FR 3262 - Final Project
Land cover change detection in the Twin Cities Metropolitan Area by Watershed, 1984 – 2009
Group members:
Alex Steele
Tobias Fimpel
1
Objectives
The aim of this project is to identify and quantify the land cover changes that have taken
place over the past 25 years within the eight primary watersheds that the Twin Cities
metropolitan area is part of. The rate of urban development in each watershed will be of
particular interest. Urban development can have severe negative impacts on ecosystems by
changing surface characteristics, which in turn affect the local water budget. When croplands or
undeveloped areas are transformed into urban or suburban landscapes, the ability of fallen
precipitation to permeate the surface and recharge soil moisture oftentimes decreases.
Consequently the amount of surface runoff increases. When undeveloped lands are transformed
into croplands, changes in the amounts of chemicals and sediments impact surface water
conditions. Vegetative and hydrologic systems are thus directly affected by such land cover
changes. A wealth of information about land cover conditions can be derived from remotely
sensed data.
Study Area and Data
The study area is composed of seven adjoining counties located in south-eastern
Minnesota: Anoka County (excluding a small portion in the northern part), Washington County,
Hennepin County, Ramsey County, Carver County, Scott County, and Dakota County. In total,
the study area covers approximately 3000 square miles and is home to a population of nearly
three million people.
Eight primary watersheds are part of the study area. These range from approximately 46
square miles to 1011 square miles in size. To delineate watershed boundaries we obtained the
shapefile “Watersheds in the Twin Cities Metropolitan Area - DNR Level 08 Catchments” from
the Metropolitan Council.
2
Land cover information was derived from two Landsat images, one taken on 08/15/1984,
and one taken on 07/25/2009. Both are Landsat scenes located at Path 37, Row 29, taken by a
Landsat TM sensor. The images are made up of a total of six bands with a spatial resolution of
30 meters and one band with a spatial resolution of 120 meters. The spectral resolution is as
follows: Band 1 0.45μm-0.52μm, band 2 0.52μm-0.60μm, band 3 0.63μm-0.69μm, band 4
0.76μm-0.90μm, band 5 1.55μm-1.75μm, band 6 10.40μm-12.50μm, band 7 2.08μm-2.35μm.
The image dating from 2009 we obtained from the United States Geological Survey’s website,
the image dating from 1984 was provided to us by the course instructor and its bands were
already stacked.
Procedures
The software used to perform the following procedures was ERDAS Imagine 2010.
Before beginning the image classification process, we created a layer stack for the later image
and visually inspected the Landsat scenes. Using the “Layer Selection and Stacking” function we
stacked the .TIF Image files we obtained from the United States Geological Survey’s website,
thereby creating an ERDAS .img file. After this step, the image dating from 8/15/1984 as well as
the image dating from 07/25/2009 were processed very similarly. Close visual inspection of the
two scenes showed no apparent radiometric or geometric errors, so that we proceeded to the
image classification process.
For the purpose of this study, we desired a classification scheme with the following four
informational classes: water, undeveloped land, agriculture, and urban land. Water was to
include wetlands and areas of open surface waters such as lakes and rivers. Undeveloped land
was to include forests, grasslands, and brush. The agricultural class was to include areas used for
growing crops. Areas occupied by streets and buildings made up the urban class.
3
After several failed attempts to devise an unsupervised pixel-based classification scheme
that produces these four informational classes we decided to perform a supervised pixel-based
classification on both images instead.
Using Erads Imagine’s signature editor we created a signature file for each image.
Because areas of barren fields showed fundamentally different radiometric properties than fields
where crops were being grown, we created two spectral classes representing agriculture which
we later merged. By creating two spectral classes, one for cultivated fields and one for barren
fields, we avoided having one single spectral class representing agriculture with a very large
variance which in turn may have led to the misclassification of grasslands as agricultural
areas.
In order to minimize negative effects of differing atmospheric conditions possibly present
in different parts of each Landsat scene, we delineated all training sites (AOIs) within the extent
of our study area in the northeastern portion of the two scenes. For the image dating from 1984
we delineated 20 AOIs for water, 27 AOIs for undeveloped lands, 15 AOIs for barren
agricultural land, 30 AOIs for cultivated agricultural lands, and 27 AOIs for urban areas. We
then merged signatures representing the same class. The resulting merged signature classes
contained the following number of pixel: 38934 pixel for water, 14173 pixel for undeveloped
lands, 2830 pixel for barren agricultural lands, 13349 pixel for cultivated agricultural lands, and
11651 pixel for urban areas. Next we ran the classification using the Maximum Likelihood
classification algorithm and, following this, merged the two resulting informational classes
representing agriculture using the thematic recode function. The image dating from 2009 was
processed similarly.
4
Next we clipped the images to the study area’s extent via Erdas Imagine’s “Subset and
Chip” operation. To do this we converted the shapefile containing the watershed boundaries into
an Erdas Imagine Area of Interest file (.aoi) and used this file to define the extent of the “Subset
and Chip” operation’s output.
Before proceeding with the change detection step, we assessed the accuracy of each
classified image subset. For the subset image dating from 1984 we used a stratified random
sample of 120 points, weighted by area, with a minimum of 15 points per class. As reference
data we used the same Landsat image that we used for the classifications. The accuracy
assessment of the image dating from 1984 produced the following results (fig 1):
Class
Reference
Classified
Number
Producers
Users
Totals
Totals
Correct
Accuracy
Accuracy
----------
----------
-------
---------
-----
Urban
25
22
20
80.00%
90.91%
Ag.
27
35
16
59.26%
45.71%
Undev.
50
48
35
70.00%
72.92%
Water
18
15
15
83.33%
100%
Totals
120
120
86
Name
----------
Fig. 1: Accuracy Totals for Subset Image 1984
The overall classification accuracy was 71.67%, the overall Kappa statistic 0.6014.
For the accuracy assessment of the subset image dating from 2009 our sample was made
up of 20 randomly chosen points for each of the four classes. Also here, the same Landsat image
that we used for the classification was used as reference data. The accuracy assessment of the
image dating from 2009 produced the following results (fig 2):
5
Class
Reference
Classified
Number
Producers
Users
Totals
Totals
Correct
Accuracy
Accuracy
----------
----------
-------
---------
-----
Undev
25
20
18
72.00%
90.00%
Urban
20
20
17
85.00%
85.00%
Ag
13
20
12
92.31%
60.00%
Water
21
20
20
95.24%
100.00%
Totals
80
80
67
Name
----------
Fig. 2: Accuracy Totals for Subset Image 2009
The overall classification accuracy was 83.75%, the overall Kappa statistic 0.7842.
When both subset images were overlaid in one Erdas Imagine viewer, it became apparent
that although the pixel boundaries of both images were geometrically identical, the Landsat
scene dating from 2009 extended slightly further towards northwest than the one dating from
1984. This difference was noticeable because both Landsat scenes did not include a small part in
the northern portion of Anoka County, which was part of the Area of Interest file delineating the
watershed boundaries. To remedy this we used the “Mask” utility to crop the extent of the 2009
subset image to the very same extent as that of the 1985 subset image.
To detect changes in the land cover between the two dates we used the “Matrix Union”
utility to create a file containing a matrix of all “from-to” class change possibilities. This changemap provided land cover change information for the entire study area summarized by zone.
Overlaying the .aoi file containing watershed boundaries that was created in an earlier
step allowed us to generate eight image files, one for each primary watershed, via the “Subset
and Chip” utility. The attribute tables of these eight image files contained “from-to” land cover
change information pertaining to the respective individual watersheds. The values contained in
6
these attribute tables we then used in ArcGIS, Adobe Illustrator, and Microsoft Office Excel to
generate maps, tables, and graphs presented in the following section.
Results
We were able to accomplish our objective, which was to identify and quantify the land
cover changes that have taken place over the past 25 years within the eight primary watersheds
of the Twin Cities metropolitan area. Tables about detailed land cover change information for
each watershed can be found in the appendices. We found that the overall pattern of land cover
change has been similar for all eight primary watersheds (fig 3).
Fig. 3: Land Cover Change by Primary Watershed in Hectare, 1984-2009
Between 1984 and 2009 agricultural lands have largely been either converted to urban
areas or have remained agricultural, all watersheds show very little conversion of agricultural
lands into undeveloped areas. Undeveloped lands show a tendency of having been changed to
agricultural and urban areas, with the exception of two watersheds, namely St. Croix-Stillwater
7
and Rum River, where more than half of the undeveloped areas from 1984 remained
undeveloped in 2009. Urban areas have mostly remained Urban, but a significant amount of
change from Urban to Agriculture was detected, too. Areas occupied by Water in 1984 largely
remained Water in 2009 but some change from Water to Undeveloped and from Water to Urban
was detected, too.
As illustrated by the following maps, the two watersheds “North Fork-Crow River” and
“Mississippi River Rush-Vermillion” saw the highest rates of urbanization at the expense of
both undeveloped lands and agricultural lands. About 16% of the area contained within each of
these watersheds has undergone a change from Agriculture to Urban, and about 20% of the area
contained within each of these two watersheds has undergone a change from Undeveloped to
Urban (fig 4, fig 5).
Fig. 4: Agriculture to Urban by Watershed, 1984 - 2009
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Fig. 5: Undeveloped to Urban by Watershed, 1984 - 2009
Since the overall accuracies of both classified subset images were moderately high (73%
and 84%) our results are most likely reflective of the general trends and their magnitudes within
individual watersheds. The values could be used to inform decisions of public planners and
natural resource managers but are not sufficiently accurate to be used as data for further
scientific studies, such as for example statistical analyses of land cover change effects on water
quality.
Discussion
To construe more definite conclusions from the results of this project, a more
comprehensive accuracy assessment would be necessary. The accuracy assessment that was
carried out has several limitations. Firstly, the number of points sampled was insufficient.
Secondly, reference data that is better interpretable than Landsat scenes should be used instead.
9
Thirdly, our samples used were biased in that not every pixel had the same chance of being
selected.
The performance of the classification process could be greatly enhanced by the inclusion
of additional data. Multi seasonal Landsat images would help to differentiate undeveloped lands
from croplands that are tilled during spring or fall. Furthermore, it would accentuate differences
in the radiometric responses of urban areas and agricultural lands. Including multispectral data
from early spring or late fall would most likely have also been beneficial for discerning
undeveloped lands from wetland areas.
Considering the very simple classification system made
up of only four classes, the acquisition of costly high-resolution imagery would certainly not be
justified.
Appendices
From-To Change
Area (Hectacres)
Percent Change
Ag to Ag
107498
14.06525263
Ag to Undev
23728.5
3.104684245
Ag to Urban
109680
14.35074986
Ag to Water
4619.52
0.604427206
Undev to Ag
166119
21.73534114
Undev to Undev
73038.9
9.556555287
Undev to Urban
128269
16.78297168
Undev to Water
8607.78
1.126259096
Urban to Ag
14378.2
1.881272353
Urban to Undev
2544.57
0.332936612
Urban to Urban
90376.5
11.82504144
Urban to Water
3196.89
0.418287461
Water to Ag
3762.81
0.492333562
Water to Undev
4485.15
0.586845968
Water to Urban
6627.6
0.867168397
Water to Water
17348.2
2.269873074
Total
764280.62
100
Fig. 6: Land Cover Change 1984-2009, Entire Study Area
10
From-To Change
Area (Hectacres)
Percent Change
Ag to Ag
11598.8
29.47531809
Ag to Undev
651.24
1.654956216
Ag to Urban
4200.93
10.67556541
Ag to Water
98.64
0.250667774
Undev to Ag
15465.2
39.30076296
Undev to Undev
1312.2
3.334613271
Undev to Urban
5351.13
13.59849803
Undev to Water
84.42
0.214531361
Urban to Ag
204.03
0.518488908
Urban to Undev
22.41
0.056949157
Urban to Urban
139.14
0.353587937
Urban to Water
3.78
0.009605882
Water to Ag
32.49
0.082564842
Water to Undev
21.42
0.05443333
Water to Urban
23.22
0.05900756
Water to Water
141.84
0.360449281
Total
39350.89
100
Fig. 7: Land cover Change1984-2009, Cannon River Watershed
From-To Change
Area (Hectacres)
Percent Change
Ag to Ag
34803.1
17.44287479
Ag to Undev
4799.34
2.405368679
Ag to Urban
30061.8
15.06659502
Ag to Water
1467.72
0.735602753
Undev to Ag
49313.6
24.71535438
Undev to Undev
10815.9
5.420792671
Undev to Urban
34805.7
17.44417787
Undev to Water
2281.86
1.143639453
Urban to Ag
3457.44
1.732825323
Urban to Undev
543.51
0.272400357
Urban to Urban
20689.9
10.36951694
Urban to Water
637.56
0.319537031
Water to Ag
485.46
0.243306429
Water to Undev
980.82
0.491574614
Water to Urban
1350
0.676602974
Water to Water
3032.46
1.519830707
Total
199526.17
100
Fig. 8: Land cover Change1984-2009, MN River-Shakopee Watershed
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From-To Change
Ag to Ag
Ag to Undev
Ag to Urban
Ag to Water
Undev to Ag
Undev to Undev
Undev to Urban
Undev to Water
Urban to Ag
Urban to Undev
Urban to Urban
Urban to Water
Water to Ag
Water to Undev
Water to Urban
Water to Water
Total
Area (Hectacres)
19453.1
1014.03
14063.6
108.63
24758.8
3143.61
16420.82
206.82
1442.7
76.23
3553.92
36.99
86.94
112.41
169.11
104.49
84752.2
Percent Change
22.9529145
1.196464517
16.59378754
0.128173664
29.21316497
3.709178051
19.37509587
0.244029064
1.702256697
0.089944568
4.193307076
0.043644885
0.102581408
0.132633725
0.199534643
0.123288835
100
Fig. 9: Land cover Change1984-2009, Mississippi River Rush-Vermillion
Watershed
From-To Change
Area (Hectacres)
Percent Change
Ag to Ag
20082.1
7.669173616
Ag to Undev
5188.05
1.981269697
Ag to Urban
41470.1
15.83705871
Ag to Water
1855.35
0.708541501
Undev to Ag
37283
14.23804283
Undev to Undev
18670.9
7.130249011
Undev to Urban
42393.7
16.18977326
Undev to Water
3923.73
1.498437245
Urban to Ag
7788.51
2.974362014
Urban to Undev
1020.15
0.389586122
Urban to Urban
62681.8
23.93761642
Urban to Water
2335.05
0.891734622
Water to Ag
1531.89
0.585015032
Water to Undev
2225.97
0.85007795
Water to Urban
3844.26
1.468088365
Water to Water
9560.25
3.650973606
Total
261854.81
Fig. 10: Land cover Change1984-2009, Mississippi River Twin Cities
Watershed
100
12
From-To Change
Percent Change
Ag to Ag
Area
(Hectacres)
3860.37
Ag to Undev
4608.45
10.75079054
Ag to Urban
6800.67
15.86489573
Ag to Water
110.97
0.258875593
Undev to Ag
5220.18
12.17786062
Undev to Undev
12217.1
28.5005768
Undev to Urban
7263.54
16.94469879
Undev to Water
191.16
0.445946277
Urban to Ag
381.78
0.890632819
Urban to Undev
419.76
0.979234198
Urban to Urban
1183.77
2.761549614
Urban to Water
17.82
0.041571263
Water to Ag
227.34
0.530348538
Water to Undev
56.07
0.13080251
Water to Urban
68.49
0.15977642
Water to Water
238.68
0.556802979
Total
42866.15
Fig. 11: Land cover Change1984-2009, Rum River Watershed
9.005637315
100
From-To Change
Area (Hectacres)
Percent Change
Ag to Ag
7875.54
9.495191844
Ag to Undev
4567.86
5.507267694
Ag to Urban
7439.22
8.96914003
Ag to Water
600.39
0.72386379
Undev to Ag
17643.9
21.27247343
Undev to Undev
20937
25.24281911
Undev to Urban
13318.7
16.05777021
Undev to Water
1284.3
1.548423967
Urban to Ag
702.9
0.847455584
Urban to Undev
335.97
0.405064237
Urban to Urban
1735.11
2.091945736
Urban to Water
138.42
0.1668869
Water to Ag
1118.79
1.348875846
Water to Undev
781.02
0.941641428
Water to Urban
978.21
1.17938473
Water to Water
3485.07
4.201795463
Total
82942.4
100
Fig. 12: Land cover Change1984-2009, St. Croix-Stillwater Watershed
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From-To Change
Area (Hectacres)
Percent Change
Ag to Ag
1963.35
16.46104509
Ag to Undev
669.87
5.616298812
Ag to Urban
1908.09
15.99773628
Ag to Water
94.59
0.793057914
Undev to Ag
2861.28
23.98943596
Undev to Undev
1179
9.884927372
Undev to Urban
2431.8
20.38860592
Undev to Water
108.18
0.906998679
Urban to Ag
126.36
1.05942275
Urban to Undev
49.14
0.411997736
Urban to Urban
231.57
1.941520468
Urban to Water
8.55
0.071684588
Water to Ag
40.59
0.340313148
Water to Undev
80.37
0.673835125
Water to Urban
64.98
0.544802867
Water to Water
109.53
0.918317299
Total
11927.25
100
Fig. 13: Land cover Change1984-2009, North Fork-Crow River Watershed
From-To Change
Area (Hectacres)
Percent Change
Ag to Ag
8127.81
18.90767035
Ag to Undev
2262.33
5.262843233
Ag to Urban
4110.57
9.562391653
Ag to Water
282.87
0.6580386
Undev to Ag
13994.6
32.55554491
Undev to Undev
4872.96
11.33593444
Undev to Urban
6775.47
15.7617308
Undev to Water
527.67
1.227515212
Urban to Ag
301.05
0.700330613
Urban to Undev
79.92
0.185917364
Urban to Urban
318.15
0.740110229
Urban to Water
18.45
0.042920112
Water to Ag
251.55
0.585179092
Water to Undev
238.32
0.554402231
Water to Urban
149.04
0.346710761
Water to Water
676.08
1.572760408
Total
42986.84
100
Fig. 14: Land cover Change1984-2009, South Fork-Crow River Watershed
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