Remote Sensing – Classification of Habitat
NR505-GIS Applications in Wildlife Sciences
In this exercise you will use the ERDAS Imagine software to:
1) View a subset of a Landsat 7 ETM+ image from August 2nd 2002
2) Delineate different vegetation types and test the separability of selected types
3) Classify the image into vegetation types
You will be working with a subset of an image from the Owyhee Mountains in
southwestern Idaho. This area is dominated by western juniper (Juniperus occidentalis)
woodlands and sagebrush steppe. Two sagebrush species exist in the area, mountain big
sagebrush (Artemisia tridentata subsp. vaseyana) and low sagebrush (Artemisia
arbuscula). Other vegetation types are broadleaf riparian areas (Salix spp.), wet
meadows, and broadleaf mountain shrub species such as Prunus spp.
Step 1: Open ERDAS Imagine and load the image /RemoteSensing/landsat7-aug22002.img:
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Start – Leica Geosystems – ERDAS Imagine – ERDAS Imagine – open with a
classic View.
Open the image by selecting File – Open – Raster layer… - navigate to the
image
The image displays in the false color infrared color combination where band 4 is
displayed in the red color gun, band 3 in the green, and band 2 in blue. Landsat
has 6 bands. You can change the way the bands are displayed by selecting Raster
– Band combinations – then modify which layer is displayed in what color gun
(red:green:blue). Try the 1-2-3 and the 6-4-3 color combinations. Notice how
different cover types display using the different color combinations. For
example, in the 6-4-3 color combinations recent burns appear as bright red (see
the upper left corner of the image).
Zoom in and out
Burn
Wet meadow
Low sagebrush
Western juniper
Mountain big sagebrush
Mountain shrub
Right-click in the image and select ‘fit image to
window’ if you want to zoom to the full extent
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Step 2: Delineate training data for the 7 vegetation types: Low sagebrush, Mountain big
sagebrush, western juniper woodlands, Willow riparian, mountain shrub, and wet
meadows.
Open the AOI (Area Of Interest) tool by
selecting Tools… in the AOI menu.
Use the Create polygon AOI tool to delineate
three polygons for each vegetation type.
The image below will help you determine
where to place your training areas.
Riparian
Mountain big sagebrush
Low sagebrush
Juniper woodlands
Wet meadows
Mountain shrub
Riparian – see arrows
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Use the Create Polygon AOI to delineate three low sagebrush
steppe areas.
Select the three polygons using the Box Select tool (draw a
box around all polygons.
Group the selected polygons using the Group Selected
Objects tool
Open a Signature editor by selecting Signature Editor in the
Classifier menu (options on the top bar or the ERDAS tool). You
will collect signatures for all 7 vegetation types in the signature editor.
Click on the Create new signature from AOI tool
Name the class ‘Low sage’.
Remove the Low sage AOI :
Select Arrange layers in the View menu – right-click on the AOI layer and select
delete then Apply (save the AOI layer if you wish, in case you have to redo
something)
Repeat from the op of this page and delineate training areas for the other vegetation
types. You should have 7 vegetation types in the Signature Editor when you are finished.
Meadow is
here
selected
Save the signature file.
Step 3: Now you can evaluate the separability between the vegetation classes. Based on
visual examination it looks like the mountain shrub class and the riparian classes are very
similar. Also the two sagebrush types may be difficult to separate.
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Separability between classes can be evaluated by computing the M statistic (Pereira
1999). To compute the M statistic you must find the mean pixel value for each vegetation
type you want to test and also the standard deviation.
M = (μ1 – μ2) / (σ1 + σ2)
where
μ1 = mean value of the reflectance for vegetation type 1
μ2 = mean value of the reflectance for vegetation type
σ1 = the standard deviation of the reflectance for veg 1
σ2 = the standard deviation of the reflectance for veg 2
M > 1 indicates adequate separability
Let’s test the separability between low sagebrush and mountain big sagebrush in all 6
spectral bands to determine which bands is most important for separability.
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Select the Low sage signature by clicking in the box to the left of the signature
name in the Signature Editor.
Click View – Statistics in the Signature Editor menu.
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The statistics for the Low sage signature for each spectral band will appear in
a table.
Copy the numbers to an Excel spreadsheet and repeat the procedure to find the statistics
for mountain big sagebrush. Compute the M statistic according to the formula above.
Question: Are low sagebrush and mountain big sagebrush separable according to this
test? What spectral band gives the best separability?
Signatures that are not separable can be merged in the signature editor. For example, the
mountain shrub class and the riparian class are difficult to separate and can be merged
into one class (broadleaf vegetation).
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Highlight the Mountain shrub class and the Riparian class by holding down the <ctrl>.
key while clicking on the number in front of the class. Click on Merge signatures.
A new class is created – name this class Broadleaf and delete the mountain shrub and
riparian classes. Save the signature editor.
You can also visually view
the histograms for the
classes.
Select two or more
signatures.
Click on the Histogram icon
in the Signature editor.
Click All Selected
Signatures and All Bands in
the histogram tool – then
click Plot
The histograms show the
spectral distribution of the
training data. You can
determine if signatures are
bimodal (not ideal, maybe
they should be two separate
classes) or overlap.
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Step 4: You are now ready to classify the image into the six remaining vegetation classes.
Open the Supervised classification
tool from the Classifier (main menu).
Brows for the input image.
Brows for the signature editor file.
Name the output file.
Choose maximum likelihood rule.
OK.
ERDAS Imagine will now classify
the image into six vegetation classes
based on the reflectance values and
the maximum likelihood
classification rule.
Your classification will
probably look somewhat
different.
View the classified image! This image can now be
displayed in ArcGIS and converted to a grid if desired.
Assignment: The separability calculation for low sage and mountain big
sage for each band and a short interpretation of the results.
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