Digital Change Detection

- Introduction -

Digital change detection allows for analysis of biophysical, environmental, cultural, and socioeconomical change across the Earth's surface. By examining and measuring change in LULC over time, humans gain a more complete understanding of how Earth systems and processes function and interact. This knowledge can lead to better land planning and management and more effective environmental monitoring. Important considerations for change detection are an appropriate time period, the temporal, spatial, spectral, and radiometric resolution of each image, and the environmental factors present in the imagery. In this lab exercise, qualitative change detection will be performed on Landsat 7 (ETM+) imagery of western Wisconsin from the years 1991 and 2011 and quantitative digital change detection will be performed using National Land Cover Datasets of the Milwaukee metropolitan statistical area from the years 2001 and 2006.

- Methods -

Qualitative change detection was performed using the Write Function Memory Insertion method in ERDAS IMAGINE. The red band from 2011, NIR band from 1991, and a copy of the 1991 NIR band from the Landsat 7 (ETM+) imagery of western Wisconsin were stacked. By setting the red band to the red color gun and the NIR bands to the blue and green color guns, areas that showed change over the time period were displayed in red (Results - Figure 2). Qualitative visual analysis of LULC change could then be accomplished.



National Land Cover Datasets of the Milwaukee metropolitan statistical area from the Multi-Resolution Land Characteristics Consortium (MRLC) were used to quantify change for each LULC class and then map five specific LULC to-from changes. To quantify LULC change, each dataset was opened in ERDAS IMAGINE and the histogram values for each class were copied from their attribute tables into a Microsoft Excel spreadsheet. A series of calculations was then done to convert the histogram pixel values into area (Ha) values making the data more user friendly. The percent change for each LULC class was then calculated and can be seen in Table 1.

Figure 1: The model used to create the to-from LULC changes

To map the specific LULC to-from changes, a model was made in ERDAS IMAGINE to create five images each showing a different to-from LULC change (Figure 1). The model uses the Wilson-Lula algorithm and begins with both the 2001 and 2006 National Land Cover Dataset rasters. These rasters are then connected to 5 Either-If-Or functions that masks all LULC classes except one desired class. A pair of functions containing the desired masked values for each date of imagery are then connected to a temporary raster file which in turn connects to a binary masking function that masks the values that do not overlap between the two LULC classes. The resulting raster file contains the areas that overlapped between the two LULC classes or in other words, the area that changed from on class to another. The five raster files were then opened in ArcMap and symbolized appropriately.

- Results -



Figure 2: The result of the Write
Function Memory Insertion.

Map 1: The combined result of the desired LULC to-from changes
produced through the model.

 - Discussion -

Urban features are easily distinguishable as showing change when examining the image created through the Write Function Memory Insertion method (Figure 2). The area between the city of Eau Claire and Chippewa Falls shows exceptional change compared to the rest of the image. Major road networks show up bright red in the image which is likely due to new paving or re-surfacing. Some agricultural fields and areas of bare soil show change while others do not. This is likely due to spectral differences created by farmers engaging in various stages of crop rotation and ley farming. Water features showed change throughout the image due to the inevitable variability in how water is distributed on the Earth's surface over time. The Write Function Memory Insertion method allows for a quick qualitative assessment of change between two or more dates of imagery however, provides no quantitative information.

The five LULC to-from changes in Map 1 were chosen based on a hypothetical situation in which the Wisconsin DNR wished to know about LULC changes in the Milwaukee MSA. The to-from changes were: agriculture to urban, wetlands to urban, forest to urban, wetland to agriculture, and agriculture to base soil. Milwaukee County experienced the least amount of these changes. This is because Milwaukee County has more urban and less vegetated land cover in relation to its size than the other counties. Because such an over welling majority of land in Milwaukee county is already urban, little change was depicted. However, in the southern third of the county, below the city of Milwaukee, there are significant sections of agriculture to urban and forest to urban. Overall, agriculture to urban is the most prevalent change throughout the study area.

- Conclusion -

For a quick and simple qualitative change detection assessment of multiple dates of imagery, the Write Function Memory Insertion method is a viable option. If quantitative information is desired, the histogram values for classified LULC images can be compared and by using the Wilson-Lula algorithm, specific to-from LULC changes can be analyzed. Image differencing, not included in this lab exercise, can also be used by comparing pixel values between bands of multi-date imagery. Identifying changes in LULC through these techniques is a preliminary step in further understanding the relation between the Earth and its processes, and human activities.

- Sources -

Earth Resources Observation and Science Center, USGS. Landsat 7 (ETM+).

Multi-resolution Land Characteristics Consortium (MRLC). National Land Cover Datasets (2001, 2006).