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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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ANALISIS SPASIAL KESESUAIAN BUDIDAYA KERAPU BERBASIS DATA PENGINDERAAN JAUH (STUDI KASUS: PULAU AMBON MALUKU) Anggraini, Nanin; Adawiah, Syifa Wismayati; Ginting, Devica Natalia Br; Marpaung, Sartono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 2 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i2.3358

Abstract

Indonesian waters have abundant marine aquaculture potential. This activity need to be maximized with remote sensing technology approach to determining locations that have the potential aquaculture areas. The research location is Ambon Island, Maluku Province. The method used for suitability site is Weighted Overlay Technique from biophysical parameters such as total suspended solids (TSS), sea surface temperature (SST), chlorophyll, and bathymetry. In addition, mangrove and coral reef data are used as a limiting factor for the suitability site. Based on the results of processing data, classes were quite suitable dominated in Piru Bay, Banguala Bay, and Ambon Bay; the appropriate classes were detected in Ambon Dalam Bay, and very suitable classes were detected in Piru Bay and Ambon Bay. The results of field measurement verification showed that the temperature of the image data with the insitu data correlated with the value of R2 0.74 and TSS image with insitu data shown R2 of 0.63.
IDENTIFIKASI MATERIAL PIROKLASTIK PASCA ERUPSI GUNUNG KELUD MENGGUNAKAN CITRA HYPERSPEKTRAL Rijal, Seftiawan Samsu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 17 No. 1 (2020)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v17i1.3359

Abstract

Analisis Perubahan Tutupan Lahan dari Citra TerraSAR-X Menggunakan Metode Analisis Texture dan Segmentasi di Jakarta Dyatmika, Haris Suka; Sari, Inggit Lolita; Muchsin, Fadila; Indriasari, Novie; Budiono, Marendra Eko
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 17 No. 1 (2020)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v17i1.3360

Abstract

Rapid urban growth in Jakarta is indicated by the increases of building area, such as settlements, roads, commercial and others. Identification of land cover extent and its changes is a vital data for urban planning. One method for land cover mapping and its changes is obtained by the utilization of remote sensing data that characterize as having a continuity data, covers a vast area, and cost-effective. Remote sensing data can be obtained from optical and radar imagery. Radar data importance for mapping land cover and land cover changes because radar data does not constrain by time and weather. In early 2018, the TerraSAR-X (TSX) data can be acquired at the LAPAN Parepare ground station. This research uses the TSX Stripmap image mode with a spatial resolution of 3 m in 2010 and 2013. The TSX data will be used to map the land cover and land cover changes in Jakarta using method of the texture analysis and image segmentation. The accuracy assessment of the map will be assessed visually and quantitatively using the Pleiades images (0.5 m) and Google Earth images. The results show that the TSX images detect the current developments of settlements, new roads construction and provide information on the loss of green open space in Jakarta.
PEMANFAATAN METODE SEMI-ANALITIK UNTUK PENENTUAN BATIMETRI MENGGUNAKAN CITRA SATELIT RESOLUSI TINGGI Setiawan, Kuncoro T.; Winarso, Gathot; Ginting, Devica N. BR.; Manessa, M.D.M.; Surahman, Surahman; Anggraini, Nanin; Hartuti, Maryani; Asriningrum, Wikanti; Parwati, Ety
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3361

Abstract

Semi-Analytical methods for detecting bathymetry using medium resolution satellite image data is the development of methods for determining satellite-based bathymetry. This method takes into account the principle of the propagation of light waves in water and the intensity of incident light which decreases according to the increase in depth traversed. The satellite image used is SPOT 7. The image is the latest generation of SPOT satellites which have 4 multispectral channels with a spatial resolution of 6 meters. Therefore, this high-resolution image is expected to produce bathymetry in shallow marine waters more accurately. Semi-analytical methods used to detect bathymetry are Benny and Dawson's methods. This method uses a comparison of the reflectance value between deep water and shallow water by taking into account the approach of the water column attenuation coefficient and the elevation angle of the satellite. The purpose of this study is to detect bathymetry in shallow sea waters. The study area is Karimunjawa Island coastal waters, Jepara, Central Java. The data used is the SPOT 7 acquisition image dated 18 May 2017 has been analysed, in situ depth data as well as tide data. The results showed that off the three SPOT 7 channels, the depth range of 0 - 11.45 meters for the blue channel band, 0 - 10.49 meters for the green channel and 0 - 9.72 meters for the channel red. The accuracy of the bathymetry detection results from the green channel shows quite good results to a depth of less than 5 meters. Green channel parameters of the Benny Dawson algorithm used are 0.3274 for Ld, 0.8932 for Lo, attenuation coefficient of 0.823 and Cosec E '0.6311272.
PEMANFAATAN DATA CITRA SENTINEL-3 SEA AND LAND SURFACE TEMPERATURE RADIOMETER (SLSTR) PAGI DAN MALAM HARI UNTUK ANALISIS INTENSITAS FENOMENA PULAU BAHANG PERMUKAAN (Studi Kasus: Kota Bandung) Mirnayani, Mirnayani; Rapang, Sry Kurnia; Aini, Andi Nursuasri; Bayanuddin, Athar Abdurrahman
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3362

Abstract

Suhu permukaan tanah perkotaan lebih tinggi dibanding pedesaan merupakan fenomena alam yang dikenal sebagai Surface Urban Heat Island (SUHI). SUHI memberikan dampak negatif yang besar seperti mempengaruhi kesehatan manusia, kualitas air, udara serta konsumsi energi makhluk hidup sehingga perlu ditemukan solusi yang tepat. Penelitian ini bertujuan untuk : (1) menganalisis pola spasial SUHI Intensity (SUHII) Kota Bandung pada pagi dan malam hari menggunakan data citra Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) dan (2) rancangan mitigasi iklim perkotaan bagi pemerintah dan masyarakat Kota Bandung. Penelitian SUHII ini menggunakan data multiwaktu Land Surface Temperature (LST) citra Sentinel-3 SLSTR pagi dan malam hari musim kemarau tahun 2019 (Agustus-Oktober) untuk menghitung selisih LST urban (Kota Bandung) dan area sub-urban. Berdasarkan pengolahan data tersebut, diperoleh SUHII maksimum pagi dan malam hari musim kemarau mencapai 5,6ºC dan 2,1ºC. Selain itu, diperoleh pula pola spasial SUHII di Kota Bandung menunjukkan dua area cenderung terjadi fenomena SUHI yaitu di pusat kota di sisi barat (Kecamatan Babakan Ciparai) dan di area permukiman padat (Kecamatan Antapani dan sekitarnya). Rancangan mitigasi pada area terindikasi SUHII tinggi bagi pemerintah dan masyarakat Kota Bandung yaitu berupa penambahan vegetasi.
IDENTIFIKASI AWAN PADA DATA TIME SERIES MULTITEMPORAL MENGGUNAKAN PERBANDINGAN DATA SEKUENSIAL Hayati, Anis Kamilah; Sunarmodo, Wismu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3363

Abstract

Cloud identification is an important pre-processing step of remote sensing data.Generally, cloud identifications could be classified into single-date and multi-date methods. Furthermore, the single-date method could be divided into physical-rules-based and machine-learning-based. Physical-rules-based method generally need data with sufficient spectral resolution while machine-learning-based method depend on training dataset. While the multi-date method usually using clear data as a reference. The clear data itself could be a whole scene or built from many scenes. Processing cloud-free data is a challenge in areas with high cloud coverage such as Indonesia. In this paper, a cloud identification method using multi-date time series scenes is proposed. This method only uses RGB channels which are common in remote sensing visual data. In addition, this method does not require or process cloud-free data mosaics in advance. A pixel value from an examined scene is compared to other pixel values from other scenes in the same position. The other scenes are the scenes that were acquired before and after the examined scene. The value differences between the examined pixel and it's before and after then evaluated using some thresholds to determine whether the pixel is a cloud or not. Assessment is done by using L8 Biome as a reference. The result shows that using some thresholds in our proposed method has a Kappa coefficient higher than 0.9.
KESESUAIAN WILAYAH BUDI DAYA IKAN KERAPU BERDASARKAN CITRA SATELIT LANDSAT-8 OPERATIONAL LAND IMAGER (OLI)/THERMAL INFRARED SENSOR (TIRS) (STUDI KASUS PERAIRAN KECAMATAN GEROKGAK, KABUPATEN BULELENG, PROVINSI BALI) Azizah, Febiana Nur; Afgatiani, Pingkan Mayestika; Adawiah, Syifa Wismayanti; Anggraini, Nanin; Ginting, Devica Natalia Br; Patwati, Ety; Asriningrum, Wikanti
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3364

Abstract

he waters in Gerokgak District are one of the aquatic region in Indonesia that have potential as regional land for the development of aquaculture, one of which is grouper cultivation. To increase the potential of grouper cultivation, it is necessary to know the right location of grouper cultivation. This study applies a method using an overlay between oceanographic parameters, namely sea surface temperature (SST), salinity, chlorophyll, and Total Suspended Solid (TSS). In addition, this study also uses a remote sensing approach by utilizing Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) satellite imagery data. The results of this study indicate that the waters in the Teluk Penerusan, Gerokgak District, Bali have waters that are suitable for grouper cultivation. Based analysis result between the values of sea surface temperature and chlorophyll with in situ values, it shows good accuracy with values of R2 = 0,661; 0,686 for chlorophyll in situ, and 0,658 for TSS with in situ.
KLASIFIKASI PENUTUP LAHAN MENGGUNAKAN DATA LIDAR DENGAN PENDEKATAN MACHINE LEARNING Hariyono, Mochamad Irwan; Dewi, Ratna Sari; Rokhmatullah, Rokhmatullah; Tambunanan, Mangapul P
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3365

Abstract

Lidar is a remote sensing technology. Lidar data is widely used and has been developed for mapping, detailed spatial planning, and natural disaster analysis. In its development for Lidar data management, software applications are widely used as well as by using built algorithms such as machine learning. The research aims to utilize Lidar data for land cover classification using machine learning, namely Support Vector Machine (SVM). The research location is Tanjung Karang village, Mataram City, Lombok. The classification applied is a supervised classification in which the training data is needed to perform the classification. The predicted land cover class in this study is limited to buildings, vegetation, roads, open land. The data used for classification is derived from Lidar, namely DTM, DSM, nDSM, and Intensity. The classification scheme used is one data input and a combination of data. The reference data used is a topographic map (Topographic map of Indonesia). The results showed that the classification with a data combination scheme had a better accuracy value than the one data classification scheme, which increased accuracy by about 15-20%. This shows that there are complementary factors between the data to be able to identify objects in the classification process.
PERANCANGAN SISTEM MONITORING CLOUD COVER UNTUK PEMANTAUAN DAN PREDIKSI CLOUD COVER MENGGUNAKAN METODE DATABASE MANAGEMENT SYSTEM DAN LONG SHORT-TERM MEMORY Hestrio, Yohanes Fridolin; Pradono, Kuncoro Adi; Widipaminto, Ayom
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3366

Abstract

The quality of optical satellite image data obtained by the Center for Remote Sensing Data and Technology is affected by weather conditions and cloud cover. Based on these conditions, the satellite image data obtained are divided into three categories including very cloudy, cloudy, and cloud-free. Based on annual data information, it is found that the amount of cloudy satellite image data is three times greater than the amount of cloud-free satellite imagery data. So we need a system that can monitor the percentage of the extent of cloud cover from the acquisition of satellite image data. In addition, it is hoped that the creation of a system that can predict cloud cover, where the results of this cloud cover prediction can be used as a reference at the time of the next satellite image acquisition. . Through research and development of this cloud cover monitoring system, both the user and the acquisition officer can monitor the cloud cover of the acquisition result and also determine the location of cloud-free image data acquisition with predictive data. The method used for the development of the monitoring system uses a DBMS (Database Management System), while predictive research on cloud cover in an area wear the LSTM (Long short-term memory) method for Time Series Forecasting. The results of this research and development are in the form of a monitoring system that can monitor the results of acquisitions with data management principles and predict cloud cover conditions from cloud cover monitoring data.
Comparative Analysis of SAVI and NDVI Correlations with Land Surface Temperature in Mandalika Special Economic Zone Using Landsat 8 Imagery Sampelan, David; Pratiwi, Anggitya; Baihaqi, Anas
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.v19i1.4442

Abstract

The rapid infrastructure development within the Mandalika Special Economic Zone (SEZ) has significantly altered land cover and potentially influenced land surface temperature (LST). This study aims to compare the correlation strength of two remote sensing-based vegetation indices, Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) with LST to determine which index better represents surface temperature variability in areas undergoing rapid development. Landsat 8 imagery from 2014 to 2023 was used to derive NDVI, SAVI, and LST values. Spearman’s Rho correlation and simple linear regression were employed to evaluate the strength and consistency of the relationships between vegetation indices and LST. The Shapiro – Wilk test confirmed that all variables were not normally distributed, leading to the use of Spearman's rho correlation. Both indices showed significant negative correlations with LST, with NDVI slightly stronger (r = -0.555) than SAVI (r = -0.536). Simple linear regression revealed NDVI had a higher R² (0.392) and lower residual error than SAVI, indicating a more robust model fit. Although SAVI is more suitable in mixed land cover conditions due to its soil background correction, NDVI provides stronger statistical performance in modeling LST in Mandalika SEZ. These findings support the strategic use of NDVI as a primary indicator in environmental planning and sustainable development monitoring or for Urban Heat Island mitigation policy in developing regions.

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