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All Journal Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES)
Sayidah Sulma
Indonesian National Institute of Aeronautics and Space (LAPAN)
Computer Science & IT Earth & Planetary Sciences

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PERBANDINGAN HASIL KLASIFIKASI LIMBAH LUMPUR ASAM DENGAN METODE SPECTRAL ANGLE MAPPER DAN SPECTRAL MIXTURE ANALYSIS BERDASARKAN CITRA LANDSAT - 8 Sayidah Sulma; Junita Monika Pasaribu; Hana Listi Fitriana; Nanik Suryo Haryani
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 13 No. 1 Juni 2016
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (966.575 KB) | DOI: 10.30536/j.pjpdcd.2016.v13.a2935

Abstract

The utilization of remote sensing data is an alternative way that could be used for rapid detection of large coverage hazardous waste area. This study aims to classify the acid sludge contaminated area using Landsat 8 by applying Spectral Angle Mapper (SAM) classification method with two spectral reference sources, namely field spectral measurement using a spectrometer and endmember spectral from the image, and then compare the classification results. The accuracy level of SAM classification result showed that classification using endmember spectral from the image as the reference spectral reached 66,7%, whereas classification using field spectral measurement as spectral reference only reached 33,3%. The accuracy level of Spectral Mixture Analysis (SMA) classification result showed that classification using endmember spectral from the image as the reference spectral reached 62,5%. The affecting factors for the low accuracy is the significant differences of the spectral profiles obtained from spectrometer with spectral Landsat-8 due to differences of spatial and altitude Keywords: Acid sludge Waste, Spectral Angle Mapper, Spectral Mixture Analysis, Landsat-8 ABSTRAKPemanfaatan data penginderaan jauh merupakan salah satu alternatif yang dapat digunakan untuk deteksi daerah tercemar limbah B3 secara cepat dengan wilayah yang luas. Penelitian ini bertujuan untuk mengklasifikasi daerah tercemar lumpur asam menggunakan data Landsat 8 dengan metode Spectral Angle Mapper (SAM), kemudian membandingkan hasil klasifikasi SAM menggunakan spektral referensi berdasarkan pengukuran spektrometer dengan spektral yang diperoleh dari endmember citra. Tingkat akurasi klasifikasi SAM dengan spektral referensi berdasarkan endmember citra adalah sebesar 66,7 %, sedangkan dengan menggunakan referensi spektrometer hanya mencapai 33,3 %. Tingkat akurasi klasifikasi Spectral Mixture Analysis (SMA) dengan spektral referensi berdasarkan endmember citra adalah sebesar 62,5 %. Faktor yang mempengaruhi rendahnya akurasi adalah perbedaan yang signifikan antara profil spektral yang diperoleh dari spektrometer dengan spektral Landsat-8 akibat perbedaan spasial dan ketinggian.
DETECTION OF GREEN OPEN SPACE USING COMBINATION INDEX OF LANDSAT 8 DATA (CASE STUDY: DKI JAKARTA) Sayidah Sulma; Jalu Tejo Nugroho; Any Zubaidah; Hana Listi Fitriana; Nanik Suryo Haryani
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 1 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (826.111 KB) | DOI: 10.30536/j.ijreses.2016.v13.a2712

Abstract

Spatial information about the availability and presence of green open space in urban areas to be up to date and transparent was a necessity. This study explained the technique to get the green open spaces of spatial information quickly using an index approach of Landsat 8. The purpose of this study was to evaluate the ability of the method to detect the green open spaces, especially using Landsat 8 with a combination of several indices, namely Normalized Difference Build-up Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Build-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) with a study area of Jakarta. This study found that the detection and identification of green open space classes used a combination of index and band gave good results with an accuracy of 81%.
Co-Authors Any Zubaidah Hana Listi Fitriana Hana Listi Fitriana Jalu Tejo Nugroho Junita Monika Pasaribu Nanik Suryo Haryani Nanik Suryo Haryani