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Extracting Land-Use Information Related to Socio-Economic Function From Quickbird Imagery: A Case Study of Semarang Area, Indonesia
Projo Danoedoro
Department of Cartography and Remote Sensing,
Faculty of Geography Gadjah Mada University, Yogyakarta Indonesia /
Centre for Remote Sensing and Spatial Information Science (CRSSIS)
The University of Queensland, StLucia Australia
ABSTRACT
Recently, high-spatial resolution imagery has widely been used for environmental assessment and mapping in Indonesia. However, most of these studies made use of visual interpretation instead of digital classification for generating land-cover/land-use information from such imagery. On the other hand, digital classification was usually applied to deliver land-cover information or mixed information between land-cover and land-use with relatively general categorisation, so that the results were not adequate to support planning. This study tried to extract land-use information related to socio-economic function by combining spectral classification, image segmentation and visual interpretation of Quickbird imagery covering Semarang area, Indonesia. To do so, a multi-spectral classification was run to derive detailed spectral-related land-cover classes. Image segmentation and visual interpretation were also carried out to generate spatial pattern of the land-cover features. A classification scheme under the versatile land-use classification system (VLUIS) was used as a reference. Integration of the spectral-related land-cover and spatial pattern maps was controlled using a knowledge-based approach, by formalising knowledge about spatial relationship between land-cover, socio-economic function, spatial pattern and their ecological context into a set of GIS rules. The result showed that Quickbird imagery could be used for generating socio-economic function of land-use at 83.63% accuracy (Kappa=0.821). In addition, several limitations related to the methods used and inaccurately mapped categories were identified.
1. INTRODUCTION: BACKGROUND AND PROBLEM
Land-cover/land-use information is recognised as an important input to planning (Lindgren, 1985; Lein, 2003). However, Fresco (1994) claimed that accurate data on actual land-use were not easily found at both global/continental and national/regional scales. In many developing countries, problems related to the availability of land-use information were caused by the lack of coordination between technical institution and the rarely available spatial data in terms of compatibility, relevance and newness. These lead to the need for the development of a flexible, multipurpose classification system (Danoedoro, 2004). From remote sensing point of view, the problems were caused by difficulties in generating land-use information from automatic classification procedures, since the automatic classification could only derive land-cover classes.
Land-use and land-cover are different. Clear differentiation between land-cover and land-use concepts has been made by several authors. Campbell (1983), for example, showed the difference in concrete-abstract dichotomy, where land-cover is concrete and land-use is abstract. That is, land-cover can be mapped directly from images, while land-use requires land-cover and additional information on how the land is used. However, both concepts were sometimes mixed in use (Anderson et al., 1976; Malingreau and Christiani, 1981), although land-use information normally contains attributes of land-cover. As a result, many land-use maps derived from remotely-sensed imagery could not consistently present land-use information, and these could not be effectively used in various applications related to planning. Due to its abstract character, the land-use information extraction is still a challenging task in remote sensing studies.
2. PREVIOUS WORKS
Visual image interpretation could normally generate land-use information by combining a set of interpretation elements including colour/tone, texture, shape, shadow, size, pattern, site and association (Lillesand et al., 2004). With digital multspectral classification, only land-cover could usually be extracted, as the land-cover types are related to their spectral responses recorded by the remote sensors (Jensen, 2004; Mather, 2004). The recorded spectral responses are comparable to tones or colours in visual interpretation. Further development in image analyses provided textural approaches based on statistical measure of a pixel group within a particular size of a moving window (Chen et al., 2004; Danoedoro, 2005). Current development of image processing system showed that shape and pattern could be recognised and classified using object-based image segmentation approach (Baatz and Schappe 2000; Ranasinghe, 2006).
However, land-use could not be simply interpreted from the colour, texture and shape of pixel groups. There are other factors that should be incorporated such as association and location of the object. If the land-use is viewed from its socio-economic function of the land cover, there would be more efforts required for deriving such information from a digital image dataset. Image-based LC/LU mapping, according to van Gils et al. (1990) could be carried out using photo-guided, photo-key, and land-ecological approaches. The photo key approach uses aerial photographs or printed imagery as a guide during field observation and measurement, while the photo key approach relies heavily on the photomorphic features as basis for objects recognition. Land-ecological approach, on the other hand, views land-cover, land-use and land characteristics as a unity. Therefore, interpretation of more detailed information related to LC and LU could be done by taking land characteristics into consideration.
Classification schemes also played an important role in the success of image classification. Danoedoro (2006) showed that the use of a classification scheme with classes that could not be directly related to the spectral classification might lead to a lower accuracy result. Similarly, classification scheme with classes relevant to spectral differentiation could lead to less accurate result when it was used for classifying imagery using non-spectral approach. In addition, there was another problem of relating classification schemes and remote sensing methods. Most digital classification methods were used for land-cover/land-use with limited number of classes, i.e. equal or less than 10 (Aplin and Atkinson, 2000; Sawaya et al., 2003; Wang et al., 2004; Puissant et al., 2005), while many applications related to planning require more detailed categorisation. Detailed categorisations used in remote sensing projects were performed by some authors, e.g. Loveland and Belward (1997), which dealt with global land cover mapping. Detailed FAO land-cover classification was prepared for visual interpretation (Jansen and Gregorio, 2003). Detailed categorisation was also found in the USGS land-cover/land-use classification system (Anderson et al., 1976). But in general detailed classification of land-use based on digital processing of remotely sensed imagery was rarely available. The previous works showed that developing method for land-use information extraction using digital remotely sensed data is important. Land-use information should contain information related to ‘uses’, or socio-economic function, rather than land-cover types. In addition, the use of appropriate classification scheme is also critical to the success of such efforts.
3. OBJECTIVE AND GOALS
This study tried to generate information related to socio-economic aspect of land-use based on a Quickbird image dataset covering Semarang area, Central Java, Indonesia. The derived information related to the socio-economic function referred to a newly developed classification system called Versatile Land-use Information System (VLUIS, Danoedoro et al., 2004). In the VLUIS, land-use information is broken down into five dimensions mappable from remotely sensed imagery, i.e. spectral-related land-cover, spatial, temporal, ecological, and socio-econonomic function. The goals of this study were (a) to produce a map showing the socio-economic dimension of land-use of the study area, generated from the Quickbird imagery; and (b) to carry out accuracy assessment of the derived map.
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