Peer-Reviewed Articles
257 Assessing Spatial Uncertainty of Lidar-derived Building
Model: A Case Study in Downtown Oklahoma City
Mang Lung Cheuk and May Yuan
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Light Detection and Ranging (lidar) technology enables costeffective
rapid production of digital models that capture
topography and vertical structures of surface features at a
fine spatial resolution. The capability has promoted lidar
applications for mapping terrain, buildings, forest stands, and
coastal features that cannot be adequately captured by other
remote sensing means over a large area. However, in complex
terrain, lidar data and lidar-derived products may contain
significant uncertainty. This research provides a simple
method to assess the spatial uncertainty of lidar-derived
building model, using downtown Oklahoma City, Oklahoma
as an example. Results indicate that significant uncertainty
could be found in urban environment where: (a) building
structures are complex, (b) buildings are constructed with
reflective materials, and (c) vegetation grows near-by. In
addition, cities under rapid development also challenge the
accuracy assessment of 3D building models. To conclude, we
suggest: (a) careful pre-flight planning before data collection,
(b) improve the feature extraction algorithm if possible,
(c) use of other remote sensing data, and (d) accuracy
assessment on suggested urban environments to reduce the
spatial uncertainty of lidar data and lidar-derived products.
271 Reconstruction of Complex Shape Buildings from Lidar
Data Using Free Form Surfaces
Nizar Abo Akel, Sagi Filin, and Yerach Doytsher
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Building reconstruction from lidar data offers promising
prospects for rapid generation of large-scale 3D models
autonomously. Such reconstruction requires knowledge on a
variety of parameters that refer to both the point cloud and
the modeled buildings. The complexity of the reconstruction
task has led researchers to use external information to
localize buildings and assume that they consist of only
planar parts. These assumptions limit the reconstruction of
complex buildings, particularly those having curved faces.
We present in this paper a detection and reconstruction
model that considers the point cloud as the only information
source and supports the reconstruction of general
shape surfaces. Nonetheless, since many of the buildings
are composed of planar faces, we maintain the planar
based partitioning whenever possible and model non-planar
surfaces only where needed. This way, standard models are
extended to support free-form roof shapes without imposing
artificial models. In addition to the free-form surface
extension, we demonstrate the effect of imposing geometric
constraints on the reconstruction as a means to generate
realistic building models.
281 An Adaptive Approach to Topographic Feature Extraction
from Digital Terrain Models
Yonghak Song and Jie Shan
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This study presents an adaptive solution to topographic
feature extraction from digital terrain model. First, a slope
map is produced by the proposed slope estimator that
combines the well-known D8 and finite difference methods.
In the second step, the Laplacian of Gaussian (LOG) operator
with multiple thresholds is applied to the resultant
slope map to determine edge pixels that have local maximum
curvature and maximum connectivity. The third step
adopts the original and robust marching square algorithms
to trace the topographic features. Modification is made to
selectively introduce shoulder points according to the local
topographic complexity. In comparison to the existing
algorithms, the performance of the proposed adaptive
marching square algorithm is evaluated in terms of precision
and resolution of the extracted features. Digital terrain
models over three locations in Antarctica are used for
this study. It is shown overall reducing 75 percent of the
shoulder points from the robust algorithm will cause
24 percent precision drop in the adaptive method.
291 Scaling Effect on the Relationship between Landscape
Pattern and Land Surface Temperature: A Case Study
of Indianapolis, United States
Hua Liu and Qihao Weng
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The objective of this paper is to examine the scaling-up
effect on the relationship between landscape patterns
and land surface temperatures based on a case study of
Indianapolis, United States. The integration of remote
sensing, GIS, and landscape ecology methods was used in
this study. Four TERRA ASTER images were acquired to derive
the land-use and land-cover (LULC) patterns and land
surface temperatures (LST) in different seasons. Each LULC
and LST image was resampled to eight spatial scales: 15, 30,
60, 90, 120, 250, 500, and 1,000 m. The scaling-up effect on
the spatial and ecological characteristics of landscape
patterns and LSTs were examined by the use of landscape
metrics. Optimal spatial resolutions were determined on the
basis of the minimum distance in the landscape metric
spaces. The results show that the patch percentages of LULC
and LST patches were not strongly affected by the scaling-up
process in different seasons. The patch densities and
landscape shape indices and LST patches kept decreasing
across the scales without distinct seasonal differences.
Thirty meters was found to be the optimal resolution in the
study of the relationship between urban LULC and LST
classes. Ninety meters was found to be the optimal spatial
resolution for assessing the landscape-level relationship
between LULC and LST patterns. This paper may provide
useful information for urban planners and environmental
practitioners to manage urban landscapes and urban
thermal environments as a result of urbanization.
305 Role of Tie Points in Integrated Sensor Orientation for
Photogrammetric Map Compilation
Kourosh Khoshelham
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Direct measurement of exterior orientation parameters has
been a challenge in photogrammetry for many years. Direct
sensor orientation using a calibrated GPS/INS system can
potentially eliminate the need for ground control points and
aerial triangulation, and consequently, result in a great
reduction in the cost and time of aerial photogrammetry.
Previous studies have shown that, compared to conventional
aerial triangulation, direct sensor orientation yields larger
errors in the image and object space. It has also been shown
that including a number of tie points within an integrated
orientation approach can result in a reduction of errors in
the image space. In this paper, the influence of the number
and distribution of tie points on integrated orientation is
investigated. Experiments with various numbers of tie points
regularly as well as randomly distributed are presented.
Results indicate that an increase in the number of tie points
up to one point per model results in a considerable reduction
of the errors in the image space.
313 Effects of Mismatches of Scale and Location between
Predictor and Response Variables on Forest Structure
Mapping
Yaguang Xu, Brett G. Dickson, Haydee M. Hampton, Thomas
D. Sisk, Jean A. Palumbo, and John W. Prather
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Measurement error introduced by mismatches of scale and
location between response and predictor variables is one of
the major sources of error in forest structure mapping. It
affects the evaluation of analytical models, compromises
the results of accuracy assessments, and reduces the accuracy
of mapping products. Using forest structure attributes measured
in a specifically-designed ground plot system, we isolated
the measurement error from the total mapping errors that are
related to multiple factors, and examined the distribution and
magnitude of this error caused by a scale mismatch between a
relatively larger forest unit and a relatively smaller forest unit,
as well as location mismatch of a specific distance between
two forest units of the same size. We demonstrated the effects
of measurement error on the analytical models and resulting
maps for three common mapping scenarios linking ground
data with remote sensing imagery. Our results indicated that
this scale- and location-related error can be analyzed using
the Classical and Berkson error models in most practical
mapping exercises involving data measured on-ground and
remotely-sensed imagery, and that the distinct error pattern of
each type of measurement error can be used to identify the
major error source. Based on this analysis, we can adjust the
plot design or adjust the resolution of imagery, and select the
optimal analytical method to achieve the best mapping result.
323 Optimization of Stereo-matching Algorithms Using Existing
DEM Data
D.G. Milledge, S.N. Lane, and J. Warburton
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Here we present a new method for using existing Digital
Elevation Model (DEM) data to optimize performance of
stereo-matching algorithms for digital topographic determination.
We show that existing DEM data, even those of a
poor quality (precision, resolution) can be used as a means
of training stereo-matching algorithms to generate higher
quality DEM data. Existing data are used to identify and to
remove gross surface errors. We test the method using true
vertical aerial imagery for a UK upland study site. Results
demonstrate a dramatic improvement in data quality
even where DEM data derived from topographic maps are
adopted. Comparison with other methods suggests that
using existing DEM data improves error identification and
correction significantly. Tests suggest that it is applicable
to both archival and commissioned aerial imagery.
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