Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province
This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics. The model was...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2018-12-01
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| Series: | Indonesian Journal of Geography |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijg/article/view/36113 |
| _version_ | 1818334813624467456 |
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| author | Ardiansyah Ardiansyah Revi Hernina Weling Suseno Faris Zulkarnain Ramadhani Yanidar Rokhmatuloh Rokhmatuloh |
| author_facet | Ardiansyah Ardiansyah Revi Hernina Weling Suseno Faris Zulkarnain Ramadhani Yanidar Rokhmatuloh Rokhmatuloh |
| author_sort | Ardiansyah Ardiansyah |
| collection | DOAJ |
| description | This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics. The model was constructed using several predictor variables i.e. Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%. |
| first_indexed | 2024-12-13T14:13:30Z |
| format | Article |
| id | doaj.art-c704a73b24d84e97a4caa48ae4152ea0 |
| institution | Directory Open Access Journal |
| issn | 0024-9521 2354-9114 |
| language | English |
| last_indexed | 2024-12-13T14:13:30Z |
| publishDate | 2018-12-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | Indonesian Journal of Geography |
| spelling | doaj.art-c704a73b24d84e97a4caa48ae4152ea02022-12-21T23:42:23ZengUniversitas Gadjah MadaIndonesian Journal of Geography0024-95212354-91142018-12-0150215416110.22146/ijg.3611323118Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta ProvinceArdiansyah Ardiansyah0Revi Hernina1Weling Suseno2Faris Zulkarnain3Ramadhani Yanidar4Rokhmatuloh Rokhmatuloh5Faculty of Mathematics and Natural Science, Universitas IndonesiaFaculty of Mathematics and Natural Science, Universitas IndonesiaPT Infimap Geospasial SistemCenter for Applied Geography Research, Universitas IndonesiaFaculty of Landscape Architecture and Environmental Technology, Trisakti UniversityFaculty of Mathematics and Natural Science, Universitas IndonesiaThis study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics. The model was constructed using several predictor variables i.e. Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%.https://jurnal.ugm.ac.id/ijg/article/view/36113Percent of building densitymulti-index approachurban environmentLandsat 8DKI Jakarta Province |
| spellingShingle | Ardiansyah Ardiansyah Revi Hernina Weling Suseno Faris Zulkarnain Ramadhani Yanidar Rokhmatuloh Rokhmatuloh Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province Indonesian Journal of Geography Percent of building density multi-index approach urban environment Landsat 8 DKI Jakarta Province |
| title | Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province |
| title_full | Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province |
| title_fullStr | Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province |
| title_full_unstemmed | Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province |
| title_short | Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province |
| title_sort | percent of building density pbd of urban environment a multi index approach based study in dki jakarta province |
| topic | Percent of building density multi-index approach urban environment Landsat 8 DKI Jakarta Province |
| url | https://jurnal.ugm.ac.id/ijg/article/view/36113 |
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