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Lalu Muhamad Jaelani
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Department of Geomatics Engineering, Faculty of Civil, Planning, and Geo-engineering; Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia. Phone 031-5929486, 031-5929487
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Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 14128098     EISSN : 2549726X     DOI : https://doi.org/10.12962/inderaja
Core Subject : Science, Agriculture, Engineering,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
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.
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 11 No. 1 (2014)" : 5 Documents clear
VALIDASI HOTSPOT MODIS DI WILAYAH SUMATERA DAN KALIMANTAN BERDASARKAN DATA PENGINDERAAN JAUH SPOT-4 TAHUN 2012 Zubeidah, Any; Vetrita, Yenni; Khomarudin, M. Rokhis
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i1.3296

Abstract

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%.
UJICOBA MODEL PEMETAAN LAHAN SAWAH BERBASIS PERUBAHAN PENUTUP LAHAN CITA LANDSAT MOSAIK TAHUNAN DI JAWA BARAT Parsa, I Made
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i1.3297

Abstract

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%.
ANALISIS MATHEMATIK FRAKTAL UNTUK KLASIFIKASI MENGGUNAKAN CITRA PENGINDERAAN JAUH SPOT-4 Arief, Muchlisin
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i1.3298

Abstract

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).
ESTIMASI LIMPAHAN PERMUKAAN DARI DATA SATELIT UNTUK MENDUKUNG PERINGATAN DINI BAHAYA BANJIR DI WILAYAH JABODETABEK Sofyan, Parwati; Febrianti, Nur; Prasasti, Indah
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i1.3299

Abstract

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 %.
PERBANDINGAN KLASIFIKASI BERBASIS OBJEK DAN KLASIFIKASI BERBASIS PIKSEL PADA DATA CITRA SATELIT SYNTHETIC APERTURE RADAR UNTUK PEMETAAN LAHAN Sutanto, Ahmad; Trisakti, Bambang; Arymurthy, Aniati Murni
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i1.3300

Abstract

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.

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