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Handling uncertainty in GIS and environmental models : An application in forest management Joy, Michael Wilfrid
Abstract
The study of uncertainty in Geographic Information Systems (GIS) and environmental models has received increasing attention in recent years, due in part to the widespread use of GIS for resource management. This study used GIS-based techniques in order to compare several different forest inventory and forest cover datasets. These datasets pertain to an area of the boreal mixedwood forest in northeastern Alberta which covers roughly 73,000 km2, and which has recently been approved for logging. The datasets include two forest inventories based on aerial photographs, and a forest cover classification based on remotely sensed satellite data. Simple logical operations were used to transform the datasets to a form suitable for comparison. Standard GIS overlay techniques were used to compare the agreement among different datasets. Visualization techniques were used to display patterns of agreement in attribute space (contingency tables), and in geographic space (maps of uncertainty). Agreement between the two forest inventories was about 50% (Percent Correctly Classified), with a Kappa value of 0.4, for a classification based on species composition. In general, much of the misclassification was between ecologically similar types, particularly between different combinations of aspen and white spruce. Comparison of the forest inventories with the classified satellite image was done using a simplified land cover classification with five categories. Agreement was about 55% (Percent Correctly Classified), with a Kappa value of 0.3. Possible sources of discrepancy among datasets include change over time, differences in spatial scale, differences in category definitions, positional inaccuracy, boundary effects and misclassification. Analyses were conducted to characterize the effect of each of these sources of disagreement. The agreement was strongly affected by the distance to boundary, indicating a boundary effect extending to more than 100 meters. Differences in spatial scale accounted for a small proportion of discrepancy. None of the other possible sources had a measurable effect on the discrepancy. It was therefore inferred that misclassification accounted for a large proportion of the discrepancy. Estimated levels of uncertainty were propagated through models including simple growth and yield tables and a more complex harvest scheduling model. It was found that uncertainty in model outputs was strongly affected by uncertainty in inventory data, uncertainty in volume yield curves, and perhaps most importantly, by a poor understanding of disturbance and forest dynamics in the region. The results of the analysis show that these uncertainties may have significant economic and ecological implications.
Item Metadata
Title |
Handling uncertainty in GIS and environmental models : An application in forest management
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1995
|
Description |
The study of uncertainty in Geographic Information Systems (GIS) and environmental
models has received increasing attention in recent years, due in part to the widespread use
of GIS for resource management. This study used GIS-based techniques in order to
compare several different forest inventory and forest cover datasets. These datasets pertain
to an area of the boreal mixedwood forest in northeastern Alberta which covers roughly
73,000 km2, and which has recently been approved for logging. The datasets include two
forest inventories based on aerial photographs, and a forest cover classification based on
remotely sensed satellite data.
Simple logical operations were used to transform the datasets to a form suitable for
comparison. Standard GIS overlay techniques were used to compare the agreement among
different datasets. Visualization techniques were used to display patterns of agreement in
attribute space (contingency tables), and in geographic space (maps of uncertainty).
Agreement between the two forest inventories was about 50% (Percent Correctly
Classified), with a Kappa value of 0.4, for a classification based on species composition.
In general, much of the misclassification was between ecologically similar types,
particularly between different combinations of aspen and white spruce. Comparison of the
forest inventories with the classified satellite image was done using a simplified land
cover classification with five categories. Agreement was about 55% (Percent Correctly
Classified), with a Kappa value of 0.3.
Possible sources of discrepancy among datasets include change over time, differences in
spatial scale, differences in category definitions, positional inaccuracy, boundary effects
and misclassification. Analyses were conducted to characterize the effect of each of these
sources of disagreement. The agreement was strongly affected by the distance to
boundary, indicating a boundary effect extending to more than 100 meters. Differences in
spatial scale accounted for a small proportion of discrepancy. None of the other possible
sources had a measurable effect on the discrepancy. It was therefore inferred that
misclassification accounted for a large proportion of the discrepancy.
Estimated levels of uncertainty were propagated through models including simple growth
and yield tables and a more complex harvest scheduling model. It was found that
uncertainty in model outputs was strongly affected by uncertainty in inventory data,
uncertainty in volume yield curves, and perhaps most importantly, by a poor
understanding of disturbance and forest dynamics in the region. The results of the analysis
show that these uncertainties may have significant economic and ecological implications.
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Extent |
16405417 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-01-12
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0086885
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
1995-05
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.