Remote Sensing – Classification of HabitatNR505-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 20022) Delineate different vegetation types and test the separability of selected types3) 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-aug2-2002.img:

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).

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Burn

Western juniper

Mountain shrub

Wet meadow

Low sagebrush

Mountain big sagebrush

Zoom in and out

Right-click in the image and select ‘fit image to window’ if you want to zoom to the full extent

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.

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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.

Mountain big sagebrushLow sagebrushJuniper woodlandsWet meadowsMountain shrubRiparian – see arrows

Riparian

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.

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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Meadow is here selected

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) whereμ1 = mean value of the reflectance for vegetation type 1μ2 = 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.

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.

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.

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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.

Step 4: You are now ready to classify the image into the six remaining vegetation classes.

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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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.