Classification of Landsat 8 Satellite Data using Fuzzy c-means

Taufik, Afirah; Ahmad, Sharifah Sakinah Syed; Khairuddin, Nor Fatin Emiliza

doi:10.1145/3036290.3036330

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Pemilihan tiga klaster ini didasarkan pada studi-studi sebelumnya yang menggunakan data penginderaan jauh seperti yang dilakukan oleh [11], di mana tiga klaster wilayah dalam Daerah Aliran Sungai (DAS) Karkeh di Iran dihasilkan dari klasterisasi data citra satelit Terra MODIS. Selain itu, [12] mengklasifikasikan tiga kelas yaitu vegetasi, non-vegetasi, dan air dari penggabungan dua indeks (NDVI dan NDWI) dari citra satelit Landsat 8.…”

Section: Fuzzy C-meansunclassified

“…Pada Gambar 4, sumbu y melambangkan nilai rata-rata NDVI > 0,3 terstandarisasi sedangkan sumbu y melambangkan persentase luas wilayah yang memiliki nilai NDVI > 0,3 di masing-masing kelurahan. Penyajian dua data pada sumbu x dan y untuk menyajikan klaster hasil FCM sudah pernah dilakukan oleh [12] ketika mengklasifikasikan citra satelit Landsat 8 di daerah Klang, Malaysia.…”

Section: Fcmunclassified

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Penerapan Fuzzy C Means Pada Nilai Ndvi Landsat 8 Untuk Klasterisasi Kehijauan Kelurahan Di Kota Bogor

Wicaksono,

Hernina

2022

INTV

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This study aims to cluster sub-districts (kelurahan) in Bogor Municipality based on greenness level. Normalized Difference Vegetation Index (NDVI) values were processed from Landsat 8 OLI recorded on 24 May 2020, downloaded from United States Geological Survey (USGS) website. NDVI values greater than 0,3 indicate that vegetation pixels are separated from overall raster maps. These NDVI values over 0,3 were extracted based on each sub-district poligon within Bogor Municipality. For sub-districts with NDVI 0,3, the percentage of the area and NDVI mean values were generated using Geographic Information System (GIS). In order to cluster 68 sub-districts in Bogor Municipality, two variables of NDVI, namely area percentage and mean NDVI values, were processed using the Fuzzy C Means (FCM) method. Greenness level clustering using the FCM method shows 14 sub-districts in high class, 28 in medium class, and 26 in low cluster class. Overlay analysis among clusters shows two sub-districts (7.69%) in the low cluster class inside the medium class, one sub-district (3.57%) in the medium class within the low class, and one sub-district (7.14%) in the high cluster class inside the medium class. There are two main indications for an overlapping sub-district located in multiple clusters, namely the sub-district that has little different values with neighbouring cluster centres, and the sub-district that has similar different values with two cluster centres.

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Section: Fuzzy C-meansunclassified

Section: Fcmunclassified