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AR4-50 MODEL, THE EXTRACTOR OF SPECTRAL VALUES INTO REMOTE SENSING IMAGE DATA-BASED LAND USE CLASS

AGRIVITA, Journal of Agricultural Science · Vol. 35 No. 3 (2014) · hlm. 255–262

DOI
10.17503/agrivita.v35i3.313
ISSN
2477-8516, 0126-0537
Terbit
2014-05-12
Penerbit
Faculty of Agriculture University of Brawijaya in collaboration with PERAGI
Bahasa
eng

Abstrak

This study attempted to develop an extraction model of spectral values ​​of land objects into land use/land cover classes on remote sensing image in the provision of land database for planning, evaluation, and monitoring in agriculture and forestry. This study employed an Isodata method and Knowledge-Based Systems (KBS) using the Landsat 7 ETM+ image in the coverage area of ​​117,799.06 ha, and the SPOT 5 XS image in the coverage area of ​​113,241.37 ha in Palu, Sigi and Donggala. The study found two image models labelled as AR4-50 and SBP-AR4-50. The separability image AR4-50 model has an average capability for separating land object pixels which are statistically 1811.98 to 1972.08 (moderate-good), with the class accuracy of land use/land cover using the image homogeneity model of SBP-AR4-50, which is totally (confusion matrix) 72.15% -87.17%, the accuracy level of land map generator for agricultural land/forestry is in good-excellent category on the Landsat 7 ETM+ and SPOT 5 XS images. Keywords: Image, Class, Land Use, Model, Separability, Homogeneity.

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25 penulis · 68 tautan kepenulisan bersama · 13 lintas organisasi

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  • Hasanuddin University
  • Institut Agama Islam Negeri Syaikh Abdurrahman Siddik Bangka Belitung
  • Mulawarman University
  • Tadulako University
  • Institut Agama Islam Negeri Langsa
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Rekan penulisOrganisasiArtikel bersama
Ahmad IsmailHasanuddin University1
Pawennari HijjangAfiliasi tidak diketahui1
Alberto Alves PereiraAfiliasi tidak diketahui1
Yahya YahyaAfiliasi tidak diketahui1
Jayana Suryana KembaraAfiliasi tidak diketahui1
Fivo FreitasAfiliasi tidak diketahui1
Hamka NapingAfiliasi tidak diketahui1
Hardiyanti MunsiAfiliasi tidak diketahui1
Ansar ArifinAfiliasi tidak diketahui1
Muhamad ArsatAfiliasi tidak diketahui1
Hardiyanti MunsiHasanuddin University1
Muhammad Yasir BasirHasanuddin University1
Ansar ArifinHasanuddin University1
Muhamad ArsatHasanuddin University1
Nur FitriyahMulawarman University1
Ice AnugrahsariMulawarman University1
Mustofa Agung SardjonoMulawarman University1
Winarno WinarnoInstitut Agama Islam Negeri Syaikh Abdurrahman Siddik Bangka Belitung1
Muhammad Nur AminInstitut Agama Islam Negeri Langsa1
Ahmad FadholiInstitut Agama Islam Negeri Syaikh Abdurrahman Siddik Bangka Belitung1
Muhammad Saidun AnwarInstitut Agama Islam Negeri Syaikh Abdurrahman Siddik Bangka Belitung1

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Sumber metadata: OAI-PMH (metadata jurnal) https://agrivita.ub.ac.id/index.php/agrivita/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">AR4-50 MODEL, THE EXTRACTOR OF SPECTRAL VALUES  INTO REMOTE SENSING IMAGE DATA-BASED LAND USE CLASS</dc:title>
	<dc:creator>Akhbar, Akhbar</dc:creator>
	<dc:creator>Basir, Muhammad</dc:creator>
	<dc:creator>Somba, Bunga Elim</dc:creator>
	<dc:creator>Golar, Golar</dc:creator>
	<dc:description xml:lang="en-US"> This study attempted to develop an extraction model of spectral values ​​of land objects into land use/land cover classes on remote sensing image in the provision of land database for planning, evaluation, and monitoring in agriculture and forestry. This study employed an Isodata method and Knowledge-Based Systems (KBS) using the Landsat 7 ETM+ image in the coverage area of ​​117,799.06 &amp;nbsp;ha, and the SPOT 5 XS image in the coverage area of ​​113,241.37 ha in Palu, Sigi and Donggala. The study found two image models labelled as AR4-50 and SBP-AR4-50. The separability image AR4-50 model has an average capability for separating land object pixels which are statistically 1811.98 to 1972.08 (moderate-good), with the class accuracy of land use/land cover using the image homogeneity model of SBP-AR4-50, which is totally (confusion matrix) 72.15% -87.17%, the accuracy level of land map generator for agricultural land/forestry is in good-excellent category on the Landsat 7 ETM+ and SPOT 5 XS images. Keywords: Image, Class, Land Use, Model, Separability, Homogeneity. </dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Agriculture University of Brawijaya in collaboration with PERAGI</dc:publisher>
	<dc:contributor xml:lang="en-US">Computing and Center for Tropical Forest Margins/CTFM (ex. Storma) Laboratory, Faculty of Forestry, Tadulako University</dc:contributor>
	<dc:date>2014-05-12</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:format>application/pdf</dc:format>
	<dc:identifier>https://agrivita.ub.ac.id/index.php/agrivita/article/view/313</dc:identifier>
	<dc:identifier>10.17503/agrivita.v35i3.313</dc:identifier>
	<dc:source xml:lang="en-US">AGRIVITA Journal of Agricultural Science; Vol 35, No 3 (2013); 255-262</dc:source>
	<dc:source>2477-8516</dc:source>
	<dc:source>0126-0537</dc:source>
	<dc:source>10.17503/agrivita.v35i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/view/313/530</dc:relation>
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