Supervised Classification of Landsat TM Imagery of the Yolo Bypass Diana Craft American River College Geography 342, Fall 2010

Project Goals Perform supervised classification on Landsat TM imagery using Spatial Analyst in ArcGIS Obtain images showing seasonal variations in the Yolo Bypass Show that selected fields in the Yolo Bypass have different uses throughout the year, supporting agriculture, wildlife, and flood control

The Yolo Bypass 59,000 acre engineered flood bypass Used for agriculture, wildlife habitat, flood protection Diversion channel for Sacramento River; part of the Sacramento River Flood Control Project Fed by Cache and Putah Creeks, Colusa Basin Drain, and Willow Slough Crossed by Interstates 5 and 80 and railroads

Step 1: Acquire Images

Image Source USGS’s Global Visualization Viewer (GLOVIS) Several years’ worth of monthly satellite images Download issues: CTRL for Pop-ups; opening .tar.gz files (use Winzip, not 7-zip); some images have to be ordered (delay); cloudiness may limit choices

GLOVIS Images: Easy to view month-by-month; seasonal trends emerge Screenshots from GLOVIS lined up to compare 2009 & 2010 month-to-month

Step 2: Preprocess Images

Composite Bands Problem: Unzipped Landsat image is set of 6 single-band images that each display as B/W Use ArcGIS Toolbox > Data Management > Raster > Raster Processing > Composite Bands This creates new single multiple-band raster dataset from multiple single-band raster datasets

Clip & Symbolize Problem: Landsat TM image is huge; want to zero in on area of interest Use ArcGIS Toolbox > Data Management > Raster > Raster Processing > Clip This clips the image to extent of given coordinates Next, go to Symbolize in Layer Properties to assign image bands to color channels: R=4, G=5, B=2

Step 3: Select Training Sites

Attribute Table & Land Cover Types

Select Training Sites

Step 4: Perform Classification

Classification Use Spatial Analyst in ArcGIS Run Create Signatures Run Dendrogram routine Run Maximum Likelihood Classification

Evaluate Results

Step 5: Refine and Repeat

Refine & Re-evalulate

Make Changes to Attribute Table

Repeat Classification & Evaluate

Conclusion and Learning Outcomes

What went wrong: Supervised Classification Using multiple images was too ambitious for the allotted time; zeroed in on one image only Preprocessing took too much time (batch processing might have helped) Image interpretation was difficult without actual field data (or experience) Initial bad result: Flawed training site selection, or problem with Max Likelihood Classification?

Lessons Learned: Project Design Diagram the whole process first Do small sample tests (rather than run multiple images down a dead end) Keep a log to document steps including those that don’t work (for future reference) Allow enough time to work it out when things go awry Refine and repeat as needed