Lab 7: Digital Change Detection

Goals

The objective of this lab exercise is to conduct digital change detection. This skill is extremely important and can be applied to a variety of different fields. For example, digital change detection can be used to monitor vegetation health, urban growth, pollution and more. In this particular lab we will learn how to perform a quick qualitative change detection method as well as quantifying post-classification change detection and how to design a model which will map detailed "from-to" changes in the land use/land cover changes over a specific amount of time.

Methods

Qualitative Change Detection

While there are a few different ways to produce a change detection image, one which is relatively simple and quick is qualitative change detection. To do this, I put the near-infrared bands of the two different images from different dates in the red, green and blue color guns in ERDAS Imagine 2013. This will cause the pixels which changed over time to become highlighted in a different color than the rest of the image so that I can draw qualitative conclusions about the changes over time of the study area. An example can be seen in Fig. 1. This area looks at the changes in the features in the Eau Claire and Chippewa Counties from 1991 to 2011. 

(Fig. 1) The regions of the bright red color are where there was change in the features within the Eau Claire and Chippewa counties between the years 1991 to 2011.

Calculating Quantitative Changes in Multidate Classified Images

In this portion of the lab, I assessed the quantitiative changes in the land use/land cover classes in the Milwaukee Metropolitan area from 2001 to 2006. The first step of this process involved bringing both the original image from 2001 and 2006 into ERDAS Imagine (Fig. 2). These images have already been classified which makes things easier.  

(Fig. 2) This image shows LULC classification of the Milwaukee Metropolitan Area in 2001 (left) and 2006 (right).

The next step, however was more complicated. I had to determine the percent change in the land use/land cover (LULC) classes between the two images. To do this I created an excel document which contained the area (in hectares) which was classified as each LULC class. To calculate the area I needed to convert the histogram of the images into square meters, then square meters to hectares. To get the histogram values I looked in the attribute tables of each of the images (Fig. 3) and the same is then done for the 2006 image. Then, I subtracted the 2006 values from the 2001 values and multiplied by 100 to determine the percent change.

(Fig. 3) Attributes of the 2001 image are used to determine the area in hectares of each LULC class within the image.

Developing a "from-to" Change Map of Multidate Images

Producing a map to show the changes of the LULC classes over the study area is a bit more of a complicated process. The "from-to" change map more specifically looks at LULC classes which change from one class to another over the time we are looking at (2001 to 2006). To measure these changes we will be using the Wilson-Lula algorithm. This involves a complex model which can be used in Fig. 4. Particularly in this exercise I focused on the areas which changed from: agriculture to urban/built-up, wetlands to urban/built-up, forest to urban/built-up, wetland to agriculture and agriculture to bare soil. I used conditional formulas like the either-or in the second set of formulas applied to the images. 

(Fig. 4) The model maker which shows the process involved in creating a "from-to" change detection image.

Results

While the qualitative methods of change detection are useful in gaining general information about areas which changed over a period of time, however no quantitative conclusions can be drawn from these types of images. Using a "from-to" change detection model we can draw conclusive data from the output image (as can be seen in Fig. 5). Figure 5 makes it difficult to see the changes in the pixels within the maps of the 4 counties but conclusions can be drawn from this map. 

Sources

The data used in this lab exercise contains images from Earth Resources Observation and Science Center, US Geological Survey and ESRI U.S. Geodatabase

The 2001 and 2006 National Land Cover Datasets were provided by the following: 

Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A., VanDriel, J.N., and Wickham, J. 2007. Completion of the 2001 National Land Cover Database for the Conterminous United States.Photogrammetric Engineering and Remote Sensing, Vol. 73, No. 4, pp 337-341.

Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2011. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864.