Peer-Reviewed Articles
1325 Habitat Mapping in Rugged Terrain Using Multispectral
Ikonos Images
Janet Nichol and Man Sing Wong
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Due to the significant time and cost requirements of
traditional mapping techniques, accurate and detailed
habitat maps for large areas are uncommon. This study
investigates the application of Ikonos Very High Resolution
(VHR) images to habitat mapping in the rugged terrain of
Hong Kong’s country parks. A required mapping scale of
1:10 000, a minimum map object size of 150 m2 on the
ground, and a minimum accuracy level of 80 percent were
set as the mapping standards. Very high quality aerial
photographs and digital topographic maps provided
accurate reference data for the image processing and
habitat classification. A comparison between manual
stereoscopic aerial photographic interpretation and image
classification using pixel-based and object-based classifiers
was carried out. The Multi-level Object Oriented Segmentation
with Decision Tree Classification (MOOSC) was devised
during this study using a suite of image processing techniques
to integrate spectral, textural, and spatial criteria
with ancillary data. Manual mapping from air photos
combined with fieldwork obtained the best result, with
95 percent overall accuracy, but both this and the MOOSC
method, with 94 percent, easily met the 80 percent specified
accuracy standard. The MOOSC method was able to
achieve similar accuracy aerial photographs, but at only
one third of the cost.
1335 Estimation of Forest Stand Characteristics Using Spectral
Histograms Derived from an Ikonos Satellite Image
Jussi Peuhkurinen, Matti Maltamo, Lauri Vesa, and Petteri Packalén
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The aim of this paper was to examine the potential of
Ikonos satellite images for estimating boreal forest stand
characteristics using frequency distributions of radiometric
values. The spectral features selected for use in the estimation
were medians, standard deviations, and the parameters
of the two-parametric Weibull distribution derived from the
standwise spectral histograms of the Ikonos image. Ancillary
map information, such as land-use and peatland classes,
was also included. The method of estimation was nonparametric
k-most similar neighbors (K-MSN) method. The
most accurate results were achieved using spectral features
that were derived from the multispectral images. The
lowest RMSEs for the mean total stem volume, basal area,
and mean height were 52.2 m3/ha (31.3 percent), 5.6 m2/ha
(25.3 percent), and 3.1 m (20.6 percent), respectively. When
only the panchromatic image was used in the analysis, the
RMSEs for the mean total stem volume and basal area were
about 3 percentage points higher. No differences in the
mean height estimates were observed between the multispectral
and panchromatic images. The most efficient predictor
variables were the medians and the scale parameters of the
Weibull distribution. The use of classified map information
did not improve the results. The findings suggest that Ikonos
satellite images can be used in to estimate forest stand
characteristics giving an accuracy that corresponds to that
achieved with aerial photographs.
1343 Pixel-based Minnaert Correction Method for Reducing
Topographic Effects on a Landsat-7 ETM+ Image
Dengsheng Lu, Hongli Ge, Shizhen He, Aijun Xu, Guomo
Zhou, and Huaqiang Du
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The topographic effect on land surface reflectance is an
important factor affecting quantitative analysis of remotely
sensed data in mountainous regions. Different approaches
have been developed to reduce topographical effects. Of
the many methods, the Minnaert correction method is most
frequently used for topographic correction, but a single global
Minnaert value used in previous research cannot effectively
reduce topographic effects on the remotely sensed data,
especially in the areas with steep slopes. This paper explores
the method to develop a pixel-based Minnaert coefficient
image based on the established relationship between Minnaert
coefficients and topographic slopes. A texture measure
based on homogeneity is used to eva-luate the topographic
correction result. This study has demonstrated promising in
reducing topographic effects on the Landsat ETM+ image with
the pixel-based Minnnaert correction method.
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1351 Orthogonal Transformation of Segmented SPOT5 Images:
Seasonal and Geographical Dependence of the
Tasselled Cap Parameters
Eva Ivits, Alistair Lamb, Filip Langar, Scott Hemphill, and
Barbara Koch
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Brightness, Greenness, and Wetness Tasselled Cap parameters
were derived for the SPOT5 sensor. Their robustness
through space and time and their discrimination power in
land-cover classes was investigated. Four images were
acquired from March and September 2003, and in July and
November 2004 over Germany. A fifth SPOT5 image was
acquired from Cameroon, West Africa in January 2003. The
Tasselled Cap parameters were extracted with the Gram-Schmidt orthogonalization technique for each image independently.
One set of combined parameters was created for
Germany using samples from the four SPOT5 images simultaneously.
Each SPOT5 image was transformed into Brightness,
Greenness, and Wetness with their own with the combined
and the July parameters. Spearman’s Rho correlation
analysis was carried out between the Tasselled Cap counterparts
acquired with the various parameters. Brightness
exhibited nearly perfect correlations between the images in
Germany; in Cameroon however, the images were inconsistent.
Greenness and Wetness displayed a difference of up to
35 percent in November in Germany. The Wetness counterparts
in Cameroon exhibited a 7 percent difference. Canonical
discrimination analysis revealed that the components
from July had the highest discrimination power and that
Greenness expressed the highest association to the first
canonical axis in all images. In March, July, and November,
Brightness was the second most important Tasselled Cap
component, in September the Wetness and in Cameroon the
Greenness. These results indicate that the Tasselled Cap
components are not stable between different seasons and
geographical locations. They can be successfully used for
land-cover discrimination if the images are transformed with
parameters appropriate to the investigated season respective
biogeographical zone.
1365 A Polygonal Approach for Automation in Extraction of
Serial Modular Roofs
Yair Avrahami, Yuri Raizman, and Yerach Doytsher
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This paper presents a novel approach for automation in roof
extraction from two solved aerial images. The approach
assumes that roofs are composed of several spatial polygons,
and that they can be obtained by extracting all or even only
some of them if the model is known. In view of this assumption,
innovative algorithms for semi-automatic spatial
polygon extraction were developed. These algorithms are
based on a 2D approach to solving the 3D reality. Based on
these algorithms, an interactive and semi-automatic modelbased
approach for automation in roof extraction was
developed. The approach is composed of two phases:
manual (interactive) and automatic. In the manual (interactive)
phase, the operator needs to choose an Expanded
Parameterized Model (EPM) from a knowledge base and
select one pre-prepared Interactive Option for Extraction
(IOE) of the roof. Then, the operator needs to point according
to the guidelines of the chosen option in the left image
space. In the automatic phase, the selected spatial polygons
are extracted, the parameters of the selected model are
calculated and the roof is reconstructed. The approach was
examined and the results we obtained had standard
accuracy. It appears that the approach can be implemented
on many types of roofs and under diverse photographic
conditions. In this paper, the algorithms, the experiments
and the results are detailed.
1379 Conterminous U.S. and Alaska Forest Type Mapping
Using Forest Inventory and Analysis Data
B. Ruefenacht, M.V. Finco, M.D. Nelson, R. Czaplewski, E.H.
Helmer, J.A. Blackard, G.R. Holden, A.J. Lister, D. Salajanu, D.
Weyermann, and K. Winterberger
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Classification-trees were used to model forest type groups and
forest types for the conterminous United States and Alaska.
The predictor data were a geospatial data set with a spatial
resolution of 250 m developed by the U.S. Department of
Agriculture Forest Service (USFS). The response data were plot
data from the USFS Forest Inventory and Analysis program.
Overall accuracies for the conterminous U.S. for the forest
type group and forest type were 69 percent (Kappa = 0.66)
and 50 percent (Kappa = 0.57), respectively. The overall
accuracies for Alaska for the forest type group and forest type
were 78 percent (Kappa = 0.69) and 67 percent (Kappa =
0.61), respectively. This is the first forest type map produced
for the U.S. The forest type group map is an update of a
previous forest type group map created by Zhu and Evans
(1994).
1389 Leaf Area Index (LAI) Change Detection Analysis on
Loblolly Pine (Pinus taeda) Following Complete Understory
Removal
J.S. Iiames, R.G. Congalton, A.N. Pilant, and T.E. Lewis
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The confounding effect of understory vegetation contributions
to satellite-derived estimates of leaf area index (LAI)
was investigated on two loblolly pine (Pinus taeda) forest
stands located in Virginia and North Carolina. In order to
separate NDVI contributions of the dominant-codominate
crown class from that of the understory, two P. taeda 1 ha
plots centered in planted stands of ages 19 and 23 years
with similar crown closures (71 percent) were analyzed for
in situ LAI and NDVI differences following a complete
understory removal at the peak period of LAI. Understory
vegetation was removed from both stands using mechanical
harvest and herbicide application in late July and early
August 2002. Ikonos data was acquired both prior and
subsequent to understory removal and were evaluated for
NDVI response. Total vegetative biomass removed under the
canopies was estimated using the Tracing Radiation and
Architecture of Canopies (TRAC) instrument combined with
digital hemispherical photography (DHP). Within-image NDVI
change detection analysis (CDA) on the Virginia site showed
that the percentage of removed understory (LAI) detected by
the Ikonos sensor was 5.0 percent when compared to an
actual in situ LAI reduction of 10.0 percent. The North
Carolina site results showed a smaller percentage of reduced
understory LAI detected by the Ikonos sensor (1.8 percent)
when compared to the actual LAI reduction as measured in
situ (17.4 percent). Image-to-image NDVI CDA proved problematic
due to the time period between the Ikonos image
collections (2.5 to 3 months). Sensor and solar position
differences between the two collections, along with pine LAI
increases through multiple needle flush, exaggerated NDVI
reductions when compared to in situ data.
1401 An Initial Study on Vehicle Information Extraction from
Single Pass QuickBird Satellite Imagery
Zhen Xiong and Yun Zhang
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Vehicle information is useful in many fields. In this paper,
a technique is presented to extract vehicle information from
single pass QuickBird images. While passing a target area,
the satellite acquires panchromatic (PAN) and multi-spectral
(MS) images simultaneously. Because of a small time interval
difference between the PAN and MS images, it is theoretically
possible to extract two sets of vehicle ground positions
from the PAN and MS images, respectively, to identify whether
or not a vehicle is in motion, and to calculate the vehicle’s
velocity and direction. Practically, however, this extraction
and calculation are challenging. Since the time interval is very
short, a small error in information extraction will result in an
unacceptably large error in the calculated vehicle position and
velocity. Another challenge is that satellite image pairs (PAN
and MS) do not have the same resolution. Therefore, traditional
change detection techniques are incapable of providing
reliable results, due to varying scales and relief distortions in
the co-registered images or slight pixel shifts in the orthorectified
images caused by resampling of the PAN and MS images.
In order to avoid these errors, this research presents an
algorithm through which a vehicle’s ground positions can be
directly calculated from the raw PAN and MS images. Experiments
demonstrate that it is feasible to use this technique to
extract vehicle information from high-resolution images
obtained from a single satellite pass.
1413 A Comparison of Coincident Landsat-5 TM and Resourcesat-1 AWiFS Imagery for Classifying Croplands
David M. Johnson
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A comparison of land-cover maps, emphasizing row crop
agriculture, resulting from independent classifications of
coincident Landsat-5 Thematic Mapper (TM) and Resourcesat-1
Advanced Wide Field Sensor (AWIFS) imagery is presented.
Three agriculturally intensive study areas within the midsection
of the United States were analyzed during the peak of
their growing season. For each region the data were collected
within the same hour during August 2005. Identical decision
tree style classification methodologies relying on ground truth
from the June Agricultural Survey were applied to the image
pairs for each of the three cases. The direct comparison of
mapping accuracy results show, on average, the TM output to
perform slightly better than that of the complimentary AWIFS.
It is concluded AWIFS is a valid alternative to TM for classifying
cultivated agriculture in areas with reasonably large field sizes.
Furthermore, AWIFS offers increased benefits due to larger
swath widths and shorter revisit frequencies.
1425 Using a Binary Space Partitioning Tree for Reconstructing
Polyhedral Building Models from Airborne Lidar Data
Gunho Sohn, Xianfeng Huang, and Vincent Tao
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During the past several years, point density covering
topographic objects with airborne lidar (Light Detection
And Ranging) technology has been greatly improved.
This achievement provides an improved ability for reconstructing
more complicated building roof structures; more
specifically, those comprising various model primitives
horizontally and/or vertically. However, the technology for
automatically reconstructing such a complicated structure
is thus far poorly understood and is currently based on
employing a limited number of pre-specified building
primitives. This paper addresses this limitation by introducing
a new technique of modeling 3D building objects
using a data-driven approach whereby densely collecting
low-level modeling cues from lidar data are used in the
modeling process. The core of the proposed method is to
globally reconstruct geometric topology between adjacent
linear features by adopting a BSP (Binary Space Partitioning)
tree. The proposed algorithm consists of four steps: (a)
detecting individual buildings from lidar data, (b) clustering
laser points by height and planar similarity, (c) extracting
rectilinear lines, and (d) planar partitioning and
merging for the generation of polyhedral models. This
paper demonstrates the efficacy of the algorithm for
creating complex models of building rooftops in 3D space
from airborne lidar data.