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Nuansa Chandra Lintang, Nuansa Chandra
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Earth & Planetary Sciences

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KAJIAN KERAPATAN VEGETASI HUTAN LINDUNG GUNUNG UNGARAN JAWA TENGAH TAHUN 2016 MENGGUNAKAN METODE INDEKS VEGETASI Lintang, Nuansa Chandra; Sanjoto, Tjaturahono Budi; Tjahjono, Heri
Geo-Image Vol 6 No 1 (2017)
Publisher : Geo-Image

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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%.
KAJIAN KERAPATAN VEGETASI HUTAN LINDUNG GUNUNG UNGARAN JAWA TENGAH TAHUN 2016 MENGGUNAKAN METODE INDEKS VEGETASI Lintang, Nuansa Chandra; Sanjoto, Tjaturahono Budi; Tjahjono, Heri
Geo-Image Vol 6 No 1 (2017)
Publisher : Geo-Image

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/geoimage.v6i1.15243

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%.
Co-Authors Heri Tjahjono Tjaturahono Budi Sanjoto