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<![CDATA[KAJIAN DAMPAK PERUBAHAN IKLIM TERHADAP KEBAKARAN HUTAN DAN DEFORESTASI DI PROVINSI KALIMANTAN BARAT ]]>
<![CDATA[Anggraini , Nanin]]>;
<![CDATA[Trisakti, Bambang]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 8 No. 1 (2011) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v8i1.3247
<![CDATA[Increasing or decreasing of rainfall intensity, due to the climate change, affects the environment condition in many Indonesia areas. For instance: low rainfall intensity causes high number of forest fire occurrence in Kalimantan Island. The impact of climate change is studied by analyzing the correlation among rainfall intensity, number of forest fire occurrence and forest area change in West Kalimantan Province. The rainfall is extracted using Tropical Rainfall Measurement Mission (TRMM) data for 2001-2008. The number of forest fire occurrence is identified by the number of hotspot extracted from thermal sensor of satellite data MODIS for 2001 - 2008. The forest area is calculated from MODIS data for 2003, 2005, 2007 and 2009. Pixel which has Normalized Difference Vegetation Index (NDVI) value more than 0,7 along a year round is assumed as forest pixel. The NDVI value is obtained by doing training sample in forest area. The result shows that the rainfall has slightly upward trend in Kalimantan. The rainfall has negative correlation with the number of hotspot. When the rainfall was the lowest and the number of hotspot was the highest in 2004, the forest area between 2003 and 2005 decreased (deforestation) significantly. On the other hand, when the rainfall was high and the hotspot was low in 2008, no decreasing in forest area otherwise we found the increasing of forest area. It is probably due to reforestation and expansion of plantation area (such as oil palm).]]>
<![CDATA[STANDARISASI KOREKSI DATA SATELIT MULTIWAKTU DAN MULTISENSOR (LANDSAT TM/ETM+ DAN SPOT-4) ]]>
<![CDATA[Trisakti, Bambang]]>;
<![CDATA[Nugroho, Gagat]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 1 (2012) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v9i1.3257
<![CDATA[Remote sensing satellite data has been widely used to support watershed and lake managements. However researches conducted in Indonesia are facing common problems related with standardization of data pre-processing, particularly that are related to orthorectification and radiometric correction. The objective of this research is to standardize the satellite data correction to monitor Total Suspended Material (TSM) in Limboto lake along 1990-2010 period using Landsat TM/ETM+ and SPOT-4. The data correction process was performed included orthorectification, sun correction, terrain correction and normalization of data with different time and different sensor. The result of each correction process was examined to evaluate the quality improvement before and after correction. The corrected data was then used to monitor the degree of turbidity of Limboto Lake during 1990-2010 periods. The study results show that data correction reduces position error and object spectral difference due to differences in acquisition time and sensor. The examined correction provides more accurate and consistent results. The quality of Limboto Lake was monitored decreases gradually, where the higher TSM concentration was found during the period of 1990-2010.]]>
<![CDATA[PEMANFAATAN DATA SATELIT UNTUK ANALISIS POTENSI GENANGAN DAN DAMPAK KERUSAKAN AKIBAT KENAIKAN MUKA AIR LAUT ]]>
<![CDATA[Anggraini, Nanin]]>;
<![CDATA[Trisakti, Bambang]]>;
<![CDATA[Soesilo, Tri Edhi Budhi]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 2 (2012) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v9i2.3266
<![CDATA[Increasing of ocean water volume caused sea level rise (SLR) that threatens the existence of small islands and coastal areas, such as North Jakarta. Besides the SLR, North Jakarta is also threatened by land subsidence. This study aims to predict the height of SLR in 2030 and to analyze the impact of SLR on the coastal areas of North Jakarta. The total height of SLR in 2030 was predicted using tidal data, land subsidence data, and SLR prediction by B2 scenario from the Intergovernmental Panel on Climate Change (IPCC). Potential inundation area due to SLR was estimated using Digital Elevation Model Shuttle Radar Topography Mission (DEM SRTM) X-C band with spatial resolution 30 m. The damage was analyzed by doing the overlay between the inundation areas with the land use information extracted from QuickBird data. The result shows that the SLR predictions in 2030 are 2.88 m caused by the tide, 2.28 m caused by the land subsidence, and 1.29 m caused by the B2 scenario IPCC. The total height of SLR prediction is 6.45 m. The potential damages of land use are dominated by urban area (1045 ha) and industrial area (563 ha). The most inundated areas are located in Penjaringan sub-district for urban (523 ha) and in Cilincing sub-district for industrial area (311 ha).]]>
<![CDATA[PEMANFAATAN CITA Pi-SAR2 UNTUK IDENTIFIKASI SEBARAN ENDAPAN PIROKLASTIK HASIL ERUPSI GUNUNGAPI GAMALAMA KOTA TERNATE ]]>
<![CDATA[Suwarsono, Suwarsono]]>;
<![CDATA[Yudhatama, Dipo]]>;
<![CDATA[Trisakti, Bambang]]>;
<![CDATA[Sambodo, Katmoko Ari]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v10i1.3268
<![CDATA[This research aims to identify the distribution of pyroclastic deposits from the eruption volcano by using Pi-SAR2 imagery. The object of research is Gamalama Volcano, located in the city of Ternate in North Maluku Province. Research methods include radiometric calibration Pi-SAR2 to get the value of backscatter intensity sigma naught, calculation of statistical values (mean, standard of deviation and coefficient of correlation between bands) backscatter intensity of sigma naught among pyroclastic deposits and other surface objects, as well as the separation distribution of pyroclastic deposits using thresholding methods. This research concludes that the Pi-SAR2 imagery can be used to identify the distribution of volcanic pyroclastic deposits from the eruption. Concurrent use of polarization HH, VV and HV will give better results than using a single polarization HH and VV. This research suggests further research to be done by applying the method of verification is supported by the use of field data (ground check).]]>
<![CDATA[PEMANFAATAN KANAL POLARISASI DAN KANAL TEKSTUR DATA PISAR-L2 UNTUK KLASIFIKASI PENUTUP LAHAN KAWASAN HUTAN DENGAN METODE KLASIFIKASI TERBIMBING ]]>
<![CDATA[Noviar, Heru]]>;
<![CDATA[Trisakti, Bambang]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v10i1.3273
<![CDATA[Polarimetric and Interferometric Airborne SAR in L band (PiSAR-L2) is an upgraded PiSAR program, which has a purpose for experimental activities of PALSAR-2 sensor equiped by ALOS-2 in 2013. Japan Aerospace Exploration Agency (JAXA) and Ministry of Reseach and Technology Indonesia have collaborated to explore the utilization PiSAR-L2 data for some applications in Indonesia. The purpose of this research is to utilize full polarimetric band of PiSAR-L2 data to classify land cover of forest area in Riau province. Field data conducted by JAXA team was used as reference data to collect input and verification training samples. SAR data pre-processing was conducted by doing backscatter conversion (digital number to Sigma naught) and filtering process using Lee filter. Classification was carried out by Maximum Likelihood classifier using Maximum Likelihood Enhanced Neighbour method. The research used three treatments for input data, using three SAR polarization bands (HH, VV and HV), and using six bands (three SAR polarization bands and three texture bands (deviation HH, VV and HV), and using six bands (three polarization dan 3 texture bands) with training samples improvement based on confusion matrix result. Verification of classification results were done using confusion matrix for each treatment. The result shows that texture band can enhance the degree of separation between object classes of vegetation, especially between forest and acacia plantation. Classification using six bands (three polarization dan 3 texture bands) with training sample improvement increased the overall accuracy and kappa statistic of the classification result to be 80% and 0.612 respectively.]]>
<![CDATA[PERBANDINGAN KLASIFIKASI BERBASIS OBJEK DAN KLASIFIKASI BERBASIS PIKSEL PADA DATA CITRA SATELIT SYNTHETIC APERTURE RADAR UNTUK PEMETAAN LAHAN ]]>
<![CDATA[Sutanto, Ahmad]]>;
<![CDATA[Trisakti, Bambang]]>;
<![CDATA[Arymurthy, Aniati Murni]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v11i1.3300
<![CDATA[Utilization of remote sensing data for land mapping has long been developed. In Indonesia, as a tropical region, the cloud becomes a classic problem in observing the Earth’s surface using optical remotely sensor satellite. Synthetic Aperture Radar (SAR) sensor satellite has the ability to penetrate clouds so it can solve cloud cover problems. In this study, the ALOS PALSAR data were used to assess object-based and pixel-based classification techniques. This data was chosen due to its capacity for object recognition based on backscatter characteristics. Object-based classification using the methods of Statistical Region Merging (SRM) for the object segmentation process and Support Vector Machine (SVM) for the classification process, whereas the pixel-based classification using SVM method. In the classification stage, several features of Target Decomposition and Image Decomposition of ALOS PALSAR data have been tested. The accuracy assessment of the classification was conducted using confusion matrix of the Region of Interest (ROI) data using the QuickBird data. Implementation of the object-based classification produced better result comparing to pixel-based classification. The number of optimal features is seven which consisted of three features Freeman Decomposition (Red, Green, Blue), Entropy, Alpha Angle, Anisotropy and Normalized Difference Polarization Index (NDPI). Overall accuracy reached 73.64% for the result of the object-based classification and 62.6% for the pixel-based classification.]]>
<![CDATA[PENDUGAAN LAJU EROSI TANAH MENGGUNAKAN DATA SATELIT LANDSAT DAN SPOT ]]>
<![CDATA[Trisakti, Bambang]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 2 (2014) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v11i2.3302
<![CDATA[The damage in catchment area (DTA) and the decrease of lake water quality have been happened in Indonesia, therefore Indonesian government has created a lake management and rescue program. This research aims to study soil erosion rate estimations using Landsat TM/ETM+ and SPOT-4 temporal data in the DTA of Kerinci Lake. Data standardization was carried out to maintain the consistency of the Normalized Difference Vegetation Index (NDVI) values from some disturbances caused by the differences of acquisition time, sensor and the effect of cloud cover. NDVImin and NDVImax were extracted from 19 Landsat TM / ETM + data in 2000-2009 period, slope was extracted from the Digital Elevation Model (DEM). Spatial distributions of soil erosion rate for 2009 and 2012 in the DTA were mapped using NDVI-slope method. The generated soil erosion rates in the DTA were analysed and verified by comparing the change of the soil erosion rate to the change of surface runoff coefficient. The results showed that the soil erosion rate in the DTA had a increasing trend, which is consistent with the increasing trend of surface runoff coefficient during 2009-2012 period. The soil erosion rate in the DTA of Lake Kerinci was estimated to increase form 0,39 mm/year in 2009 to be 0,46 mm/year in 2012.]]>
<![CDATA[PERBANDINGAN METODE KLASIFIKASI PENUTUP LAHAN BERBASIS PIKSEL DAN BERBASIS OBYEK MENGGUNAKAN DATA PiSAR-L2 ]]>
<![CDATA[Manalu, R. Johannes]]>;
<![CDATA[Sutanto, Ahmad]]>;
<![CDATA[Trisakti, Bambang]]>
<![CDATA[ Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 13 No. 1 (2016) ]]>
Publisher : <![CDATA[Institut Teknologi Sepuluh Nopember ]]>
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DOI: 10.30536/inderaja.v13i1.3319
<![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.]]>
<![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 Vol. 13 No. 2 (2016) ]]>
Publisher : <![CDATA[BRIN ]]>
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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[TECHNIQUE FOR IDENTIFYING BURNED VEGETATION AREA USING LANDSAT 8 DATA ]]>
<![CDATA[Bambang Trisakti]]>;
<![CDATA[Udhi Catur Nugroho]]>;
<![CDATA[Any Zubaidah]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016) ]]>
Publisher : <![CDATA[BRIN ]]>
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DOI: 10.30536/j.ijreses.2016.v13.a2447
<![CDATA[During the last two decades, forest and land fire is a catastrophic event that happens almost every year in Indonesia. Therefore, it is necessary to develop a technic to monitor forest fires using satellite data to obtain the latest information of burned area in a large scale area. The objective of this research is to develop a method for burned area mapping that happened between two Landsat 8 data recording on August 13rd and September 14th 2015. Burned area was defined as a burned area of vegetation. The hotspot distribution during the period August - September 2015 was used to help visual identification of burned area on the Landsat image and to verify the burned area resulted from this research. Samples were taken at several land covers to determine the spectral pattern differences among burned area, bare area and other land covers, and then the analysis was performed to determine the suitable spectral bands or indices and threshold values that will be used in the model. Landsat recorded on August 13rd before the fire was extracted for soil, while Landsat recorded on September 14th after the fire was extracted for burned area. Multi-temporal analysis was done to get the burned area occurring during the certain period. The results showed that the clouds could be separated using combination of ocean blue and cirrus bands, the burned area was extracted using a combination of NIR and SWIR band, while soil was extracted using ratio SWIR / NIR. Burned area obtained in this study had high correlation with the hotspot density of MODIS with the accuracy was around 82,4 %.]]>
