Peer-Reviewed Articles
1321 Geometric Accuracy Assessment of QuickBird Basic
Imagery Using Different Operational Approaches
Manuel A. Aguilar, Fernando J. Aguilar, Francisco Agüera, and
Jaime A. Sánchez
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The new very high-resolution space satellite images, such
as QuickBird and Ikonos, open new possibilities in cartographic
applications. This work has as its main aim the
assessment of a methodology to achieve the best possible
geometric accuracy in orthorectified imagery products
obtained from QuickBird basic imagery which will include
an assessment of the methodology’s reliability. Root Mean
Square Error (RMSE), mean error or bias, and maximum error
in 79 independent check points are computed and utilized
as accuracy indicators.
The ancillary data were generated by high accuracy methods: (a) check and control points were measured with a differential global positioning system, and (b) a dense digital elevation model (DEM) with grid spacing of 2 m and RMSEz of about 0.31 m generated from a photogrammetric aerial flight at an approximate scale of 1:5000 that was used for image orthorectification. Two other DEMs with a grid spacing of 5 m (RMSEz = 1.75 m) and 20 m (RMSEz = 5.82 m) were also used.
Four 3D geometric correction models were used to correct the satellite data: two terrain-independent rational function models refined by the user, a terrain-dependent model, and a rigorous physical model. The number and distribution of the ground control points (GCPs) used for the sensor orientation were studied as well, testing from 9 to 45 GCPs. The best results obtained about the geometric accuracy of the orthorectified images (two dimensional RMSE of about 0.74 m) were computed when the dense DEM was used with the 3D physical and terrain-dependent models. The use of more than 18 GCPs does not improve the results when those GCPs are extracted by stratified random sampling.
1333 Assessment of SRTM, ICESat, and Survey Control
Monument Elevations in Canada
Alexander Braun and Georgia Fotopoulos
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The Shuttle Radar Topography Mission (SRTM) has provided
homogeneous and highly accurate data for Digital Elevation
Models (DEMs). It is important to realize that the SRTM DEM
represents the Earth’s surface (vegetation, snow) as seen by
a C-band SAR in February 2000 and not necessarily the bare
terrain. The accuracy of the DEMs depends on various
factors including the terrain roughness/slope, land-cover,
snow/ice, and vegetation. Herein, an accuracy assessment
of the C-band SRTM DEM is performed through comparisons
with independent elevation data obtained from the laser
altimetry mission ICESat (4.7 million footprints) and
terrestrial height information at 33,000 survey control
monuments in Alberta, Canada. Temporal elevation
changes due to snow cover and vegetation are considered
and presented for the provinces of Quebec and Alberta.
The analysis focuses on both data and datum issues,
which are required to provide a realistic assessment of
the achievable accuracy.
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1343 Consequences of Land-cover Misclassification in
Models of Impervious Surface
Gerard McMahon
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Model estimates of impervious area as a function of landcover
area may be biased and imprecise because of errors in
the land-cover classification. This investigation of the effects
of land-cover misclassification on impervious surface models
that use National Land Cover Data (NLCD) evaluates the
consequences of adjusting land-cover within a watershed to
reflect uncertainty assessment information. Model validation
results indicate that using error-matrix information to adjust
land-cover values used in impervious surface models does
not substantially improve impervious surface predictions.
Validation results indicate that the resolution of the landcover
data (Level I and Level II) is more important in
predicting impervious surface accurately than whether the
land-cover data have been adjusted using information in the
error matrix. Level I NLCD, adjusted for land-cover misclassification,
is preferable to the other land-cover options for use
in models of impervious surface. This result is tied to the
lower classification error rates for the Level I NLCD.
1355 Estimating Basal Area and Stem Volume for Individual
Trees from Lidar Data
Qi Chen, Peng Gong, Dennis Baldocchi, and Yong Q. Tian
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This study proposes a new metric called canopy geometric
volume G, which is derived from small-footprint lidar data,
for estimating individual-tree basal area and stem volume.
Based on the plant allometry relationship, we found that
basal area ß is exponentially related to G (ß1G3⁄4, where
ß1 is a constant) and stem volume V is proportional to
G (V = ß2G, where ß22 is a constant). The models based on
these relationships were compared with a number of models
based on tree height and/or crown diameter. The models
were tested over individual trees in a deciduous oak woodland
in California in the case that individual tree crowns are
either correctly or incorrectly segmented. When trees are
incorrectly segmented, the theoretical model ß=ß1G3⁄4 has
the best performance (adjusted R2, Ra2 = 0.78) and the model
V =ß2G has the second to the best performance ( Ra2 = 0.78).
When trees are correctly segmented, the theoretical models
are among the top three models for estimating basal area
( Ra2 = 0.77) and stem volume ( Ra2 = 0.79). Overall, these
theoretical models are the best when considering a number
of factors such as the performance, the model parsimony,
and the sensitivity to errors in tree crown segmentation.
Further research is needed to test these models over sites
with multiple species.
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1367 A Theoretical Approach to Modeling the Accuracy
Assessment of Digital Elevation Models
Fernando J. Aguilar, Francisco Agüera, and Manuel A. Aguilar
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In this paper, a theoretical analysis is presented of the degree
of correctness to which the accuracy figures of a grid Digital
Elevation Model (DEM) have been estimated, measured as
Root Mean Square Error (RMSE) depending on the number
of checkpoints used in the accuracy assessment process.
The latter concept is sometimes referred to as the Reliability
of the DEM accuracy tests.
Two theoretical models have been developed for estimating the reliability of the DEM accuracy figures using the number of checkpoints and parameters related to the statistical distribution of residuals (mean, variance, skewness, and standardized kurtosis). A general case was considered in which residuals might be weakly correlated (local spatial autocorrelation) with non-zero mean and non-normal distribution. Thus, we avoided the “strong assumption” of distribution normality accepted in some of the previous works and in the majority of the current standards of positional accuracy control methods. Sampled data were collected using digital photogrammetric methods applied to large scale stereo imagery (1:5 000). In this way, seven morphologies were sampled with a 2 m by 2 m sampling interval, ranging from flat (3 percent average slope) to the highly rugged terrain of marble quarries (82 percent average slope).
Two local schemes of interpolation have been employed, using Multiquadric Radial Basis Functions (MRBF) and Inverse Distance Weighted (IDW) interpolators, to generate interpolated surfaces from high-resolution grid DEMs. The theoretical results obtained were experimentally validated using the Monte Carlo simulation method.
The proposed models provided a good fit for the raw simulated data for the seven morphologies and the two schemes of interpolation tested (r2 > 0.96 as mean value). The proposed theoretical models performed very well for modeling the non-gaussian distribution of the errors at the checkpoints, a property which is very common in geographically distributed data.
1381 Extraction of Urban Built-up Land Features from Landsat
Imagery Using a Thematic-oriented Index Combination
Technique
Hanqiu Xu
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This paper proposes a technique to extract urban built-up
land features from Landsat Thematic Mapper (TM) and
Enhanced Thematic Mapper Plus (ETM+) imagery taking two
cities in southeastern China as examples. The study selected
three indices, Normalized Difference Built-up Index (NDBI),
Modified Normalized Difference Water Index (MNDWI), and
Soil Adjusted Vegetation Index (SAVI) to represent three
major urban land-use classes, built-up land, open water
body, and vegetation, respectively. Consequently, the seven
bands of an original Landsat image were reduced into three
thematic-oriented bands derived from above indices. The
three new bands were then combined to compose a new
image. This considerably reduced data correlation and
redundancy between original multispectral bands, and thus
significantly avoided the spectral confusion of the above
three land-use classes. As a result, the spectral signatures of
the three urban land-use classes are more distinguishable in
the new composite image than in the original seven-band
image as the spectral clusters of the classes are well separated.
Through a supervised classification, a principal
components analysis, or a logic calculation on the new
image, the urban built-up lands were finally extracted with
overall accuracy ranging from 91.5 to 98.5 percent. Therefore,
the technique is effective and reliable. In addition, the
advantages of SAVI over NDVI and MNDWI over NDWI in the
urban study are also discussed in this paper.
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1393 Seasonal Sensitivity Analysis of Impervious Surface
stimation with Satellite Imagery
Changshan Wu and Fei Yuan
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Numerous approaches have been developed to quantify the
distribution of impervious surfaces using remote sensing
technologies. Most of these approaches have been applied
to data from a single time period, typically in the summer
season (June to September). Presently, it is not clear whether
there is an optimal time for impervious surface estimation
with these methods. In this paper, the seasonal sensitivity
of impervious surface estimation is examined. In particular,
Landsat TM/ETM+ imagery for four different seasons has
been acquired for the environs of Franklin County, Ohio.
Two impervious surface estimation methods, spectral mixture
analysis and regression modeling, are used to test for seasonal
variations. Results indicate that the summer image
provides better accuracy with the spectral mixture analysis
method, while consistent accuracies are obtained for all four
seasons with regression modeling.
1403 Employing Spatial Metrics in Urban Land-use/Landcover
Mapping: Comparing the Getis and Geary Indices
Soe W. Myint, Elizabeth A. Wentz, and Sam J. Purkis
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We examine the potential of supplementing per-pixel classifiers
with the Getis index (Gi) in comparison to the Geary’s C
on a subset of Ikonos imagery for urban land-use and landcover
classification. The test is pertinent considering that the
Gi is generally considered more capable of identifying clusters
of points with similar attributes. We quantify the impact of
varying distance thresholds on the classification product and
demonstrate how well the Gi identified cold and hot spots in
comparison to Geary’s C. The exercise also provides a rule of
thumb for effectively measuring spatial association in connection
to adjacency. We are able to support existing literature
that measuring local variability improves classification over
spectral information alone. The results, however, neither
confirm nor deny the challenge on whether measuring cold
and hot spots rather than just spatial association improves
classification accuracy.
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1417 Designing and Implementing Software Components
for an Automated Mapping System
Jae-Hong Yom and Jane Drummond
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This paper summarises a research project that examined
the usefulness of Object-oriented Programming (OOP) and
the Unified Modelling Language (UML) in producing welldesigned
software. This leads to the cost effective implementation
of new automated mapping tools as and when
they emerge. In this project, software for an automated
mapping system was designed, implemented and successfully
tested. The system consists of the following subsystems:
(a) image acquisition, (b) positioning, (c) image point
referencing, (d) spatial object extraction, and, (e) visualization.
This paper addresses parts of this work (the first three
subsystems) in order to demonstrate the flexibility of this
approach to software design.