The International Journal of Remote Sensing and Earth Sciences (IJReSES)
The International Journal of Remote Sensing and Earth Sciences (IJReSES), published by Badan Riset dan Inovasi Nasional (BRIN) in collaboration with the Ikatan Geografi Indonesia (IGI) and managed by the Department of Geography Universitas Indonesia, is a pivotal platform in the global dissemination of research in earth sciences and remote sensing. It aims to enrich the literature in these fields and serves as a key resource, particularly in Indonesia and Asian countries, while extending its reach worldwide. The journal is instrumental in complementing the body of knowledge in Remote Sensing and Earth Sciences and is committed to fostering the participation of young scientists, especially from Indonesia and Asian countries. Scope and Focus: IJReSES encompasses a wide spectrum of topics related to remote sensing and earth sciences, including but not limited to: - Remote sensing technologies and methodologies - Geospatial data acquisition, processing, and analysis - Earth observation and satellite imagery - Geographic Information Systems (GIS) - Environmental monitoring and management - Climate change and its impacts - Natural resource management - Land use and land cover change - Urban and rural development - Disaster risk reduction and response - Geology and geomorphology - Soil and water sciences - Biodiversity and ecosystem studies
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327 Documents
<![CDATA[LAPAN-A3 SATELLITE DATA ANALYSIS FOR LAND COVER CLASSIFICATION (CASE STUDY: TOBA LAKE AREA, NORTH SUMATRA) ]]>
<![CDATA[Jalu Tejo Nugroho]]>;
<![CDATA[Dony Kushardono]]>;
<![CDATA[Zylshal]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2782
<![CDATA[LAPAN-A3 is the 3rdgeneration satellite for remote sensing developed by National Institute of Aeronautics and Space (LAPAN). The camera provides imagery with 15 m spatial resolution and able to view a swath 120 km wide. This research analyzes the performance of LAPAN-A3 satellite data to classify land cover in Toba Lake area, North Sumatera. Data processing starts from the selection of region of interest up to the assessment of accuracy. Supervised classification with maximum likelihood approach and confusion matrix method was applied to classify and evaluate the assessment results. The land cover is classified into five classes; water, bare land, agriculture, forest and secondary forest. The result of accuracy test is 93.71%. It proves that LAPAN-A3 data could classify the land cover accurately. The data is expected to complement the need of the satellite data with medium spatial resolution.]]>
<![CDATA[Front Pages IJReSES Vol. 13, No. 2(2016) ]]>
<![CDATA[Editorial Journal]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016) ]]>
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<![CDATA[Front Pages IJReSES Vol. 13, No. 2(2016)Â Â *Note:Â This cover is a revision of the Peer Reviewers section of the cover that was uploaded on May 26, 2017]]>
<![CDATA[BATHYMETRY DATA EXTRACTION ANALYSIS USING LANDSAT 8 DATA ]]>
<![CDATA[Kuncoro Teguh Setiawan]]>;
<![CDATA[Syifa Wismayati Adawiah]]>;
<![CDATA[Yennie Marini]]>;
<![CDATA[Gathot Winarso]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016) ]]>
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DOI: 10.30536/j.ijreses.2016.v13.a2448
<![CDATA[The remote sensing technique can be used to produce bathymetric map. Bathymetric mapping is important for the coastal zone and watershed management. In the previous study conducted in Menjangan Island of Bali, bathymetric extractin information from the top of the atmosphere (TOA) reflectance image of Landsat ETM+ data has R2 = 0.620. Not optimal  correlation value produced is highly influenced by the reflectance image of Landsat ETM+ data, were used, hence the lack of the research which became the basis of the present study. The study was on the Karang Lebar water of Thousand Islands, Jakarta. And the aim was to determine whether there was an increased correlation coefficient value of bathymetry extraction information generated from Surface reflectance and TOA reflectance imager of Landsat 8 data acquired on August 12, 2014. The method of extraction was done using algorithms Van Hengel and Spitzer (1991). Extraction   absolute depth information obtained from the model logarithm of Landsat 8 surface reflectance images and pictures TOA produce a correlation value of R2 = 0.663 and R2 = 0.712.]]>
<![CDATA[DETECTING THE LAVA FLOW DEPOSITS FROM 2018 ANAK KRAKATAU ERUPTION USING DATA FUSION LANDSAT-8 OPTIC AND SENTINEL-1 SAR ]]>
<![CDATA[Suwarsono]]>;
<![CDATA[Indah Prasasti]]>;
<![CDATA[Jalu Tejo Nugroho]]>;
<![CDATA[Jansen Sitorus]]>;
<![CDATA[Djoko Triyono]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a3078
<![CDATA[The increasing volcanic activity of Anak Krakatau volcano has raised concerns about a major disaster in the area around the Sunda Strait. The objective of the research is to fuse Landsat-8 OLI (Operational Land Imager) and Sentinel-1 TOPS (Terrain Observation with Progressive Scans), an integration of SAR and optic remote sensing data, in observing the lava flow deposits resulted from Anak Krakatau eruption during the middle 2018 eruption. RGBI and the Brovey transformation were conducted to merge (fuse) the optical and SAR data. Â The results showed that optical and SAR data fusion sharpened the appearance of volcano morphology and lava flow deposits. The regions are often constrained by cloud cover and volcanic ash, which occurs at the time of the volcanic eruption. Â The RGBI-VV and Brovey RGB-VV methods provide better display quality results in revealing the morphology of volcanic cone and lava deposits. The entire slopes of Anak Krakatau Volcano, with a radius of about 1 km from the crater is an area prone to incandescent lava and pyroclastic falls. The direction of the lava flow has the potential to spread in all directions. The fusion method of optical Landsat-8 and Sentinel-1 SAR data can be used continuously in monitoring the activity of Anak Krakatau volcano and other volcanoes in Indonesia both in cloudy and clear weather conditions.]]>
<![CDATA[UTILIZATION OF NEAR REAL-TIME NOAA-AVHRR SATELLITE OUTPUT FOR EL NIÑO INDUCED DROUGHT ANALYSIS IN INDONESIA (CASE STUDY: EL NIÑO 2015 INDUCED DROUGHT IN SOUTH SULAWESI) ]]>
<![CDATA[Amsari Mudzakir Setiawan]]>;
<![CDATA[Yonny Koesmaryono]]>;
<![CDATA[Akhmad Faqih]]>;
<![CDATA[Dodo Gunawan]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016) ]]>
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DOI: 10.30536/j.ijreses.2016.v13.a2450
<![CDATA[Drought is becoming one of the most important issues for government and policy makers. National food security highly concerned, especially when drought occurred in food production center areas. Climate variability, especially in South Sulawesi as one of the primary national rice production centers is influenced by global climate phenomena such as El Niño Southern Oscillation or ENSO. This phenomenon can lead to drought occurrences. Monitoring of drought potential occurrences in near real-time manner becomes a primary key element to anticipate the drought impact. This study was conducted to determine potential occurrences and the evolution of drought that occurred as a result of the 2015 El Niño event using the Vegetation Health Index (VHI) from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite products. Composites analysis was performed using weekly Smoothed and Normalized Difference Vegetation Index (or smoothed NDVI) (SMN), Smoothed Brightness Temperature Index (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI).  This data were obtained from The Center for Satellite Applications and Research (STAR) - Global Vegetation Health Products (NOAA) website during 35-year period (1981-2015). Lowest potential drought occurrences (highest VHI and VCI value) caused by 2015 El Niño is showed by composite analysis result. Strong El Niño induced drought over the study area indicated by decreasing VHI value started at week 21st. Spatial characteristic differences in drought occurrences observed, especially on the west coast and east coast of South Sulawesi during strong El Niño. Weekly evolution of potential drought due to the El Niño impact in 2015 indicated by lower VHI values (VHI < 40) concentrated on the east coast of South Sulawesi, and then spread to another region along with the El Nino stage. Â]]>
<![CDATA[GEOMETRIC ASPECTS EVALUATION OF GNSS CONTROL NETWORK FOR DEFORMATION MONITORING IN THE JATIGEDE DAM REGION ]]>
<![CDATA[Made Ditha Ary Sanjaya]]>;
<![CDATA[T. Aris Sunantyo]]>;
<![CDATA[Nurrohmat Widjajanti]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2901
<![CDATA[Many factors led to dam construction failure so that deformation monitoring activities is needed in the area of the dam. Deformation monitoring is performed in order to detect a displacement at the control points of the dam. Jatigede Dam deformation monitoring system has been installed and started to operate, but there has been no evaluation of the geometry quality of control networks treated with IGS points for GNSS networks processing. Therefore, this study aims to evaluate the geometric quality of GNSS control networks on deformation monitoring of Jatigede Dam area. This research data includes the GNSS measurements of five CORS Jatigede Dam stations (R01, GG01, GCP04, GCP06, and GCP08) at doy 233 with network configuration scenarios of 12 IGS points on two quadrants (jat1), three quadrants (jat2), and four quadrants (jat3 and jat4). GNSS networks processing was done by GAMIT to obtain baseline vectors, followed by network processing usingparameter method of least squares adjustment. Networks processing with least squares adjustment aims to determine the most optimal by precision and reliability criterion. Results of this study indicate that network configuration with 12 IGS stations in the two quadrants provides the most accurate coordinates of CORS dam stations. Standard deviations value of CORS station given by jat1 configuration are in the range of 2.7 up to 4.1 cm in X-Z components, whereas standard deviations in the Y component are in the range 5.8 up to 6.9 cm. An optimization assessment based on network strength, precision, and reliability factors shows optimum configuration by jat1.]]>
<![CDATA[PRELIMINARY STUDY OF HORIZONTAL AND VERTICAL WIND PROFILE OF QUASI-LINEAR CONVECTIVE UTILIZING WEATHER RADAR OVER WESTERN JAVA REGION, INDONESIA ]]>
<![CDATA[Abdullah Ali]]>;
<![CDATA[Riris Adriyanto]]>;
<![CDATA[Miming Saepudin]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a3075
<![CDATA[One of the weather phenomena that potentially cause extreme weather conditions is the linear-shaped mesoscale convective systems, including squall lines. The phenomenon that can be categorized as a squall line is a convective cloud pair with the linear pattern of more than 100 km length and 6 hours lifetime. The new theory explained that the cloud system with the same morphology as squall line without longevity threshold. Such a cloud system is so-called Quasi-Linear Convective System (QLCS), which strongly influenced by the ambient dynamic processes, include horizontal and vertical wind profiles. This research is intended as a preliminary study for horizontal and vertical wind profiles of QLCS developed over the Western Java region utilizing Doppler weather radar. The following parameters were analyzed in this research, include direction pattern and spatial-temporal significance of wind speed, divergence profile, vertical wind shear (VWS) direction, and intensity profiles, and vertical velocity profile. The subjective and objective analysis was applied to explain the characteristics and effects of those parameters to the orientation of propagation, relative direction, and speed of the cloud system’s movement, and the lifetime of the system. Analysis results showed that the movement of the system was affected by wind direction and velocity patterns. The divergence profile combined with the vertical velocity profile represents the inflow which can supply water vapor for QLCS convective cloud cluster. Vertical wind shear that effect QLCS system is only its direction relative to the QLCS propagation, while the intensity didn’t have a significant effect.]]>
<![CDATA[COMPARATIVE TEST OF SEVERAL RAINFALL ESTIMATION METHODS USING HIMAWARI-8 DATA ]]>
<![CDATA[Nanda Alfuadi]]>;
<![CDATA[Agie Wandala]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016) ]]>
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DOI: 10.30536/j.ijreses.2016.v13.a2453
<![CDATA[Indonesian society needs information on potential hydrometeorological disasters, therefore the development of rainfall estimation methods becomes an important research activities to support disaster risk reduction. Central Kalimantan were selected as research location for comparative test of rainfall estimation methods based on Himawari-8 IR1 (11μm) data, because it has area with cloud cover fairly intensive throughout the year. Some rainfall estimation methods tested in this research are AE, CST, CSTM, IMSRA. Non Linear Relation, and Non Linear Inversion. Each of these methods tends to have a weakness in the value of accuracy, so this research aims to determine the most accurate method to be applied in Palangkaraya (27 meters above sea level) city and Muratewe (60 meters above sea level) district in Central Kalimantan. The experiment was conducted during the period of highest rainfall in January and February 2016 by converting the temperature data cloud tops (IR1) into a precipitation with AE, CST, CSTM, IMSRA, Non Linear Relation and Non Linear Inversion method. Based on the results of quantitative analysis, it was known that IMSRA was the best method which can be applied in rainfall estimation in Muarateweh’s and Palangka Raya’s winter period. The Accuracy of all estimation methods decreased when it was applied in Palangka Raya at afternoon and in Muarateweh at night until early morning. The estimation method with the lowest score was the AE with an average MSE value > 90 and the best estimation method was IMSRA with MSE value <12.]]>
<![CDATA[PRELIMINARY DETECTION OF GEOTHERMAL MANIFESTATION POTENTIAL USING MICROWAVE SATELLITE REMOTE SENSING ]]>
<![CDATA[Atriyon Julzarika]]>;
<![CDATA[Udhi Catur Nugroho]]>
<![CDATA[ International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018) ]]>
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DOI: 10.30536/j.ijreses.2018.v15.a2772
<![CDATA[The satellite technology has developed significantly. The sensors of remote sensing satellites are in the form of optical, Microwave, and LIDAR. These sensors can be used for energy and mineral resources applications. The example of those applications are height model and the potential of geothermal manifestation detection. This study aims to detect the potential of geothermal manifestation using remote sensing. The study area is the Northern of the Inverse Arc of Sulawesi. The method used is remote sensing approach for its preliminary detection with 4 steps as follow (a) mining land identification, (b) geological parameter extraction, (c) preparation of standardized spatial data, and (d) geothermal manifestation. Mining lands identification is using Vegetation Index Differencing method. Geological parameters include structural geology, height model, and gravity model. The integration method is used for height model. The height model integration use ALOS PALSAR data, Icesat/GLAS, SRTM, and X SAR. Structural geology use dip and strike method. Gravity model use physical geodesy approach. Preparation of standardized spatial data with re-classed and analyzed using Geographic Information System between each geological parameter, whereas physical geodesy methods are used for geothermal manifestation detection. Geothermal manifestation using physical geodesy approach in Barthelmes method. Grace and GOCE data are used for gravity model. The geothermal manifestation detected from any parameter is analyzed by using geographic information system method. The result of this study is 10 area of geothermal manifestation potential. The accuracy test of this research is 87.5 % in 1.96 σ. This research can be done efficiently and cost-effectively in the process. The results can be used for various geological and mining applications.]]>
