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International Journal of Geoinformatics, Vol. 2, No. 1, March 2006
1. Introduction
Urban environments are characterized by dif-
ferent types of materials and land cover charac-
teristics than found in natural landscapes (Ben
Dor et al., 2001, Roberts and Herold, 2004). The
analysis of earth observation data has to consider
these unique spectral and spatial characteristics if
urban mapping is to be beneficial to a variety of
applications. In fact, there is a growing need for
improved maps of urban surface materials, such as
roof types for energy conservation and fire danger
assessment (Cohen, 2000, Medina, 2000), imper-
vious surfaces for improved estimation of flood
potential and urban source pollution (Schueler,
1994, Ridd, 1995), the mapping of transportation
assets (Herold and Roberts, 2005), and for security
applications (Clark et al., 2001). The dynamic
nature of urban environments necessitates tech-
nologies that are rapid, repeatable and provide
large areal coverage at a reasonable cost, making
remote sensing one of the most viable technolo-
gies (Herold et al., 2003a).
Until recently, most analysis in urban areas has
relied upon aerial photography as a data source.
Urban environments are especially challenging
because urban objects typically have a small spatial
extent, making aerial photography well suited to
these areas. Recent advances in spaceborne sys-
tems, such as IKONOS (www.spaceimaging.com)
provide alternatives to aerial photography. For
example, IKONOS provides 1 m panchromatic
Martin Herold1 and Dar A. Roberts2
1 ESA GOFC-GOLD Land Cover Project Office, Dep. of Geography, FSU Jena, Loebedergraben 32, 07743
Germany,E-amil : m.h@uni-jena.de2 Dep. of Geography, University of California Santa Barbara, Ellison Hall, Santa Barbara, CA, 93106,
USA, E-mail : dar@geog.ucsb.edu
Abstract
Urban mapping limitations exist for multispectral satellites (e.g. IKONOS), where the location and
broadband character of the spectral bands only marginally resolve the complex spectral characteristics
of many built surface types. Imaging spectrometry provides improved spectral characterization of
urban materials enabling greater discrimination of urban land cover at improved accuracy. Map
accuracy using hyperspectral data remains low, however, due to spectral confusion between specific
land cover types (e.g. roofs versus roads). The use of three-dimensional information from LIDAR
overcomes some of these limitations, and, when combined with multispectral or hyperspectral data,
provides significantly higher accuracies. However, both multi-spectral and LIDAR data require
fine spatial resolution data to achieve the highest accuracies. Hyperspectral data proved to be less
sensitive to changes in spatial resolution, and outperformed combined broadband multispectral
data and LIDAR for urban land cover mapping at spatial resolutions coarser than 16 m.
International Journal of Geoinformatics, Vol.2, No. 1, March 2006ISSN 1686-6576/ Geoinformatics International
Multispectral Satellites - ImagingSpectrometry - LIDAR: Spatial - SpectralTradeoffs in Urban Mapping
IJG_Urban Mapping 24/04/2006, 10:22 AM1
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Multispectral Satellites - Imaging Spectrometry - LIDAR: Spatial - Spectral Tradeoffs in Urban Mapping
and 4 m multispectral data, thereby meeting the
minimum spatial resolution of 5 m considered
necessary for accurate spatial representation of
urban materials such as buildings and roads
(Woodcock and Strahler, 1987, Jensen and Cowen,
1999).
Multispectral systems such as IKONOS do not
provide sufficient spectral information needed to
spectrally discriminate many urban materials
(Herold et al., 2003b). Both, the spectral position
and broadband character of the multi-spectral bands
only marginally resolve the complex spectral
characteristics of urban environments (Herold et
al., 2003b). For example senesced (dead) grass
and wood shingle roofs can be definitively separated
from bare soil, road surfaces and non-wooden
roofs based on the expression of ligno-cellulose
bands in the short-wave infrared (SWIR, Roberts
et al., 1993), yet these wavelengths are not sam-
pled by common very high spatial resolution
satellite sensors. Multispectral sensors were
designed primarily for mapping natural and quasi-
natural land surfaces. Different spectral sensor
configurations are required to resolve the unique
spectral properties and complexity of urban areas
(Herold et al., 2003b, 2004). Imaging spectro-
metry is a relatively new technology with consi-
derable potential for mapping urban materials
(Ben-dor et al., 2001, Clark et al., 2001, Herold et
al., 2003b, Roberts and Herold, 2004). However,
the use of imaging spectrometry has also proven
problematic for some aspects of urban mapping.
Herold et al., (2003b) showed considerable
spectral confusion between classes when using the
Airborne Visible Infrared Spectrometer (AVIRIS)
to map twenty-six urban land cover classes. These
limitations were due to spectral similarity between
specific land-cover types, and considerable within-
class variability due to surface geometry, condition,
and age that modify reflected radiance (Herold et
al., 2004).
One approach for reducing spectral confusion
between some land cover types would be to incor-
porate a third dimension into the analysis as pro-
vided by Light Detection and Ranging (LIDAR).
LIDAR systems emit rapid pulses of laser light
(usually in near infrared wavelengths) to precisely
measure distances from the sensor to targets on
the ground based on the time delay (Jensen, 2000).
The LIDAR pulse is sent coherently, but might
be extended in its return especially from surfaces
with complex three-dimensional structures. For
example, the first part of the pulse might be re-
flected off a tree canopy (first response) while the
second response could transmit to the ground
and be reflected off of these surfaces (last re-
sponse). Advanced LIDAR systems allow for a
detailed representation and analysis of the re-
flected signal. Both first and later return signals
vary in time delay and return intensity but provide
important information about canopy height or
other vertical structures. Although LIDAR has
been widely applied in atmospheric (e.g., Cooper
et al., 2003), oceanographic (Irish and Lillycrop,
1999) and vegetation remote sensing (Lefsky et
al., 2002, Clark et al., 2004), the use in urban areas
is quite new. A few studies have explored LIDAR
data in extraction of buildings, roads and other
surface features in urban areas (Gamba and
Houshmand, 2000, Priestnall et al., 2000, Steel et
al., 2001, Gamba and Houshmand, 2002) and
highlighted the potential of LIDAR to capture the
three-dimensional surface structure of the urban
environment. However, the potential, limitations
and synergies among those different data sources
for urban mapping remains poorly established.
In this paper, we evaluate potential improve-
ments in classification accuracy by combining
LIDAR-derived height information with AVIRIS
data in an urban area. We further evaluate the
potential importance of spectral and spatial reso-
lution by synthesizing IKONOS data from AVIRIS
and by degrading both data sets to spatial resolu-
tions ranging from 4 to 16 m. We assess the impor-
tance of spectral, spatial and vertical height infor-
mation by classifying image data and comparing
classified results to a common reference data set.
Our main objective is to explore problems related
IJG_Urban Mapping 24/04/2006, 10:22 AM2
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International Journal of Geoinformatics, Vol. 2, No. 1, March 2006
to spatial-spectral tradeoffs and assess the capa-
bilities and limitations of new and innovative
remote sensing datasets for urban mapping.
2. Data and Methodology
2.1 Study area
In this study, we focused on a specific region in
the urban area of Santa Barbara/Goleta, California,
located 170 kilometers northwest of Los Angeles
in the foothills of the California Coast Ranges.
The study area is characterized by a mixture of
urban land cover types and surface materials
including various categories of roof and road
types of different age and condition. A 4x2.5 km
image subset of low topography was chosen for
analysis. Parts are characterized by quasi-natural
landscapes including agriculture, grasslands,
shrublands, riparian areas and a lake. Other parts
of the image consists of single-family housing in a
high-density residential area with different roof
and road types, commercial and educational
areas, and industrial land use in the southern
central area. The eastern part is dominated by
residential areas, including some multi-family
housing complexes, as well as a downtown area
representing an additional mix of urban materials.
Development in the area occurred over several
decades and therefore includes the many urban
materials that have evolved in that time frame.
This factor also contributes to the great spectral
complexity of the area because spectral charac-
teristics of materials can change over time.
2.2 Remote Sensing data
2.2.1 LIDAR data
Airborne1 LIDAR data were acquired in
October 2001 in the area of Goleta. The AIRBORNE
1 LIDAR (www.airborne1.com) is an advanced
system that records the first and last response
elevation (time delay measurement) and intensity
(overall 4 measurements). The ground sampling
density of the system was approximately 2 m
(Figure 1). The initial LIDAR point data were
transformed into a Triangular Irregular Network
(TIN). Based on the TIN, a grid was derived with a
spatial resolution of 4 m spatial resolution.
The main information provided by the LIDAR
is elevation measured by the time distance from
the sensor source to the reflecting object. Based
on the position of the sensor and the pointing
direction, the LIDAR signal can be used to accu-
Figure 1: Subset of the LIDAR data showing the original LIDAR point measurements (first return, left)
and the related interpolated TIN in shaded relief presentation (right).
IJG_Urban Mapping 24/04/2006, 10:22 AM3
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Multispectral Satellites - Imaging Spectrometry - LIDAR: Spatial - Spectral Tradeoffs in Urban Mapping
rately calculate the three-dimensional position
and reflectance characteristics of the object. The
LIDAR pulse is first reflected by the top of the
surface object (first return) representing the object
elevation (tree top or top of buildings, Figure 1
and 2). The last return LIDAR signal is similar to
the first one if the surface is flat (e.g. parking lot).
Differences between first and last return appear
if the sensed surface is rough or the LIDAR beam
partly penetrates through the surface material, e.g.
vegetation is partly transparent to near-infrared
radiation. In this case the last return elevation
signal represents the ground elevation in contrast
to the first return that provides the surface signal
(Figure 2). For urban mapping, the building eleva-
tions can be removed from the last return signal
using different acquisition and processing tech-
niques, i.e. minimum filters, large footprint LIDAR
data or existing ground elevation models. In this
study, the LIDAR data provider processed the
LIDAR last return signals to a bare earth model
with all buildings removed. Then the difference
between the first and the last response elevations
normalizes large-scale topographic variations and
emphasizes the three-dimensional surface structure
of the urban environment from buildings and vege-
tation (Figure 2). The LIDAR elevation difference
was used as pseudo spectral band in image classi-
fications.
2.2.2 Hyperspectral AVIRIS data
This study used AVIRIS data acquired on
June 9th, 2000. The data were acquired with a
ground-instantaneous field of view of approxi-
Figure 2: Examples of the LIDAR data for the Goleta test site compared to IKONOS false color compositeand digital vector data.
IJG_Urban Mapping 24/04/2006, 10:22 AM4
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International Journal of Geoinformatics, Vol. 2, No. 1, March 2006
mately 4 meters, which is similar to current high
spatial resolution space-borne systems like
IKONOS. The AVIRIS sensor acquires 224 indi-
vidual bands with a nominal full-width half maxi-
mum (FWHM) of 8-11 nm, covering a spectral
range from 370 to 2510 nm (Green et al., 1998).
The data were processed by the Jet Propulsion
Laboratory (JPL) and the University of California,
Santa Barbara (UCSB) for motion compensation
and reduction of geometric distortions due to
topography. The data were further geo-rectified to
match current digital databases of the study region.
Radiometrically corrected/georectified AVIRIS
data were processed to apparent surface reflec-
tance using a modified Modtran radiative transfer
algorithm (Green et al., 1993, Roberts et al., 1997).
A further spectral adjusted used a ground reflect-
ance target from a spectral library (Clark et al.,
1993). Due to atmospheric contamination, the
number of AVIRIS bands was reduced to 180, with
the bands 1-7, 105-119, 152-169, 221-224 excluded
from the analysis (Herold et al., 2003b).
Figure 3: Representation of different urban surface types in different AVIRIS color composites compared to ground spectral measurements convolved to AVIRIS spectral configurations. The VIS and VIS/NIR composites
would be similar to measurements taken by the IKONOS satellite. Spectra 1-3 represent roofs, spectra 4 - 6transportation surfaces, spectra 7 green vegetation and spectra 8 bare soil to refer these spectra to land cover
classes used in the classification
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Multispectral Satellites - Imaging Spectrometry - LIDAR: Spatial - Spectral Tradeoffs in Urban Mapping
2.2.3 Synthesized multispectral IKONOS data
The multispectral band configuration of
IKONOS were synthesized from the June 9, 2000
AVIRIS data (Figure 3, 4). This was accomplished
by convolving the IKONOS spectral response
functions (Figure 3) to AVIRIS equivalents using
the spectral calibration data (band center and
FWHM) for the June 9th AVIRIS scene, then
applying these functions to the AVIRIS data to
synthesize a four band IKONOS data set. The
IKONOS spectral response functions are available
with 5 nm increments from the system operator
Space Imaging (Figure 4). For more information
on this step please refer to Herold et al., (2003b).
2.2.4 Simulated spatial resolutions
Another important consideration is spatial
resolution. In fact, spatial and spectral resolution
are strongly related since an uncontaminated
spectrum can only be acquired if the spatial
resolution is sufficiently fine enough to represent
the land cover object in ìpureî pixels. To study the
effect of spatial sensor resolution and spatial-
spectral tradeoffs in land cover mapping, the
AVIRIS, simulated IKONOS and LIDAR data
were degraded to different spatial resolutions.
This step involved a bilinear aggregation to spatial
resolutions of 6, 8, 10, 12, 14 and 16 m to simulate
these sensor configurations. This is a simplified
way of representing spatial resolution affects but
has been widely used and proven successful for
such purposes (Woodcock and Strahler, 1987).
Studies have shown that important spatial resolu-
tion for changes in remotely sensed urban images
is in the range from 5-15 m (Welch, 1982, Wood-
cock and Strahler, 1987). This is the critical scale
given the size of common urban objects.
2.3 Image classifications
All image analysis steps were applied using the
public domain program “Multispec”. This program
was designed for the processing and analysis of
hyper-dimensional spectral datasets and contains
procedures for the analysis of class separability
and selection of most suitable spectral bands
based on the Bhattacharrya distance (B-distance)
and image classification (Landgrebe and Biehl,
2001). Image classification was performed using
a standard Maximum Likelihood classification
technique implemented in “Multispec”. The training
areas were selected from the ground mapping
database (Herold et al., 2003b). The image classi-
fication was performed for IKONOS and AVIRIS
individually, and including the LIDAR data (first/
last response elevation difference).
This study was confined to a few major urban
land cover types usually considered in multispec-
tral data analysis (Figure 3). The built up classes
Figure 4: Spectral response function for 4 multi-spectral IKONOS bands in normalized transmittance valuesconvolved to 10 nm increments
IJG_Urban Mapping 24/04/2006, 10:22 AM6
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International Journal of Geoinformatics, Vol. 2, No. 1, March 2006
included roofs/buildings (class 1) and transporta-
tion surfaces (e.g. roads, parking lots, class 2). Two
vegetation classes were included, green vegetation
(class 3) and non-photosynthetic vegetation (NPV,
class 4). We considered a bare soil class (class 5),
which mainly represents construction sites. Class
6 was open water.
This study required a comprehensive reference
database for classification accuracy assessment.
The reference data mapping was based on three
different spatial sampling schemes: spatial random
sampling, and two systematic methods including
neighborhood sampling and class-specific sampl-
ing. The three sampling schemes allowed a com-
prehensive database to be acquired that was digi-
tally processed to support the image classification
and data analysis (e.g. more than 350 individual
roofs were mapped representing 10 % of all roofs
in the study area, Herold et al., 2003b). The accu-
racy assessment for the individual classifications
was used to evaluate the urban mapping perfor-
mance of remote sensing data with different sensor
characteristics.
3. Results
Image classification was applied to four sensor
configurations: IKONOS and AVIRIS indivi-
dually and each combined with the LIDAR height
difference. Figure 5 shows classification results as
they varied depending on the sensor configuration
for four of the six land cover classes. Producer’s
accuracy reports the percentage of reference data
of a specific land cover type that was correctly
classified. User’s accuracy, in contrast, describes
the percentage of image pixels of a specific class
that were correctly classified (Jensen 2000). Thus,
if all of the reference data of a specific class were
correctly classified, yet twice as many pixels were
labelled as a specific class than actually were pre-
sent in the reference data, the Producer’s accuracy
would be 100%, yet User’s accuracy only 50%.
For the classification of buildings and roofing
material at 4 m resolution, accuracy was quite
different for AVIRIS versus IKONOS. Producer’s
accuracy for AVIRIS was 35% higher than in
simulated IKONOS data. Decreased accuracy for
IKONOS can be attributed to a lack of important
spectral information in this sensor, most notably
bands in the SWIR (Figure 3, Herold et al., 2003b).
However, the Producer’s accuracy of AVIRIS
data was still only approximately 70%. This rather
low accuracy can be attributed to the spectral
similarity of roof types, road surfaces, and other
non-built land cover types such as bare soil
(Herold et al., 2004). Spectral confusion between
composite shingle roofs and roads is well illus-
trated by comparing composite shingles to asphalt
roads, surface types that are composed of similar
materials (Figure 4, spectra 1 & 4). Much of the
spectral confusion between asphalt roads and
composite shingles is eliminated by adding a
measure of vertical height (Figure 5). For example,
when combined with LIDAR, IKONOS provided
better separation between roads and roofs than
AVIRIS alone, producing Producerís and User’s
accuracies that exceeded 90%. While the highest
classification accuracies were still achieved with
AVIRIS when combined with LIDAR, they were
not significantly higher than those achieved with
IKONOS and LIDAR.
Producer’s accuracy for buildings/roofs was
highly sensitive to spatial resolution, decreasing
between 12-20% from 4 to 16 m spatial resolution
depending on sensor configuration. In this case, an
increase in spatial resolution had less of an impact
than the choice of sensor (e.g., AVIRIS was 35%
more accurate than IKONOS at 4 m). Most not-
ably, AVIRIS classifications proved to be less
sensitive to a change in spatial resolution than
IKONOS. For example, from 4 to 16 m, Producer’s
accuracy using AVIRIS decreased by 12%, where
as for IKONOS accuracy decreased by 18% and
IKONOS+LIDAR by 20%.
The highest classification accuracies for tran-
sportation areas were also achieved when combin-
ing AVIRIS with LIDAR data at the finest spatial
resolutions. In this case, the difference in Pro-
IJG_Urban Mapping 24/04/2006, 10:22 AM7
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Multispectral Satellites - Imaging Spectrometry - LIDAR: Spatial - Spectral Tradeoffs in Urban Mapping
Figure 5: Producer’s and User’s classification accuracies for four land cover classes and four different sensorconfigurations with degraded spatial resolutions. All original sensor resolutions are 4 m with IKONOS
four multispectral bands, the AVIRIS data, and the LIDAR data with the difference between the first andthe last response elevations
IJG_Urban Mapping 24/04/2006, 10:22 AM8
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International Journal of Geoinformatics, Vol. 2, No. 1, March 2006
ducer’s accuracies between AVIRIS and IKONOS
was much lower and improvements using LIDAR
were much less pronounced (~ 10%). As was the
case for buildings/roofs, AVIRIS Producer’s accu-
racy was much less sensitive to changes in spatial
resolution than with IKONOS. User’s accuracy,
in contrast, showed considerable improvements
with the use of LIDAR. For example, both AVIRIS
and IKONOS tended to overmap transportation
surfaces, producing User’s accuracies of 60 and
40%, respectively. When combined with LIDAR,
Userís accuracies increased to over 90% for both
sensors at 4 m resolution. However, unlike the Pro-
ducer’s accuracy, Userís accuracies were highly
sensitive to spatial resolution, decreasing by over
35% for IKONOS+LIDAR and over 20% for
AVIRIS+LIDAR.
Producer’s accuracy of green vegetation was
high for all sensors configurations and spatial
resolutions. The unique spectral signal of vegetation
(see Figure 4) is well represented by both sensors
and, in terms of Producer’s accuracy, allows for
very accurate classification. User’s accuracy for
vegetation, on the other hand showed a tremendous
decrease for all sensor configurations, especially
from 4 to 10 m spatial resolution. In this case
vegetation becomes increasingly overmapped as
resolution coarsens. Pixels adjacent to green
vegetation areas increasingly merge with non-
vegetation land cover types and form mixed pixels.
The strong NIR to read spectral contrast of
vegetation leads to an increase in the number of
pixels incorrectly mapped as vegetation, even if the
actual proportion of vegetation in the pixel is
relatively low.
In contrast to the other classes, bare soil
classification accuracies improved at coarser
spatial resolutions. Bare soil usually represents
areas with larger spatial extents that do not require
fine spatial resolutions for their accurate mapping.
Finer spatial resolutions appeared to provide too
much detail and decreased the map accuracy of
bare soil. The User’s accuracy also indicates the
importance of the detailed spectral information for
accurate separation of bare soil from other land
cover types. The spectral signal from IKONOS,
and IKONOS and LIDAR, is quite limited in this
context and resulted in significant overmapping of
bare soil.
The overall results are confirmed by the two
other land cover categories not discussed in detail,
i.e. AVIRIS/LIDAR provided the highest overall
classification accuracies (Figure 6). When com-
pared with LIDAR+IKONOS, AVIRIS+LIDAR
was also less sensitive to changes in spatial resolu-
tion, showing an overall decrease of 11 % from 4 to
16 m spatial resolutions compared to a decrease of
20% for IKONOS. AVIRIS data, without LIDAR
produced significantly lower classification accu-
racies (~ 60%) but was also less sensitive to changes
in spatial resolution, decreasing by only 7% from
4 to 16 m.
4. Conclusions
The results of this study have shown poten-
tials, limitations, and synergies between different
sources of remote sensing data. As shown in pre-
vious studies, IKONOS multispectral data pro-
vides insufficient accuracy in urban areas. Imag-
ing spectrometry (AVIRIS) data provide signifi-
cant spectral improvements. However, urban land-
cover classification with AVIRIS is still limited by
considerable spectral confusion between materials
with similar chemistry, such as composite shingles
and asphalt roads (Herold et al., 2004).
The use of three-dimensional information pro-
vided by LIDAR data significantly improves of the
accuracy of urban land cover maps. LIDAR is
particularly valuable for discriminating buildings/
roofs from roads based on height differences bet-
ween these two surfaces. In fact, the combination
of IKONOS and LIDAR data produced more
accurate results than using only spectral data from
AVIRIS. AVIRIS, on the other hand, performed
better for other classes like vegetation and bare
soil. The combination of AVIRIS and LIDAR pro-
vided the best land cover classification perfor-
IJG_Urban Mapping 24/04/2006, 10:22 AM9
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Multispectral Satellites - Imaging Spectrometry - LIDAR: Spatial - Spectral Tradeoffs in Urban Mapping
mance with over 90 % overall accuracy for 6
classes.
The land cover classification results showed
a strong dependence on the spatial resolution. Map
accuracy significantly decreased between 4 and
16 m spatial resolution. At coarser spatial resolu-
tions the spectral signals from individual urban
land cover features (Mainly buildings, roads, and
green vegetation) increasingly merge into mixed
pixels. The individual classification accuracies for
the categories steadily decreased, i.e. green veget-
ation is increasingly overmapped due its distinct
spectral characteristic, whereas built areas tends to
be underestimated. This trend is evident for all
sensor configurations and reflects the general
limitations of lower spatial resolution data in
mapping urban land cover. For coarser spatial
resolution the use of spectral mixture analysis
helps to map urban land cover on the sub-pixel
level (Rashed et al., 2001, Wu and Murray, 2003).
In terms of spatial-spectral tradeoffs, the impact
of spatial resolution (4 - 16 m) on map accuracy
was generally smaller than those for changing
spectral information (IKONOS, AVIRIS, LIDAR).
This suggests that it would be better to pick a lower
spatial resolution AVIRIS dataset over a high-
spatial resolution IKONOS dataset, at least from
a pixel-based spectral mapping perspective.
Moreover, the decrease in overall accuracy from
4 to 16 m for the AVIRIS data was only seven
percent. For the combination of IKONOS/LIDAR
this change was nearly 20 %. The accuracy decrease
is greatest between 12 – 16 m spatial resolution
and at 16 m spatial resolution the classification
performance of IKONOS/LIDAR dropped below
AVIRIS accuracy. Hence, AVIRIS data analyses are
less sensitive to changes in spatial resolution.
Although the trends varied for individual land
cover classes, IKONOS and LIDAR classification
data strongly depended on uncontaminated repre-
sentation of individual urban land cover features
and should only be used at the finest spatial
resolutions. If only coarse spatial resolution data
are available, a hyperspectral dataset is preferable.
It should be noted that these results reflect a
purely pixel-based spectral mapping perspective.
Thus, if the mapping objective is focused on the
spatial and geometric properties of land cover
structures (e.g. the shape and size of buildings),
fine spatial resolution data on to order of 3-5 m
are required for a clear representation of the urban
environment (Jensen and Cowen 1999). Also,
the use of object-oriented, segmentation image
analysis approaches can add an additional level
of information to the image classification and
help to resolve some of the limitations shown here
for spatial-spectral resolution dependent mapping
approaches (Blaschke and Strobl, 2001, Herold
et al., 2003c).
Figure 6: Overall accuracies and KAPPA coefficient for different sensor configurations and varying spatial resolution.
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International Journal of Geoinformatics, Vol. 2, No. 1, March 2006
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Green, R.O., Conel, J.E. and Roberts D.A., 1993,
Estimation of Aerosol Optical Depth, Pressure
Elevation, Water Vapor and Calculation of
Apparent Surface Reflectance from Radiance
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Imaging Spectrometer (AVIRIS) using
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T.G. Chrien et al. 1998. Imaging spectroscopy
and the Airborne Visible Infrared Imaging Spec-
trometer (AVIRIS). Remote Sensing of Environ-
ment, 65, 3, 227-248.
Herold, M, Goldstein, N. C. and Clarke K. C. 2003a.
The spatio-temporal form of urban growth:
measurement, analysis and modeling, Remote
Sensing of the Environment, 86, 286-302.
Herold, M., Gardner, M. and Roberts D. A. 2003b.
Spectral Resolution Requirements for Mapp-
ing Urban Areas, IEEE Transactions on Geo-
science and Remote Sensing, 41, 9, 1907-1919.
Herold, M., Liu X. and Clarke K. C., 2003c, Spatial
metrics and image texture for mapping urban
land use, Photogrammetric Engineering and
Remote Sensing, 69, 9, 991-1001.
Herold, M., Roberts, D., Gardner, M. and Dennison
P., 2004, Spectrometry for urban area remote
sensing - Development and analysis of a spectral
library from 350 to 2400 nm, Remote Sensing
of Environment, 91, 3-4, 304-319.
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racteristics of asphalt road aging and deterio-
ration: Implications for remote sensing appli-
cations, Applied Optics, 44, 20, 4327-4334.
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13
International Journal of Geoinformatics, Vol. 2, No. 1, March 2006
Martin Herold (SM98) was
born in Leipzig, Germany in
1975. He received his first gra-
duate degree (Diplom in Geo-
graphy) in 2000 from the
Friedrich Schiller University
of Jena, and the BAUHAUS
University of Weimar, Germany, and his PhD
study at the Department of Geography, University
of California-Santa Barbara in 2004. Dr. Herold
is currently coordinating the ESA GOFC GOLD
Land Cover project office at the Friedrich Schiller
University Jena, Germany. In his earlier career,
his interests were in multi-frequency, polarimetric
and interferometric SAR-data analysis for land
surface parameter derivation and modeling. He
joined the Remote Sensing Research Unit, Uni-
versity of California Santa Barbara in 2000 where
his research focused on remote sensing of urban
areas, imaging spectrometry for urban mapping,
and the analysis and modeling of urban growth
and land use change processes. Dr. Herold’s most
recent interest are in international coordination
and cooperation towards operational terrestrial
observations with specific emphasis on the
harmonization and validation of land cover data-
sets. Dr. Herold is a member of IEEE, the German
Society of Photogrammtry and Remote Sensing
(DGPF), and the Thuringian Geographical Union
(TGG).
Dar Roberts was born in
Torrance, California in 1960.
He received a Bachelor of Arts
Double Major in Environmen-
tal Biology and Geology from
the University of California,
Santa Barbara in 1982, a
Master of Arts in Applied Earth Sciences from
Stanford University in 1986 and a PhD in
Geological Sciences from the University of
Washington in 1991. He is currently a Professor
of Geography at the University of California,
Santa Barbara where he has taught since 1994.
He has published over 60 articles in refereed
journals, contributed 14 book chapters and
published over 100 non-refereed papers and
proceedings. His primary research interests are in
spectroscopy, land-use/land-cover change, fire
danger assessment and vegetation analysis,
primarily using remote sensing. He has worked
with a large variety of sensors, including hyper-
spectral thermal (SEBASS), several hyperspec-
tral VNIR sensors (AIS, HYDICE, Hyperion,
HYMAP, AVIRIS), active sensors (SAR, LIDAR,
IFSAR) and broad band data (MSS, ETM+, TM,
IKONOS, MODIS). Research sites include a
diversity of sites in North America, all of North
Africa, Madagascar and the Brazilian Amazon. He
has been a major participant in several large cam-
paigns, including DOE sponsored research at the
Wind River Canopy Crane site in south-central
Washington, LBA in Brazil and most recently the
North American Carbon Program. Recently he has
worked in urban environments, studying the spec-
tral properties of urban materials and evaluating
methods for mapping urban infrastructure includ-
ing road quality. He teaches advanced courses in
optical and microwave remote sensing.
IJG_Urban Mapping 24/04/2006, 10:22 AM13
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