Articles
<![CDATA[Methane Emission from Digestion of Palm Oil Mill Effluent (POME) in a Thermophilic Anaerobic Reactor ]]>
<![CDATA[I Irvan]]>;
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Vivian Wongistani]]>;
<![CDATA[Yoshimasa Tomiuchi]]>
<![CDATA[ International Journal of Science and Engineering Vol 3, No 1 (2012) ]]>
Publisher : <![CDATA[Chemical Engineering Diponegoro University ]]>
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DOI: 10.12777/ijse.3.1.32-35
<![CDATA[As the issue of global warming draws increasing concern, many studies to reduce CO2 and CH4 gases (greenhouse gases, GHG) have been implemented in several countries, including in Indonesia. Considering that Indonesia has a huge numbers of palm oil mills, no doubt if their waste water treatment as one of the major sources in GHG. This paper presents the results from a research project between Metawater Co., Ltd.-Japan and University of Sumatera Utara-Indonesia. The objective of the research is to study the methane emission of thermophilic fermentation in the treatment of palm oil mill effluent (POME) on a laboratory scale. Anaerobic digestion was performed in two-litre water jacketed biodigester type continuous stirred tank reactor (CSTR) and operated at a thermophilic temperature (55 oC). As raw material, a real liquid waste (POME) from palm oil mill was used. Fresh POME was obtained from seeding pond of PTPN II waste water treatment facility which has concentration of 39.7 g of VS/L and COD value of 59,000 mg/L. To gain precise results, complete recording and reliable equipment of reactor was employed. As the experimental results, for hydraulic retention time (HRT) 8 days, VS decomposition rate of 63.5% and gas generation of 6.05-9.82 L/day were obtained, while for HRT 6 and 4 days, VS decomposition rate of 61.2, 53.3% and gas generation of 6.93-8.94 and 13.95-16.14 L/day were obtained respectively. Keywords—methane (CH4), palm oil mill effluent (POME), anaerobic digestion, thermophilic, green house gases (GHG)]]>
<![CDATA[PERBANDINGAN METODE KLASIFIKASI PENUTUP LAHAN BERBASIS PIKSEL DAN BERBASIS OBYEK MENGGUNAKAN DATA PiSAR-L2 (COMPARISON BETWEEN PIXEL-BASED AND OBJECT-BASED METHODS FOR LAND COVER CLASSIFICATION USING PiSAR-L2 DATA) ]]>
<![CDATA[Johannes Manalu]]>;
<![CDATA[Ahmad Sutanto]]>;
<![CDATA[Bambang Trisakti]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 13 No. 1 Juni 2016 ]]>
Publisher : <![CDATA[Indonesian National Institute of Aeronautics and Space (LAPAN) ]]>
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DOI: 10.30536/j.pjpdcd.2016.v13.a2936
<![CDATA[PiSAR-L2 program is an experimental program for PALSAR-2 sensor installed on ALOS-2. Research collaboration had been conducted between the Japan Aerospace Exploration Agency (JAXA) and Ministry for Research and Technology of Indonesia in 2012 to assess the ability of PiSAR-L2 data for some applications. This paper explores the utilization of PiSAR-L2 data for land cover classification in forest area using pixel-based and object-based methods, then carried out comparison between the two methods. PiSAR-L2 data full polarization with 2.1 level for Riau province was used. Field data conducted by JAXA team and landcover map from WWF were used as references to collect input and evaluation sample. Pre-processing was done by doing backscatter conversion and filtering, then classification was conducted and it`s accuracy was tested. Two methods were used, 1) Maximum Likelihood Enhance Neighbor classifier for pixel-based and 2) Support Vector Machine for object based classification. The effect of spatial resolution on classification result was also analyzed. The results show that pixel-based produced mixed pixels "salt and pepper", the classification accuracies were 62% for 2.5 m and 83% for 10 m spatial resolution. While the object-based has some advantages: high homogeneity (absence of mixed pixels), clear and sharp boundary among classes, and high accuracy (97% for 10 m spatial resolution), although it was still found errors in some classes. ABSTRAKProgram Polarimetric Interferometric Airborne Synthetic Aperture Radar of L-band version 2 (PiSAR-L2) adalah program eksperimen sensor Phased-Array Synthetic Aperture RADAR-2 (PALSAR-2) yang dipasang pada satelit Advanced Land Observing Satellite-2 (ALOS-2). Kerjasama riset telah dilakukan antara JAXA dan Kementerian Riset dan Teknologi pada 2012 untuk mengkaji kemampuan data PiSAR L-2 yang direkam menggunakan pesawat untuk beberapa aplikasi. Kegiatan ini menggunakan data PiSAR L-2 untuk klasifikasi penutup lahan di wilayah hutan dengan metode klasifikasi berbasis piksel dan berbasis obyek, kemudian membandingkan kedua metode tersebut. Data yang digunakan adalah data PiSAR L-2 polarisasi penuh dengan level 2.1 untuk wilayah Provinsi Riau. Data lapangan diperoleh dari survei lapangan tim JAXA dan peta penutup lahan dari World Wildlife Fund dijadikan sebagai referensi untuk sampel masukan dan pengujian. Pengolahan awal melakukan konversi backscatter dan filtering, kemudian melakukan klasifikasi dan uji akurasi. Dua metode klasifikasi yang digunakan, 1) Metode Maximum Likelihood Enhance Neighbor classifier untuk klasifikasi berbasis piksel dan 2) Metode Support Vector Machine untuk klasifikasi berbasis obyek. Pada kegiatan ini dilakukan analisis pengaruh resolusi spasial terhadap hasil klasifikasi. Hasil memperlihatkan bahwa metode berbasis piksel mempunyai piksel bercampur “salt and pepper”, akurasi klasifikasi adalah 62% untuk spasial resolusi 2.5 m dan 83% untuk spasial resolusi 10 m. Sedangkan klasifikasi berbasis obyek mempunyai kelebihan dengan homogenitas obyek yang tinggi (tidak adanya piksel bercampur), batas antara kelas yang jelas dan tegas, serta akurasi yang tinggi (97% untuk resolusi spasial 10 m), walau masih ada kesalahan pada beberapa kelas penutup lahan.]]>
<![CDATA[UTILIZATION OF MULTI TEMPORAL SAR DATA FOR FOREST MAPPING MODEL DEVELOPMENT ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Rossi Hamzah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2013.v10.a1844
<![CDATA[Utilization of optical satellite data in tropical region was limited to free cloud cover. Therefore, Synthetic Aperture Radar (SAR) becomes an alternative solution for forest mapping in Indonesia due to its capability to penetrate cloud. The objective of this research was to develop a forestmapping model based on multi temporal SAR data. Multi temporal ALOS PALSAR data for 2007 and 2008 were used for forest mapping, and one year mosaic LANDSAT data in 2008 was used as references data to obtain training sample and to verify the final forest classification. PALSAR processing was done using gamma naught conversion and Lee filtering. Samples were made in forest and water area, and the statistical values of the each object were calculated. Some thresholds were determined based on the average and standard deviation, and the best threshold was selected to classify forest and water in 2008. It was assumed that forest could not change in 1-2 years period. The classification of forest, water, and the change were combined to produce final forest in 2008, and then it was visually verified with mosaic LANDSAT in 2008. The result showed that forest, water, and the change could be well classified using threshold method. The forest derived from PALSAR was visually consistent with forest appearance in LANDSAT and forest produced from INCAS. It has better performance than forest derived from INCAS for separating oil palm plantation from the forest.]]>
<![CDATA[MONITORING OF LAKE ECOSYSTEM PARAMETER USING LANDSAT DATA (A CASE STUDY: LAKE RAWA PENING) ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Nana Suwargana]]>;
<![CDATA[Joko Santo Cahyono]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 1 (2015) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2015.v12.a2674
<![CDATA[Most lakes in Indonesia have suffered (decrease in quality) caused by land conversion in the catchment area, soil erosion, and water pollution from agriculture and households. This study utilizes remote sensing data to monitor several parameters used as ecosystem status assessors in accordance with the guidelines of Lake Ecosystem Management provided by the Ministry of Environment. The monitoring was done at Lake Rawa Pening using Landsat TM/ETM+ satellite data over the period of 2000-2013. The data standardization was done for sun angle correction and also atmospheric correction by removing dark pixels using histogram adjustment method. RGB color composites (R: NIR + SWIR, G: NIR, B: NIR-RED) were used for water hyacinth identification; thus, the lake water surface area can be delineated. Further samples were collected for water hyacinth and water classification with Maximum Likelihood method. Total Suspended Matter (TSM) by Doxaran model and the water clarity from field measurement was correlated to build water clarity algorithm. The results show that Lake Rawa Pening was deterioting in term of quality during the period of 2000-2013; it can be seen from the dynamic rate of the shrinkage and the expansion of the lake water surface area, the uncontrolled distribution of water hyacinth which it covered 45% of the lake water surface area in 2013, the increased of TSM concentration, and the decreased of water clarity. Most parts of Rawa Pening’s water have clarity less than 2.5 m which indicated that the thropic status is hypertrophic class.]]>
<![CDATA[UTILIZATION OF IKONOS IMAGE AND SRTM AS ALTERNATIVE CONTROL POINT REFERENCE FOR ALOS DEM GENERATION ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Gathot Winarso]]>;
<![CDATA[Atriyon Julzarika]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 7, No 1 (2010): Vol 7,(2010) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2010.v7.a1539
<![CDATA[Abstract. Digital Elevation Model (DEM) was generated from Advanced LandObservation Satellite - The Panchromatic Remote-Sensing Instrument for Stereo Mapping(ALOS PRISM) stereo data using image matching and collinear correlation based on LeicaPhotogrametry Suite (LPS) software. The process needs three dimension of Ground ControlPoint (GCP) or Control Point (CP) XYZ as input data for collinear correlation to determineexterior orientation coefficient. The main problem of the DEM generation is the difficultyto obtain the accurate field measurement GCP in many areas. Therefore, another alternativeCP sources are needed. The aim of this research was to study the possibility of (IKONOS)image and Shuttle Radar Topography Mission (SRTM) X-C band to be used as CPreference for ALOS PRISM DEM generation. The study area was Sragen and Bandungregion. The DEM of each study area was generated using 2 methods: generated using fieldmeasurement GCPs taken by differential GPS and generated using CPs from IKONOSimage (XY coordinat) and SRTM for (Z elevation). The generated DEMs were compared.The accuracy of both DEMs were evaluated using another field measurement GCPs. Theresult showed that the generated DEM using CPs from IKONOS and SRTM X-C hadrelatively same height pattern and height distribution along transect line with the DEMusing GCPs. The absolute accuracy of the DEM using CPs was about 60% - 80% lessaccuracy comparing to the DEM using GCPs. This research showed that IKONOS imageand SRTM X-C band can be considered as good alternative CP source to generate highaccuracy DEM from ALOS PRISM stereo data.]]>
<![CDATA[LAND COVER CLASSIFICATION ALOS AVNIR DATA USING IKONOS AS REFERENCE ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Dini Oktaviana Ambarwati]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 9, No 1 (2012) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2012.v9.a1822
<![CDATA[Abstract. Advanced Land Observation Satellite (ALOS) is a Japanese satellite equipped with 3 sensors i.e., PRISM, AVNIR, and PALSAR. The Advanced Visible and Near Infrared Radiometer (AVNIR) provides multi spectral sensors ranging from Visible to Near Infrared to observe land and coastal zones. It has 10 meter spatial resolution, which can be used to map land cover with a scale of 1:25000. The purpose of this research was to determineclassification for land cover mapping using ALOS AVNIR data. Training samples were collected for 11 land cover classes from Bromo volcano by visually referring to very high resolution data of IKONOS panchromatic data. The training samples were divided into samples for classification input and samples for accuracy evaluation. Principal component analysis (PCA) was conducted for AVNIR data, and the generated PCA bands were classified using Maximum Likehood Enhanced Neighbor method. The classification result was filtered and re-classed into 8 classes. Misclassifications were evaluated and corrected in the post processing stage. The accuracy of classifications results, before and after post processing, were evaluated using confusion matrix method. The result showed that Maximum Likelihood Enhanced Neighbor classifier with post processing can produce land cover classification result of AVNIR data with good accuracy (total accuracy 94% and kappa statistic 0.92). ALOS AVNIR has been proven as a potential satellite data to map land cover in the study area with good accuracy.]]>
<![CDATA[DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN ]]>
<![CDATA[Tatik Kartika]]>;
<![CDATA[Inggit Lolita Sari]]>;
<![CDATA[Bambang Trisakti]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2013.v10.a1843
<![CDATA[Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal classification in Sumatera Island using Landsat imagery in 25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was redetermined to obtain the final stratification zone. The indices was generated by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each zone described forest as 100, non-forest as 0, and uncertain forest between 50-99. The result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification.]]>
<![CDATA[DEM GENERATION FROM STEREO ALOS PRISM AND ITS QUALITY IMPROVEMENT ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Atriyon Julzarika]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 8, (2011) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2011.v8.a1740
<![CDATA[Digital elevation mode (DEM) is important data for supporting many activities. One of DEM generation methods is photogrametry of optical stereo data based on image matching and collinear correlation. The problem of DEM from optical stereo data is bullseye due to low contrast in relatively flat area and cloud cover. The research purpose is to generate DEM from ALOS PRISM stereo data level 1B2R and improve the quality of the DEM. DEM was generated using Leica Photogrametry Suite (LPS) software. The study area is located in Sragen district and its vicinity. The process needed three dimension of Ground Control Point (GCP) XYZ, as input data for collinear correlation. Ground measurement was conducted using differential GPS to collect 30 GCPs that used for input (21 GCPs) and for accuracy evaluation (9 GCPs). The generated DEM has good detail (10 m), but it has bullseye which mostly occurred in relatively flat area. The quality improvement was carried out by combining the DEM with SRTM DEM (30 m) using DEM fusion method. Both DEMs were processed for geoids correction (EGM 2008), co-registration and histogram normalization. The fusion method was conducted by considering height error map (HEM) of each DEM. The quality of fused DEM was evaluated by comparing HEM, the number of bullseye, and vertical accuracy before and after the fusion. The result shows that DEM fusion can preserve detail information of the DEM and significantly reduce the bullseye (decreasing more than 66% of bullseye). It also shows the improvement (from 7.6 m to 7.3 m) of vertical accuracy. Keywords: Digital Elevation model, Optical stereo data, ALOS PRISM, DEM fusion, Bullseye]]>
<![CDATA[COMPARING ATMOSPHERIC CORRECTION METHODS FOR LANDSAT OLI DATA ]]>
<![CDATA[Esthi Kurnia Dewi]]>;
<![CDATA[Bambang Trisakti]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 2 (2016) ]]>
Publisher : <![CDATA[National Institute of Aeronautics and Space of Indonesia (LAPAN) ]]>
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DOI: 10.30536/j.ijreses.2016.v13.a2472
<![CDATA[Landsat data used for monitoring activities to land cover because it has spatial resolution and high temporal. To monitor land cover changes in an area, atmospheric correction is needed to be performed in order to obtain data with precise digital value picturing current condition. This study compared atmospheric correction methods namely Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The correction results then were compared to Surface Reflectance (SR) imagery data obtained from the United States Geological Survey (USGS) satelite. The three atmospheric correction methods were applied to Landsat OLI data path/row126/62 for 3 particular dates. Then, sample on vegetation, soil and bodies of water (waterbody) were retrieved from the image. Atmospheric correction results were visually observed and compared with SR sample on the absolute value, object spectral patterns, as well as location and time consistency. Visual observation indicates that there was a contrast change on images that had been corrected by using FLAASH method compared to SR, which mean that the atmospheric correction method was quite effective. Analysis on the object spectral pattern, soil, vegetation and waterbody of images corrected by using FLAASH method showed that it was not good enough eventhough the reflectant value differed greatly to SR image. This might be caused by certain variables of aerosol and atmospheric models used in Indonesia. QUAC and DOS made more appropriate spectral pattern of vegetation and water body than spectral library. In terms of average value and deviation difference, spectral patterns of soil corrected by using DOS was more compatible than QUAC.]]>
<![CDATA[Stabilitas Reaktor Uplow Anaerobic Sludge Blanket-Hollow Centered Packed Bed dalam Produksi Biogas pada Kondisi Ruangan ]]>
<![CDATA[Dana Sembiring, Surya]]>;
<![CDATA[Irvan]]>;
<![CDATA[Trisakti, Bambang]]>;
<![CDATA[Novita Sari Sihombing, Dewi]]>
<![CDATA[ Jurnal Teknik Kimia USU Vol. 8 No. 2 (2019): Jurnal Teknik Kimia USU ]]>
Publisher : <![CDATA[Talenta Publisher (Universitas Sumatera Utara) ]]>
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DOI: 10.32734/jtk.v8i2.1883
<![CDATA[Anaerobic digestation was the docomposition of microbes from organic matter into methane, carbon dioxide, organic nutrients and compost in oxygen depletion and hydrogen gas. This study aimed to obtain the stability of the Uplow Anaerobic Sludge Hollow Centered Packed Bed reactor in biogas production at an ambient state that was seen through pH and alkalinity parameters. The process was carried out by varying hydraulic retention time, ei 45 days, 25 days, 10 days and 6 days with pH maintained 7 (±0,2). Analysis of pH and alkalinity was carried out to assess the stability of the reactor at ambient conditions using samples taken from the reactor overflow. The pH profile produced was relatively stable with a pH range between 5.8 - 7.2. The resulting alkalinity value was relatively stable with a pH range between 2,000-4,000 mg/L. The volume of biogas produced was 470 ml with concentration of methane (CH4), carbon dioxide (CO2) and trace hydrogen sulfide (H2S) respectively by 88.00%, 11.00% and 0.10%.]]>
