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Methodology to integrate hyperspectral Remote Sensor data with GIS for decision support systems:- A case of hail storm damage in Sydney
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. Objectives 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:
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) |