Brian Anderson
W00928851
ESCI442 – Lab 6
10 Years of Change Detection of the Baker to Bay Area using Landsat Imagery
Abstract
Using the ENVI 5.0 software package I performed ISODATA Clustering unsupervised
classification on remotely sensed imagery, obtained from Landsat TM datasets, of the Baker to
Bay area of Whatcom County, Washington. My objectives were to detect clear cuts that occurred
from 1988-1992 and 1995-1995. My next objective was to determine the hectares and overall
rate of cutting for the study area for two time periods. My final objective was to determine what
percentage of the harvest occurred on what land management agency’s land, i.e. Department of
Natural Resources, Forest Service, private, etc. In the Wilderness area managed by the National
Park Service, there was an 11.52% (27 400 hectares) harvest of the total study area from 19881992. In National Forest land, there was an 18.81% (44 739 hectares) harvest rate. In the lands
managed by the Department of Natural Resources there was a 16.43% (39 084 hectares) harvest
rate. The greatest area of change was in the private sector with a 53.22% (126 558 hectares)
harvest rate. Finally, there was .02% (44 hectares) of misclassified area. This was a successful
approach to accurately classify the forest coverage and my classification techniques can serve for
future tree harvesting analysis.
*Note 1: All images and tables will be provided at the end after my Discussion and before my
references
Methods
Data
The data used in this study was collected using the Landsat satellite series, collecting images in a
30 meter pixel, providing a 1200 x 1260 pixel image of Whatcom County, Washington which
includes Mount Baker on the east side of the image and Bellingham Bay on the west. All bands
of the Thematic Mapper sensor attached to Landsat were analyzed, with the exception of Band 6
due to the pixel size difference. The final source of the imagery was Western Washington
University. The images were collected on August 31, 1998, August 10, 1992 and July 18, 1995.
Analyses
First, preprocessing work was done on the images to create a classified image that detailed all
changes from 1988-1995 using the Tassel Cap Transformation, primarily by contrasting the
digital number values in the red and Near Infrared sections of the spectrum. This was done to
determine differences in brightness and greenness from one image to another because these
differences would indicate whether the change detected was one from logging (decrease in
greenness and increase in brightness) or from a wildfire (decreases in both greenness and
brightness) (Cohen, et al., 2002). Finally, 1995 greenness and brightness digital numbers were
subtracted from 1992 greenness and brightness digital numbers, and 1992 brightness and
Brian Anderson
W00928851
ESCI442 – Lab 6
greenness digital numbers from 1988 brightness and greenness digital numbers to create an
image showing land coverage differences from 1998-1995. This preprocessing work was done
for me, with my analysis starting by running an ISODATA classification on the image (Anatova,
Lab 4: Unsupervised Classification with ENVI, 2014). This classification produced 50 spectral
classes which I then narrowed to three information classes: No Change, Timber harvest between
1988 & 1992 and Timber harvest between 1992 & 1995. I subjectively determined which
spectral class belonged to an information class based on both visual analysis and observations of
the digital numbers for each spectral class. During my visual analysis I compared individual
spectral classes to color-infrared (bands 4, 3, 2 as red, green and blue) images of 1988, 1992 and
1995 images to determine what information class they belonged in. When complete, I compared
the 50 spectral classes to the digital numbers to determine their information class. After this was
complete I subjectively determined which spectral class belonged in which information class to
combine all the spectral classes into their respective information class. Next, I masked out the
low (<150 meters) and highlands (>1400 meters) to reassign misclassified change detections in
the agriculture areas and from where the glaciers had retreated. I then eliminated small polygons
(<2 hectares) because this change wouldn’t be from forest harvesting. These small polygons
were eliminated using Sieve Classes and Clump Classes techniques to detect and reclassify small
numbers of grouped pixels. The Sieve Classes technique was used to reclassify small polygons to
the No Change information class, while the Clump Classes technique was used to smooth the
image and reclassify small, isolated clumps of pixels to match the values of the surrounding
pixels. The final step was determining what the area of harvest was, overall rate of harvest
(percent of area harvested per year), the harvest rate of forested area per year and finally the
percent of forested area harvested per year for different ownership categories.
Results
The total area harvested was 237 825 hectares of land. Of this, 27 400 hectares (11.52%) was in
the National Park System’s Wilderness area. 44 739 hectares (18.81%) of timber was harvested
in the National Forest’s jurisdiction. A huge 126 558 hectares (53.22%) of timber was harvested
off of private lands. 39 084 hectares (16.34%) of timber was harvested off of Department of
Natural Resources lands. Finally, there were 44 hectares (0.02%) was labeled as Unclassified.
The 1998-1995 harvest was determined to be 3.90% (9280 hectares) harvest rate per year and the
1992-1995 harvest was 1.90% (4525 hectares) harvest rate per year.
Discussion
During part of my analysis work, I imported a Land Use/Land Cover (LULC) file to aid in
determining the final step of my work. This LULC image had a very low (43.6%) classification
accuracy. One of the best ways to improve my analysis methods would to use a LULC image
with higher classification accuracy. Another way to improve my results would be to spend more
time in the subjective classification I did in the beginning, assigning spectral classes to
information classes. While I did do this twice, there’s a possibility that I could have misclassified
a spectral class. With more time, I am sure I can improve my results by ensuing my subjective
classifications are as accurate as possible. This careful subjective classification would eliminate
the Unclassified section of detected change. Finally, to improve my results, the timber harvested
in the Wilderness area should have been reclassified to No Change. This should have been done
because there was no harvesting in the Wilderness area; instead this detected change was a
natural disturbance, namely a wildfire. In the end, I do not believe that these inaccuracies were
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Brian Anderson
W00928851
ESCI442 – Lab 6
enough to affect the results of my analysis. This study is relevant because it serves as a baseline
for forest harvesting classification to aid in detecting land cover change. If an increase in timber
harvesting is detected, further analysis, including water flow and erosion, can be done to
determine the effects to the local ecosystems. To further the analysis on the Baker to Bay area,
my research could be used to aid in analysis in determining how much the clear cuts of the past
and present impact local and regional ecosystems. These impacts could include an increase in
water flow, which would lead to an increase of erosion rate, leading to a decrease in salmon
habitat, negatively impacting the economy. Analysis in air quality could also be done to see if the
air quality has changed at all over the decades, and if it’s correlated to clear cutting.
Conclusion
On a whole, this study was a success and was a valid approach that could be used in the future on
other areas. I do not believe that there were any outstanding biases that would have affected my
data in any way, nor was there any problem with the original data or study set-up.
Figure 1 – Ownership areas in the Baker to Bay area
Ownership Category
Map Color
Private Land
Department of Natural Resources
Mount Baker National Forest
National Park System Wilderness
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Brian Anderson
W00928851
ESCI442 – Lab 6
Figure 2 – Initial 50 Classifications of the Baker to Bay area; specific colors irrelevant currently
Figure 3 - The Elevation Mask used to factor out crop land change and snowmelt changes. Black is masked out (<150 meters,
>1400 meters), white is left the same.
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Brian Anderson
W00928851
ESCI442 – Lab 6
Figure 4 - Clear cuts of the Baker to Bay area. Black is No Change, red is 1988-1992 harvest, green is 1992-1995 harvest
Ownership Category
Land Harvested (m2)
NPS Wilderness
National Forest
Private Land
Dept. Nat. Res.
Unclassified
Total
273,996,250
447,386,875
1,265,584,375
390,842,500
440,000
2,738,250,000
Land Harvested
(hectares)
27,399.625 hectares
44,738.6875 hectares
126,558.4675 hectares
39,084.25 hectares
44 hectares
237,825 hectares
Percent of total
harvest
11.52092%
18.8116%
53.21494%
16.43404%
.018501%
Table 1 – Area of harvested land, per ownership category
Category
Land Harvested (m2)
No Change
1998-1992 Harvest
1992-1995 Harvest
Total
2,240,201,250
92,801,250
45,247,500
2,378,250,000
Land Harvested
(hectares)
224,020.1
9,280.125
4,524.75
237,825
Percent of Harvest
94.19536%
3.902081%
1.902554%
Table 2 – Areas of Forest Harvested
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Brian Anderson
W00928851
ESCI442 – Lab 6
Works Cited
Anatova, N. (2013, December). Lab 6: Change Detection Using Unsupervised Classification with ENVI.
Retrieved Feburary 2014, from ESCI 442/542: Introduction to Remote Sensing:
http://staff.wwu.edu/antonon/envr442/ENVI/442_change_lab_ENVI.htm
Anatova, N. (2014, January 17). Lab 4: Unsupervised Classification with ENVI. Retrieved Feburary 2014,
from ESCI442/542: Introduction to Remote Sensing:
http://staff.wwu.edu/antonon/envr442/ENVI/442_unsup_class_ENVI.html
Cohen, W. B., Florella, M., Gray, J., Helmer, E., & Anderson, K. (1998, April). An Efficient and
Accurate Method for Mapping Forest Clearcuts in the Pacific Northwest Using Landsat Imagery.
Photogrammetric Engineering & Remote Sensing, 64(4), 293-300. Retrieved Feburary 2014,
from http://staff.wwu.edu/antonon/envr442/pdf_files/cohen_etal_1998_apr_293-300.pdf
Cohen, W. B., Spies, T. A., Alig, R. J., Oetter, D. R., Maiersperger, T. K., & Fiorella, M. (2002,
September 24). Characterizing 23 Year (1972-95) of Stand Replacement Disturbance in Western
Oregon Forests with Landsat Imagery. Ecosystems, 5, 122-137. doi:10.1007/s10021-001-0060-X
*Note 2: Unless specified, the analysis citation is (Anatova, Lab 6: Change Detection Using
Unsupervised Classification with ENVI, 2013)
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