Peer-Reviewed Article Abstracts
377-391 Modeling Uncertainty in Photointerpreted Boundaries
G. Edwards and K.E. Lowell
Abstract
Download
Full Article
A model based on multiple photointerpretations for estimating local boundary
uncertainty (or 'fuzzy boundary width') between forest stands is developed
and presented, using an artifical data set consisting of textured images
of known class characteristics and locations. A fuzzy width estimator has
been developed by breaking down the perceptual process of photointerpretion
into two components: discrimination and variability. Discrimination consists
of the ability of the photointerpreter to detect a difference in texture.
Variability consists of the intrinsic spatial variability of the texture
itself. A quantitative analysis of these effects led to a model relating
the image construction parameters to the fuzzy boundary widths.
393-399 Evaluation of the Potential for Providing
Secondary Labels in Vegetation Maps
Curtis E. Woodcock, Sucharita Gopal, and William Albert
Abstract
Download
Full Article
For thematic maps made from remote sensing at the resolution of polygons, there
are frequently more data available than the single class assigned to the
polygon. One way of using these additional data is to provide secondary labels
in maps. A key question concerns the reliability of these data. The optimistic
view is that the distribution of classes at the pixel level is representative
of the polygon, while the pessimistic view is that classifications are noisy
and thus unreliable at this level of detail. Secondary labels for a vegetation
map of the Plumas National Forest mirror the errors in the original vegetation
map, indicating caution in the use of secondary labels. Results from the
analysis of three decision rules indicate that class- conditional thresholds
perform better than either of the approaches based on a single threshold.
401-407 Estimating the Kappa Coefficient and Its
Variance Under Stratified Random Sampling
Steve Stehman
Abstract
Download
Full Article
The kappa coefficient of agreement is frequently used to summarize the results
of an accuracy assessment used to evaluate land-use or land-cover classifications
obtained by remote sensing. The standard estimator of the kappa coefficient
along with the standard error of this estimator require a sampling model
that is approximated by simple random sampling. Formulas are presented for
estimating the kappa coefficient and its variance for stratified random sampling.
Empirical results demonstrate that these estimators have little bias, and
confidence intervals perform well, often even at relatively small sample
sizes.
409-412 Unbiased Estimates of Class Proportions
from Thematic Maps
Paul C. Van Deusen
Abstract
Download
Full Article
A statistical overview is presented for estimating various components related
to map accuracy assessment. The emphasis is on estimation of the true proportions
of each map class under several common sampling designs. A complete system
is presented for relating alternative approaches and estimators using standard
rules of probability theory. Covariance matrices for estimates of true class
proportions are derived in the Appendices for each of the sampling designs
discussed.
413-417 Natural Constraints for Inverse Area Estimate
Corrections
Ding Yuan
Abstract
Download
Full Article
Though it has been used for marginal area estimate correction in image classification
for years, the inverse correction technique has been the most controversial
compared with several other marginal area estimate correction techniques,
such as the direct and additive methods. In the reported practices, the inverse
correction technique provided acceptable corrections to the marginal area
estimates. In statistical simulation comparison, however, the inverse method
was found unstable and systematically inferior to the direct method. Through
theoretic analysis and discussions on the characteristics of inverse correction
for image classification, the author concludes that (1) the inverse correction
exists if the classifier is minimum practically acceptable and (2) the inverse
is not ill-conditioned (ie it is stable) if the classifier is reasonably
acceptable.
419-428 Error Propagation through the Buffer Operation
for Probability Surfaces
Howard Veregin
Abstract
Download
Full Article
This study explores the propagation of error through the buffer operation in
GIS. The study focuses on probability based raster databases, or probability
surfaces, in which cell values show the probabilities associated with membership
in different land-cover classes. Results indicate that there is a strong
positive relationship between error levels in source and derived layers.
The strength of the relationship is affected by the degree to which source
probabilities tend to be under- or over-estimated, and by the interaction
between buffer size and spatial covariation in source probabilities.
429-433 Estimating Positional Accuracy of Data
Layers within a GIS through Error Propagation
Lawrence V. Stanislawski, Bon A. Dewitt, and Ramesh L. Shrestha
Abstract
Download
Full Article
The positional accuracy of a GIS layer can be separated into absolute and relative
components. Accepted standards for estimating horizontal accuracy in cartographic
data quantify absolute positional accuracy only. However, relative accuracy
values that describe variability in spatial relationships of coordinate information
- such as variance of area, azimuth, and distance computations - can be valuable
to research and decision making. This paper presents a technique for quantifying
absolute and relative positional accuracy estimated through error propagation
from a covariance matrix for affine transformation parameters. This technique
was developed and tested with a spatial data set manually digitized from
a simulated 1:24 000-scale map whose errors were restricted to those of the
electrostatic plotter. A sequence of transformation tests was performed,
using from 4 to 40 control points per test.