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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 147 Documents
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ANALISIS PENINGKATAN KUALITAS GEOMETRI DENGAN MENGGUNAKAN TITIK IKAT BUNDLE ADJUSTMENT (STUDI KASUS DATA PLEIADES WILAYAH KABUPATEN MADIUN DAN KABUPATEN MAGETAN) Sari, Inggit Lolita; Brahmantara, Randy Prima
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3347

Abstract

Recently, the utilization of very high spatial resolution data such as Pleaides has reached at a high demand. Particularly to support disaster mitigation, where automation and fast image processing are necessary and unavoidable. Pleiades imagery has been acquired at LAPAN ground station starting in 2018. This study examines the improvement of the Pleiades images geometry accuracy processed using the bundle adjustment (BA) method in order to support image mosaicking where case study is located in the Madiun regency and the Magetan regency. This method uses parameters to relate the geometry between scenes by using tie points. These points are located in the intersection area between scenes. Geometry quality assessment of the imagery produced using BA correction are measured by comparing between the coordinate of the imagery and the coordinates obtained from the field measurement. The assessment shows that BA geometry correction has improved the geometry quality of the images which nearly similar to the field measurement and achieved a better geometry accuracy compare to the images processed without BA method.
ANALISIS METODE KOMPRESI BERDOMAIN WAVELET PADA CITRA SATELIT RESOLUSI SANGAT TINGGI Widipaminto, Ayom; Indradjad, Andy; Monica, Donna; Rokhmatullah, Rokhmatullah
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3348

Abstract

A problem that often arises in remote sensing images, especially very high-resolution images, is the large storage and bandwidth needed to transmit those images. On satellite images processing, a compression needs to be done on those satellites images to make it easier in terms of transmission and storage. This paper compare several wavelet-domain methods namely wavelet method, bandelet method, and CCSDS to find the best method to compress the very high-resolution satellites imageries Pleiades. Experiment results show that the method wavelet and bandelet is better in preserving the images quality with around 50 dB PSNR, while CCSDS is better in reducting the image size to the eighth of original image.
PENGEMBANGAN METODE KLASIFIKASI LAHAN SAWAH BERBASIS INDEKS CITRA LANDSAT MULTIWAKTU Parsa, Made; Dirgahayu, Dede; Harini, Sri
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3349

Abstract

Research on the development of a paddy field classification model based on Landsat remote sensing images aims to obtain a rapid classification of paddy field models. This study uses input multitemporal Landsat images (path/row 122/064) in 2017. The research was conducted in Subang regency, which is one of the center of West Java rice production. The method used in this study is the threshold method for the multi-temporal Landsat image index. As a reference, detailed scale spatial information on paddy fields base is used which is supplemented with data from field surveys using drones. First, an atmospheric correction of Landsat images was carried out using DOS (Dark Object Subtraction) Method, then transformation image to several indices: Enhance vegetation Index (EVI), Normal Difference Water Index (NDWI), and Normal Difference bare Index (NDBI) was carried out. For cloudy images, the index is filled with interpolation techniques from the index value before and after. The next step is smoothing index and statistical analysis to obtain minimum, maximum, mean, median, range (maximum - minimum), EVI_planting, EVI_harvesting, mean_planting-harvesting, mean_vegetative, mean_generative, NDWI_planting, NDWI_harvesting, NDBI_planting, and NDBI_harvesting. Classification accuracy is calculated by using the confusion matrix technique using detailed scale spatial information references. Based on the analysis and test of accuracy that has been done on several models, the highest accuracy is generated by the 1.5 stdev threshold model four index parameters (EVI_min, EVI_Max, EVI_range, and EVI_mean) with an accuracy of 86.56% and a kappa value of 0.716.
Pengembangan Tiling database untuk Penyimpanan Data Penginderaan Jauh pada Pembangunan LAPAN Engine Widipaminto, Ayom; Safitri, Yuvita Dian; Sunarmodo, Wismu; Rokhmatullah, Rokhmatullah
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3350

Abstract

Remote sensing image data is included in the unstructured data category which is characterized by large volumes of data and is regularly updated. Special techniques are needed in large capacity data storage and supported by high-capacity data processing machines. This study aims to find a design representation of remote sensing image data that is more efficient in storage and processing than conventional methods. The design proposed is with the concept of tiling databases, namely the method of breaking down image data into small size pieces with certain identities and then entering them into a database. The test results compared to the conventional method found that the storage volume can be reduced by up to 25%, the speed of reading the data also increases by about 21%. This system can support the development of LAPAN Engine because it offers a storage strategy that is more effective in terms of volume, and efficient in terms of the speed of reading data even though the tiling process into the database takes pretty long time.
ANALISIS TINGKAT AKURASI TITIK HOTSPOT DARI S-NPP VIIRS DAN TERRA/AQUA MODIS TERHADAP KEJADIAN KEBAKARAN Indradjad, Andy; Purwanto, Judin; Sunarmodo, Wismu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3351

Abstract

Accuracy analysis of the forest fire detection by using remote sensing data hotspots from SNPP and TERRA/AQUA has been carried out. The sensors used were MODIS sensors for TERRA/AQUA satellites and VIIRS sensors for S-NPP satellites. The detection of hotspots from remote sensing satellite data can be used as an early warning of forest fires. Hotspot can be derived from 2 sensors, namely MODIS and VIIRS sensors using algorithms that have been developed by science team from satellite developer. This hotspot information need to be accurately analysis by ground thruth of the fire events. This aims to analize the accuracy of hotspot information detection for forest fires. By comparing fire event data in 2018 and hotspot information data on hotspot databases owned by LAPAN. The results show that MODIS sensors are 39% and for VIIRS sensors are 20%. That result using 2 km of buffer radius which is the most significant result comparing others. It is clearly indicates that improvements are needed to improve the accuracy of hotspot derived from VIIRS data.
EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu) Kartika, Tatik; Dirgahayu, Dede; Sari, Inggit Lolita; Parsa, I Made; Carolita, Ita
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.v16i2.3353

Abstract

The use of remote sensing in vegetation monitoring has been widely applied, including vegetation density monitoring. However, the use to evaluate rehabilitation program on critical land is still limited. Evaluation of forest cover and land rehabilitation activities become important due to the increase of critical land. The current method to evaluate the land condition is conducted by ground check at the rehabilitation site held at the end of the year after the initial implementation of the rehabilitation program until the third year. This method requires a lot of time, labour, and money. Based on the standard regulation to evaluate the rehabilitation program, the program is successful if 90% the new vegetation planted can grows until the third year. Therefore, this research uses an effective and efficient method for evaluating land rehabilitation programs using remote sensing data by understanding vegetation conditions and their densities using multi-temporal analysis for large areas. A multi-temporal Landsat-8 images from 2015-2018 will be used to analyze the Normalized Difference Vegetation Index (NDVI) trend in the time-based sequence method using spatial analysis. The results show that the non-forest area in Serayu Hulu Watershed consist of non-critical land, moderate critical land, critical land, and severe ciritical land. According to the ground check and NDVI trend analysis, the rehabilitation in non-critical land of the non-forest area was generally unsuccessful due to the area rehabilitation plant were harvested before the rehabilitation evaluation time ended. On the otherhand, on critical land; moderate critical land; and severe critical land of the non-forest area, the success of rehabilitation program was indicated by the achievement of the NDVI threshold value at 0.4660; 0.4947. 0.4916, respectively.
ANALISIS KONSENTRASI TSS DAN PENGARUHNYA PADA KINERJA PELABUHAN MENGGUNAKAN DATA REMOTE SENSING OPTIK DI TELUK KENDARI Nurgiantoro, Nurgiantoro; Mustika, Wayan; Abriansyah, Abriansyah
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.v16i2.3354

Abstract

KOREKSI ATMOSFER DATA LANDSAT-8 MENGGUNAKAN PARAMETER ATMOSFER DARI DATA MODIS Muchsin, Fadila; Fibriawati, Liana; Rahayu, Mulia Inda; Hendayani, Hendayani; Pradhono, Kuncoro Adhi
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.v16i2.3355

Abstract

Landsat-8 data (level 1T) received by user are still in digital number and can be used directly for mapping land use / land cover. However, the data still has low radiometric accuracy when it is used to derive information such as vegetation index, biomass, land use/ land cover classification, etc. so that it requires radiometric / atmospheric correction. In this study, we use the second simulation of a satellite signal in the solar spectrum (6S) method to eliminate atmospheric disturbance and compare the results with field measurements. The atmospheric parameters used were aerosol optical depth (AOD), water vapor column and ozone thickness from MODIS data with the date and time of acquisition are close to Landsat-8 data acquisition. From the analysis conducted on the values of vegetation index (NDVI, EVI, SAVI and MSAVI) surface reflectance shows that the vegetation index that has high accuracy is NDVI (3-11) % and the lowest is MSAVI (11-24) %. Analysis of the spectral response of atmospheric corrected image shows that visible band have good accuracy with RMSE values ranging from (1 - 4) %. On the contrary the lowest accuracy is found on the near infrared channel (NIR) with values (14-27) %.
APLIKASI MODEL GEOBIOFISIK NDVI UNTUK IDENTIFIKASI HUTAN PADA DATA SATELIT LAPAN-A3 Arifin, Samsul; Carolita, Ita; Kartika, Tatik
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.v16i2.3356

Abstract

The LAPAN-A3 / IPB satellite is a micro satellite created by the nation's children in order to build the nation's independence in the field of Space. This satellite has 4 bands including 3 visible waves and 1 near infrared. Given that it is a new satellite, it is necessary to do a study and research on the ability of sensor characteristics to identify natural resources, one of which is forests. In this study besides using LAPAN-A3 satellite data, Landsat-8 data is also used as comparative data for testing the similarity of forest object classification results. Determination of extraction of geobiophysical parameters of forest identification using the Normalized Difference Vegetation Index (NDVI) model with a threshold value for forest identification. The results of the study with LAPAN-A3 satellite data show that the threshold range for forest identification is above 0.65 on the vegetation index scale -1 (minus one) to +1 (plus one). The results of the study after comparing NDVI values with Landsat-8 data have a 60% similarity.
PENGARUH DISTRIBUSI SPASIAL SAMPEL PEMODELAN TERHADAP AKURASI ESTIMASI LEAF AREA INDEX (LAI) MANGROVE Kamal, Muhammad; Kanekaputra, Tito; Hermayani, Rima; Utari, Dian
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.v16i2.3357

Abstract

Leaf Area Index (LAI) has an important role in defining the health of mangrove forest. Remote sensing images able to estimate mangrove LAI, especially through semi-empirical approach. This approach needs appropriate selection of sample location and value distribution for both modelling and accuracy assessment purposes. However, both aspects are often neglected when selecting the sample for modelling. This research aims to explor and analyze the LAI field sample collected to answer (1) if the spatial and (2) value distribution of modelling samples affect the accuracy of mangrove LAI estimation. The method used was by developing regression models between Soil-Adjusted Vegetation Index (SAVI) pixel values derived from ALOS AVNIR-2 image (10m) and field LAI measurement using LICOR LAI-2200. The modelling samples were selected randomly and purposively through three simulations based on spatial distribution and value range of the samples. The accuracy of the estimation was assessed using 1:1 relationship plots and Standard Error of Estimate (SEE). The research results show that the accuracy of LAI estimation is dependent to the spatial distribution and the range value of the modelling samples. High estimation accuracy achieved when the sample location for modelling is evenly distributed and covers the range of the field sample values.

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