Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital (the Journal of Remote Sensing and Digital Image Processing) is a scientific journal dedicated to publishing research and development in technology, data, and the utilization of remote sensing. The journal encompasses the scope of remote sensing as outlined in Law No. 21 of 2013 on Space Affairs, which includes: (1) data acquisition; (2) data processing; (3) data storage and distribution; (4) utilization and dissemination of information. The journal was first published by the Indonesian National Institute of Aeronautics and Space (LAPAN) in June 2004 and received its initial accreditation as a "B" Accredited Scientific Periodical Magazine from LIPI in 2012. In 2015, the journal successfully maintained its "B" Accredited status. From 2018 to 2021, the journal was accredited as SINTA 2 with certificate number 21/E/KPT/2018. Starting from March 2025, the journal has been managed by the Institut Teknologi Sepuluh Nopember (ITS), in collaboration with the Geoinformatics Research Center of BRIN and the Indonesian Society for Remote Sensing (ISRS/MAPIN). The journal encompasses the scope of remote sensing as outlined in Law No. 21 of 2013 on Space Affairs, which includes: data acquisition; data processing; data storage and distribution; utilization and dissemination of information.
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147 Documents
<![CDATA[VALIDASI HOTSPOT MODIS DI WILAYAH SUMATERA DAN KALIMANTAN BERDASARKAN DATA PENGINDERAAN JAUH SPOT-4 TAHUN 2012 ]]>
<![CDATA[Zubeidah, Any]]>;
<![CDATA[Vetrita, Yenni]]>;
<![CDATA[Khomarudin, M. Rokhis]]>
<![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.v11i1.3296
<![CDATA[Forest/land fire indicator can be indicated by fire smoke and hotspot. Currently hotspot information has been widely used but its accuracy remains disputed. Therefore validated hotspot is needed as a proper effort of disaster management. This study aims to examine the accuracy of the hotspot as an indicator of forest fire/land from two data sources, namely IndoFire Map Service (IndoFire) and Fire Information for Resource Management System (FIRMS-NASA). Validation is done by comparing the data hotspot with a higher resolution image, i.e. SPOT-4 for 2012. The results show that the value of hotspot FIRMS acquired by 42% with error of 20% Commissioned 38% Omission error. Furthermore, analysis showed slightly better accuracy by 66% with 19% commission error and 18% error omission for FIRMS data compared to IndoFire ID using 46% with 19% commission error and 20% omission error. The value of confidence level of hotspot is very much affected by smoke and haze that is detected by the method of MODIS algorithm which is very sensitive to the condition of the environment. The results indicate that the accuracy of hotspot data can be considered for use in the field as a warning for forest fire, but should be considered for the data with a confidence level greater than 80%.]]>
<![CDATA[UJICOBA MODEL PEMETAAN LAHAN SAWAH BERBASIS PERUBAHAN PENUTUP LAHAN CITA LANDSAT MOSAIK TAHUNAN DI JAWA BARAT ]]>
<![CDATA[Parsa, I Made]]>
<![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.v11i1.3297
<![CDATA[Land cover changes of bare land, water and vegetation can be used as a basis for paddy field mapping by using probability theory approach, that is, the probability of one area can be determined as paddy field if the changes of water, bare and vegetation in multi time series can be detected. The results of preliminary studies that have been done on Tenggamus region – Lampung showed that probability theory approach produces a mapping accuracy reaches 91.2%. Based on this results, it has been carried out the model of validation for the wide region for some districts in Province West Java. The data used in this study are multitemporal Landsat 2000-2009. Data processing methods include: 1. Unsupervised digital classification of global land cover to map the bare land, vegetation and water from Landsat images, 2. Merger of each two multitemporal land cover so that the three spatial information obtained: bare land, vegetation and water 2000-2009. The validation of land cover changes made by overlaying the three spatial information. The evaluation results conducted by the confusion matrix (error matrix) by using reference paddy field 1:50,000 scale in 2010. Results of the testing showed that the average mapping accuracy of this probability model reaches 65.5%.]]>
<![CDATA[ANALISIS MATHEMATIK FRAKTAL UNTUK KLASIFIKASI MENGGUNAKAN CITRA PENGINDERAAN JAUH SPOT-4 ]]>
<![CDATA[Arief, Muchlisin]]>
<![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.v11i1.3298
<![CDATA[Fractal is a mathematical set that typically displays self-similar patterns. Fractal have two basic characteristic suitable for modeling the topography of the earth surface self similarity and randomness; Applications of fractal geometry in remote sensing rely heavily on estimates of the non integer fractal dimension (D). The fractal dimension is calculated using the model of Surface Area Triangular Prism (TPSA). Fractal dimension is used to observe the spatial repetition (morphologie) of surface. In this study, fractal dimension is used to observe the relative height of a building / object of surface in urban area. This paper described image analysis using non integer fractal dimension used to determining the height of an object relative to the others, then do grouping of the object height by thresholding method. The result of the whole proses is presented after the density slicing proses. The analysis showed that the fractal dimension of the homogeneous object/surface is smaller than the heterogeneous objects. Based on it’s fractal dimensional objects/buildings in Jakarta city (covering 1600 ha), can be grouped in 3 classes: very high object, high object and rather high object and there are approximately 178 ha using 9 x 9 windows and approximately 80 ha using 17 x 17 windows very high object. However, the results of this study are still in the early stages that the fractal dimension can quantitatively interprets spatial structure and spatial complexity of remote sensing data. Therefore, research needs to be followed up with the field measurements and very high resolution resolution data (such as IKONOS).]]>
<![CDATA[ESTIMASI LIMPAHAN PERMUKAAN DARI DATA SATELIT UNTUK MENDUKUNG PERINGATAN DINI BAHAYA BANJIR DI WILAYAH JABODETABEK ]]>
<![CDATA[Sofyan, Parwati]]>;
<![CDATA[Febrianti, Nur]]>;
<![CDATA[Prasasti, Indah]]>
<![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.v11i1.3299
<![CDATA[The study about runoff estimation based on soil moisture conditions was conducted using remote sensing data i.e., Landsat and Tropical Rainfall Measurement Mission during flood period January – February 2013 in Jakarta and its souroundings area. The Landsat data used to analyze the landcover/landuse which one of the basin characteristics. In this study, the TRMM has ability for representing the regional rainfall as 62.5 %. The Curve Number-Soil Conservation Service (CN-SCS) method was used in this study to estimate the runoff. The results of runoff estimation was shown in hydrograph unit in order to know when the flood will occur. The antecedent soil moisture condition in wet condition showed the best hydrograph unit. It had the peak point in January 17th 2013 exactly same with the time flood occurred in Jakarta and the souroundings area. This model has a good potential to be used as a flood early warning system. Spatially, the overall accuracy of the flood identification in Jakarta region compared with the flood map produced by Disaster Management Berau was 43 % with the producer’s accuracy 96 %, and user’s accuracy 42 %.]]>
<![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[DETEKSI DAERAH TERCEMAR LUMPUR ASAM MENGGUNAKAN DATA LANDSAT 7 ETM BERDASARKAN SUHU PERMUKAAN TANAH ]]>
<![CDATA[Sulma, Sayidah]]>;
<![CDATA[Pasaribu, Junita Monika]]>;
<![CDATA[Haryani, Nanik Suryo]]>
<![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.v11i2.3301
<![CDATA[The high human activity in the mining and industrial areas increases the potency for hazardous and toxic waste pollution. One form of hazardous and toxic waste is acid sludge, a mixture of hydrocarbons and sulfuric acid derived from the disposal of plant wax. This study aims to detect and monitor the acid sludge contaminated area based on the Land Surface Temperature (LST) derived from Landsat 7 ETM multi-temporal data. The steps included data collection, development of LST algorithms for Landsat 7 ETM resulted from regression of Terra-MODIS LSTand Tb of Landsat data, calculation of LST using Landsat 7 ETM multi temporal data and monitoring LST in polluted areas. The distribution of the MODIS LST value can be used as a reference in determining the LST from Landsat 7 ETM by performing linear regression models with a coefficient determination of 0.84. Based on the analysis of LST, the contaminated areas have a higher temperature compare to the uncontaminated area. There is no significant relationship pattern to the land and land recovery process. This may indicate that the recovery process in that area did not significantly affect the temperature.]]>
<![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[DETEKSI LIMBAH ACID SLUDGE MENGGUNAKAN METODE RED EDGE BERBASIS DATA PENGINDERAAN JAUH ]]>
<![CDATA[Haryani, Nanik Suryo]]>;
<![CDATA[Hidayat, Hidayat]]>;
<![CDATA[Sulma, Sayidah]]>;
<![CDATA[Pasaribu, Junita Monika]]>
<![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.v11i2.3303
<![CDATA[In line with the growing industry and population, the contamination of hazardous and toxic waste material increased. The increases is triggered by inappropriate handling of household and industry sector. The monitoring or detection of contaminated area or zone is very crucial to identify the areas of dispersion of the hazardous waste material. Remote sensing is one of applicable tool for detecting purposes. Several research has utilized remote sensing data to detect the contaminated areas by vegetation index, surface temperature as well as other indexes. This research proposes the red edge method from Landsat TM data to detect the hazardous waste material contamination in Pertamina RU-V Balikpapan. Based on the executed review, it is acknowledged that red edge method has a potential to detect the existence of hazardous and toxic waste, in the case where the acid sludge waste detection is correlated with the land rehabilitation such as neutralization, bioremediation, solidification and non-activation of acid sludge in the contaminated area which can be observed from its spectral displacement. The detection is related to bioremediation implementation and the indication of acid sludge in contaminated area. Based on the executed review, the red edge method is potentially applicable for this activity. The red edge pattern has defined the contaminated area in Pertamina RU-V Balikpapan. Based on the obtained and reviewed data, this research concluded that the monitoring of condition of hazardous waste could be implemented to identify which hazardous waste has been treated.]]>
<![CDATA[KLASIFIKASI PENUTUP LAHAN BERBASIS OBJEK PADA DATA FOTO UAV UNTUK MENDUKUNG PENYEDIAAN INFORMASI PENGINDERAAN JAUH SKALA RINCI ]]>
<![CDATA[Sari, Nurwita Mustika]]>;
<![CDATA[Kushardono, Dony]]>
<![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.v11i2.3304
<![CDATA[The need of spatial information from detailed-scale remote sensing is increasing. Unmanned Aerial Vehicle or UAV become one of vehicles that is expected to obtain such information. Production of land cover spatial information using UAV photo data requires appropriate method for classification. This study proposes an object-based classification method for land cover based on Haralick texture information namely homogeneity, contrast, dissimilarity, entropy, angular second moment, mean, standard deviation, and correlation. As a comparison method, a conventional land cover-object-based classification is implemented using the same information features, there are brightness, compactness, and density. The result shows that method using texture feature in object-based classification has reached 95.22% accuracy or 17.5% difference that is much better than conventional method that reaches 77.71%.]]>
<![CDATA[PEMANFAATAN DATA PENGINDERAAN JAUH UNTUK MENDUKUNG PERENCANAAN OPERASI KEAMANAN LAUT DI LAUT ARAFURU ]]>
<![CDATA[Winarso, Gathot]]>;
<![CDATA[Kurniawan, Eko]]>
<![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.v11i2.3305
<![CDATA[Large sea area has been patrolled continuly by Indonesia Navy need a lot of fleets to cover all Indonesian seawaters and also spend a huge of budget. Consequently, it is important to have smart strategic to optimise the fleet and to make efficient the logistic budget. The objective of this research is to apply remote sensing analysis to get sensitive timing operation on violation and security disturbance related to fishing activity. According to assumption that security threat might occur in the area where fishing activity is high that will be concentrated in the high productivity area. The chlorophyll-a concentration estimated from satellite data MODIS level-2 were received from NASA United State of America. Daily data from 2008-2013 was calculated into monthly average to get monthly variation of chlorophyll-a concentration within a year during 5 years. The analysis was done in general area and smaller unit area to understand the different variation at smaller area. The variation of chlorophyll-a in smaller unit area will differ the plan timing in patrol activity specific for those area. The data analysis resulted that phytoplankton bloom indicated occurred to May- September every year. The month of phytoplankton bloom can be suggested become a more intense patrol activity. In general, there are no different result from smaller unit area, but only small shifting of timing of bloom and changing of each different unit area.]]>
