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All Journal Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Mulia Inda Rahayu
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Computer Science & IT Earth & Planetary Sciences

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KOREKSI ATMOSFER DATA LANDSAT-8 MENGGUNAKAN PARAMETER ATMOSFER DARI DATA MODIS Fadila Muchsin; Liana Fibriawati; Mulia Inda Rahayu; Hendayani Hendayani; Kuncoro Adhi Pradhono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 2 Desember 2019
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (646.635 KB) | DOI: 10.30536/j.pjpdcd.2019.v16.a3054

Abstract

Data Landsat-8 (level 1T) yang diterima oleh pengguna masih dalam bentuk nilai digital dan dapat digunakan secara langsung untuk pemetaan penutup /penggunaan lahan. Namun, data tersebut masih memiliki akurasi radiometrik yang rendah apabila akan digunakan untuk menurunkan informasi seperti indeks vegetasi, biomasa, klasifikasi penutup lahan /penggunaan lahan, dan sebagainya sehingga perlu dilakukan koreksi radiometrik/atmosfer. Penelitian ini menggunakan metode koreksi atmosfer second simulation of satellite in the solar spectrum (6S) untuk memperbaiki gangguan atmosfer dan membandingkan hasilnya dengan pengukuran lapangan. Parameter atmosfer yang digunakan adalah aerosol optical depth (AOD), kolom uap air dan ketebalan ozon yang bersumber dari data MODIS dengan tanggal dan jam perekaman yang mendekati dengan data Landsat-8. Dari analisis yang dilakukan terhadap nilai indeks vegetasi (NDVI, EVI, SAVI dan MSAVI) citra terkoreksi atmosfer (surface reflectance) menunjukkan bahwa indeks vegetasi yang memiliki akurasi tinggi adalah NDVI yaitu (3 – 11) % dan terendah adalah MSAVI yaitu (11 – 24) %. Hasil analisis terhadap respon spektral objek citra terkoreksi atmosfer menunjukkan bahwa kanal-kanal visible memiliki akurasi yang cukup baik dengan nilai RMSE berkisar antara (1 – 4) %. Sebaliknya akurasi terendah terdapat pada kanal inframerah dekat (NIR) dengan nilai (14 – 27) %.Kata kunci: Landsat-8, koreksi atmosfer, respon spektral, indeks vegetasi
COMPARISON OF THE RADIOMETRIC CORRECTION LANDSAT-8 IMAGE BASED ON OBJECT SPECTRAL RESPONSE AND VEGETATION INDEX Fadila Muchsin; Supriatna; Adhi Harmoko; Indah Prasasti; Mulia Inda Rahayu; Liana Fibriawati; Kuncoro Adi Pradhono
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 2 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3632

Abstract

Landsat-8 standard level (level 1T) data received by users still in digital form can be used directly for land cover/land use mapping. These data have low radiometric accuracy when used to produce information such as vegetation indices, biomass, and land cover/land use classification. In this study, radiometric/atmospheric correction was conducted using FLAASH, 6S, DOS, TOA+BRDF and TOA method to eliminate atmospheric disturbances and compare the results with field measurements based on object spectral response and NDVI values. The results of the spectral measurements of objects in paddy fields at harvest time in the Cirebon Regency, West Java, Indonesia show that the FLAASH and 6S method have spectral responses that are close to those of objects in the field compared to the DOS, TOA and TOA+BRDF methods. For the NDVI value, the 6S method has the same tendency as the object's NDVI value in the field.
NEW AUTOMATED CLOUD AND CLOUD-SHADOW DETECTION USING LANDSAT IMAGERY Kustiyo; Dianovita; Hedi Ismaya; Mulia Inda Rahayu; Erna Sri Adiningsih
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 2 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1831

Abstract

Cloud cover has become a major problem in the use of optical satellite imageries, particularly in Indonesian region located along equator or tropical region with high cloud cover almost all year round. In this study, a new method for cloud and cloud shadow detection using Landsat imagery for specific Indonesian region was developed to provide a more efficient and effective way to detect clouds and cloud shadows. Landsat Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) were used as inputs into the model. The first step was to detect cloud based on cloud physical properties using albedo and thermal bands, the second step was to detect cloud shadows using the Near Infrared (NIR), and Short Wave Infrared (SWIR) bands, and finally, the geometric relationships were used to match the cloud and cloud shadow layer, before proceeding to the production of the final cloud and cloud shadow mask. The results were then compared with other method such as tree base cloud separation. It showed that method we proposed could provide better result than tree base method, the accuracy result of this method was 98.75%.
RANDOM FOREST CLASSIFICATION OF JAMBI AND SOUTH SUMATERA USING ALOS PALSAR DATA Mulia Inda Rahayu; Katmoko Ari Sambodo
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 2 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1852

Abstract

Recently, Synthetic Aperture Radar (SAR) satellite imaging has become an increasing popular data source especially for land cover mapping because its sensor can penetrate clouds, haze, and smoke which a serious problem for optical satellite sensor observations in the tropical areas. The objective of this study was to determine an alternative method for land cover classification of ALOSPALSAR data using Random Forest (RF) classifier. RF is a combination (ensemble) of tree predictors that each tree predictor depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. In this paper, the performance of the RF classifier for land cover classification of a complex area was explored using ALOS PALSAR data (25m mosaic, dual polarization) in the area of Jambi and South Sumatra, Indonesia. Overall accuracy of this method was 88.93%, with producer’s accuracies for forest, rubber, mangrove & shrubs with trees, cropland, and water classes were greater than 92%.
Co-Authors Adhi Harmoko Dianovita Erna Sri Adiningsih Fadila Muchsin Fadila Muchsin Hedi Ismaya Hendayani Hendayani Indah Prasasti Katmoko Ari Sambodo Kuncoro Adhi Pradhono Kuncoro Adi Pradhono Kustiyo Liana Fibriawati Supriatna