Extracting Land-Use Information Related to Socio-Economic Function From Quickbird Imagery: A Case Study of Semarang Area, Indonesia
4. STUDY AREA
Semarang Municipality is the capital of Central Java Province, Indonesia. In terms of spatial data availability, the Semarang local authority for planning (Bappeda) does not have adequate data to support spatial planning in its area. The Bappeda only has a Key Dataset for Local Development (KDLD) with relatively old maps, including LU map published in 1995. Moreover, all spatial data are stored in paper format and there is no GIS system for storing and modelling the spatial data for planning purposes. Semarang is inhabited by approximately 1.33 million (2001) with most people concentrated on the north coastal alluvial plain of Java Sea. Stretching from 6º 50’ - 7º 10’ S and 109? 35’ – 110?50’E, Semarang municipality is unique among other provincial capital cities in Indonesia. In terms of area, it ranks fourth (373.7 km2) among other Indonesian cities, and it administratively covers both urban and rural areas at roughly the same proportion. The rural land-uses situated in the rolling and hilly terrain in the southern part are currently decreasing very rapidly due to the rapid urban area expansion. Young fluvial deposits in the coastal area currently experience land subsidence, causing rob – a local name for flooding process related to sea tidal activities. The rob has periodically inundated the urban area intermittently during the past 15 years, provoking inhabitants to move to the higher place with rough terrain in the southern part of the city. Figure 1 shows the study area covering the western part of the Semarang Municipality.
Figure 1. The study area as shown by a colour composite of Quickbird multispectral imagery
5. MATERIALS AND METHODS
This study used Quickbird multispectral and panchromatic datasets at 2.4 m and 0.6 m spatial resolutions respectively, covering western part of Semarang city, Central Java, Indonesia. The datasets were acquired on 31 August 2002 and delivered at standard correction mode, i.e. only the systematic geometric and radiometric errors were corrected. Research Systems Inc ENVI 4.0 software was used for creating a pan-sharpened multi-spectral dataset and multi-spectral classification. ITC’s ILWIS version 3.3 raster-based GIS and image processing software was utilised for on-screen digitisation of terrain units and spatial-related land-cover features. In addition, a trial version of eCognition version 4.0 software was also used for object-based image segmentation.
As summarised in Figure 2, a multispectral classification based on the Quickbird’s multispectral bands was carried out using maximum likelihood algorithm. The VLUIS spectral-related cover classification scheme (Table 1) was used as a reference, although the regions of interest (ROIs) of sample were taken and labelled using generic names. At this stage, 85 spectral-related land-cover classes were generated. After that, a class merging procedure followed, generating 48 land-cover classes. A selective majority filtering was applied to the merged classes in order to remove isolated classified single pixels and to aggregate the speckled classified pixels. The majority-filtered classification result was then used as a land-cover map, ready for further processing, i.e. deriving socio-economic aspect of land-use.
Figure 2. Method used in this study
Table 1. Spectral-related cover dimension classification scheme used in the study area
Visual interpretation procedure was applied using on-screen digitisation of the Quickbird pan-sharpened colour composite image, for generating the spatial dimension of land-use with respect to the VLUIS classification scheme. (Table 2). The pan-sharpened imagery was produced by integrating the higher-spatial resolution panchromatic band (0.60m) with the lower multi-spectral bands (2.4m) using Brovey transform (Vrabel, 1996). The criteria used in this visual interpretation process were location, shape, pattern, regularity, and density of the land-cover. The visual interpretation was also applied to derive a terrain unit map. The derived terrain units represent a set of land characteristics relevant to the variation of land-use in the study area. Although a geomorphological approach was used here, the map did not contain exactly the same information as landform map. Field work activity was done based on the terrain units. The terrain units served as strata in the stratified sampling for land-cover and land-use data collection. Information related to the land characteristics was also collected during the fieldwork to improve the tentative classification of the terrain.
Table 2. VLUIS spatial dimension classification scheme used in the study area
Table 3 Relationship between terrain unit and the land/terrain characteristics interpreted from the Quickbird imagery and field observation
In order to find an alternative of spatial dimension map, an object-based image segmentation was used. By using the eCognition software, an unsupervised multi-resolution image segmentation was run to a small portion of the study area (1024 x 1024 pixels), i.e. the northwestern corner of the Quickbird coverage used in this study. The criteria used in this stage were the weight of each spectral band involved, which could be modified with respect to the presumed importance of each band in the classification, the shape factor which could be set to 0.8 remaining factor 0.2 for the colour, and a factor of 0.9 for the smoothness instead of compactness. Summarised criteria for this segmentation process are presented in Figure 3. The spatial dimension map’s accuracy derived from this procedure was assessed using the visual interpretation-based spatial dimension map. A threshold of 85% overall accuracy was used in order to decide whether or not the image-segmentation would be used.
Figure 3 Summarised criteria for the unsupervised object-based image segmentation
After obtaining all required input maps, a knowledge-based image classification for generating land-use information related to socio-economic function was run. Classes for the socio-economic function referred to the VLUIS socio-economic dimension’s classification scheme (Table 4). In this process, relationship between land characteristics, spatial pattern, and the land-cover types were formalised into a set of rules, applicable in a raster-based GIS environment. The accuracy of the derived land-use map was then assessed using overall accuracy and Kappa.
6. RESULTS AND DISCUSSION
Multi-spectral classification using maximum likelihood algorithm showed that the Quickbird dataset could generate 85 spectral-related land-cover classes at 67.75% overall accuracy (Kappa = 0.6813%). This accuracy result was considered low, compared to the accuracy threshold for land-cover/land-use map (Campbell, 1983). After the classes were merged to 48, the overall accuracy increased to 79.02% (Kappa=0.7829). This resultant map actually showed a land-cover map presented using VLUIS spectral-related cover dimension. When a global majority filtering was applied to the 48 classess, the overall accuracy became 87.05% (Kappa = 0.8656). Furthermore, when a selective majority filtering process applied to the same number of classes, an overall accuracy of 85.90% (Kappa = 0.8539) was achieved. Although the selective majority filtered produced a slightly lower accuracy level, the appearance of the resultant map was better, in the sense that the linear features such as road network and water courses could be preserved. This map was then used a final land-cover map, utilised in the subsequent processing for generating a final land-use map containing socio-economic function information. Figures 4 and 5 show the accuracy assessment result and the land-cover map obtained from this stage.
Table 4. VLUIS socio-economic dimension’s classification scheme used in this study
Figure 4. Accuracy assessment result obtained from the multi-spectral classification using 85 and 48 classes, and 48 classes that were processed using global majority and selective majority filtering.
Figure 5. The spectral-related cover dimension map obtained from the multi-spectral classification of Quickbird dataset covering the study area of north-western Semarang.
Terrain classification and delineation was carried out using on-screen digitisation. The backdrop image used for this purposes was a pan-sharpened colour composite imagery. Based on the terrain classification using visual interpretation, a general terrain unit map relevant to the variability of land-use in the study area was produced. The land characteristics representing each terrain mapping unit is presented in Table 2. They were interpreted from the imagery, then were checked and corrected during the fieldwork. The land characteristics, spatial dimension map, and the land-cover maps were then correlated to the socio-economic function-related land-use classes, in order to develop a set of computer-based rules for deriving the land-use map in a raster-based GIS environment.
A spatial dimension map showing land-cover-related spatial units characterised by shape, location, regularity, pattern, and density of the land-cover was generated using on-screen digitisation. This map was interpreted from the pan-sharpened Quickbird magery, so that detailed features such as road network with 4-5 m wide could be delivered. Mapping units of such spatial dimension map served as objects that were used to transform spectral-related land cover classes to other categories, e.g. socio-economic function-related land-use classes. Figure 6 shows the spatial dimension map generated from the on-screen digitisation of the pan-sharpened Quickbird imagery.
Figure 6. The spatial dimension map obtained from the on-screen digitisation.
The immediate result of the object-based image segmentation was relabelled using the VLUIS spatial dimension classification scheme (Figure 7b). In order to assess its accuracy, this spatial dimension map was superimposed with the other spatial dimension map obtained from the visual interpretation (Figure 7d). By overlaying both maps, an error matrix could be delivered, resulting an overall accuracy of 62.27 % (Kappa = 0.5978). This accuracy level showed that the object-based image segmentation could not be used for generating spatial dimension map according to the VLUIS classification scheme. This result also lead to the use of the spatial dimension map derived using on-screen digitisation in the subsequent process, i.e. deriving socio-economic dimension of land-use information.
Figure 7 (a) small portion of the north-western part of the study area used for testing the segmentation result; (b) segmentation result; (c) skeleton of the segmentation result superimposed on the colour composite image; (d) skeleton of the segmentation result superimposed on the visual interpretation-based spatial dimension map