cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Lalu Muhamad Jaelani
Contact Email
lmjaelani@its.ac.id
Phone
+62819634394
Journal Mail Official
lmjaelani@its.ac.id
Editorial Address
Department of Geomatics Engineering, Faculty of Civil, Planning, and Geo-engineering; Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia. Phone 031-5929486, 031-5929487
Location
Kota surabaya,
Jawa timur
INDONESIA
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
 11   12   13   14   15 
Effectiveness of Normalized Difference Built-Up Index in Mapping Built-Up Features across Arid Rural Regions Oknisia, Elisabet; Nugraini, Lysa Dora Ayu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i1.5084

Abstract

Normalized Difference Built-up Index (NDBI) is a widely used remote sensing method for detecting built-up areas. However, its effectiveness in distinguishing built-up land from open land in dry rural regions remains underexplored. This study aims to evaluate the performance of NDBI in identifying built-up areas in Bayat Sub-district, Klaten Regency, Central Java, a predominantly rural area with semi-arid land characteristics during October 2023. The analysis employed Landsat 8 OLI imagery acquired in 2023, which was processed to generate NDBI values. These values were classified into four built-up intensity levels using the natural breaks (Jenks) method: Very Low, Low, Medium, and High. Validation was conducted using 36 ground truth points representing land cover types such as vegetation, built-up land, open land, and water bodies. Classification accuracy was assessed through a confusion matrix. The results revealed a significant degree of misclassification. NDBI is computed from the difference in reflectance between the Shortwave Infrared (SWIR) and Near Infrared (NIR) bands, where built-up areas typically exhibit high SWIR and low NIR values. However, dry open land (e.g., bare soil or unvegetated areas) displays a similar spectral pattern, high SWIR reflectance due to dry surfaces, and low NIR reflectance from the absence of biomass. This similarity causes elevated NDBI values for dry open areas, making them difficult to distinguish from actual built-up regions. The confusion matrix yielded an overall accuracy of 75.00% and a Kappa coefficient of 0.628, indicating moderate agreement between the classification results and ground data. These findings highlight the limitations of NDBI in differentiating built-up land from non-vegetated open land in semi-arid rural settings.
Spatial Temporal Analysis of Mesoscale Convective System to Asia-Australia Monsoon in East Java Firdianto, Prasetyo; Sukojo, Bangun Muljo; Zakir, Achmad; Mulsani, Adi
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i1.5136

Abstract

Indonesia maritime continent has the formation of clouds that can develop and evolution into MCSs (Mesoscale Convective System). Asian-Australian monsoon has an important influence in determining activities of MCSs. Research gap is analysis of relation between monsoon and MCSs in East Java where is greatly influenced by the monsoon. The data are weather satellite of Himawari, zonal wind and meridional wind ERA-Interim Model 850 mb. Determination of the MCSs follows the physical characteristics in the Maddox algorithm and the AUSMI index follows the Kajikawa algorithm. The method used is quantitative analysis of coefficient of correlation and determination, and qualitative in the form of descriptive analytic. It can be known that the Asian-Australian monsoon has weak influence on the MCSs in the East Java. AUSMI index has the same pattern and phase with frequency of MCSs on seasonal. 
Analysis of Mangrove Species Detection Performance on Multiresolution Satellite Imagery Using Linear Spectral Unmixing Fultriasantri, Indah; Alina, Aldea Noor; Jaelani, Lalu Muhamad; Sanjaya, Hartanto; Abdul Rasam, Abdul Rauf
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i1.6548

Abstract

The Pamurbaya mangrove conservation area in East Surabaya is crucial for coastal protection, but it is vulnerable to degradation due to human activities and land-use changes. Species distribution maps are essential for understanding ecological functions, such as carbon sequestration, salinity tolerance, and ecosystem stability. This study utilizes multiresolution remote sensing data from WorldView-2 satellite imagery to map mangrove and detailed species-level. Random Forest is utilized to differentiate mangrove and non-mangrove, while Linear Spectral Unmixing allows for detailed mangrove species distribution. Further analysis was carried out to determine at what resolution the LSU works optimally. The imagery was served in 0.5 meter resolution and down-sampled to 5 meter, 10, 20, 30, and 50 meter resolutions. This study obtained that LSU were able to differentiate mangroves according to its endmember and working optimally at medium resolution (10–30 m), with overall accuracy increasing from 70% (10 m) to 75% (30 m) and Kappa value increasing from 53.7 to 60.41. High resolution (0.5–10 m) provides more detailed mapping but is optimal for species with small and scattered distributions. Meanwhile, low resolution (20–50 m) tends to cause overestimation or aggregation of species.
Flood Prone Area Analysis using Landsat 9 and MCDA Method in Bekasi Regency Suharyanto, Zahra Putri Callibri; Bioresita, Filsa
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 2 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i2.7758

Abstract

Mapping reveals Bekasi (total 126,266.77 ha) has four flood vulnerability classes. Most land (68.5% or 86,454.87 ha) is Medium risk, primarily in transitional zones prone to inundation from extreme rain or land-use changes. High-risk areas cover 21,831.52 ha, while Low-risk zones span 17,980.39 ha. This distribution shows the regency is predominantly moderate-to-highly vulnerable, driven by lowland topography and proximity to rivers. As West Java's most flood-damaged region in the past decade, a study integrated Landsat 9 imagery and MCDA to map flood risk using five parameters (land cover, elevation, rainfall, soil, river buffers). Validated with BNPB historical data, the model confirmed northern areas (Tambun, Muara Gembong, Babelan) as highest risk due to low elevation (<10 m), alluvial soil, and frequent flooding.
The Use of Active Remote Sensing Data and Adaptive Threshold Method for Analysing Oil Spill in West Side of Java Sea Wardhana, Bhisma Kusuma; Bioresita, Filsa; Hayati, Noorlaila
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 2 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i2.7799

Abstract

Oil spill phenomena, particularly in the West Side of Java Sea, occur due to the dense oil industry and maritime activities causing potential vulnerability to oil pollution. Rapid detection of oil spill distribution needs to be conducted to minimize the resulting impacts. By developing an early detection method for oil spills in the Western Java Sea using Synthetic Aperture Radar (SAR) technology from Sentinel-1A Satellite using SNAP software with an Adaptive Threshold approach. The detection method is based on the principle that oil causes the sea surface to become calm, resulting in a drastic reduction in radar wave reflection values. Research results show oil spill detection in June 2023 with an area reaching 73,823 km² and an accuracy level of 93,75% based on confusion matrix validation. This research also integrates windfield analysis to support radar image interpretation, with wind speed estimation results of 1-12 m/s and dominant direction toward northwest to north. Windfield data was validated using BMKG reanalysis data and Copernicus Marine My Ocean Pro. The developed method is superior to optical imagery in terms of detection visualization and object classification capability within the spill area. The findings of this research provide important contributions to the development of effective monitoring and response systems to protect marine ecosystems, and can serve as a basis for planning environmental impact mitigation from oil spills in the region.
Air Temperature-based Spatial Modeling of Remote Sensing Data Using Machine Learning Approaches: a Systematic Literature Review Sampelan, David; Pratiwi, Anggitya; Baihaqi, Anas; Agustiarini, Suci
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 2 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i2.8450

Abstract

This study presents a systematic review of spatial air temperature modeling based on remote sensing data using machine learning approaches during the period 2016–2025. Using the PRISMA framework, we conducted literature searches in Google Scholar (998 articles) and Scopus (489 articles).. After merging the datasets, removing duplicates, and applying inclusion–exclusion criteria, 12 articles were retained for in-depth analysis. The findings indicate a marked increase in publications since 2021, reflecting growing global interest in integrating remote sensing and machine learning for air temperature estimation. Ensemble algorithms such as Random Forest and XGBoost dominate due to their balance of accuracy and computational efficiency, while temporal deep learning approaches such as LSTM and TCN are emerging as powerful tools for capturing complex atmospheric dynamics. Among remote sensing predictors, Land Surface Temperature (LST) is the most frequently used, often complemented by NDVI, albedo, and elevation to improve spatial accuracy. Geographical context strongly influences methodological performance. XGBoost proves effective in heterogeneous urban areas, Random Forest performs well in mountainous regions, and artificial neural networks demonstrate higher adaptability in extreme environments such as the Greenland ice sheet. Nonetheless, limited ground-based observations and sparse station networks remain key challenges, particularly across tropical and archipelagic regions. This review identifies three major directions for future research: (1) expanding studies to underrepresented tropical regions, (2) leveraging temporal deep learning methods for detecting extreme events, and (3) integrating multisensor data with innovative validation strategies to enhance the robustness and reliability of air temperature modeling.
Analysis of SO2 Emissions and Thermal Anomalies from the Eruption of Mount Lewotobi Laki-laki in November 2024 Using Google Earth Engine Pratama, Febryanto; Jaelani, Lalu Muhamad
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i1.5968

Abstract

Mount Lewotobi is one of the active volcanoes located in Wulanggitang District, East Flores Regency, East Nusa Tenggara. Mount Lewotobi Laki-Laki in November 2024 has been detected showing significant volcanic activity. This volcanic activity has been detected emitting volcanic gas emissions and significant lava flows that could affect air quality, structures, and the surrounding ecosystem. SO2 emissions and hotspot areas were analyzed using remote sensing data from Sentinel-5P (TROPOMI), Sentinel-2 (MSI), and Landsat-8 (OLI). Data processing was conducted using the Google Earth Engine platform to obtain spatial and temporal analyses of SO2 concentrations in the air and heat sources generated by volcanic activity. The Normalized Hotspot Indices (NHI) method was applied to identify and map hotspots generated by volcanic activity. The results of SO2 levels showed a maximum value of 300,831 µg/m³ and an average of 71,928 µg/m³ occurring on November 9, 2024. The classification of hotspot distribution indicated a range from high to moderate to low. The total number of hotspots measured was 51 on Landsat-8 and 278 on Sentinel-2. The statistical test results for Landsat-8 data showed no significant correlation between SO2 measurements and hotspot measurements, whereas the results for Sentinel-2 showed an inverse correlation.

Page 15 of 15 | Total Record : 147

 11   12   13   14   15 

Filter by Year

2004 2025


Reset
Filter By Issues
All Issue Vol. 19 No. 2 (2025) Vol. 19 No. 1 (2025) Vol. 18 No. 1 (2021) Vol. 17 No. 1 (2020) Vol. 16 No. 2 (2019) Vol. 16 No. 1 (2019) Vol. 15 No. 2 (2018) Vol. 15 No. 1 (2018) Vol. 14 No. 2 (2017) Vol. 14 No. 1 (2017) Vol. 13 No. 2 (2016) Vol. 13 No. 1 (2016) Vol. 12 No. 2 (2015) Vol. 12 No. 1 (2015) Vol. 11 No. 2 (2014) Vol. 11 No. 1 (2014) Vol. 10 No. 2 (2013) Vol. 10 No. 1 (2013) Vol. 9 No. 2 (2012) Vol. 9 No. 1 (2012) Vol. 8 No. 1 (2011) Vol. 6 No. 1 (2009) Vol. 5 No. 1 (2008) Vol. 4 No. 1 (2007) Vol. 3 No. 1 (2006) Vol. 1 No. 1 (2004) More Issue