ES 775 Lab 3

IKONOS--KANSAS

Introduction

Ikonos is a commercial earth-resources satellite launched in 1999. It provides high-resolution multispectral imagery. Individual blue, green, red, and near-infrared bands have 4-meter resolution (linear cell size), and the panchromatic band (green+red+near-infrared) has 1-meter cell size. This spatial resolution rivals conventional aerial photography. Imagery may be ordered from a global archive, or users may specify acquisition for certain dates, locations and cloud-cover conditions.

Ikonos from GeoEye.

For this exercise, you will work with multispectral data for a portion of Cheyenne Bottoms in central Kansas--see title image. Cheyenne Bottoms is the premier wetland of Kansas; it is considered to be the most important wetland site for shorebird migration in the central United States. Cheyenne Bottoms occupies approximately 64 square miles (166 km²) in Barton County near Great Bend and Hoisington. It is managed in part by the Kansas Department of Wildlife and Parks (CBWA) and partly by the Nature Conservancy. The dataset for this exercise covers Nature Conservancy land in the northwestern portion of the bottoms.

Aerial photographs of Cheyenne Bottoms.

Download Idrisi Andes files for Ikonos multispectral bands: CB_BLUE, CB_GREEN, CB_RED AND CB_NIR. This dataset was acquired in July 2003 during a drought period. The imagery has been resampled into UTM zone 14 grid system. Note: the RST files are ~6 MB in size each; the panchromatic image is 16 times larger, much too big to supply for this exercise.

Exercise

Open Idrisi Explorer and examine the metadata for one of the images. Check information about the raster image.

1. Provide the following spatial details about the raster grid: number of rows and columns, linear size (resolution) for each cell, and total ground area (in kmē).

Now create two standard color composites: natural color (blue, green, red) and false-color (green, red, near-infrared). Use the color composite function, as you have done in previous exercises. Select the appropriate bands, and provide a suitable output file name and image title. Your resulting images should appear similar to the following examples.

Note: The output files are large, three times the file size of input files.
Make sure you have sufficient disk space available.

Natural-color composite and false-color composite.
Annotation of geographic features (left).

Describe the appearance of the following features in each of the composites, paying attention to color, shadows, and patterns. Note: you need to zoom and pan, in order to display full resolution for selected features.

2. City of Hoisington.

3. Sewage treatment plant southeast of Hoisington.

4. Marshes in Nature Conservancy land.

5. State wildlife area (CBWA).

6. On this basis what are the smallest features you are able to identify visually, and how are you able to recognize these features?

Much of this scene represents wetland conditions, for which the distributions of active vegetation and water bodies are key environmental indicators. In order to highlight these elements, use the red and near-infrared bands to create a vegetation index for the scene. Open the VEGINDEX module (under Image Processing, Transformation). Select NDVI (normalized difference vegetation index), enter the appropriate bands, and provide a suitable output image name. Click OK, and then examine the resulting image and its numerical values.

7. What is the range of numerical values for NDVI? What are the data and file types? How large is the NDVI image (in bytes) compared to the input files?

NDVI files consist of real numbers, which require much more storage space than do byte-binary numbers. It is customary to convert raw NDVI values into a byte-binary scale, as follows.

Do this now, employing a series of temporary files, which you can delete later. Display your final image using the "ndvi256" palette. Examine the image and look at values for different types of land cover.

8. Give NDVI values typically associated with the following features.

From your analysis, it should be clear that water bodies have NDVI values of zero. RECLASS (2nd icon from right) the NDVI image so that water bodies have a new value = 1, and all other features have a value = 2. Name the output file "water" and display it with the "qualcoolw4" palette.

9. How effective was this technique for separating water bodies from all other types of land cover? Explain you answer.

10. What is the surface area (in kmē) of the water class (1) in this image? What percentage of the total area does this represent?

Next conduct an unsupervised cluster analysis, as you did in exercise 1. Given the file sizes, this may take a few moments to execute.

Result of the cluster analysis of Ikonos dataset based on all four bands. Eight clusters are indicated.

11. To the best of your ability, identify the type of land cover represented by each cluster.

As your final task, prepare a false-color composite based on a combination of bands, including the NDVI values as one component. Prepare a composition that includes suitable title, subtitle, scale bar and north arrow. Name your composition CB_BEST and turn in an image file with the exercise.

12. Explain the band combination and color assignments you employed to make this composite image. How do various types of land cover appear on this image?

Turn in:

Return to course schedule.
ES 775 © J.S. Aber (2007).