Heru Noviar, Heru
Pusat Pemanfaatan Penginderaan Jauh, LAPAN

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PENGEMBANGAN METODE ZONASI DAERAH BAHAYA LETUSAN GUNUNG API STUDI KASUS GUNUNG MERAPI Asriningrum, Wikanti; Noviar, Heru; suwarsono, -
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol 1, No.1 Juni (2004)
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Abstract

Merapi volcano which has height 2.986 m is located at central of Java Island. This volcano is one of 129 active volcano in Indonesia. Considering the amount of volcano, we need a method as a mitigation system of eruption hazard. MOS-MESSR (1991) dan Landsat-ETM (2002) sata and supported by secondary data are used to identify and classify landform, drainage pattern, and land cover. The result are 10 classes of landform, 3 leruption hazard level of drainage pattern, and 9 classes of landform. Based on gemorphogical analysis during 11 years show that forest area decrease 13.062 Ha and hazard risk pattern increasa.
ENVIRONMENTAL QUALITY CHANGES OF SINGKARAK WATER CATCHMENT AREA USING REMOTE SENSING DATA Carolita, Ita; Trisakti, Bambang; Noviar, Heru
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 2 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1853

Abstract

Lake Singkarak in west Sumatera is currently in very poor condition and become one of the priorities in the government lake rescue program. High sedimentation rate from soil erosion has caused siltation, decreasing of quality and quantity of lake water. Monitoring of the environment quality changes of the lake and its surrounding are required. This study used Landsat and SPOT satellite data in periods of 2000-2011 to evaluate environmental quality parameters of the lake such as land cover, lake water quality (total suspended solid), water run-off, and water discharge in Singkarak lake catchment area. Maximum likelihood classifier was used to obtain land cover. Total suspended solid was extracted using Doxaran algorithm. The look up table and rational method were used to estimate run-off and water discharge. The results showed that the decreasing of forest area and the increasing of settlement were consistent with the increasing of average run-off and water discharge in Paninggahan and Sumpur sub-catchment area. The results were also consistent with the increasing of TSS in Singkarak lake, where TSS increased from around 2-3 mg/l up to 5-6 mg/l in the periods of 2000-2011.
Model SPASIAL INDEKS LUAS DAUN (ILD) PADI MENGGUNAKAN DATA TM-LANDSAT UNTUK PREDIKSI PRODUK PADI Sitanggang, Gokmaria; Dirgahayu Domiri, Dede; Carolita, Ita; Noviar, Heru
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol.3, No.1 Juni (2006)
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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The spatial model for irrigated paddy yield acreage and yield prediction use the Landsat-TM of remote sensing data which has been producted by LAPAN using the Vegetation Index (VI) as a single parameter. Verification of the model mentioned above has also been done for Java Island showing that the accuracy result is acceptable for the operational although there are some limitation of the model. The objective of this research is to develop a spatial model for the paddy yield acreage and the yield prediction using Landsat-TM data, based on another parameter i.e the single parameter of Leaf Area Index (LAI), or using both parameter of LAI and VI to improve the accuracy prediction, compared to the accuracy using the single parameter of VI. The spatial model based on the Leaf Area Index (LAI) reduces dynamic factor of the parameter which control the growth stage of the paddy in the field such as the soil moisture (level of water) and the weather condition such as the temperature and the moisture radition, pests and diseases. In this research phase, the profile of LAI against the paddy age based on the field measurement shows that the LAI value increases a long with the vegetative growth and reaches the peak value of 4,567 at the maximum vagatative index (8-9weeks after the planting time). Furthermore, the LAI value decreases a long with the generative growth. The LAI value at the maximum vegetative phase can be used to predict the paddy production. The relation between the LAI and the spectral bands combination of Landsat-TM can be obtained by using the Power Regression Model as follows: LAI=0,2219*(TM4/TM3)2,1005(R2=0,95) where LAI means the value Leaf of Area Index on the paddy object at the paddy field area, which represents the pixel in the image spatial distribution. While TM3 means the digital number (gray level value) of the pixel in the spectral band 3 of Landsat-TM image data which represent the paddy object at the paddy field area, and TM4 means the digital number of the pixel in the spectral band 4 of Landsat-TM image data, which represent the paddy object at the paddy field area. The research also shows the application example or the model or the algorithm whivh is obtained in this research by using Landsat-TM. The LAI spatial of the paddy field area in Kabupaten Subang/Sukamandi West Java can be produced.
PENGUKURAN SUHU PERMUKAAN LAHAN UNTUK PREDIKSI LETUSAN GUNUNG API Noviar, Heru; Asriningrum, Wikanti; Rijono, Yon
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol.3, No.1 Juni (2006)
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Temperature is one of the important parameter for volcano eruption prediction. Remote Sansing Data can be used to measure land surface temperature. The land surface temperature can be calculated with the band 4 and 5 of NOAA Satellite data by implementing the land surface temperature algorithm (LST). From field observation and measurement of volcano Merapi temperature indicate a significant pattern between the creater temperature and the land surface temperature derived from satellite data which shows increasing near eruption.
PEMANFAATAN KANAL POLARISASI DAN KANAL TEKSTUR DATA PISAR-L2 UNTUK KLASIFIKASI PENUTUP LAHAN KAWASAN HUTAN DENGAN METODE KLASIFIKASI TERBIMBING (UTILIZATION OF POLARIZATION AND TEXTURE BANDS OF PISAR-L2 DATA FOR LAND COVER CLASSIFICATION IN FOREST AREA USING SUPERVISED CLASSIFICATION METHOD) Noviar, Heru; Trisakti, Bambang
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 Juni 2013
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Abstract

Polarimetric and Interferometric Airborne SAR in L band (PiSAR-L2), yang merupakan kelanjutan dari program PiSAR, bertujuan untuk melakukan eksperimen sensor PALSAR-2 yang akan dibawa oleh ALOS-2. Selanjutnya pada tahun 2012, Japan Aerospace Exploration Agency (JAXA) dan Kementerian Ristek dan Teknologi Indonesia telah melakukan kerjasama riset untuk mengkaji pemanfaatan data PiSAR-L2 di wilayah Indonesia. Penelitian ini bertujuan untuk memanfaatkan kanal-kanal polarisasi data PiSAR-L2 untuk klasifikasi penutup lahan kawasan hutan di Provinsi Riau. Hasil survei lapangan tim JAXA setelah perekaman data PiSAR-L2 dijadikan sebagai data referensi untuk pembuatan training data dan training pengujian hasil klasifikasi. Pengolahan data dilakukan dengan merubah nilai dijital menjadi backscatter (Sigma naught) dan melakukan Lee filter, kemudian melakukan klasifikasi terbimbing dengan metode Maximum Likelihood Enhanced Neighbour dengan 3 perlakuan, yaitu menggunakan input 3 kanal polarisasi SAR (HH, VV dan HV), menggunakan input 3 kanal polarisasi dan 3 kanal tekstur (deviasi HH, deviasi VV dan deviasi HV), serta menggunakan input 6 kanal (3 kanal polarisasi dan 3 kanal tekstur) dan perbaikan training sampel berdasarkan hasil confusion matrix. Selanjutnya dilakukan pengujian akurasi dengan menggunakan metode confusion matrix. Hasil menunjukkan bahwa kanal tekstur dapat menaikkan tingkat pemisahan antara kelas obyek vegetasi, khususnya hutan dan akasia. Hasil klasifikasi dengan menggunakan 6 kanal dan perbaikan training sampel berhasil meningkatkan akurasi klasifikasi penutup lahan sehingga diperoleh nilai overall accuracy sebesar 80% dan nilai kappa sebesar 0.612. Kata kunci: PiSAR-L2, klasifikasi maximum likelihood, Kanal polarisasi, Kanal tekstur, Confusion matrix
IDENTIFICATION AND CLASSIFICATION OF FOREST TYPES USING DATA LANDSAT 8 IN KARO, DAIRI, AND SAMOSIR DISTRICTS, NORTH SUMATRA Noviar, Heru; Kartika, Tatik
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 2 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2477

Abstract

Forests have important roles in terms of carbon storage and other values. Various studies have been conducted to identify and distinguish the forest from non-forest classes. Several forest types classes such as secondary forests and plantations should be distinguished related to the restoration and rehabilitation program for dealing with climate change. The study was carried out to distinguish several classes of important forests such as the primary dryland forests, secondary dryland forest, and plantation forests using Landsat 8 to develop identification techniques of specific forests classes. The study areas selected were forest areas in three districts, namely Karo, Dairi, and Samosir of North Sumatera Province. The results showed that using composite RGB 654 of Landsat 8 imagery based on test results OIF for the forest classification, the forests could be distinguished with other land covers. Digital classification can be combined with the visual classification known as a hybrid classification method, especially if there are difficulties in border demarcation between the two types of forest classes or two classes of land covers.