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Using ENVI for False Color Imaging and Tree Type Detection: A Step-by-Step Guide

This guide provides a detailed walkthrough using ENVI software for processing false color images to detect tree types, specifically coniferous and deciduous trees. It covers loading Landsat 7 ETM+ bands, employing false color composition with RGB selections, and enhancing image quality through pan-merging and image sharpening techniques. Additionally, the guide outlines how to overlay shape files for supervised classification, allowing for effective analysis and identification of tree boundaries.

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Using ENVI for False Color Imaging and Tree Type Detection: A Step-by-Step Guide

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  1. ENVI False Color Imagesfor tree type detection False Color for conifer and deciduous identification Boundary Shape Files

  2. To begin processing data open ENVI:

  3. To Load L7 ETM+ Bands • Go to File • Open External File • Landsat • GEOTiff • Select the bands • False Color: 80, 40, 30 & 20 • A widget should open with the bands specified

  4. Selection of bands • Select RGB on Widget (Available Bands List box)

  5. Selection of Bands • Select 40, 30, 20 by clicking on Band1 and Click Load RGB

  6. Image Output

  7. Darker Reds = ConifersLighter Reds = Deciduous

  8. Pan-Merging to Sharpen Image • Locate Transform on ENVI 4.3 Toolbar • Click on Transform and navigate to Image Sharpening • Move Cursor to Image Sharpening • Click on HSV • A Widget will open: • Select Display #1 • Click OK

  9. A New Widget will open:

  10. Select Band 80 and click OK

  11. This Widget will open:

  12. Leave default settings and choose file output:

  13. Output file Widget:

  14. Click OK

  15. The pan-merged bands will be loaded into the widget, click Load RGB

  16. Pan-merged Image

  17. Adding Shape Files; Click “Overlay, Vectors”

  18. And this Widget will appear

  19. Click File, Open Vector File

  20. Navigate to the Menominee Folder and chose menrbda4_.evf and open

  21. The vector file will open and load automatically

  22. Supervised Classification

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