This remote sensing workshop is
designed for spatial analysis
personnel for creating live
vegetation fractional cover maps
with Sentinel data. We will
show download and import,
optical and SAR image data, use
image-processing tools to
transform the satellite data to
maps that represent live
vegetation cover, use SAR image-processing
tools to create maps
indicating the vegetation cover density,
add value to the
produced maps by portraying
thematic information with color
rendering. The objective is,
although the procedures learned
can be applied to processing of
any raster data, this course is
specific to the production of
live cover status and change
maps in the management of NRT [Northern Rangelands Trust]
natural resources. For
downloading Sentinel optical and
SAR data, this is the Copernicus
Open Access Hub. The link is
provided in the handbook.
One has to register to download data.
We can go change our layer.
Let's bring up the
satellite image.
OK
We can zoom in and
zoom out.
We are OK.
That is the navigation tool.
Then, we go for selection, and we
take an area of interest, drop
this down. OK. The sensing period,
when we want to collect the data,
I usually go month to month.
Let's see if we have any data.
We'll take January 1. You can go
to any year, any period, and all
this data is free of cost.
And, we have different...
You can take the radar data is
Sentinel-1. The optical data is
Sentinel-2. We take Sentinel-2,
and you can select 2A
and 2B. Both these satellites
are in tandem, five days of
difference in collection of data
and product type, and 1C is non-
atmospheric corrected data, and
2A is atmospherically corrected data
so we select 2A.
Next, we hit the search button, and it
gives you the number of images
over that area in that period.
If you go see, it seems like
January 2 was also very cloudy
over our area of interest, you
can just scroll down.
This image looks good, if you want to
look at it. We select this,
which is this image. Then, go view it.
It brings to the image,
it brings up the image. You can
see all the detail here. And then if
you're OK, this has a lot of
clouds, but if it's a cloud-free
image and you're OK with the image,
you hit the download
button, or we close it, and you can
go and search each image.
That's the problem. I'm in the, I have to be
in this mode. You select the
image, and it goes to that
particular image. You can go and
look at your images and see if
they are cloud free, you
download them. OK? Similarly, you
can go up here again.
You can change your satellite.
You go to Sentinel-1. You select the
platform 1A or 1B.
So the product type will be, we select GRD
SLC is Single Look Complex
which is used when
applying inSAR processing.
GRD is Ground Range Detected,
is georeferenced data projected
using Earth ellipsoid model.
For polarization, we take both
VV and VH. In sensor mode, we
take IW which is Interferometric Wide.
It gives us a swath of 250
kilometers. We hit OK.
That shows our coverage, and similarly,
you select the image that you want,
and you can view it and then download.
For live vegitation
map products, nine
Sentinal images were used. Here, as example,
I will use two Sentinel images,
which cover part of the core and
NRT region. So open image data in
SNAP. We go to File,
Import, Optical Sensors, Sentinel-2,
L2A, that is atmospherically
corrected data. Go to the
right folder. We bring up Tile 5
and Tile 8. I'm calling my nine
images as tiles 1 to 9. So we
go to 819. We go to
Tile 5, May, original, and we open
this folder and select the XML
and import products.
Another way of bringing in the product
is go to the Folder, Tile 9, and
just drag and drop the XML file.
We right click on the product,
Open RGB Image Window. Select
the bands for false color
composite.
OK. Do the same for the next one. OK.
So now we have two tiles, one and two.
We can display these tiles
vertically. As we scroll this
image up, we can see the vertical, this overlap
overlap right here. Now we will
mosaic these images. To mosaic
the images, we first have to
resample a requirement in SNAP
Processing. Go to Raster,
Geometric Operations,
resampling.
It's the first product. We can
rename this. Make it shorter.
OK, Save as di-map and go
to the libr...
to the folder you want to save
it in. We have a folder created
and resampling parameters, and
it should be... [no speaking]
We will select
band 4, and... [no speaking]
[no speaking] Resampling is bi-linear, and
we hit Run. So this shows that the tile
has been successfully resampled,
and now the product is opened in
SNAP. We hit OK. Close this
and run the same on our second image.
We have both the tiles resampled.
Next, we go and mosaic
them. So we add the images,
which is one and two. Open, and
we will give it a name. This is S2A.
We select our folder,
which is...
So we are in Tile 9 and 8.
We select Mosaic, and select, Go to Image
Project Definition, Custom,
CRS reference system. We take it
as WGS84. Next, we go and set
our pixel size. Zero. We will display
our source products and zoom
into the area. Then we have
Variable Conditions. We go and
select the bands we need.
We want mosaic. We will select Band 3,
Band 4, and Band 8.
OK, and then we run it.
It is very important that each time we
create a new band, we must save
the product, Save Product.
This is the two tile mosaic May 2019 image. Let's view the RGB.
This is a false color image. In
Remote Sensing, false color is
preferred over the natural color
because the use of NIR band
increases the spectral
separation and thus enhances the
interpretability of the data.
The vegetation is all in shades
of red. Next, we create the
MSAVI2. MSAVI2 is the
abbreviation of modified soil
adjusted vegetation index.
This index was selected for this
project as it has proven
capability to minimize the soil
influence in remote sensing data
and can easily be used in an
operational mapping system.
Before we apply the MSAVI2 equation
we will scale Band 4
and Band 8. We go to Band Math
and give it a name. Go to Edit
Expressions. Band 400, and hit OK
After scaling Band 4 and 8,
we apply the MSAVI2 equation
using these scale bands.
Band Maths
Edit it for the
expression. The formula,
the equation is there in the
handbook. We can just copy it
and paste it, and hit OK.
Displayed here is the MSAVI2
band created. Next step is the
calibration of this satellite,
MSAVI2. The model equation to be
used here was created using the
MSAVI2 calculated from field and
satellite data collected at the
same land surface locations.
Using the equation, the
satellite MSAVI2 is calibrated
to field MSAVI2. Right click
on the product Band Maths,
Edit Expression. We have the equation
in our handbook. Paste it and
hit OK. This calibrated
satellite, MSAVI2 to field
MSAVI2 will be used to replace
the field MSAVI2 in the equation,
field MSAVI2 to vegetation LFC
calibrations. The detailed
explanation of the procedure is
given in the USGS Open-File
Report referenced in the
handbook. Now we create the
live vegetation fractional cover
map using the developed model
equation 2. Right click on the
Product, Band Maths, Edit
Expression. We will copy the
expression from the handbook,
the model expression. Here, we
have multiplied the vegetation
map by 2.55
to stretch 0 percent to 100
percent range to the full 8U
range of 0 percent to 255 percent.
Next hit OK.
We have created
a May 2019 veg status map. Next,
we will convert the veg map from
decimal format to integer format.
We go to Raster, Data Conversion,
Convert Datatype. We select
the product. We will change the name,
VegMap. Go to the right
folder, select the folder. In
the Processing Parameters,
select the image that you want
converted. We will select 8U. We
will truncate it. Run it. Once
it's done, the product will open
in SNAP. We see here that the
product is opened here. We close this.
As I said before, every
time you create a product
or a band, you have to save the
product. Save Product. Also, we
see that when we convert it,
it keeps the same name of the Band
image file. We will display this.
[no speaking]
OK. We go to Pixel Information. You can
see the value at that point
where your cursor is. Just to
show the difference, we will go
to the Band before it was
converted to an integer.
We highlight it. Go to the Product.
Go to Pixel Information,
and you see the decimals there.
We will now create 2019-2018 difference vegetation
map. We must have both the 2018
and 2019 vegetation
map products open in SNAP. So we
have 2019 up here. We will go
and bring up our 2018
map. OK. Next, we go to
Band Math. In the handbook you
will see that we have done...
I've shown the process of using
2018 map. Here, we will use 2019
and we will name the project,
name the Band as 2018 VegMap...
then go to Edit. OK.
Expression. We have to dropdown
and select the 2018 image.
And index. I'll go before that,
sorry. We selected before. Then times
one. We want to copy it
into this folder. OK and OK.
[no speaking] As we had seen that we get an
error message. So before we do
anything, we go and save the
file and save it as a session.
Save Session As, and save it,
give it a name.
2019-2018DiffVegMap. Save it in the
folder and hit Save. OK.
Here we see that a virtual band has
been created. We go right-click
and Convert Band. Then we go
here to the Product
and go Save Product.
It is very important that you
save the product. Otherwise,
when you reopen the product, it will not... the
band will not be there. [no speaking] OK.
OK, and we will display it, right there.
We now have the 2019 and 2018
veg maps in the same product
folder. We will now calculate
the difference image.
Go to the product, right-click Band
Maths, and change the file name.
Edit Expression. This is our
expression, one difference.
We select 2019 and we
select 2018. OK, OK, and OK.
Displayed is the difference
vegetation map. Now we will
transform this decimal to an
integer format. Right-click Band
Maths and we will give it a name,
difference veg map 2019
2018
and 16 S. Edit
Expression. Select the... We will first go and
take a function. We want to convert it,
round it. Where is it? Round. We
select this, and we select a
difference map. OK and OK.
Displayed is the difference map
that we just created. Every time
SNAP creates a virtual band, we
have to go and convert the band
and save the product. After the
product has been saved, we will
go and display the band again.
Displayed is the 16S integer
image we just created. We will
go and we look at the histogram,
and we will clip it to 100. So, the
minimum value is -114,
and the maximum is 163.
Basically 163 is an outlier. The values are between... the highest
value goes to 113. We go to
Band Maths. -114 is to zero and the high
upper limit, we will set it to
100. We'll give it a file name
and Edit Expression. We'll
select an operator if/then.
This will be our band, 16S.
If it is greater than 100, zero,
then it is 100, else it is equal
to this. OK. OK and OK.
Displayed here is the clipped
to 100 difference vegetation map.
If you look at the histogram,
you'll see that the minimum
still shows is -114 and the max
is 100. This will be truncated
when we convert this image to 8U.
Again, it's a virtual band.
We convert it to Convert Band,
and go
Save Product. [no speaking]
So the Clip 100 image band is being
created. To display it, we
double-click on the band.
Next we're going to scale the 0 to 100
to 0 to 255.
[no speaking]
We go to Band Maths.
Name the image. Edit Expression.
We'll use the expressions as
given in the handbook. We'll use
the round functions.
[no speaking]
And this... 114 divided by (114--100))*255).
We have couple of parenths here.
One, two. OK, and OK, and OK. Display the
scaled image from to 255.
If you go and look at the histogram,
you see the scale image, 0 to 255.
Virtual Image Band, Convert Band,
and Save Product.
After saving the product, we display
the band. OK next step is to
convert the 16S to 8U numerical format.
We go
to Raster,
Data Conversion, Convert Data Type.
And our source is 2019.
And we want to... convert the
difference image.
DiffVegMap
2019-2018
We write it. Here put, oops!
black and white.
OK it will go to the
Mosaic, same folder. Select
Processing Parameters. We'll go
to the image. We want to convert it.
We want to convert it to 8U.
it. We want to convert it to 8U.
We will truncate it to and run
We will truncate it to zero.
and run it.
[no speaking while file renders]
The conversion to 8U from 16S
provides a less complex data
structure for color renditions
and GIS applications.
Now we will export this product created the 8U format product.
We click on the Product. Go to
File, Export. We will export in
this format which is recognized
by QGIS. Slash and go to the folder.
[no speaking while going to the folder]
We have created the vegetation status map, and the vegetation difference map. Next, we will
develop a color ramp for both
these maps. We go to the
Vegetation Status Map and hit
Color Manipulation, which gives
you the histogram. We will set
it to 100. OK. So now, we will apply
the colors that we have given
in the handbook. There's color 1
and 2 for the vegetation status
map. To set the colors, to
change colors, I'll just show
you how we do it. You click on
this, and you can get the colors here.
Next, if you click on this,
you can go and change the text,
and if you click between the
two sliders, left-click, you
can add a new slider. To remove
a slider, you go left-click on
the slider and you can remove
the slider and you can add a
slider also from here.
So we will select the lower limit and
choose our color. So we have the
color. This dialog appears.
We'll select the RGB, either you
can give the RGB colors. [no speaking while color selections are being made]
And hit OK. Next, we will take the
upper limit, choose the color
RGB, and we can give the color
code, which is 9ACD32, and OK.
We don't need the center slider,
So we will left-click and remove
the slider. So we have the color set.
Now, we can also export
these, can apply to other bands,
or we can save the colors
to a text palette. So, we click on this,
and you can save your
colors. I already have it saved
here, so you can go and give it
a name, VegStatus. Then you can
use it for other years.
Cancel
So this is a classified vegetation
status map. Normally, in
a classified map, we have
thematic classes such as soil,
grass, trees, etc. This is a
biophysical classification,
meaning, mapping a quantity of
something, for example biomass.
In this case, we are mapping the
percent cover of live vegetation,
more specifically, grasses and
forbs per 10 by 10 meter pixel
because it is
a Sentinel image.
For Landsat, it would be
30 meter by 30 meter.
We will create the legend in QGIS later,
but what do the colors represent?
The very bright green shows
higher percent coverage of live
vegetation. The lighter
shades of green
are low percent cover,
and the shades of brown are less
cover or no vegetation cover at all.
We can also, if we go to the World
View, it shows us where we are.
This frame, you can zoom in. We
see all the forest trees, very
high trees. Forested area is
appearing very bright. We have
some cloud cover, which is shown
by the brown. This is a
vegetation status map. I've also
displayed the false color
composite. We will link
both these images, and zoom in
to look at the landscape more
closely. So you see, same variable.
It shows vegetation percent cover,
less vegetation, no
vegetation. You can see the
bright. As we know in the
false color composite, the red
represents vegetation.
We can go into any area. And another thing
that we, I wanted to point out was
if we go for View Pixel
Information, so these areas, you
see high green. We have to
remember we had stretched the
image from 0 to 255. So when
we are taking percent cover,
we need to divide it by 2.55
to get the percent from 0 to 100 percent. OK?
We will now create a color ramp for the 2019, '18 difference
vegetation map with six
colors. We'll use the break
points given in the table of the
handbook on page 15. We go
to the Color Manipulation.
We set the histogram to 100
percent. As we said, we'll
use six colors. We'll first add
a new slider.
[no speaking while sliders are being set up]
OK... Now, we go to the table,
and we will set our positions and colors.
First color. Off, Sorry!
So, we will add a color. First color is blue.
Go RGB, and we'll type the code.
One, two, three, four. f f
And OK. Next color. [no speaking while colors are being selected]
Sure does look bad. We havn't sent the relative positions as yet.
I have zero zero. [no speaking while histogram colors are manipulated]
Now, we'll set the breakpoints. We start from the extreme level,
the upper limit, which is 189.81
Enter.
OK where is my red? [no speaking]
[no speaking during color selections on the histogram]
OK, so we can go and right-click, and we can export the color
legend as image. You can save it.
I have it already, so I'll
cancel it. Then, you can also
save the color palette to a text file.
So, the difference veg map shows
colors ranging from reds to
yellows to greens to cyan
and blues. There is a mixture of
colors. How do we represent it
as far as the live vegetation
change from 2018 to 2019?
The reds, for sure,
have shown an increase in live
vegetation from 2018 to 2019.
The blue show a
decrease from 2018 to 2019.
That is live vegetation was
higher in 2018 as compared
to 2019. The science
also show that the live
vegetation in 2018 was higher
than 2019, but not
as much as the blues. On the
other side, the yellows are
showing that the live vegetation
in 2019 was higher than
2018. Then we have a big
middle zone, which are the
greens. Here, there is actually
a small increase as well as a
decrease shown. We have to
really zoom into the areas to
look at the shades of green. If
there was an increase from 2018
to 2019, all are decreased.
Shades of yellow green will show
live vegetation high in 2019.
Shades of cyan and green
means lower in 2019 than in
2018. If we
zoom into an area,
we see the yellowish green areas.
They show a very small increase
from 2018 to '19. Areas
of bluish green, which are
around here, in this area and
these areas, show a very small
decrease from 2018 to '19. We
should keep in mind also that
middle zone has very low values.
The increases and decreases are
very small numbers.
In this exercise we will process Sentinel-1 SAR image data.
We have already downloaded the data
so we will go to... Now we will import
this downloaded data. Go to
Import, SAR Sensors, Sentinel-1.
Go to the right folder. We have
the SAR data in this folder
[no speaking] and import product.
So we are there. Import Product.
As I had shown earlier, we can also
directly drag and drop the
products. We'll do the same. We
go to this folder and drag
this Manifest file.
We have imported the four SAR images
that cover the core and NRT
Conservancies. If we go and
open the Product, see bands, so we
have amplitude as VH and VV.
What does that mean?
SAR satellites have an active sensor
on both. It sends a signal and
then receives back the signal.
Here, the VH, the signal is
transmitted vertically and
receives the signal back
horizontally. The sensor
receives the signal back
horizontally. In VV, the signal
is transmitted vertically and
received vertically. SAR is
an active sensor, so it
does not need any light
to record the image. Therefore, the
data collections are not impeded
by any cloud cover. Also, the
data can be collected day or night.
Here, we are going to
mosaic these four images, for
mosaicing. Before mosaicing, we
have to calibrate each product.
We go to Radar,
Radiometric, and Calibrate. So we
will shorten our name. That is
12, 1st of December.
We will take that away and then go
to the right folder... [no speaking during folder selection]
and Select. The processing
parameters, we have VH and VV.
The output will be sigma naught band,
and we will hit Run. So our calibrated
file is done. It's opened in SNAP,
and we will go and display it.
The bands, our VH and VV, and I have it.
It takes a little bit time to
display the band, so I have it
displayed. So this is the calibrate.
The next step is we will
go and do terrain correction. So we
go to Radar, Geometric, and
Terrain Correction, Range
Doppler terraine
Correction.
It's our file with an abbreviation
terrain correction. Next, we
select our folder. Go to the
same folder. Locate the processing
parameters, we have our source
bands, the VH, VV. Then we will
select our map projection. We go
to Predefined CRS. We select WGS
Auto UTM. OK, OK, Hit Run.
We have
our terrain corrected
image. I have it displayed.
Go and look at it. That
is our terrain corrected image.
If we go to the one before it, just
the calibrated image, that's
what it looked like. Now it is
terraine corrected.
Next, we are going to go ... (georeference entered incorrected),
to go and do speckle filtering.
Speckle filtering helps remove
the noise from the image.
I'm not going to go into the
details of it. It's not actually
noise. That is also data, but
here we do not require that.
We go to Radar, Speckle
Filtering, and Single Product.
Speckle Filter. And you see that it, then we have the
Speckle Filter and then we will...
our folder there. We go to
Processing Parameters. We have
the Source Bands and we are
going to select the Lee filter,
3x3, and hit Run. This is
the result of our speckle filter.
This is before applying the
speckle filter. Before speckle filter,
after speckle filter.
We really
have to zoom into an area to know.
So let's look at it again
if...OK. Before speckle
filter. Next step is to
create a mosaic. Before we do
that, we have to apply the
calibration to terraine correction
and speckle filter to all
the other three SAR images. Once
all these images are processed
with the speckle filter, we will
use this for creating the mosaic.
OK,
I have run the speckle filter on
these other three images. We go
to Radar, Geometric, and SAR
Mosaic. Either you go and
add one image at a time that or
you will add Add Opened. We'll
just go and remove the files we
don't need. We just need the
files that have been processed
with speckle filter for the
mosaic. [no speaking] I just open this up so
that we know what we are
removing. We have the four
images. All processed through speckle filter
Next is the SAR
mosaic. It takes the VV and the
VH bands, and we will not
normalize the image. Then, we
will write it. We give it a name.
We go to the right folder, and
OK and hit Run. Displayed is the mosaic.
Once the mosaic is created, it
takes about 20 minutes for it to
display. We have the two to open
up the mosaic. You see, we have the two
bands, the VH and the VV.
Next, we will create a subset.
Before we create the subset, we
will use the nine tile mosaic of
optical data, and that of SAR
2018, as a reference image, and
we'll use the coordinates of
that image of the SAR 2018 image.
I have already displayed the
mosaic of SAR 2018.
This is my image. We need to use
the geocoordinates of this image
to subset our 2019 SAR image.
It is not straightforward way.
I have not yet seen in SNAP how
to just bring in an image and
use the coordinates of that
image. We have to go first to
get the coordinates of this. We will
go to Analysis and
Geocoding. We will know the
upper-right latitude, upper-
right longitude, and lower-right
latitude and lower-right
longitude. These are given in
degrees, minutes, seconds.
We have to convert these to decimal
degrees to use in the subset.
We'll use Excel and convert this.
Next, we go to our subset.
We go to Raster and Subset.
It brings up the thumbnail image.
We go to Geo Coordinates, bring
up our Excel, and Copy-Paste.
Backspace, then last one.
[no speaking] We put a negative, because it is
a south latitude here. Then we
just hit OK. It takes about
20 minutes to create the
subset. Here we have displayed
the subset of SAR image 2019.
Next, we will create a
ratio band. That is VH over VV.
Simply we go to... and as soon as the
product is created, you go right-click
and save the product as
I have said before. We right-
click Band Math, give it a
name, VH_VV. Then Edit
Expression. We want to
create a ratio. We go here, VH
over VV. I have already created
the band. You see that? Just hit
OK. You see here that the ratio
band is created, but I would
like to see the ratio band is
created as a virtual band. You
will go right-click and convert
band. It's grayed out because
it's already converted. Convert
band, and then hit Save Product.
Once that is done, then we
will again use Band Math and use
the expression equation given in the
handbook to calculate the final product.