Using IDRISI to analyze Landsat 5 data of
Squaw Creek National Wildlife Refuge

Band 4 of Squaw Creek showing features in and around the refuge


Sara Acosta

ES 775 Advanced Image Processing


Image processing of remotely sensed data is a useful means of obtaining information about the Earth’s surface.  The purpose of this project is to demonstrate the capabilities of IDRISI and to discuss how the information obtained from it can be used to further the understanding of an area of interest.  In this case, the area of focus is the Squaw Creek National Wildlife Refuge in Mound City, Missouri.
Squaw Creek National Wildlife Refuge is a protected area within the Missouri River Valley.  The habitat is made up of some of the last remaining of its kind and consists of agricultural fields, bottomland forest, loess bluff hills, wetlands, and wet prairie grasslands.  It sustains a variety of wildlife including threatened and state endangered species such as the eastern massasauga rattlesnake that resides in the wet prairie.  The wildlife here depends on the refuge management to maintain or manipulate the habitat as necessary.  The use of satellite imagery is one way in which more information can be gained about the land and then used in order to improve management practices.
Eastern massasauga rattlesnake

Here, an image from the Landsat 5 satellite from July 02, 1997 is being used.  This satellite carries a Thematic Mapper (TM) sensor system, an optical-mechanic sensor that operates in 7 bands of visible, reflective-infrared, middle-infrared, and thermal infrared regions of the electromagnetic spectrum.  Each of the spectral bands represents a specific wavelength and demonstrates separate characteristics.  The following chart shows each of the bands, their wavelength, and the applications for each of them (Jenson, 2000).

Wavelength (micrometers)
Band 1 
0.45- 0.52
provides increased penetration of water bodies, supports analyses of landuse, soil, and vegetation characteristics
Band 2
0.52- 0.60
corresponds to green reflectance of healthy vegetation
Band 3
0.63- 0.69
important for vegetation discrimination, soil boundary, and geological-boundary delineations
Band 4
Reflective infrared
0.76- 0.90
Responsive to the amount of vegetation biomass present, useful for crop ID and emphasizes soil/crop and land/water contrasts
Band 5
1.55- 1.75
sensitive to the turgidity of water in plants, useful in crop drought studies/ plant vigor investigations, and discriminates between clouds, snow, and ice
Band 6
Thermal infrared
10.4- 12.5
measures amount of infrared radiant flux emitted from surfaces, useful for locating geothermal activity, vegetation classification, vegetation stress analysis and soil moisture studies
Band 7
2.08- 2.35
discriminates geologic rock formations


Obtaining satellite imagery of Squaw Creek

A Landsat satellite image can be obtained from the U. S. Geological Survey (USGS) in a raster format that will work in IDRISI.  Therefore, no conversions are necessary.  The images are organized by path and rows across the Earth’s surface.  A window including only the refuge boundaries can be extracted from the rest of the image.

The Squaw Creek Refuge lies within Path 27 and Row 32 in northwest Missouri.  First, a composite that will show distinct features can be made in order to determine what portion of the image needs to be extracted.  A 345 composite of the refuge works well because roads, pools, fields, and cities all can easily be distinguished.  Then, zoom into the refuge and determine the four corners of the area that needs to be extracted.  The four corners determined here are:

Upper left column 1441
Upper left row 2413
Lower right column 2094
Lower right row 2891
To extract the window, go to Reformat--> Window.  Use the corners determined above and give file names to the new, extracted images.  The new images now isolate the refuge and can be analyzed in a variety of ways using IDRISI.

A 345 composite of Squaw Creek National Wildlife Refuge

extracted from a Landsat 5 satellite image.

Composite Images

Composite images are made when selecting three different bands of the Landsat image.  Each composite image emphasizes different features in the image.  Some will bring out features of vegetation, while others bring out man-made features such as roads or cities, and others may highlight water bodies.  Below are several composites with a description of what is shown in the image.

The standard false color composite is made up of bands 2, 3, and 4.  Vegetation is shown in shades of pink and red.  Darker shades represent thicker, more active vegetation while the lighter shades are generally or and less dense.  This image shows that much of the refuge is covered in vegetation, including most wetland units.  It was taken in July when the pools/wetland units have dried up or drained for the summer.  Notice the darker shade of red that represents the forest compared to the lighter shades of the prairie or wetland units.  The agricultural fields are primarily bare and are shown in shades of blue.
This image is a natural color composite made up of bands 1, 2, and 7.  It shows objects in their natural color and can be helpful in its interpretation.Vegetation is shown in shades of green while fields are in browns.  Roads and cities are shown in white or blue.

Another false-color composite image is made up using bands 3, 4, and 5.  This image seems to best represent areason the refuge that contain water.  Water bodies are shown in dark blues.  Due to the time of year, the areas of water are not extensive, but small patches of water can be seen in some parts on the refuge.
Roads, cities, and bare fields are represented best in this 124 false-color composite image.  In this image these areas include Mound City, highways, and bare fields which are located in the southwest corner.


Another image can be created to analyze the vegetation on the refuge.  NDVI stands for the Normalized Difference Vegetation Index.  This is an index derived from reflectance measurements in the red and infrared portions of the electromagnetic spectrum.  It describes the relative amount of green biomass from one area to the next.  To create an NDVI image, bands 3 (red) and 4 (near-infrared) are used.  First go to the Overlay module to enter in band 4 as the first image and band 3 as the second image.  Then select the overlay option: First – Second / First + Second and use “raw ndvi” as the value units.  The NDVI values range from –0.76 to 0.82 where vegetation is shown in green while other features are shown in yellow, red, and blue (Figure 1).
Raw NDVI values are fractional real numbers ranging from –1.0 which means no vegetation, to +1.0 which refers to the maximum vegetation.  These raw NDVI values are changed to the byte-binary data type by using Scalar to add 1 (Figure 2).  Scalar is then used to multiply the second image (Figure 2) by 125.  This third image (Figure 3) is then used to Convert the image from real-binary to byte-binary with rounding. The final NDVI image (Figure 4) can be viewed with the NDVI palette and then used for vegetation analysis.
                 Figure 1                                                                Figure 2                                                    Figure 3
Figure 4

The final NDVI image shows neutral values (yellows) representing bare ground, lower values (browns) represent bodies of water, and higher values (green) are active vegetation.  This image makes it very clear the amount of vegetation covering the refuge and the lack of water during the summer months.


More vegetation analysis can be done with the TASSCAP (tasselled cap) module.  TASSCAP can be found under Program --> Analysis --> Image Processing.  When using a Landsat TM image, 3 images will be derived.  They include a greenness image, a brightness image, and a moistness image.  The brightness image refers to soil brightness, the greenness (or Green Vegetation Index – GVI) refers to green vegetation cover or vegetation biomass above ground, and the moistness image refers to soil moisture.
One image that shows interesting results is the moistness image.  The wet prairie definitely stands out apart from the surrounding areas of wetlands and forest habitat.  To further look at wet prairie habitat, the ISOCLUST module can be used.  It can be found under Analysis --> Image Processing --> Hard Classifiers.  Using ISOCLUST will separate different features by their spectral reflectance.
The ISOCLUST image combines like spectral signatures together into 16 clusters.  So, similar objects and features are represented by the same color.  In this image the wet prairie is represented in 2 different colors, cluster 4 and cluster 7.  The wet prairie habitat can be further isolated by creating a Boolean Image.

Boolean Image of the Wet Prairie

A Boolean image is an image in which the attribute of any cell is the integer 1 or 0.  Use EDIT to make an attribute file.  Enter in the clusters 4 and 7 to assign them a value of 1 as shown below.  All other features will then be assigned to 0 and will appear blacked out in the image.  Save this as an attribute file to be used in the next step.
4 1
7 1


Now run ASSIGN found under Analysis --> Database Query.  Choose the ISOCLUST image as the feature definition image and choose the attribute file that was just created.  The image now isolates the wet prairie habitat.

The majority of the wet prairie habitat can be seen in the center of the refuge.  A benefit of creating a Boolean image that isolates this habitat is to see how much of the same type of habitat can be found surrounding the refuge as well.

This could be useful information when applied to studying the state endangered eastern massasauga rattlesnake discussed before.  This species primarily uses the wet prairie habitat.  By finding surrounding patches of wet prairie habitat, another population of the snakes could be found living off the refuge boundaries.

Wet Prairie grassland


There are many possibilities to explore satellite imagery using IDRISI software.  From creating composites that best depict the land features to manipulating the imagery to highlight specific habitat, much information can be gained about the Earth.  In the case of the Squaw Creek Refuge, analyzing satellite imagery can greatly aid in management techniques and simply gaining more information about the habitat.


Clark Labs IDRISI Software (2003). (4/7/03).
Region 3, U.S. Fish and Wildlife Service.  Squaw Creek National Wildlife Refuge, URL: (4/7/03).
Friends of Squaw Creek National Wildlife Reufuge (2003).  General Refuge Information, URL: (4/7/03).
Jenson, John R. 2000. Remote Sensing of the Environment An Earth Resource Perspective.  Prentice-Hall, Inc., Upper Saddle River, New Jersey.

This webpage was created to fulfill the requirements for ES 775 Advanced Image Processing at Emporia State University.  Created May 2003.