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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 : [email protected] Dep. of Geography, University of California Santa Barbara, Ellison Hall, Santa Barbara, CA, 93106,

USA, E-mail : [email protected]

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


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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).


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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.


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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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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


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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-


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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


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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-


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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.


11


References

Ben-Dor, E., Levin, N. and Saaroni H., 2001, A

spectral based recognition of the urban envi-

ronment using the visible and near-infrared

spectral region (0.4-1.1 µm), A case study over

Tel-Aviv, International Journal of Remote

Sensing, 22, 11, 2193-2218.

Blaschke, T. and Strobl J., 2001, What’s wrong with

pixels?: Some recent developments interfac-

ing remote sensing and GIS, GeoBIT, 6, 6,

12-17.

Clark, R. N., R. O. Green, G. A. Swayze, G. Meeker,

S. Sutley, T. M. Hoefen, K. E. Livo, G. Plumlee,

B. Pavri, C. Sarture, S. Wilson, P. Hageman,

P. Lamothe, J. S. Vance, J. Boardman I.

Brownfield, C. Gent, L. C. Morath, J. Taggart,

P. M. Theodorakos, and M. Adams, 2001.

Environmental Studies of the World Trade

Center area after the September 11, 2001

attack. U. S. Geological Survey, Open File

Report OFR-01-0429, URL: http://speclab.cr.

usgs.gov/wtc/ (access: February 2004).

Clark, R.N., Swayze, G.A., Heidebrecht, K.B.,

Goetz, A.F.H. and Green R.O., 1993, Com-

parison of methods for calibrating AVIRIS

data to ground reflectance, Proceedings of the

4th Annual Airborne Geosciences Workshop,

JPL Publication 93-26, 31-34.

Clark M. L., Clark D. B.and Roberts D A. 2004.

Small-footprint lidar estimation of sub-canopy

elevation and tree height in a tropical rain forest

landscape, Remote Sensing of Environment,

91: 68-89.

Cohen, J.D., 2000, Preventing Disaster, Home

Ignitability in the Wild-Urban Interface, Jour-

nal of Forestry, 98, 15-21.

Cooper, D.I., Eichenger, W.E., Archuleta, J., Hipps,

L., Kao, J., Leclerc, M.Y., Neale, C.M. and

Prueger, J., 2003, Spatial and temporal footprint

analysis of three dimensional moisture fields

from lidar, eddy covariance, and a footprint

model. Agricultural and Forest Meteorology,

114:213-234.

Gamba, P. and Houshmand B., 2000, Digital

surface models and building extraction: a

comparison of IFSAR and LIDAR data, IEEE

Transactions on Geoscience and Remote Sen-

sing, 38, 1959 –1968.

Gamba, P. and Houshmand B., 2002, Joint analy-

sis of SAR, LIDAR and aerial imagery for

simultaneous extraction of land cover, DTM

and 3D shape of buildings, International Jour-

nal of Remote Sensing, 23, 20, 4439-4450.

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

Measured by the Airborne Visible-Infrared

Imaging Spectrometer (AVIRIS) using

MODTRAN2. Proceedings of SPIE Conference

no. 1937, Imaging Spectrometry of the

Terrestrial Environment, 2-5.

Green, R.O., Eastwood, M.L., Sarture, C.M. and

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.

Herold, M. and Roberts D. A., 2005, Spectral cha-

racteristics of asphalt road aging and deterio-

ration: Implications for remote sensing appli-

cations, Applied Optics, 44, 20, 4327-4334.


12


Irish J.L. and Lillycrop W. J. 1999. Scanning laser

mapping of the coastal zone: the SHOALS

system, ISPRS Journal of Photogrammetry &

Remote Sensing, 54, 123–129

Jensen, J.R. and Cowen D.C., 1999, Remote sensing

of urban/suburban infrastructure and socio-

economic attributes, Photogrammetric Engi-

neering and Remote Sensing, 65, 5, 611-622.

Jensen, J.R, 2000, Remote Sensing of the Environ-

ment: An Earth Resource Perspective, Prentice

Hall.

Landgrebe, D.A. and Biehl L, 2001, An introduc-

tion to MultiSpec, URL: http://www.ece.purdue.

edu/~biehl/MultiSpec/2001, (access September

2004).

Lefsky, M.A., W.B. Cohen, G.G. Parker and D.J.

Harding (2002), Lidar remote sensing for

ecosystem studies. Bioscience 52(1) 19-30,

Medina, M. A., 2000, Effects of shingle absorpti-

vity, radiant barrier emissivity, attic ventila-

tion flowrate, and roof slope on the perfor-

mance of radiant barriers, Int. J. Energy Res.

24(8): 665-678.

Priestnall, G., Jaafar, J. and Duncan A., 2000,

Extracting urban features from LIDAR digital

surface models, Computers, Environment and

Urban Systems, 24, 2, 65-78.

Rashed, T., Weeks, J.R., Gadalla, M.S. and Hill

A., 2001, Revealing the Anatomy of Cities

through Spectral Mixture Analysis of Multi-

spectral Imagery: A Case Study of the Greater

Cairo Region, Egypt. Geocarto International,

16, 4, 5-16.

Ridd, M.K., 1995, Exploring a V-I-S- (vegetation ñ

impervious surface- soil) model for urban

ecosystem analysis through remote sensing:

comparative anatomy for cities, International

Journal of Remote Sensing, 16, 2165-2185.

Roberts, D. A. and Herold M., 2004, Imaging

spectrometry of urban materials, in King, P.,

Ramsey, M.S. and G. Swayze, (eds.), Infrared

Spectroscopy in Geochemistry, Exploration

and Remote Sensing, Mineral Association of

Canada, Short Course Series Volume 33,

London, Ontario, pp. 155-181. URL: http://www.

ncgia.ucsb.edu/ncrst/research/pavementhealth /

urban/imaging_spectrometry_of_urban_

materials. pdf

Roberts, D.A., Adams, J.B., & Smith, M.O., 1993,

Discriminating Green Vegetation, Non-

Photosynthetic Vegetation and Soils in AVIRIS

Data, Remote Sensing of Environment, 44 (2/

3), 255-270.

Roberts, D.A., Green, R.O. and Adams J.B., 1997,

Temporal and spatial patterns in vegetation

and atmospheric properties from AVIRIS,

Remote Sensing of Environment, 62, 3, 223-

240.

Schueler, T.R., 1994, The importance of imper-

viousness. Watershed Protection Techniques,

1, 3, 100–111.

Steel, A.M., Barnsley, M.J. and Barr, S.L., 2001,

Inferring urban uand cover and land use

through analysis of airborne LIDAR data, in

Juergens, C. (2001): Proceedings of the Sym-

posium on Remote Sensing of Urban Areas.

Welch, R. 1982. Spatial resolution requirements

for urban studies. International Journal of

Remote Sensing, 3, 2, 139-146.

Woodcock, C.E. & Strahler, A.H. 1987, The factor

scale in remote sensing, Remote Sensing of

Environment, 21, 311-332.

Wu, C. and Murray A.T., 2003, Estimating imper-

vious surface distribution by spectral mixture

analysis, Remote Sensing of Environment, 84,

493-505.


13


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.


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