Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province

Indonesian Journal of Geography · Vol. 50 No. 2 (2018) · hlm. 154–161

DOI
10.22146/ijg.36113
ISSN
2354-9114, 0024-9521
Terbit
2018-12-26
Penerbit
Faculty of Geography, Universitas Gadjah Mada
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

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%.

Kata kunci

Jaringan kolaborasi para penulis

33 penulis · 165 tautan kepenulisan bersama · 53 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • University of Indonesia
  • National Research and Innovation Agency
  • Universitas Trisakti
  • Indonesian National Institute of Aeronautics and Space
  • Ministry of Public Works, Republic of Indonesia
  • Meteorological, Climatological, And Geophysical Agency
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Muhammad DimyatiUniversity of Indonesia3
Iqbal Putut Ash ShidiqUniversity of Indonesia2
Mangapul Parlindungan TambunanUniversity of Indonesia2
Kuswantoro MarkoUniversity of Indonesia2
Ratna SaraswatiUniversity of Indonesia2
Dimas Bayu IchsandyaUniversity of Indonesia1
Nurul Sri RahatiningtyasUniversity of Indonesia1
Riza Putera SyamsuddinUniversity of Indonesia1
Farhan Makarim ZeinUniversity of Indonesia1
Ananda FitrianiAfiliasi tidak diketahui1
Nurwita Mustika SariIndonesian National Institute of Aeronautics and Space1
Masita Dwi Mandini ManessaUniversity of Indonesia1
Ratih DewantiNational Research and Innovation Agency1
Ahmad ZubairUniversity of Indonesia1
Triarko NurlambangUniversity of Indonesia1
Babag PurbantoroNational Research and Innovation Agency1
Siswanto SiswantoMeteorological, Climatological, And Geophysical Agency1
Evi AnggraheniUniversity of Indonesia1
Pranita GiardiniUniversity of Indonesia1
Kintan MaulidinaUniversity of Indonesia1
Raisya AfifahUniversity of Indonesia1
Anang MuchlisMinistry of Public Works, Republic of Indonesia1
Andry RustantoUniversity of Indonesia1
Muhammad DimyatiAfiliasi tidak diketahui1
Irma SusantiUniversity of Indonesia1
Hayuning AnggrahitaUniversity of Indonesia1
Saipiatuddin SaipiatuddinUniversity of Indonesia1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://jurnal.ugm.ac.id/ijg/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Percent of building density (PBD) of urban environment:  a multi-index approach based study in DKI Jakarta Province</dc:title>
	<dc:creator>Ardiansyah, Ardiansyah</dc:creator>
	<dc:creator>Hernina, Revi</dc:creator>
	<dc:creator>Suseno, Weling</dc:creator>
	<dc:creator>Zulkarnain, Faris</dc:creator>
	<dc:creator>Yanidar, Ramadhani</dc:creator>
	<dc:creator>Rokhmatuloh, Rokhmatuloh</dc:creator>
	<dc:subject xml:lang="en-US">Geography</dc:subject>
	<dc:subject xml:lang="en-US">Percent of building density; multi-index approach; urban environment; Landsat 8; DKI Jakarta Province</dc:subject>
	<dc:subject xml:lang="en-US">Urban remote sensing</dc:subject>
	<dc:description xml:lang="en-US">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%.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Geography, Universitas Gadjah Mada</dc:publisher>
	<dc:contributor xml:lang="en-US">Department of Geography, Faculty of Mathematics and Natural Science, Universitas Indonesia</dc:contributor>
	<dc:contributor xml:lang="en-US">PT Infimap Geospasial Sistem</dc:contributor>
	<dc:date>2018-12-26</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijg/article/view/36113</dc:identifier>
	<dc:identifier>10.22146/ijg.36113</dc:identifier>
	<dc:source xml:lang="en-US">Indonesian Journal of Geography; Vol 50, No 2 (2018): Indonesian Journal of Geography; 154-161</dc:source>
	<dc:source>2354-9114</dc:source>
	<dc:source>0024-9521</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijg/article/view/36113/pdf</dc:relation>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:coverage xml:lang="en-US"></dc:coverage>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Authors</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>