Studying urban areas using remote sensing imagery has become a challenge, both visually and digitally. Supervised classification, one of the digital classification approaches to differentiate between built-up and non-built-up area, used to be leading in digital studies of urban area. Then the next generation uses index transformation for automatic urban data extraction. The extraction of urban built-up land can be automatically done with NDBI although it has one limitation on separating built-up land and bare land. The previous studies provide opportunities for further research to increase the accuracy of the extraction, particularly using index transformation. This study aims to obtain the maximum accuracy of the extraction by merging several indices including NDBI, NDVI, MNDWI, NDWI, and SAVI. The merging of the indices is using four stages: merging of two indices, three indices, four indexes and five indices. Several operations were experimented to merge the indices, either by addition, subtraction, or multiplication. The results show that merging NDBI and MNDWI produce the highest accuracy of 90.30% either by multiplication (overlay) or reduction. Application of SAVI, NDBI, and NDWI also gives a good effect for extracting urban built-up areas and has 85.72% mapping accuracy.
Topographic feature is one of the several factors affecting the distortion of the real reflectance value of objects. Digital processing used the surface reflectance values of satellite imagery needs the corrected images with the most minimized disturbances, hence several topographic correction methods using digital elevation data have been developed. This study examined the different result of topographic correction from several available elevation data in Indonesia, including SRTM DEM, topographic map (RBI), and DEMNAS. Sun-Canopy-Sensor+C (SCS+C) correction using different DEMs was applied on Landsat-8 data over Menoreh Mountains, Indonesia. The results obtained showed that DEMNAS produced the most topographically normalized images based on statistical and visual analysis. The availability of DEMNAS throughout Indonesia is the advantage to be used as an input of this pre-processing method. However, it needs to be examined first since the quality is not surely similar to our study site.
Suitable land-cover/land-use information is rarely available in most developing countries, particularly when newness, accuracy, relevance, and compatibility are used as evaluation criteria. In Indonesia, various institutions developed their own maps with considerable differences in classification schemes, data sources and scales, as well as in survey methods. Redundant land-cover/land-use surveys of the same area are frequently carried out to ensure the data contains relevant information. To overcome this problem, a multidimensional land-use classification system was developed. The system uses satellite imagery as main data source, with a multi-dimensional approach to link land-cover information to land-use-related categories. The land-cover/land-use layers represent image-based land-cover (spectral), spatial, temporal, ecological and socio-economic dimensions. The final land-cover/land-use database can be used to derive a map with specific content relevant to particular planning tasks. Methods for mapping each dimension are described in this paper, with examples using Quickbird satellite imagery covering a small part the Semarang area, Indonesia. The approaches and methods used in this study may be applied to other countries having characteristics similar to those of Indonesia
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