Peer-Reviewed Articles
1171 Digital Reproduction of Historical Aerial Photographic Prints for Preserving
a Deteriorating Archive
Donald E. Luman, Christopher Stohr, and Leta Hunt
Abstract
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Aerial photography from the 1920s and 1930s is a unique
record of historical information used by government agencies,
surveyors, consulting scientists and engineers, lawyers,
and individuals for diverse purposes. Unfortunately, the use
of the historical aerial photographic prints has resulted in
their becoming worn, lost, and faded. Few negatives exist for
the earliest ph photography. A pilot project demonstrated that
high-quality, precision scanning of historical aerial photography
is an appealing alternative to traditional methods for
reproduction. Optimum sampling rate varies from photograph
to photograph, ranging between 31 and 42 µm/pixel
for the USDA photographs tested. Inclusion of an index, such
as a photomosaic or gazetteer, and ability to view the imagery
promptly upon request are highly desirable.
1181 Efficient Handling of Large Digital Images in Geographic Information Systems
Fayez S. Shahin
Abstract
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There has been a tremendous demand for handling digital
images in geographic in formation systems (GIS) and real time
mapping applications, mainly due to interest in multi-media
information systems. Due to the large amount of data involved
in large digital images, efficient storage, management,
and processing of such images in geographic information
systems presents a challenge that needs to be addressed
properly. This paper presents a new methodology for handling
digital images in GIS. This methodology is based on
partitioning large digital images into smaller tiles and using
a hierarchical image compression algorithm.
1185 A Machine-Learning Approach to Automated Knowledge-Base Building for Remote
Sensing Image Analysis with GIS Data
Xueqiao Huang and John R. Jensen
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A machine learning approach to automated building of
knowledge buses for image analysis expert systems incorporating
GIS data is presented. The method uses on inductive
learning algorithm to generate production rules from training
data, With this method, building a knowledge base for a
rule-based expert system is easier than using the conventional
knowledge acquisition approach. The knowledge base
built by this method was used by an expert system to perform a
wetland classification of Par Pond on the Savannah River Site,
South Carolina using SPOT multispectral imagery and GIS data.
To evaluate the performance of the resultant knowledge base,
the classification result was compared to classifications with
two conventional methods. The accuracy assessment and the
analysis of the resultant production rules suggest that the
knowledge base built by the machine learning method was of
good quality for image analysis with GIS data.
1195 Measuring Uncertainty in Class Assignment for Natural Resource Maps Under
Fuzzy Logic
A-Xing Zhu
Abstract
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There are two kinds of uncertainty associated with assigning
a geographic entity to a class in the classification process.
The first is related to the fuzzy belonging of the entity to the
prescribed set of classes and the second is associated with
the deviation of the entity from the prototype of the class to
which the entity is assigned. This paper argues that these
two kinds of uncertainty con be estimated if a similarity
model is employed in spatial data representation. Under this
similarity model, the uncertainty of fuzzy belonging can be
approximated by an en entropy measure of membership distribution
or by a measure of membership residual The uncertainty
associated with the deviation from the prototype
definitions can be estimated using a membership exaggeration
measure. A case study using a soil map shows that high
entropy values occur in areas where soils seem to be transitional
and that areas which are mis-classified hove higher
entropy values. The membership exaggeration is high for areas
where soil experts have low confidence in identifying soil
types and predicting their spatial distribution. These measures
helped in identifyng that the high elevation areas were
mapped with high accuracy and that error reduction efforts
are needed in mapping the soil resource in the low elevation
areas.
1203 Building the Estimation Model of Digitizing Error
Huang Youcai and Liu Wenbao
Abstract
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Moving a digitizer along a smooth and continuous line could
be regarded as a discrete time stochastic process consisting of
trend motion and random motion. A stochastic stationary observation
series of digitizing error may be generated by adopting
a backward difference process (filtering the trend motion
from the stochastic series). To separate the trend motion from
the stochastic series effeciently, several mathematical formulae
have been developed for measuring the complexity of line related
to the determination of order of the backward difference
operators. The stochastic motion may be simulated by using
an autoregressive process in terms of time series analysis theory.
The estimation model of digitizing error, consisting of
these two processes, has been built. Numerical examples presented
in this paper show how to use the model to estimate
the digitizing error after having a set of digitized data.
1211 Spatial Error Analysis of Species Richness for a Gap Analysis Map
Denis J. Dean, Kenneth R. Wilson, and Curtis H. Flather
Abstract
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Variation in the distribution of species richness as a result of
introduced errors of omission and commission in the Gap
Analysis database for Oregon was evaluated ed using Monte
Carlo simulations. Random errors, assumed to be independent
of o species' distribution. and boundary errors, assumed
to be dependent on the species' distribution, were simulated
using ten rodent species. Error rates of omission and commission
equal to 5 and 20 percent were used in the simulations.
Indications are that predictions of species richness
within a Gap Analysis database can be very sensitive to both
types of errors with sensitivity to random error being much
greater. Implications are that the inclusion of error modeling
in applied GIS databases is critical to spatially explicit conservation
recommendations.
1219 GIS-Based Evaluation of Salmon Habitat in the Pacific Northwest
Ross S. Lunetta, Brian L. Cosentino, David R. Montgomery, Eric M. Beamer,
and Timothy J. Beechie
Abstract
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Categorization of 164,083 kilometers of stream length has
provided the first quantitative measure of the extent and location
of potential salmon stream habitat throughout western
Washington State. Reach slope and forest seral stage provided
a coarse indicator of channel condition across the region.
Reach-average slopes calculated for individual Stream
reaches using 30-metre digital elevation model (DEM) data,
correctly identified low-gradient (less than 4.0 percent slope)
response reaches that typically provide habitat for anadromous
salmon with an accuracy of 96 percent (omission and
commission error rates of 24.0 and 4.0 percent, respectively).
Almost one-quarter (23.2 percent) of all stream length categorized
consisted of response reaches. of which only 8.7 percent
were associated with late-seral and 20.7 percent with
mid-seral forest stages. Approximately 70 percent of the total
stream length potentially providing anadromous salmon habitat
is associated with non-forested and early-seral stage forests.
GIS-based analytical techniques provided a rapid,
objective, and cost-effective tool to assist in prioritizing locations
of salmon habitat preservation and restoration efforts
in the Pacific North west.
1231 Using Bayesian Statistics, Thematic Mapper Satellite Imagery, and Breeding
Bird Survey Data to Model Bird Species Probability of Occurrence in Maine
Jeffrey A. Hepinstall and Steven A. Sader
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A Bayesian modeling technique was used to predict probability
of occurrence for 14 species of Maine land birds. The relationships
between bird species survey data to and the spectral
values of Landset Thematic Mapper bands 4 and 5 as well
as a derived texture measure were used to build conditional
probabilities for input into Bayes' Theorem. The conditional
probabilities form decision rules for reclassifying the input
spectral data into probability of occurrence estimates with
associated estimates of error inherent in the model prediction.
This methodology removed the costly and time-consuming
step of creating a habitat map before modeling species
occurrence. The output resolution of the species predictions
is not degraded from the original 30-m TM pixel size to the
coarse resolution of the wildlife survey data. Model results
can be compared to results from other habitat modeling
techniques and used by natural resource managers to predict
the effects of land-use changes on available habitat.