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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 6 Documents
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Search results for , issue "Vol. 16 No. 2 (2019)" : 6 Documents clear
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.
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.

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