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KAJIAN KERAPATAN VEGETASI HUTAN LINDUNG GUNUNG UNGARAN JAWA TENGAH TAHUN 2016 MENGGUNAKAN METODE INDEKS VEGETASI

Geo-Image Journal · Vol. 6 No. 1 (2017) · pp. 1–7

DOI
10.15294/geoimage.v6i1.15243
ISSN
2549-0362, 2252-6285
Published
2017-07-10
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

The research aims are to know the vegetation density level using vegetation index method of NDVI, SAVI, ARVI, DVI and RVI and to know the highest accuracy of vegetation index methods to determine the vegetation density in protected forest Mount Ungaran. The sampling method use purposive random sampling, with 82 sampel points. This research uses descrpitive research method. The first variable is vegetation density and the second ones is distribution and area produced by vegetation index transformations. The result of this research shows that the distribution and the area of classification results for each vegetation index transformation are different. There was little differenceof wide in the transformation of NDVI and SAVI is an area of 900 m2, equevalent to one pixel on the Landsat-8 imagery. Meanwhile classification generated by the transformation of ARVI, a lot of generating area is included dense classification, and the classification generated by the transformation of DVI and RVI is more a lot included rare classification. The confusion matrix shows NDVI transformation has the best accuracy with an overall accuracy percentage of 75,61%.

Collaboration network of the authors

40 authors · 139 co-authorship links · showing the 40 most connected

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  • University of Brawijaya
  • University of August 17, 1945 Banyuwangi
  • State University of Semarang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fahrudin HanafiAffiliation unknown5
Ananto AjiAffiliation unknown4
Sriyanto SriyantoAffiliation unknown3
Aprillia FindayaniAffiliation unknown3
Moch. ArifienAffiliation unknown3
Suroso SurosoAffiliation unknown3
Wahyu SetyaningsihAffiliation unknown3
Sriyono SriyonoAffiliation unknown3
Erni SuhariniAffiliation unknown3
Juhadi JuhadiAffiliation unknown3
Eva BanowatiAffiliation unknown2
Retno Salaras MahatiAffiliation unknown2
Vina Nurul HusnaAffiliation unknown2
Dewi Liesnoor SetyowatiState University of Semarang2
Hairumini HairuminiAffiliation unknown2
Apik Budi SantosoAffiliation unknown2
Saptono PutroUniversity of Brawijaya2
Sunarko SunarkoUniversity of August 17, 1945 Banyuwangi2
Andi Irwan BenardiAffiliation unknown1
Erin Dian WinarniAffiliation unknown1
AnifahtunAffiliation unknown1
Tegar Iis RomadhonAffiliation unknown1
Edi KurniawanAffiliation unknown1
Dwi JatmikoAffiliation unknown1
R SugiyantoAffiliation unknown1
Eka Anjar SariAffiliation unknown1
Nina RosianaAffiliation unknown1
Fandi Yusuf MaldiniAffiliation unknown1
Sisna Yuliana Diah Utami PutriAffiliation unknown1
Ratna Dwi SetyowatiAffiliation unknown1
Edi PranotoAffiliation unknown1
Eko Adi SaputroAffiliation unknown1
Dwi Utami PutriAffiliation unknown1
Putri Inmas PAffiliation unknown1
Nurrahim Adhadina Eka PutriAffiliation unknown1
Elvita SafitriAffiliation unknown1
Mohammad Nur supraptoAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/geoimage/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">KAJIAN KERAPATAN VEGETASI HUTAN LINDUNG GUNUNG UNGARAN JAWA TENGAH TAHUN 2016 MENGGUNAKAN METODE INDEKS VEGETASI</dc:title>
	<dc:creator>Lintang, Nuansa Chandra</dc:creator>
	<dc:creator>Sanjoto, Tjaturahono Budi</dc:creator>
	<dc:creator>Tjahjono, Heri</dc:creator>
	<dc:description xml:lang="en-US">The research aims are to know the vegetation density level using vegetation index method of NDVI, SAVI, ARVI, DVI and RVI and to know the highest accuracy of vegetation index methods to determine the vegetation density in protected forest Mount Ungaran. The sampling method use purposive random sampling, with 82 sampel points. This research uses descrpitive research method. The first variable is vegetation density and the second ones is distribution and area produced by vegetation index transformations. The result of this research shows that the distribution and the area of classification results for each vegetation index transformation are different. There was little differenceof wide in the transformation of NDVI and SAVI is an area of 900 m2, equevalent to one pixel on the Landsat-8 imagery. Meanwhile classification generated by the transformation of ARVI, a lot of generating area is included dense classification, and the classification generated by the transformation of DVI and RVI is more a lot included rare classification. The confusion matrix shows NDVI transformation has the best accuracy with an overall accuracy percentage of 75,61%.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2017-07-10</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://journal.unnes.ac.id/sju/geoimage/article/view/15243</dc:identifier>
	<dc:identifier>10.15294/geoimage.v6i1.15243</dc:identifier>
	<dc:source xml:lang="en-US">Geo-Image Journal; Vol 6 No 1 (2017); 1-7</dc:source>
	<dc:source>2549-0362</dc:source>
	<dc:source>2252-6285</dc:source>
	<dc:source>10.15294/geoimage.v6i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/geoimage/article/view/15243/8249</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2017 Geo-Image</dc:rights>
</oai_dc:dc>