index
is to pit the blue wavelength,
basically subtract it from the red wavelength
in the denominator of the equation.
Finally-
Oh, and I guess I should also add that
in terms of the application of EVI monitoring,
vegetation is again typically how it's used,
but it's got higher precision for agriculture,
and like I said, dense forest land,
especially in the tropics.
And so sometimes I've also seen it used
to calculate crop yield.
So again, you know, you will be doing this
this week in Google Earth Engine,
but you won't be calculating it using the coefficients
or the L either.
So finally, I also wanted to talk
a little bit about the normalized difference water index,
which the simplified equation for, again,
is shown down at the bottom right here.
The normalized difference water index
is remote sensing derived index,
which measures the change in water content of leaves
using the near infrared and the short wave infrared,
so SWIR wavelengths.
This was a method that was developed by a researcher,
his last name is Gao, back in 1996.
So NDWI is used to analyze vegetation's water contents
because of its sensitivity to the water content
of vegetation and water bodies.
So again, the value of NDWI varies between zero,
or sorry, negative 0.1 to 1.0,
and it depends on the hardwood content
and the nature of the vegetation cover.
The higher value of NDWI represents
the higher water content in vegetation
and a higher fraction of vegetation cover.
And the low NDWI indicates low vegetation water content
and also low vegetation fraction cover.
But when we subtract these, we can also look at stress.
So when water is the stress factor in an image,
the value of NDWI will decrease.
It's also used to determine the moisture content
in vegetation cover during an assessment of fire risk.
So the Forest Service actually can use NDWI values
to indicate whether there are
sufficient moisture in fuel loads
and use that to calculate
how an area might be increasing in drought stress
and increasing in fire risk and vice versa.
So, yeah, as I said,
the applications of NDWI are drought monitoring,
fire risk assessment, water stress analysis.
It's also used in reservoir mapping and discharging,
and then it can also be used in agriculture
in the assessment of yield reductions,
or to determine whether ground water levels are lowering.
So how do we detect changes between remote sensing images?
Due to the geospatial format of raster imagery
and repeat measurement by satellites,
remote sensing is a very well suited scientific tool
with which to detect changes across the landscape
on a pixel by pixel basis.
So as with other processes in remote sensing,
there are numerous ways to detect change.
The methods are typically specific
to the question that the user is asking.
So here at the bottom, I am showing a figure,
or the figure here is showing
four of the most commonly used change detection methods
as applied to a registered normalized
multi temporal Landsat image.
So the methods shown from left to right
that are mapped are,
on the left, we've shown a post classification detection.
The second one is using image differencing,
so we just subtract one image from the other.
The third is using image ratioing.
So we can look at on a pixel by pixel basis
the ratios of changes in certain areas.
And finally, the fourth one
is looking at a principle components analysis,
so using a principle components analysis of the image
to basically gain an understanding
of where there is a high level of variance
in the features on the landscape.
So in Google Earth Engine,
you can accomplish each of these change detection methods,
and you'll be going into this a little bit more
I think in the future of this course.
We might touch on it briefly
in the tutorials this week as well.
But just know that these change detection methods
can and are applied both to individual images,
and also they're actually even more classically
often applied to image composites.
So I think I said sort of in a previous example,
you know, if you're interested in looking at
changes to vegetation, you know,
you would set out to first gather your image collection
and then filter it by dates.
If it were me, I'd be filtering out dates
that are not included in the growing season
so as not to add more sort of noise to my data set.
So I'd only take images
that were during a given growing season.
I might stack those images up and then use maybe a mean
to sort of summarize
where there's vegetation during the growing season.
And then if we do this for a bunch of years,
you can then use band math
and other tools in Google Earth Engine
to subtract out and create difference images.
So there's the whole different way of,
there's a bunch of different ways to do this,
and I think we're definitely gonna touch on it
a bit in this course and it's something to think about
as we start to consider what we might wanna be doing
for our end of semester projects.
So please let me know if you have any questions this week
as you go through the tutorials
that are embedded in the readings that were assigned
and I look forward to seeing you all on Yellowdig
and also hopefully in the live class
that is coming up here pretty soon too.
All right, thank you, take care.