Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index
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 g...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Muhammadiyah Surakarta
2018-04-01
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| Series: | Forum Geografi |
| Subjects: | |
| Online Access: | http://journals.ums.ac.id/index.php/fg/article/view/5907 |
| _version_ | 1797642819720970240 |
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| author | Iswari Nur Hidayati R Suharyadi Projo Danoedoro |
| author_facet | Iswari Nur Hidayati R Suharyadi Projo Danoedoro |
| author_sort | Iswari Nur Hidayati |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T14:05:48Z |
| format | Article |
| id | doaj.art-03356deb0ec448c5a79623a287828516 |
| institution | Directory Open Access Journal |
| issn | 0852-0682 2460-3945 |
| language | English |
| last_indexed | 2024-03-11T14:05:48Z |
| publishDate | 2018-04-01 |
| publisher | Universitas Muhammadiyah Surakarta |
| record_format | Article |
| series | Forum Geografi |
| spelling | doaj.art-03356deb0ec448c5a79623a2878285162023-11-02T01:47:22ZengUniversitas Muhammadiyah SurakartaForum Geografi0852-06822460-39452018-04-0132110.23917/forgeo.v32i1.59074013Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation IndexIswari Nur Hidayati0R SuharyadiProjo DanoedoroStudent of Doctorate Degree, Faculty of Geography, UGM Department of Geographic Information Science, Gadjah Mada University, Yogyakarta, IndonesiaStudying 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.http://journals.ums.ac.id/index.php/fg/article/view/5907built-up area extractionremote sensingindex transformationLandsat 8 OLI |
| spellingShingle | Iswari Nur Hidayati R Suharyadi Projo Danoedoro Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index Forum Geografi built-up area extraction remote sensing index transformation Landsat 8 OLI |
| title | Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index |
| title_full | Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index |
| title_fullStr | Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index |
| title_full_unstemmed | Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index |
| title_short | Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index |
| title_sort | developing an extraction method of urban built up area based on remote sensing imagery transformation index |
| topic | built-up area extraction remote sensing index transformation Landsat 8 OLI |
| url | http://journals.ums.ac.id/index.php/fg/article/view/5907 |
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