Digital Change Detection

Introduction:

Digital change detection is the process of identifying areas that have experienced changes in land use/land cover (LULC) between two time periods. There are two main methods of performing change detection, Write Function Memory Insertion and Post-Classification Change detection.

Methods:

The Write Function Memory Insertion method is an extremely simple, yet effective process. By stacking the NIR bands of two images, it is possible to identify LULC changes that occurred as they appear as a different color than those that remain constant.

Figure 1: The output from Write Function Memory Insertion.
Areas that changed between 1991 and 2011 appear in red.

Post-classification comparison change detection is a slightly more complicated process than Write Function Memory Insertion, as it first requires both images to be classified. Post-classification comparison change detection is done by comparing the number of pixels in a LULC class in each image, and calculating the percentage of change between the two images (Figure 2).

Figure 2: The percent change between 2001 and 2011 in the Milwaukee MSA.

It is also possible to map the changed pixels, so it shows not only which pixels changed, but which classes they changed to and from (Figure 3). By combining this method with the comparison graphing, it would be possible to identify how much of each LULC classes' change was from each other class.

Figure 3: Milwaukee MSA LULC change.

Conclusions:

LULC change is one of the biggest areas of study within remote sensing, and this lab was extremely helpful for understanding the primary methods of analysis within this sub-field of the discipline. By performing post-classification change detection, specifically, the remote sensing expert can add extremely valuable information to satellite imagery. This information is extremely helpful for planning and environmental modeling.