Lab 7: Digital Change Detection

Background and Goal

The goal of this lab is to develop the knowledge and skills necessary to compute changes that occur over time using land use/land cover images. This digital change detection is important because it allows the monitoring of environmental and socioeconomic transitions that happen through time. In this lab we learn a couple techniques, from quick qualitative analysis to a statistical analysis and even a model that displays specific from-to changes between the different classes over time.

Methods

To do a quick qualitative change detection, we used a Write Function Memory Insertion technique. The study area was the surrounding counties of Eau Claire. For this change detection, we used band 3 from a 2011 image, and two copies of band 4 from a 1991 image. These images were then stacked using the Layer Stacking function. This new image was opened, and the layers were then adjusted so that the band 3 image would be viewed through the red color gun, while the band 4 images were viewed through the green and blue color guns. Since band 4 is black and white, while band 3 is red, this highlights any changes that occurred between 1991 and 2011.

Next we learned post-classification comparison change detection. For this exercise, we used images from 2001 and 2006 of the Milwaukee Metropolitan Statistical Area (MSA). This change detection technique calculates quantitative changes and is used to create a model that shows specific class changes between the two years. To quantify the land change, we examined the histogram values for each class in both of the images. In an Excel spreadsheet the different histogram values for all classes in both images were recorded and converted to hectares. The histogram shows how many pixels are within each class. For this specific sensor the spatial resolution is 30 meters, so one square pixel is 900 square meters. With this knowledge we were able to convert the histogram values to square meters. We then converted the values to hectares by multiplying the square meters by 0.0001. Figure 1 shows the class area data for both the years. A table was then created to calculate the percent change of square hectares for each class between 2001 and 2006.

Figure 1: The histogram data and conversion process for the 2001 and 2006 images.

To calculate specific from-to change between classes, we used a quite sophisticated model in ERDAS’ Model Maker. This model consisted of two input rasters, five pairs of functions, five pairs of temporary rasters, five more functions, and finally five output rasters. These were then connected as shown in Figure 2. The input rasters are the images from 2001 and 2006. For this exercise we are trying to reveal change from Agriculture to Urban, Wetlands to Urban, Forest to Urban, Wetland to Agriculture, and Agriculture to Bare Soil. These classes were extracted into the temporary rasters (in the respective order), using a Either IF OR function. This function assigns a 1 IF a pixel is the desired class, OR a 0 if a pixel is not the desired class, creating a binary model of the desired classification. In the next function, these binary models are overlapped using a Bitwise function. Areas that overlap are kept, while the rest are discarded (creating another binary model). This effectively reveals the specific areas of change between the two time periods, between the desired classes.

Figure 2: The complex model that was used for the from-to digital change detection.

Results

Figure 3 shows the final result for the Write Function Memory. This technique is quick an easy, thought it doesn’t create very concrete or specific results. All that you can do with the image is a visual examination to try and reveal areas of change. Figure 4 shows the final table for the quantitative analysis of the Milwaukee MSA. According to this table, Bare Soil shows the greatest change with a 23% increase in the five year period. Figure 5 Shows the final map of the from-to digital change detection method. From this model it is easy to see where change occurred in the study area. Waukesha County went through the most change, while Milwaukee County went through the least change.

Figure 3: The result of the Write Function Memory digital change detection technique. areas that are a light pink color show change between 1991 and 2011, while the red and white areas show no change.

Figure 4: The results of the quantitative analysis of change between 2001 and 2006 in the Milwaukee MSA. Bare Soil changed the most with an increase by 23.6%, while Wetlands changed the least with a decrease of 0.7%.

Figure 5: The results of the from-to digital change detection technique. Waukesha County saw the most change, while Milwaukee County saw the least.

Sources

Earth Resources Observation and Science Center. (1991). [Landsat image of counties surrounding Eau Claire]. United States Geological Survey. Provided by Cyril Wilson.

Earth Resources Observation and Science Center. (2011). [Landsat image of counties surrounding Eau Claire]. United States Geological Survey. Provided by Cyril Wilson.

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.

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.