Lab 6 - Digital Change Detection

Goal and Background

The goal of this lab exercise was to allow us to develop an understanding of the methods to evaluate and measure LULC changes that occur over time.  In this lab, we performed quick, qualitative measurements of LULC change, quantified the change detection, and developed a model that was used to map from-to changes in LULC over time.

Methods

Part 1: Change detection using Write Function Memory Insertion

For part 1 of this lab, we performed a write memory memory insertion to quickly be able to view pixels that changed from one image to another.  To do this, the provided band 3 image and the two provided band 4 images were layer stacked and the output image was saved.  Next, under the Multispectral tab, the Set Layer Combinations window was opened so that we could specify which band was to be inserted into which color gun.  The band 3 red image was inserted into the red band color gun and then the two band 4 NIR band images were inserted into the green and blue bands.  The output image of this was an image that showed which pixels experienced change over time as represented by the color pink.

Part 2: Post-classification comparison change detection

For part 2 of this lab, we first began by calculating the quantitative change that occurred between the two provided LULC classification images of the Milwaukee WI area. To do this, the Raster Attribute Editor was opened for both images so that the data in the table could be copied to an Excel sheet for calculations. The data that was copied over was the class names column and the histogram values column.  Using this data, the next step was to convert the histogram values to hectares for each of the LULC classes and then calculate the percent increase or decrease for each of the LULC classes.  Once this was done, a table was created to show how much each LULC class changed over the time period between the images capture dates.  The next portion of this lab was devoted to creating a model that would take these same LULC classification images of the Milwaukee area and output images that showed LULC change-to over time.  To accomplish this, the Wilson-Lula Algorithm was used.  The model that was developed has an input for each of the LULC images, a pair of functions for each of the change-to possibilities, and a second function for each change-to which combines the outputs from the first function to create the final image showing which pixels experienced change from one LULC class to another LULC class.  For this example, the change-to options that were shown were Agriculture to urban/built-up, Wetlands to urban/built-up, Forest to urban/built-up, Wetland to agriculture, and Agriculture to bare soil. In the first set of functions, the first function was changed from analysis to conditional and then set to EITHER IF OR.  For example, the first function, which measured Agriculture to urban/built-up was EITHER 1 IF ( $n1_milwauke_2001==7 ) OR 0 OTHERWISE with the image being set equal to 7 because this is the value that corresponds to agriculture.  In the same set of functions, the second function was set to EITHER 1 IF ($n2_milwauke_2011==3 ) OR 0 OTHERWISE with the image set equal to 3 because this is the value of urban/built-up.  A temporary raster was created between the two layers of functions and the second group of functions was set to bitwise and the '&' symbol was used to add the two separate values from the previous functions together.  This final function output an image that showed which pixels went from one LULC class to another.  These various change-to images were then brought into Arcmap to create a map showing all the changes experienced by areas in the Milwaukee area between 2001 and 2011.

Results

Figure 1. Write Function Memory Insertion

Figure 2. Table showing quantitative change

Figure 3. Example of Model using Wilson-Lula algorithm to develop from-to imagery

Figure 4. Final output map showing from-to change in Milwaukee area

Sources

 Landsat satellite image is from Earth Resources Observation and Science Center, United States Geological Survey. National Land Cover Dataset is from Multi-Resolution Land Characteristics Consortium (MRLC). Appropriate citation for the 2001 and 2011 National Land Cover Dataset can be found at http://www.mrlc.gov/nlcd2001.php and http://www.mrlc.gov/nlcd2011.php respectively. Milwaukee shapefile is from ESRI U.S geodatabase.