Landisch and Zahler
Quantifying Land Cover Change
And Urban Development
In Rochester, MN
FR 3262
Patrick Landisch and Stephanie Zahler
5/10/13
1
Landisch and Zahler
2
Introduction
Humans have had an incredible impact on the environment over the course of our
existence. Over recent years, our population has continued to grow exponentially and this has
caused major changes in land use and land cover. With more than 80% of the world’s
population living in urban areas, studying the growth and development of cities is critical in
assessing land cover change (Veldkamp and Verburg 2004). It is essential to study the effects of
urbanization and understand the various implications a changing land cover can have on the
environment such as the loss of forest and agricultural fields, increased runoff due to
impervious surfaces, CO2 emissions from urban areas etc. There are many more environmental
concerns out there and awareness and action are necessary, but so is urban development.
People need a place to live, but we need to consider Earth, our true home, as we change it in
developing more living space.
For our project we decided to monitor urban development in Olmsted County. The city
of Rochester is located in this county and is the third largest city in Minnesota. From 1980 to
2000 the city saw a rise in population from 57,890 to 70,745 (U.S. Census Bureau). In order to
accurately study the land cover change in this area, we used data downloaded from the
internet and the ERDAS Imagine software program to assess urban development from 1985 to
2001.
Landisch and Zahler
3
Methodology
-Defining the study areaOlmsted County has a total of 654.50 square miles of land and water. In order to do any
analysis of Rochester, we first needed to define an exact location as our area of interest. After
examining the Public Land Survey (PLS) township boundaries, we concluded that a total of four
townships will be sufficient enough for the scope of this project: Cascade, Haverhill, Marion,
and Rochester. This amounted to a total of 106.7 square miles. These four counties encompass
the city of Rochester as well as the surrounding area that may or not be subjected to urban
development and land use change.
-Data acquisitionTo obtain the necessary data for this project, we used two sites to gather the
preliminary resources. First, we accessed the MN Data Deli and downloaded the PLS township
boundary shapefile. Second, through the United States Geologic Survey Glovis website, we
were able to select Landsat 5 satellite images of our study area. We selected images from the
spring of 1985 and 2001. However, we were not able to find images from the same month so
we settled for April and May. To eliminate cloud cover, we used a 10% cloud cover filter on
both of the images in hopes that it would negate any obstruction that clouds may present. Both
images were downloaded and imported to ERDAS Imagine for further image processing
-Image PreprocessingIn order to successfully import Landsat images, it is necessary to use the Layer Stack tool
in ERDAS. This step requires the user to stack the .tif files on top of each other, creating a final
Landisch and Zahler
4
output image. Once this was successfully done the next step was to georeference each image
so that they were both projected in the same reference system. We used the UTM NAD83 Zone
15N projection and datum for our images.
Meanwhile, we imported the PLS township boundary data into ArcMap to define our
study area. By opening the attribute table and selecting the appropriate townships, we
highlighted our study area. In order to use this file in ERDAS, we exported the selected features
to a USB flash drive as a shapefile and were then able to import it to ERDAS.
Within the same 2D Viewer of ERDAS Image, we opened both our Landsat satellite
raster layer and our four township vector layer. To cut out our study area from the satellite
images, we needed to perform a simple clip operation. The first step in doing this was to open a
new Area of Interest file. By clicking on each township and copying it, we were able to paste it
to our Area of Interest file. The clip tool allowed us to essentially cut out our study area from
the Landsat image. After doing this for both of our satellite images, we were ready for the
classification step.
-Image ClassificationWe chose to perform a supervised classification on both of our images. It was agreed
upon that through a supervised classification, we would be able to more accurately select our
training areas. We decided that a total of 5 classes would be used for this classification: Urban,
Water, Bare Soil/Vegetation, Green Vegetation, and Forest. The choice to separate bare
soil/vegetation and green vegetation was in hopes that it would make the identification,
classification and quantification easier. We selected 10 to 20 training areas for each class. After
Landisch and Zahler
5
each training area was created, it would be added to the Signature Editor tool. Finally, with all
the training areas added we then merged them for each defined class. After which we were
able to run the classification. The distinction between agriculture and forest was fairly easy to
make in that forested areas had a courser texture, irregular shape, and a darker shade of red
than the brighter color and uniform shape of agricultural fields. Urban areas were easy to
detect, however some areas appeared to have mixed pixels, which we believed were suburban
areas.
-Change DetectionAfter our classifications were complete, it was necessary to quantify the amount of
change that happened from 1985 to 2001. In order to observe this change quantitatively, we
performed a thematic change detection on both the images. With the thematic change we
were able to also create a Summary Report. This gave us the amount of land changed from
each zone throughout the 16 year period and to what class it was converted to. This is the
advantage of a thematic change detection, it gives the user “from-to” information about the
two images. With this information we were able to quantify how much land from water,
vegetation, and forest was converted to urban land over the span of 16 years.
-Accuracy AssessmentWe performed an accuracy assessment on the 2001 image. To do so, we downloaded
NAIP imagery of the state of Minnesota from the USDA NRCS website. We applied the same
image processing steps to slim down the image to just our study area. After doing so, this image
was used as our reference data for the accuracy assessment. However, it is important to note
Landisch and Zahler
6
that the earliest land cover image provided from the NAIP imagery set was 2006. This might
cause some error in accuracy, but it was the best reference data we were able to find. A
stratified random sample method was used with 105 total points created and a minimum of 15
for each class. The error matrix shows our 2001 image had an accuracy of 71% and a Kappa
statistic of 0.64. These numbers are fairly close, so we feel confident in our assessment. We
believe the majority of error is due to our attempt to distinguish between green vegetation and
bare soil.
Results (all information can be found in the Appendix)
After the completion of all these steps it is clear that there has been an increase in
urban development. With the increase in urban space comes a decrease in land cover and land
use for forests and agriculture. Almost 24% of the classified vegetation was converted to urban
land. This amounted to a total of 3,685.58 acres converted from agricultural to urban
development. As for forestland converted to urban, 15% had been converted to urban which
amounted to 4,829.1 acres of land. As one can see from the images in our Appendix, the urban
growth is most prominent in the northwest and south central region of images. The central area
of Rochester also has a clear rise in urban development, most likely due to the continual
development of urban infrastructure such as commercial buildings, new roads, housing etc.
Discussion
It is comes to no surprise that the city of Rochester has experienced an increase in urban
development. This may be directly correlated to the rise in population from 1980 to 2000. The
population of 70,745 in 2000 in comparison to 57,890 in 1980 explains the significant increase
Landisch and Zahler
7
in urban land cover and the need to convert forest and agricultural land to urban areas. One of
the drawbacks to our project was the fact that we did not use Landsat 7 information from 2012.
Due to the scan lines in the Landsat 7 imagery, it would have been a challenging task to
accurately and efficiently classify and detect land cover change. However, this project would
have been more relevant. Another thing we would have done differently would be to classify
agriculture as one class instead of bare soil/vegetation and green vegetation. Another setback
to our project was the absence of a reference map for the 1985 classification image. This
prevented us from successfully performing an accuracy assessment on the 1985 image.
Land cover change poses a variety of environmental issues that require attention and
mitigation plans. An increase in impervious surfaces due to urban development may lead to an
increase in soil erosion, water pollution, C02 emissions etc. Although our objective was not to
study the environmental impacts of urban development, studying and knowing which areas
were converted to urban surfaces is important in identifying starting points for mitigation plans.
Overall, we feel confident in the work we accomplished for this project. In retrospect,
there are a couple of things we would have differently. However, for the first time using ERDAS
Imagine to complete a project with satellite imagery, we believe that we have provided a
detailed procedure for studying land cover and land use change. This was a learning experience
working with ERDAS, and upon completion we feel that we have stronger and deeper
understanding of the process of image classification and applying satellite imagery to real life
situations
Appendix
Landisch and Zahler
Supervised Classification of 1985 Image
8
Landisch and Zahler
Supervised Classification of 2001 Image
9
Landisch and Zahler 10
2006 NAIP Reference Image of MN Land Cover
Landisch and Zahler 11
Percent Change Chart
Percent Change to Urban Area 19852001
Percent Change
Acres
Bare Agriculture to Urban
0.1367
3119.48
Water to Urban
0.1998
37.42
Green Vegetation to Urban
0.1085
566.1
Forest to Urban
0.1513
4829.1
Urban to Urban (Acres in 1985)
Total
15503
0.5963
24055.1
Landisch and Zahler 12
Accuracy Assessment for 2001 Supervised Classification
Landisch and Zahler 13
References
Veldkamp, A., and P. H. Verburg. "Modelling land use change and environmental impact." Journal of
Environmental Management 72.1 (2004): 1-3.
http://www.census.gov/
FR 3262 Lab Manual 7 Image Operations and Clip
FR 3262 Lab Manual 11 & 11a Supervised Classification and Accuracy Assessment
FR 3262 Lab Manual 12 Change Detection