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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
 1 
Search results for , issue "Vol. 10 No. 1 (2013)" : 5 Documents clear
PENGEMBANGAN METODE PENDUGAAN KEDALAMAN PERAIRAN DANGKAL MENGGUNAKAN DATA SATELIT SPOT-4. STUDI KASUS: TELUK RATAI, KABUPATEN PESAWARAN Arief, Muchlisin; Hastuti, Maryani; Asriningrum, Wikanti; Parwati, Ety; Budiman, Syarif; Prayogo, Teguh
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3267

Abstract

Bathymetric estimation of shallow water depth using satellite remote sensing data becomes more prevalent. However, when these methods are implemented for areas with different environments, the results indicate the presence of irregularities. To minimize the deviation, conducted the merger of the information obtained from field measurements with reflectance values SPOT-4 satellite imagery. This paper proposed the method development for bathymetric estimation of shallow water depth based on the correlation function between the depth value of direct measurements using a "handheld echo-sounder" to the resultant of reflectance values (band 1 and band 3). The algorithm for bathymetric estimation of a shallow water depth consists of thresholding method and correlation functions. Threshold value (T) depth of 0.5 meters is determined from observations of the correlation function graph polynomial from five and magnitude is 0.35 <T <0.47. Based on the results of the calculations show that the SPOT-4 satellite data can be used to estimate the shallow water depths up to approximately 18 meters.
PEMANFAATAN CITA Pi-SAR2 UNTUK IDENTIFIKASI SEBARAN ENDAPAN PIROKLASTIK HASIL ERUPSI GUNUNGAPI GAMALAMA KOTA TERNATE Suwarsono, Suwarsono; Yudhatama, Dipo; Trisakti, Bambang; Sambodo, Katmoko Ari
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3268

Abstract

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).
OPTIMALISASI PARAMETER SEGMENTASI UNTUK PEMETAAN LAHAN SAWAH MENGGUNAKAN CITRA SATELIT LANDSAT (STUDI KASUS PADANG PARIAMAN, SUMATERA BARAT DAN TANGGAMUS, LAMPUNG) Parsa, I Made
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3270

Abstract

Pixel-based digital-image classification results often contain of salt and pepper effects, while the visual classification has weakness because frequently provide inconsistent results. Due to the above, this study describes "Segmentation Parameter Optimization for Wetland Mapping Using Landsat Satellite Image" with object-based classification. The main objective of this study is to find out the optimal combination of segmentation parameters for paddy field mapping. The study was carried out in two sites namely in Pariaman, -West Sumatera Province and in Tangamus, -Lampung Province using segmentation of Landsat acquired in 2008 and visual interpretation of multitemporal Landsat images acquired in 2000-2009. Landsat segmentation covers two steps, firstly segmentation to optimize the parameter of color and compactness values, secondly to optimize the segmentation scale parameter. For validation, the study used both the visual-based and quantitative-based classification results of 2005 and 2007 derived from Quickbird image. Qualitative test includes object separation and segmentation accuracy of the first step of segmentation, while quantitative test is performed using confusion matrix on the second step of segmentation. This study results show that within the combinations of parameter values analyzed, the combination of parameter color value of 0.9, shape of 0.1, compactness of 0.5, and smoothness of 0.5 provides the most similar segmentation to the data reference. Meanwhile, the best case that the rules of cartography is scale of 8 for Pariaman study area and scale of 6 for Tangamus study area having accuracy ranges from 90.7% to 96.3%. This study concluded that the effect of the uncertainty of geometry of Landsat images against Quickbird images shows the maximum error of segment tolerance the origin of 4 ha to 16.70 ha for Pariaman site and 13.32 ha for Tangamus site. This results are still acceptable in segmentation results. Finally it was found that, the most optimal combination of parameters for mapping paddy field is at a scale of 1:1, color of 0.9.
KLASIFIKASI FASE PERTUMBUHAN PADI BERDASARKAN CITRA HIPERSPEKTRAL DENGAN MODIFIKASI LOGIKA FUZZY Maspiyanti, Febri; Fanany, M. Ivan; Arymurthy, Aniati Murni
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3272

Abstract

Remote sensing is a technology that is capable of overcoming the problems of measurement data for fast and accurate information. One of implementation of remote sensing technology in the field of agriculture is in hyperspectral image data retrieval to find out the condition and age of the rice plant. It is necessary for the estimation of rice yield in order to support Government policy in conducting imports rice to meet food needs in Indonesia. To have a good prediction model in estimation of rice yield that has high accuracy must be preceded by the determination of the phase of the rice plant. The selection of the appropriate classifier must also supported the selection of just the right features to get the optimum accuracy. In this study, we conducted a comparison between Fuzzy Logic and Modified Fuzzy Logic to perform the classification on nine rice growth stages based on hyperspectral image. Modified Fuzzy Logic have the same procedure with Fuzzy Logic but with extra crisp rules given in Fuzzy Rules which is expected to increase the accuracy achievement. In this study, Modified Fuzzy Logic proved to be able to improve the accuracy of up to 10% compared to Fuzzy Logic.
PEMANFAATAN KANAL POLARISASI DAN KANAL TEKSTUR DATA PISAR-L2 UNTUK KLASIFIKASI PENUTUP LAHAN KAWASAN HUTAN DENGAN METODE KLASIFIKASI TERBIMBING Noviar, Heru; Trisakti, Bambang
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3273

Abstract

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.

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