Sunil Bhaskaran, Bruce Forster
School of Surveying and Spatial Information Systems
Faculty of Engineering,University of New South Wales
Sydney, 2052, NSW, Australia.
Corporate Strategy Division, New South
Wales Fire Brigades,Sydney,
Hail storms can result in substantial damage to property throughout the world. In Sydney, Australia for instance on the 14th April 1999, a thunderstorm was detected forming approximately 115 km south of Sydney near Nowra. In a span of 25 minutes the storm unleashed a maelstrom of icy fury, as the largest hailstones ever recorded in Sydney crashed down from the skies at over 200 km/h. The storm carved a path of destruction resulting in an estimated damage bill of 1.5 billion dollars (Fire News, 1999). Historically hailstorms have contributed to huge losses of property and in some rare instances lives. Table xxx shows the total estimated financial losses incurred by hail storms and other catastrophies from 1967-1998.
While the prevention of hailstorm is a myth, the management of dynamic resources for rescue and post disaster operations is an important issue. Hail storms result in damaged roofs, windows, temporary flooding, electrical short circuit, outbreak of fire, and general panic among residents. There is a high correlation of damaged roofs to the material composition of the roofing material which in turn determines their resistance to sustain the onslaught and fury of hail stones Andrew and Blong (1997a), Vorobief et al., n.d. Under these circumstances a systematic approach to the problem of rescue and repair operations hinge on a multitude of spatio-temporal geographic data. For instance, prior knowledge of areas with higher susceptibility which in this case may be explained by the types of roofing materials is vital for the allocation of dynamic resources. A clear and precise idea about the target areas which are likely to be affected by hailstones is necessary in order to manage post disaster operations efficiently (Sunil et al, 2000). This study examines the potential of a spectral analysis of urban surface materials including roofing materials which may provide some geographic details to the emergency services for strategic resource allocation and decision support .
Table 1 Largest Australian Insured Catastrophic Losses 1967-1998 Insurance Council of Australia Event Location Date Insured Loss
(A$ million) Earthquake Newcastle 1989 1125 Cyclone Tracy Darwin 1975 835 Hailstorm Sydney 1990 385 Cyclone Wanda Brisbane 1974 330 Bushfires Victoria/
South Australia 1983 325 Hailstorm Brisbane 1985 300 Thunderstorm Sydney 1991 225 Hailstorm Sydney 1986 160 Hailstorm NSW 1976 130 Cyclone Madge Northern Australia 1973 150 Cyclone Althea Townsville 1971 150
Current information about the vulnerable portions of the city of Sydney is vital for emergency preparedness. Hailstorm vulnerability can be assessed by the type of roofing material used for houses and other structures since different roofing materials have varying degrees of resistance to hail stones. The roof is the first point of impact from the hailstorm and thereafter severe damage is caused to the houses and property. Various studies indicate that tiles, gutters, windows, brittle cladding materials and metal sheeting are all at risk during heavy hail. Thin metal sheeting is dented or even penetrated, while tiles develop hairline cracks and are often shattered under the impact of hail stones. Age and impact location are important factors for many roofing materials (Vorobief et al., n.d). A study carried out by Andrew and Blong (1997b) reported that tile roofs were the most commonly damaged roof type. The study ranked roof materials were in decreasing order of susceptibility to damage: aluminium, fibro, slate, tiles and iron. Roofs contributed as one of the major cost items accounting for 22% of the total cost. The degree of resistances of roofing materials vary according to the material composition and these roofs are distributed in varying proportions over the Sydney metropolitan region which may be hit by hail storms in the future.
Remote sensing is an important technology, which provides real and near real time spatio-temporal digital data which may be analysed in many ways to derive additional knowledge about urban materials. Many studies have been carried out in the past which used remote sensor data to study urban surfaces mainly by means of classification of multi-spectral data materials (Lo, 1997, Forster 1983, Meinel et al). A methodology which used airborne remote sensor data was demonstrated to map vulnerable regions which may be affected by the potential of a hail storm in Sydney (Sunil et al, 2001). These studies brought out the potential as well as limitations of using broad band remote sensor data for analysis particularly in urban areas. For the present study such broad band sensors were not considered since most of these features will not be detected by broad band sensors (xxxxxx) due to their inadequate spectral resolution. Besides these urban features occur heterogeneously in space and do not follow any specific pattern which compounds the problem of their systematic identification. Since details extracted from broad band sensors such as Landsat, SPOT and other optical remote sensor data have proven to be either inadequate or complex, the potential of generating details for each pixel by using hyperspectral sensor data is a strong motivation for pursuing this study.
Imaging spectroscopy or hyperspectral imaging has already revolutionalised the field of remote sensing by combining the science of spectroscopy with that of imaging to render minute details about our earth surface, which was not possible until recent times with broad band sensor systems. Every object is characterised by a typical and unique spectral reflectance which is detected by reflected-light spectroscopy. Reflectance varies with wavelength for most materials because energy at certain wavelengths is scattered or absorbed to different degrees.
A spatial distribution of roofing materials may be a valuable indicator to gain insight into regions of relatively higher susceptibility from hail stones than others, which may be detected by remote sensing images. The heterogeneity of urban surface materials added to spectral confusion (Forster, 1983) and limitations of few spectral bands makes most of the available broad-band sensors such as Landsat and SPOT incapable of detecting urban surface features (Hieden et.al, 2001). However, hyperspectral remote sensing data, due to their fine spectral resolution have the potential to distinguish between various surface materials (Goetz 1982, Roessner et.al.,1998). The overall shape of a spectral curve and the position and strength of absorption bands in many cases can be used to identify and discriminate materials in an urban area. A supervised classification of materials in an urban environment was created by (Bhaskaran et.al, 2000) by forcing a reference library of endmember spectra to image spectra in urban areas of Perth, Western Australia and later in Sydney, Australia. Recent studies accomplished in hyperspectral analysis of urban areas have yielded positive indicators with respect to the potential of spectral analysis. For instance, in the terrestrial urban environment two major aspects can be remotely sensed: natural targets (e.g. soil, water, vegetation) and man-made targets e.g. buildings, pools, roads and vehicles (E.Ben-Dor et al, 2001). The potential of imaging spectroscopy has been demonstrated by a few other scientists such as Ridd (1997), Hepner et al. 1998, Bianchi et al. (1996), Fiumie and Marino (1997) and Roessner et al. (1998).
Previous studies have achieved remarkable success in imaging spectroscopy and its ability in identifying and quantifying urban features using the albedo and chemical composition of materials (Bianchi et al, 1996; Fumie and Marino (1997); Roessner et al. (1998); Lehmann et al (1998). However, we believe very little has been achieved and documented in the area of integrating such information with GIS data to develop decision support systems which may be useful for many organizations dealing with spatial data. The main purpose of this paper is to demonstrate a methodology for integrating classified hyperspectral data with available cartographic (GIS) data to address vital resource allocation issues and vulnerability mapping.
The main objective of this paper is to examine the potential of Hymap sensor data to create a surface material distribution map of vulnerable regions, which may be susceptible to the threat of hailstorm damage. Specific objectives may be summarised as follows:
- To create a laboratory spectra of urban surface materials especially roofing materials by using a fieldspec under artificial illumination and natural light
- To map the vulnerability of urban areas to the Hail Storm hazard by analysing the Hyperspectral sensor data.
- To integrate classified hyperspectral data with GIS data for the provision of intelligent information and decision support systems to emergency organizations.
Study Area and Airborne HyMap data
A narrow transect (3 by 19kms) covering the region from Concord, located to the south of the Parramatta River, to the Forestville region located to the north of the Parramatta River, was exposed using the airborne HyMap sensor in early September 1999, by Integrated Spectronics Pty Ltd Sydney, Australia. The instrument recorded 126 spectral bands which spread from 445 nm to 2543 nm in the electromagnetic spectrum. For purposes of exploring the full potential and capability of hyperspectral data, it was necessary to select a study area which had various types of roofing materials representing different land use and functions such as residential, commercial, educational, industrial and so on.
The Concord Bay region located to the south of the Parramatta River in Western Sydney and some parts to the north of the river were ideal locations. These areas had a mixed type of land use and the occurrence of a wide variety of roofing materials in close proximity. From the objective of the study and potential of future hail storm damage this was considered to be a ideal study area and a potentially vulnerable region. Figures 1 shows the study area south of the Parramatta River exposed by HyMap sensor as well as by Aerial photo.
Figure. 1 Study area (Concord Bay, Sydney) exposed by Airborne Hyperspectral Sensor (left ) & Aerial Photo (right)
A good understanding of the surface features is essential for accurate analysis of the Hymap image. A database was created for different land-uses, which showed the material composition of the surface features which ranged from roof types (terracotta tiles, concrete tiles, slate tiles, corrugated fibro and metal) to pavers and bitumen. A database was also created by surveying the field in the study area with the aid of a laptop and high resolution aerial photo image as well as other GIS layers such as street network and census layers (see Fig xxx). Most of the materials fell into the category of terracotta tiles, concrete tiles, slate tiles, corrugated fibro and metal roofs, pavers and bitumen which were found almost exclusively in some places and in a mixed form in others. Since the spatial resolution of the HyMap image was 5m by 5m care was taken to examine those areas which could be also be spatially resolved on the hymap image. Apart from the material composition, the age, location, use and function of the place where the sample feature was found was also recorded. In some instances where the roofs could not be seen directly, local but reliable knowledge was obtained from staff who were working at these places.
Methodology and Analysis
Various samples of urban surface materials, mainly consisting of roofing materials and pavers were gathered from different sources. Table 2 shows some of the different surface materials used in the analysis.
Terracotta Tile Vegetation Clay Brick (Red) Concrete Paver Wood Birch Brick Metal Concrete Brick Bitumen Slate Basalt Sandstone Corrugated Fibro Marble
Apart from the types of roofing materials collected, particular care was also taken to collect weathered and non-weathered materials. The methodology was to generate a reference spectral library consisting of different types of spectra from roofing materials. The HyMap sensor cover both the reflective V-I-S; 0.4-0.7μm, N-I-R; 0.7-1.1μm and SWIR; 1.1-2.5 μm wavelengths. A FieldSpec® Pro Full Range (FR) spectro-radiometer from Analytical Spectral Devices (ASD, Colorado, Boulder, USA) as shown in Fig 1 that measures reflectance in the VIS, SWIR I & II was used to collect reflectance (i.e in the white reference mode). The spectrometer unit incorporates 3 spectrometers to cover the 0.350-2.500μm wavelength. The HyMap image has 126 bands which were reduced to 115 bands due to poor data quality in bands 63,64,65,66,67,94,95,96,97,125 and 126. This was done for two reasons: firstly to avoid data from pronounced absorption features operating outside the atmospheric window such as water H2O near 1.4μm and carbon-dioxide CO2 at 1.9μm and secondly to avoid negative reflectance values from the image spectra, since the reference spectral library were all in positive reflectance values. The negative values from image spectra were possibly due to some inconsistencies in the sensor.
The reference spectra was calibrated and forced to the image spectra and a supervised classification Spectral Angle Mapper (SAM) was performed in order to create surface abundance maps showing roofing materials with different resistances to hail storm. Accuracy estimation was carried in the field with the aid of high spatial aerial photo images. The classified image was georeferenced to the UTM projection system zone 56. Integration with GIS data to show the areas at risk was performed by spatial overlay over the geo-referenced classified HyMap image.
- Spectral library of urban surface materials
A full range (0.350μm – 2.500μm) spectro-radiometer from ASD was used to extract reflectance values from selected urban materials mainly consisting of roofing materials, pavers, bitumen, metals, corrugated fibro and slate. Roofing materials such as slate, corrugated fibro and terracotta were identified as highly vulnerable to hail storm (see table 1). The urban materials were examined under artificial illumination and the spectra were extracted from each of the sample materials. A spectral library of pure urban materials was created and resampled to the 126 channel HyMap image configuration. The spectral characteristics were viewed and studied using a spectral display software Viewspecpro. The second batch of spectra was generated under natural light. A clear and sunny day was chosen for collecting spectra from surface materials. This exercise was carried out between 12noon and 1 pm in order to reduce the effect from the sun angle. The two urban spectral libraries were named (USAL) urban spectra artificial light and (USNL) urban spectra natural light. Fig 3 A & B shows some of the sample spectra taken in the laboratory by using a spectroradiometer and in the field under natural light.
- Image calibration
In order to directly compare hyperspectral image radiance spectra with field reflectance spectra, the encoded radiance values in the image must be converted to reflectance. The Empirical Line method was preferred over the Flat Field calibration and the Internal Average Relative Reflectance (IARR) techniques. The Flat Field calibration technique is normally applied to spectrally homogeneous areas whereas the IARR calibration technique is used to normalize images to a scene average spectrum. This technique is particularly effective in an area where no ground measurements exist and little is known about the scene. This technique was not chosen for analysis since the study area comprised of hetereogenous urban surface consisting of a variety of features.
The Empirical Line calibration technique is used to force image data to match selected field reflectance spectra (Roberts et al., 1985; Conel et al., 1987; Kruse et al., 1990). Field reflectance spectra must be acquired from two or more uniform ground target areas which should have widely different brightness and be large enough to recognize in the image. A known dark (concrete) and bright target (sandstone) from the Hymap image were selected and paired with the spectra of the same objects from the reference spectra for calibration. The reference spectra were then forced with the spectra from the image by employing the empirical line technique. A linear regression was calculated between the reference spectra and the image spectra. The regression line was used to predict the surface reflectance spectrum for each pixel from its original image spectrum (ENVI user manual, 1999). The encoded radiance values of the image were thus converted to apparent reflectance by the empirical line technique.
Figure 3A & B Spectra from Field and Image
- Interpretation of Urban roof spectra
The mapping of urban roof types is one of the key factors in understanding the vulnerability to the damage potential of hail storm hazard in urban environments. Therefore a detailed mapping and interpretation of the urban surface materials and their spectral characteristics were essential. Generally, the total reflectance of a given object across the entire visible region (also termed albedo) is strongly related to the physical condition of the relevant targets (shadowing effects, particle size distribution, refraction index etc.) whereas the spectral peaks are more related to the chemical condition of the sensed target (specific absorption). Several urban related chromophores do provide significant absorption features in the VIS-NIR, such as chlorophyll (at 0.68μm), iron oxides (at 0.50μm, 0.56, 0.88) Hunt et al, 1971). In addition to these specific absorption features the shape of the spectral curve also holds importance in distinguishing urban surface materials (Bhaskar et al, 2000)
Metal roofs show a high reflectance in the visible range due to the presence of steel and aluminium unlike concrete and terracotta. Terracotta is mainly composed of clay which shows strong absorption peaks in the VIS range and in the far infrared region at around 2160 nm due to the presence of hydroxide ions in Kaolinite, a naturally occurring mineral found in clay based materials. This absorption feature at the 2160 nm is notably absent for other roof types such as metal, slate, and concrete. However, older terracotta shows higher absorption when compared to newer terracotta due to the weathering and fading of the original colour. Concrete tile roofs show high reflectance in the VIS but generally are featureless throughout the spectrum. Roofing slate is a dense natural material that is practically non-absorbent. The colour of slate is determined by its chemical composition. Because these factors vary from region to region, slate is available in a variety of colours. These same factors also influence how susceptible slate is to changing colour upon exposure to the weather. In the study this character of slate made it difficult to detect slate roofs accurately. The presence of slate or similar material such as bitumen, asphalt made it difficult to distinguish slate from the other materials.
- Mapping Vulnerable regions by Supervised Classification (Spectral Angle Mapper)
The spectral angle mapper is an automated method for comparing image spectra to individual spectra or a spectral library (Boardman, unpublished data; CSES, 1992; Kruse et al., 1993). The spectral angle mapper (SAM) classification was used for comparing image spectra to the reference spectra. The SAM algorithm determines the similarity between two spectra by calculating the spectral angle between them as unit vectors in spectral space with dimensionality equal to the number of bands (ENVI user manual, 1999). A classified image was produced by supervised classification in which each pixel was assigned to a class (roofing material, Fig 4).
The study area is dominated by terracotta tile roofs both old and new varieties. It may be deducted that the predominant land-use is residential, interspersed with some commercial establishments along the major highways and some industrial activities which had metal roofing. Some of the residential dwellings were also made up of concrete structures which appear in a random manner in the classified image. Some educational institutions which fell in the study area had roofing materials consisting of concrete and metal where their compounds were made up of concrete pavers. From the pattern of the land use and distribution of surface materials one can assume that the main threat from hail storm hazard would be directed to residential areas since terracotta roofs are susceptible to hail stones.
In many instances by a combination of hyperspectral analysis and scientific visualization (photo-interpretation using elements such as shape, colour, pattern, association) the land use may be determined which in turn also assists the spectral analysis of urban materials. Two twin structures belonging to the department of housing were initially recorded as concrete roofs, but the spectral analysis showed that they were indeed some type of metal roof. This was confirmed later to be corrugated iron during the field verification process. Reliable local knowledge was used wherever the access to the roofs was impossible.
A field check was performed in the study area located both to the south and north of the Parramatta River. This was performed by visiting the field and also by using a database of roofing materials, which were checked and identified on the ground and created in the form of a point layer using MapInfo GIS software prior to the analysis. Forty randomly selected points were visited on the ground to verify the results of the spectral analysis and classification results. The area around the fairways to the south of the study area comprised of terracotta type roofs which were accurately detected by the SAM classification. The individual structures all along the Hilly Street were accurately identified by the classification. In some instances where the structures were found to have concrete and metal roofing materials together the classification matched the features accurately. The metal roof all along the highways were identified successfully. There were some areas which had slate roofs in combination with concrete and terracotta which were not clearly identified, which may be attributed to the inadequate spatial resolution of the HyMap image. Interestingly slate roofs were also identified accurately but there were many instances where other materials having similar spectral characteristics to slate such as bitumen, asphalt were wrongly classified as slate. It is our belief that a detailed analysis particularly of the spectral shape and curve of such practically non-absorbent features may yield better results. Figure xxx shows the spots visited for verification which were successfully identified (in white) and mapped. The field verification confirmed the immense potential in integration of Imaging Spectroscopy for Urban area analysis and their integration with GIS for emergency decision support systems.
Potential of GIS to develop decision support system DSS
The analysis of the hyperspectral data provided a major input in the form of distribution of roof types in the study area in near real time. The total risk in a certain urban area is a factor of various interactive variables such as land-use, roof types, population density, socio-economic characteristics, to name a few. Risk may be explained with the help of these variables if they can be analysed in combination and not exclusively of each another. A GIS enables such spatial analyses and assists in the development of decision support systems which may be modelled by inputting multiple risk related spatio-temporal variables.
The classified image was geo-referenced to the UTM projection system, zone 56. The census data provided by the Australian Bureau of Statistics (ABS) may be used for creating new derived layers such as population density, percentage of less mobile people and socio-economic characteristics (Table 3). As an example for this study a few layers are spatially overlain (see Fig 5 A & B; which shows the vector map of the classified image as well as the Spatial Overlay). This integration of Hymap and GIS data may provide a valuable spatial emergency decision support system (SEDSS) for carrying out emergency operations and allocation of strategic dynamic resources. Mapping the overall vulnerability may also be modelled by integrating hyperspectral data and GIS. For instance, if a certain area were to be occupied by a less resistant roof material such as terracotta tile, a high population density a majority of whom are of ethnic background, then the overall vulnerability of the area may be significantly higher as compared to an area occupied by more resistant roofs only. Although the variables which may affect the overall vulnerability are many, these would vary depending on the area to be investigated and their demographic and population characteristics.
Figure 5 A & B Vector Map of Classified Image and GIS Operations: Spatial Overlay
Result, Discussions and Future Work
Surface truthing with the aid of current aerial photo images exposed over the study area revealed 90% accuracy. It is impossible to prevent a hailstorm but there is ample scope to alleviate the hardships faced by residents in a crisis situation presented by the damage caused from hailstones. Appropriate measures in the form of emergency preparedness may be employed to ensure maximum protection and safety from such natural threats. Existing technology in the form of RS & GIS may be used to reduce the effects of such unpredictable disasters.
- Calibrating image spectra to reference spectra by using known targets
In this study several known targets such as sandstone, terracotta and vegetation were used during the SAM technique. All these images came up with different percentages of roofing materials. For instance, when terracotta tile was used as the known target during calibration the SAM yielded a high percentage of terracotta tile in the output classification. This percentage came down when other known targets such as sandstone and vegetation were used. This clearly indicates that to classify particular roofing material the best option would be to use the same feature as the known target for calibration.
Present spatial resolution of airborne hyperspectral sensor data does not enable spectral unmixing especially in this study where the roofing materials occur spatially in a juxtaposed manner. Resultantly, a single pixel at a 5m by 5m spatial resolution results in spectral confusion in heterogenous urban areas. An improved spatial resolution will result in identification of pure end-members and a more accurate surface abundance map. At the moment hyperspectral sensors are carried on aircraft. The future will see earth observations by these sensors from space-borne platforms. The proposed spatial resolution is rather broad to be useful for much urban analysis in particular, but it is expected that the resolution will improve in time. Until then airborne hyperspectral data will be the most effective scale independent source of studying urban surfaces. The high cost of acquiring hyperspectral data is an impediment in its widespread use.
Scope and Limitations of the Study
Emergency services have to make decisions on short notices, which are in turn are influenced by numerous factors most of which have a spatial and temporal dimension. For instance, the issue of resource allocation is influenced by spatio-temporal factors such as location of potential hazard prone regions, existing predominant land-use, population density, ,socio-economic characteristics and so on. Since dynamic resources have to be managed in an efficient way the combined analyses of Remote sensor data and GIS data is inevitable. There are many aspects of procuring such information which are very important such as Currency, Accuracy and Reliability. Integrated spectral analysis of Hyperspectral and GIS data enables us to study the complex urban areas in near real time.
The spatial resolution of hyperspectral data has to be sufficiently high particularly in Urban areas due to the variations in the shape and sizes, heterogeneity and dense pattern of urban features. Systematic appraisal of the features needed to be detected and analysed will lead to the selection of an appropriate spatial resolution which will in turn increase the accuracy of spatial unmixing and will improve the sub-pixel analysis of urban areas. In the present study there were some instances where the spatial resolution was not enough to determine the spectra of some features. Airborne hyperspectral sensor data has a definite advantage over proposed space borne hyperspectral sensors in that they are scale independent and may be exposed over any region to the required resolution. This aspect is important for the study of urban areas particularly given the irregularity and dynamism of urban features.
The variations within a surface material such as terracotta due to weathering, colour changes, irregularity in the chemical composition and therefore the material composition may create problems in the accurate analysis and mapping, but with a careful approach they may be addressed to some extent. For instance, tiles belonging to certain categories related to the age, colour or chemical composition may be classified and spectrally analysed separately. This may address the reasons for the differences in urban spectral characteristics of the same material over a period of time.
All this indicates that there is tremendous scope for the application of Imaging spectroscopy to problem solving but there is a need to approach this technology prudently. On the other hand it is quite clear that Imaging spectroscopy and their integration with GIS is arguably the best option for the study of urban areas.
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