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Lalu Muhamad Jaelani
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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 147 Documents
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VALUASI JUMLAH AIR DI EKOSISTEM LAHAN GAMBUT DENGAN DATA LANDSAT 8 OLI/TIRS Risdiyanto, Idung; Wahid, Allan Nur
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 1 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2017.v14.a2592

Abstract

The water content of peatland ecosystems stored as gasses in the air and as liquid in the peat soil and vegetation. The presence of water was influential to the value of spectral radians received by satellite sensors. Objective of study were develop empirical model to be applied in the Landsat 8 satellite imagery interpretation to estimate water content of peatland ecosystem. Method consisted of field measurements and satellite data interpretation. Field activities aimed to obtain weather parameters such as radiation, air temperature, surface temperature, evapotranspiration (ET), relative humidity (RH), soil water content (KAT), and biomass for each land cover in peatland ecosystems. Field measurements results were used to validate the parameters derived from Landsat 8 satellite data. Water content in the air was assessed by the ET and RH, in the soil was assessed by soil heat flux (G) and in the vegetation by biomass. The results of the validation of the data field measurement with Landsat 8 showed only ET (r2 = 0.71), RH (r2 = 0.71), and biomass (r2 = 0.87) had a strong relationship, while between G and KAT had weak relationship. Conclusion of this study indicated Landsat 8 satellite data could be used to calculate the water content in the air and vegetation. Thus, estimating water content in the peatland ecosystem with satellite data can only be done on the surface.
ESTIMASI PRODUKTIVITAS PRIMER PERAIRAN BERDASARKAN KONSENTRASI KLOROFIL-A YANG DIEKSTRAK DARI CITRA SATELIT LANDSAT-8 DI PERAIRAN KEPULAUAN KARIMUN JAWA Nuzapril, Mulkan; Susilo, Setyo Budi; Panjaitan, James P.
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 1 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2017.v14.a2548

Abstract

Sea primary productivity is an important factor in monitoring the quality of sea waters due to his role in the carbon cycle and the food chain for heterotrophic organisms. Estimation of sea primary productivity may be suspected through the values of chlorophyll-a concentration, but surface chlorophyll-a concentration was only able to explain 30% of the primary productivity of the sea. This research aims to build primary productivity estimation model based on chlorophyll-a concentration value of a surface layer of depth until depth compensation. Primary productivity model of relationships with chlorophyll concentration were extracted from Landsat-8 imagery then it could be used to calculated of sea primary productivity. The determination of the depth classification were done by measuring the attenuation coefficient values using the luxmeter underwater datalogger 2000 and secchi disk. The attenuation coefficient values by the luxmeter underwater, ranges between of 0.13-0.21 m-1 and secchi disk ranged, of 0.12 – 0.21 m-1. The penetration of light that through into the water column where primary productivity is still in progress or where the depth of compensation ranged from 28.75 – 30.67 m. The simple linier regression model between average value of chlorophyll concentration in all euphotic zone with sea primary productivity has high correlation, it greater than of surface chlorophyll-a concentration (R2 = 0.65). Model validation of sea primary productivity has high accuracy with the RMSD value of 0.09 and satellite-derived sea primary productivity were not significantly different. The satellite derived of chlorophyll-a could be calculated into sea primary productivity
METODE DUAL KANAL UNTUK ESTIMASI KEDALAMAN DI PERAIRAN DANGKAL MENGGUNAKAN DATA SPOT 6 STUDI KASUS : TELUK LAMPUNG Arief, Muchlisin; Adawiah, Syifa Wismayati; Parwati, Ety; Marpaung, Sartono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 1 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2017.v14.a2618

Abstract

Depth data can be used to produce seabed profile, oceanography, biology, and sea level rise. Remote sensing technology can be used to estimate the depth of shallow marine waters characterized by the ability of light to penetrate water bodies. One image that can estimate the depth is SPOT 6 which has three visible canals and one NIR channel with 6 meter spatial resolution. This study used SPOT 6 image on March 22, 2015. The image was first being dark pixel atmospheric corrected by making 30 polygons. The originality of this method was to build a correlation between the dark pixel value of red and green channels with the depth of the field measurement results, made on June 3 to 9, 2015. The algorithm derived experimentally consisted of: thresholding which served to separate the land by the sea and the correlation function. The correlation function was obtained: first correlating the observation value with each band, then calculating the difference of minimum pixel darkness value and minimum for red and green channel was 0.056 and 0.0692. The model was then constructed by using the comparison proportions, so that the linear equations were obtained in two channels: Z (X1, X2) = 406.26 X1 + 327.21 X2 - 28.48. Depth estimation results were for a 5-meter scale, the most efficient estimation with the smallest error relative mean occurred in shallow water depth from 20 to 25 meters, while the result of 10 meters scale from 20 to 30 meters and the estimated depth had similar patterns or could be said close to reality. This method was able to detect sea depths up to 25 meters and had a small RMS error of 0.653246 meters. Thus the two-channel method could offer a fast, flexible, efficient, and economical solution to map topography of the ocean floor.
UJI MODEL FASE PERTUMBUHAN PADI BERBASIS CITRA MODIS MULTIWAKTU DI PULAU LOMBOK Parsa, I Made; Dirgahayu, Dede; Manalu, Johannes; Carolita, Ita; KH, Wawan
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 1 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2017.v14.a2621

Abstract

Model testing is a step that must be done before operational activities. This testing aimed to test rice growth phase models based on MODIS in Lombok using multitemporal LANDSAT imagery and field data. This study was carried out by the method of analysis and evaluation in several stages, these are: evaluation of accuracy by multitemporal Landsat 8 image analysis, then evaluation by using field data, and analysis of growth phase information to calculate model consistency. The accuracy of growth phase model was calculated using Confusion Matrix. The results of stage I analysis for phase of April 30 and July 19 showed the accuracy of the model is 58-59%, while the evaluation of stage II for phase of period July 19 with survey data indicated that the overall accuracy is 53%. However, the results of model consistency analysis show that the resulting phase of the smoothed MODIS imagery shows a consistent pattern as well as the EVI pattern of rice plants with an 86% accuracy, but not for pattern data without smoothing. This testing give conclusion is the model is good, but for operational MODIS input data must be smoothed first before index value extraction.
ANALISIS PERUBAHAN GARIS PANTAI UJUNG PANGKAH DENGAN MENGGUNAKAN METODE EDGE DETECTION DAN NORMALIZED DIFFERENCE WATER INDEX Anggraini, Nanin; Marpaung, Sartono; Hartuti, Maryani
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.1017.v14.a2545

Abstract

Besides to the effects from tidal, coastline position changed due to abrasion and accretion. Therefore, it is necessary to detect the position of coastline, one of them by utilizing Landsat data by using edge detection and NDWI filter. Edge detection is a mathematical method that aims to identify a point on a digital image based on the brightness level. Edge detection is used because it is very good to present the appearance of a very varied object on the image so it can be distinguished easily. NDWI is able to separate land and water clearly, making it easier for coastline analysis. This study aimed to detect coastline changes in Ujung Pangkah of Gresik Regency caused by accretion and abrasion using edge detection and NDWI filters on temporal Landsat data (2000 and 2015). The data used in this research was Landsat 7 in 2000 and Landsat 8 in 2015. The results showed that the coastline of Ujung Pangkah Gresik underwent many changes due to accretion and abrasion. The accretion area reached 11,35 km² and abrasion 5,19 km² within 15 year period.
PENGARUH ASIMILASI DATA PENGINDERAAN JAUH (RADAR DAN SATELIT) PADA PREDIKSI CUACA NUMERIK UNTUK ESTIMASI CURAH HUJAN Paski, Jaka Anugrah Ivanda
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.1017.v14.a2642

Abstract

One of the main problems in numerical weather modeling was the inaccuracy of initial condition data (initial conditions). This study reinforced the influence of assimilation of remote sensing observation data on initial conditions for predictive numerical rainfall in BMKG radar area Tangerang (Province of Banten and DKI Jakarta) on January 24, 2016. The procedure applied to rainfall forecast was the Weather Research and Forecasting model (WRF) with a down-to-down multi-nesting technique from Global Forecast System (GFS) output, the model was assimilated to radar and satellite image observation data using WRF Data Assimilation (WRFDA) 3DVAR system. Data was used as preliminary data from surface observation data, EEC C-Band radar data, AMSU-A satellite sensor data and MHS sensors. The analysis was done qualitatively by looking at the measurement scale. Observation data was used to know rainfall data. The results of the study showed that producing rainfall predictions with different assimilation of data produced different predictions. In general, there were improvements in the rainfall predictions with assimilation of satellite data was showing the best results.
KLASIFIKASI MULTISKALA UNTUK PEMETAAN ZONA GEOMORFOLOGI DAN HABITAT BENTIK MENGGUNAKAN METODE OBIA DI PULAU PARI Anggoro, Ari; Siregar, Vincentius P.; Agus, Syamsul B.
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.1017.v14.a2622

Abstract

This study used multiscale classification and applied object-based image analysis (OBIA) for geomorphic zone and benthic habitats mapping in Pari islands. An optimized segmentation was performed to get optimum classification result. Classification methods for level 1 and 2 used contextual editing classification and for level 3 used support vector machines classifier. The results showed that overall accuracy for level 1 was 97% (reef level), level 2 was 87% (geomorphic zone), and level 3 was 75% (benthic habitats). Accuracy achieved by support vector machines classification was performed only in level 3 and optimum scale value achieved was 50 in compare with other scale values, i.e. 5, 25, 50, 75, 95. OBIA methods can be used as an alternative for geomorphic zone and benthic habitats map.
MODEL KOREKSI ATMOSFER CITRA LANDSAT-7 Muchsin, Fadila; Fibriawati, Liana; Pradhono, Kuncoro Adhi
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.1017.v14.a2595

Abstract

Three methods of atmospheric correction, Second Simulation of the Satellite Signal in the Solar Spectrum (6S), Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and the model Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), have been applied to the level 1T Landsat-7 image Jakarta area. The atmospheric corrected image is then compared with the TOA reflectance image. The results show that there is an improvement of the spectral pattern on the TOA reflectance image by the decrease of the reflectance value of each object by (1 - 11) % after the atmospheric correction of all models for visible bands (blue, green and red). In the NIR and SWIR bands there is an increase in the spectral value of about 1% to the TOA reflectance on all objects except wetland for the LEDAPS model. The percentage of the increase and the decrease in spectral values of 6S and FLAASH models have the same tendency. Analyzes were also performed on the NDVI values of each model, where NDVI values were relatively higher after atmospheric correction. The NDVI value of rice crop on FLAASH model is the same as 6S model that is equal to 0.95 and for wetland, it has the same value between FLAASH model and LEDAPS which is 0.23. NDVI value of entire scene for FLAASH model = 0.63, LEDAPS model = 0.56 and 6S model = 0.66. Before the atmospheric correction, the TOA is 0.45.
OPTIMASI PARAMETER DALAM KLASIFIKASI SPASIAL PENUTUP PENGGUNAAN LAHAN MENGGUNAKAN DATA SENTINEL SAR Chulafak, Galdita Aruba; Kushardono, Dony; Zylshal, Zylshal
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.1017.v14.a2746

Abstract

In this study, application of Sentinel-1 Synthetic Aperture Radar (SAR) data for the land use cover classification was investigated. The classification was implemented with supervised Neural Network classifier for Dual polarization (VH and VV) Sentinel-1 data using texture information of gray level co-occurance matrix (GLCM). The purpose of this study was to obtain the optimum parameters in the extraction of texture information of pixel window size, the orientation of neighboring relationships on the texture feature extraction, and the type of texture information feature used for the classification. The classification results showed that in the study area, the best accuracy obtained is 5 × 5 pixel window size, 00 orientation angle, and the use of entropy texture information as classification input. It was also found that more features texture information used as classification input can improve the accuracy, and with careful selection of appropriate texture information as classification input will give the best accuracy.
IDENTIFIKASI POTENSI REMBESAN MIKRO DI LAPANGAN MIGAS MELALUI DETEKSI MINERAL LEMPUNG MENGGUNAKAN CITRA LANDSAT 8 OLI/TIRS, STUDI KASUS LAPANGAN MIGAS CEKUNGAN JAWA BARAT BAGIAN UTARA Susantoro, Tri Muji; Wikantika, Ketut; Saepuloh, Asep; Harsolumakso, Agus Handoyo
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 1 (2018)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2018.v15.a2779

Abstract

Clay minerals in the oil and gas field have changed with an increase of the quantities in the middle of the oil and gas field and reduction in the edges. This reduction is the effect of micro seepage from oil and gas from the subsurface. The aims of the research is to identify the potential oil and gas seepage through clay mineral mapping. The data used where Landsat 8 OLI/TIRS with recording dated September 25, 2015. The method used in the mapping of clay minerals using the ratio of 1.55-1.75 µm (Short Wave Infrared 1) and 2.08-2.35 µm (Short Wave Infrared 2). The result of Landsat 8 OLI/TIRS data processing shows the potential of anomalies in edges of the oil and gas field. The anomaly is a change in the index value of clay minerals that tend to be lower with values 1.0 to 1.5 than the middle of oil and gas field with values 1.5 to 2.0. The potential pattern of the anomaly follows the border of the oil and gas field. Field surveys show that oil and gas field based on grain size analysis is dominated by clay-sized soil. The dominant clay minerals from X-Ray Diffraction analysis are smectite (56%) and kaolinite (6%).

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