Extracting Land-Use Information Related to Socio-Economic Function From Quickbird Imagery: A Case Study of Semarang Area, Indonesia
By integrating the spectral-related cover dimension, terrain unit, and spatial dimension maps, the socio-economic dimension map was derived. In this process, the matrix showing relationship between terrain units (representing land characteristics) and the socio-economic classes could be used as a basis for making decision, whether any spectral-related land-cover class exist on the particular terrain unit could be judged as a particular socio-economic class, or it should be consulted to the corresponding spatial dimension category. For example, typical shallow water (C12) in the tidal flat could (Mtf2) be judged as coastal fishpond (F1222). Old clay roof tile (C4212) in the strongly dissected piedmont (Dpmx) was labelled as lower class residential area because (F4111), according to the spatial dimension map, it was built up area with irregularly spaced small sized units interleaved by vegetation (S4111). The result was a socio-economic dimension map presented in Figure 8.
Figure 8. The land-use map containing socio-economic function information derived from the spectral-related land-cover, spatial dimension, and terrain unit maps.
Based on the matrix, a strategy could be set up, involving the use of two-dimensional tables for controlling overlay between the terrain unit, spatial dimension, and spectral-related land-cover map. The terrain unit and the spatial dimension maps were superimposed first to generate a map representing a combination of terrain and spatial aspect of land-cover. After that, the result was superimposed with the spectral-related land-cover map to derive the socio-economic dimension map. This means that the socio-economic labelling was determined by the land characteristics, land-cover types, and the spatial pattern, regularity, density, size, and pattern of the land-cover.
The socio-economic function was specified in four main categories: (a) water-based utilisation, (b) forest-based utilisation, (c) agricultural uses, and (d) settlement and infrastructures. Further categorisation details were derived with respect to this major grouping. For example, under agricultural uses, the following categories were mapped: (a) F3221/rubber estate, (b) F3412/rice field with continuous (three times) rice, and (F3523) dry field with mixed cash crops. In addition, under the settlement and infrastructures, the following categories were mapped: (a) F4111/lower class residential area, (b) F4112/ medium-higher class residential complex, (c) F4312 parking area, (d) F4341/waste recycle area, and (e) F4342/waste disposal land.
Accuracy assessment of the northern part of the Quickbird imagery showed that overall accuracy of 83.63% was achieved, with Kappa index of 0.8209. Confusions were constituted by continuous rice field with three times rice (F3411) and continuous rice field with twice rice (F3412); homestead garden (F3610) and forest garden (F3640); as well as forest garden (F3640) and mix garden (F3620). In comparison with the spaial dimension map, the socio-economic dimension map was less pixelated, because the objects, not individual pixels, played a more important role during rule-based classification. On the other hand, the features representing the socio-economic function classes looked more blocky then those of the spectral-related land-cover map, because the grainy features have been aggregated with the aid of the spatial dimension map.
Most of the mappable classes derived from the Quickbird datasets were presented at level 4. At this level, maps for supporting local planning activities are normally prepared at scale of 1:5,000 - 1:10,000. However, it was also found that several categories (particularly annual crops), which have been defined at level 4, were not possible to extract using Quickbird imagery. Crop types (spectral-related cover dimension) were the example. Due to its wide spectral range of each band and the abundance of crop types grown in the study area, the multi-spectral bands utilised by the Quickbird sensor was not adequate to differentiate various crop types grown at once in one place. However, by correlating the general categories of crop (e.g. cash crops, rice), more detailed categories at level 4 under other dimensions (particularly ecological and socio-economic dimensions) could be derived. Crop rotational pattern, for example, is strongly controlled by the terrain characteristics. By correlating the crop calendar, date/season of recording, general group of the crop types that could be recognised, and the terrain characteristics, the socio-economic dimension of land-use in terms of rotational pattern and possible planted crops could be generated.
Socio-economic function reflects how people use the land for satisfying some of their needs. The classification scheme under the dimension of socio-economic function showed a consistent categorisation in terms of “uses”. In addition, this study also showed that integration of digital image processing and raster GIS could generate relatively detailed land-use information. Some categories also required additional information with administrative boundaries, which could only be supplied by secondary data, e.g. land status. Some other categories could easily be defined using spectral approach supported by limited ancillary data, for examples teak (Tectona grandis) production forest areas, since virtually all areas with teak trees were intended for production forest.
It should also be realised that land-use is a multifaceted concept. It may not be modelled and mapped merely using five dimensions based on remotely sensed imagery. In more developed areas, land-uses are normally less correlated to the external forms of the features, e.g. building shape and pattern. The use of remote sensing technology to model the land-use into multidimensional fashion should be put in the context of complementary or alternative approach, where field surveys often fail to generate comprehensive, efficient and rapidly provided information required in a planning process. Furthermore, all these dimensions should be used in combination with field-based land-use survey and measurement, particularly for urban land-use planning purposes.
Remote sensing normally produces accurate results under certain circumstance in relation to images and processing. Indonesia is situated in a wet tropical region so that cloud cover and hazy atmosphere impede regular acquisition of good quality imagery at any day throughout a year. Increasing number of satellite systems that are capable to make recording with various viewing angles is expected to overcome this problem.
From the spatial analytical perspective, the use of simple map overlays with Boolean logic operators (If-then-else) for deriving other dimensions’ categories needs to be improved in further studies. The ecological and socio-economic function dimensions, for example, may be modelled using more sophisticated spatial analyses tools, e.g. neighbourhood analysis. The presence of particular socio-economic functions is occasionally related to their neighbours, so that analysis of adjacency or contiguity in vector data model needs to be explored.
This study has shown that the land-use information related to socio-economic function could be generated from high-spatial resolution imagery like Quickbird, at relatively high overall accuracy level. Such information could be delivered when the spectral and spatial dimensions of land-cover/land-use were integrated with terrain unit map containing land characteristics relevant to the variation of socio-economic aspect of land-use. To achieve that result, a multi-spectral classification should be combined with visual interpretation or image segmentation generating spatial dimension of the land-cover/land-use as well as terrain unit classes.
This study also showed that the use of object-oriented image segmentation was not successful yet, in the sense that the accuracy level achieved was relatively low. However, in the near future, improvement in the methods and algorithms may be carried out, so that a better result may be expected.
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