The Usefulness of Radarsat for Monitoring Land Use Change in the Multimedia Super Corridor, Malaysia
Judibal C.C.1 Kamaruzaman J.2 Marghany, M.M3
1Faculty of Forestry - UPM
2 Faculty of Forestry - UPM
3Faculty of Engineering - UPM
Abstract
The Multimedia Super Corridor (MSC), which includes University Putra Malaysia (UPM) and Serdang township was studied using radarsat data. The ESAI/PCI software was used to discriminate land use alteration cover from the EASI/PC software that discriminates vegetation alteration cover from the other cover types, and provides rate of vegetation cover in the area. This quantitative analysis using overlay techniques provides an excellent strategy for monitoring land use change in the study area. A supervised classification was used to examine the spatial impact of incorporating topography in the classification. This involved a classification and mapping of the entire study area using Radarsat data and topographic descriptors extracted from an interpolated digital elevation model (DEM) for all pixels in the study area. The results provide evidence that Radarsat and topographic data sets derived form DEM can be integrated in order to estimate rate of change on the green cover area. The combined analysis of the vegetation data showed that some forms of degradation has taken place through a few years ago.
Introduction
Accurate and up to date earth surface or terrain information is a common requirement of scientists in a wide range of disciplines since the nature of the surface affects the practical use of land. Foresters, ecologists, engineers, geographers, and other concerned decision making and planning. Information requirements include measurement of attributes may describe the vegetation type and composition at a site or the topography in the form of slope, aspect or elevation.
Quantitative analysis of terrain is gaining increasing importance as a component of spatial modelling in Remote Sensing and geographic information systems (GIS). The objective of such quantitative analysis is to assign a relevant description to all elements in a digital terrain model, with the objective of the terrain (Evans, 1979), but because of technical difficulties in producing and integrating such data with other information, it has only been utilized for a limited set of applications.
Paper presented at the 18th. Asian Conference on Remote Sensing. 20-25 October 1997. Kuala Lumpur, Malaysia.
DEMs are similar ot Radarsat data in that thay are quantitative representations of tye earth surface; however, each number in the model represents terrain elevations at knoqwn positions rather than spectral intensities (BURROUGH,1986). Such models can be generated independently from ground survey (Brinker and Wolf, 1984), topographic maps (Collins, 1975), aerial photography (Crawley, 1974), or most recently, geographic information systems (GIS) (WEIBEL and DeLotto, 1989). The application of DEM data in digital terrain classification has been proposed and attempted for MSS by Hutchinson (1978) and TM data by Moulton (1989); efforts has been documented using radar imagery in a subartic environment, although several researches (for example, Walsh, 1987) have pointed out the value of this approach of Radarsat. Several systems for automated terrain classification have already been reported been reported by (Pike (1988) and Mark (1975)).
However, they all lack a certain breath of the approach and therefore do not yield optimal results. The objective of this paper is to determine whether or not a data set composed of topographic terrain descriptors can lead to an improved classification if integrated with supervised classification by Radarsat data.
Methodology
Satellite Data
Radarsat data (fine mode) of Kuala Lumpur was taken on 07 January 1996 at 22:49:41.070 GM time. The data is located between 20 88' N to 3 40' and 1010 55'E to 1010 98'E. For the purpose of the study, sub-scenes of about 305 by 320 pixels of the study area were selected. It is located between 20 88' N to 30 05' N latitude and between 1010 41 'E to 1010 45'E Longitude.
Digital Image Processing
Textures and Filtering Analysis
The Radarsat image data were loaded into the PCI EASI/PAC image processing system. A subset image was later selected by image subset command. Digital image processing steps included textures analysis and speckle filtering The image processing analysis has conducted based on Maged et alkalinity. 1996.
DEM Analysis
Topographic map were digitized by using AutoCAD version 12. The digitizing data was then loaded to PCI EASI/ PAC and later converted into digital data to raster data in order to create DEM which was late converted to 3D DEM. Finally, the raster data was overlaid on the textures analysis on Radarsat image. The main purpose of this procedure is to determine the change of DEM since 1981 to 1996.
Supervised Classification
Different sets of classes was used to processes image classifications. These classes were selected based on the information obtained from the topographic map of study area. The statistical calculation of the classes was obtained automatically by PCI EASI/PAC software. Two types of classification were performed. The first step was to determine statistically it traditional information is available from the topographic data set. This requires a systematic analysis of the relationships between supervised classification acquired by Radarsat and the topographic component of terrain in the region selected for this study.
The next step was to determine the statistical improvement in landscape or terrain classification accuracy that can be achieved by integrating supervised classification and topographic data. The statistical analysis was based on a discriminant function derived from supervised classification, topographic data alone and a combination of supervised classification and topographic data set. Finally a spatial analysis of the integration of supervised classification and topographic data in the form of a map product was conducted.
