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The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Published by Badan Riset dan Inovasi Nasional
ISSN : 02166739     EISSN : 2549516X     DOI : https://doi.org/10.55981/ijreses
Core Subject : Science,
Earth & Planetary Sciences
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
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 327 Documents
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COMPARISON OF THE MANGROVE FOREST MAPPING ALGORITHMS IN KELABAT BAY USING RANDOM FOREST AND SUPPORT VECTOR MACHINES Rahmadi; Raldi Hendrotoro Seputro Koestoer
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3885

Abstract

One of the tropical ecosystems is the mangrove forest, which thrives on protected coastlines such as bays, estuaries, lagoons, and rivers. These are usually found in the intertidal zone. Mangroves are a valuable natural resource because they stabilize coastlines, prevent erosion, retain sediment and nutrients, protect against storms, regulate floods and currents, sequester carbon, maintain water quality, serve as spawning grounds for fish and other marine life, and provide food For plankton. With over 59.8% of the total area of mangroves on the planet, Indonesia has some of the largest mangrove forests in the world. With the case study of Kelabat Bay in Bangka Regency and the Bangka Belitung Islands, this study compares the use of random forest (RF) techniques and support vector machines (SVM) for mapping mangrove forests. Landsat-9 imagery from 2022, taken via the Google Earth Engine (GEE), is the data source used in this study. This study utilizes computer programming and accuracy testing. As a result, RF detected mangrove forests covering an area of approximately 67 ha (OA: 0.932), while SVM detected mangrove forests covering an area of approximately 62 ha (OA: 0.912).
SPATIAL ANALYSIS OF QUANTITATIVE PRECIPITATION FORECAST ACCURACY BASED ON STRUCTURE AMPLITUDE LOCATION (SAL) TECHNIQUE Abdullah Ali; Achmad Rifani; Supriatna; Yunus Subagyo Swarinoto; Umi Sa’adah
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3854

Abstract

Quantitative Precipitation Forecast (QPF) is the final product of a short-term forecasting algorithm (nowcasting) based on weather radar data which is widely used in hydrometeorological aspects. The calculation of the accuracy value using point data on a rainfall gauge often causes a double penalty problem because the QPF prediction results are in the form of spatial objects. This study aims to apply object-based spatial verification in analyzing the accuracy of QPF based on the Short Term Ensemble Prediction System (STEPS) algorithm using the SAL technique. The verification process is carried out by calculating the index value of the structure component (S), amplitude (A), and location (L) in the QPF prediction results based on the results of weather radar observations. The index values for components S and A have a range of -2 to 2, and 0 to 1 for component L with a perfect value of 0. The case study used is the occurrence of heavy rains that caused flooding in Bogor Regency in 2020. SAL verification results from 26 case studies used shows the average value of the components S, A, and L, respectively 0.51, 0.38, and 0.21. As many as 75% of all case studies have S and L component values less than 0.5 which indicate the structure and location of the QPF prediction object is close to the structure and location of the object of observation. A positive value in component A indicates that the QPF prediction results based on the STEPS algorithm tend to be overestimated but on a low scale, namely 0.38 out of 2.
SPATIAL ANALYSIS OF THE TSUNAMI RISK IN PALABUHANRATU SUB-DISTRICT, SUKABUMI REGENCY, INDONESIA BASED ON THE DISASTER CRUNCH MODELSPATIAL ANALYSIS OF THE TSUNAMI RISK IN PALABUHANRATU SUB-DISTRICT, SUKABUMI REGENCY, INDONESIA BASED ON THE DISASTER CRUNCH Inti Raidah Hidayat; Sudaryanto
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 2 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3657

Abstract

Palabuhanratu Sub-District is one of the southern coastal areas of Java that has the potential to be exposed to tsunamis, with an estimated run-up of between 12-20 meters. Accordingly, it is necessary to conduct tsunami disaster mitigation by analysing the level of tsunami risk in the district to reduce potential losses if a tsunami occurs. This study aims to map the level of tsunami risk in Palabuhanratu Sub-District based on the disaster crunch model, which is a risk model that integrates vulnerability and tsunami hazard factors. The tsunami vulnerability analysis uses a weighted overlay quantitive approach, while the tsunami hazard analysis employs simulation of tsunami propagation by COMCOT V.1.7; the tsunami inundation reduction model; cost distance analysis; and fuzzy membership analysis. The results of the tsunami risk analysis show that villages included in the high-, medium-, and low-risk categories are Citepus, Palabuhanratu, and Jayanti. The percentage of high-risk areas in the three villages are 10% (139 hectares), 20.3% (114 hectares), and 0.01% (0.13 hectares) respectively. The higher the risk of a tsunami in an area, the higher the losses that will be incurred by the local population.Palabuhanratu Sub-District is one of the southern coastal areas of Java that has the potential to be exposed to tsunamis, with an estimated run-up of between 12-20 meters. Accordingly, it is necessary to conduct tsunami disaster mitigation by analysing the level of tsunami risk in the district to reduce potential losses if a tsunami occurs. This study aims to map the level of tsunami risk in Palabuhanratu Sub-District based on the disaster crunch model, which is a risk model that integrates vulnerability and tsunami hazard factors. The tsunami vulnerability analysis uses a weighted overlay quantitive approach, while the tsunami hazard analysis employs simulation of tsunami propagation by COMCOT V.1.7; the tsunami inundation reduction model; cost distance analysis; and fuzzy membership analysis. The results of the tsunami risk analysis show that villages included in the high-, medium-, and low-risk categories are Citepus, Palabuhanratu, and Jayanti. The percentage of high-risk areas in the three villages are 10% (139 hectares), 20.3% (114 hectares), and 0.01% (0.13 hectares) respectively. The higher the risk of a tsunami in an area, the higher the losses that will be incurred by the local population.
RADAR-BASED STOCHASTIC PRECIPITATION NOWCASTING USING THE SHORT-TERM ENSEMBLE PREDICTION SYSTEM (STEPS) (CASE STUDY: PANGKALAN BUN WEATHER RADAR) Abdullah Ali; Supriatna; Umi Sa’adah
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 1 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3527

Abstract

Nowcasting, or the short-term forecasting of precipitation, is urgently needed to support the mitigation circle in hydrometeorological disasters. Pangkalan Bun weather radar is single-polarization radar with a 200 km maximum range and which runs 10 elevation angles in 10 minutes with a 250 meters spatial resolution. There is no terrain blocking around the covered area. The Short-Term Ensemble Prediction System (STEPS) is one of many algorithms that is used to generate precipitation nowcasting, and is already in operational use. STEPS has the advantage of producing ensemble nowcasts, by which nowcast uncertainties can be statistically quantified. This research aims to apply STEPS to generate stochastic nowcasting in Pangkalan Bun weather radar and to analyze its advantages and weaknesses. Accuracy is measured by counting the possibility of detection and false alarms under the 5 dBZ threshold and plotting them in a relative operating characteristic (ROC) curve. The observed frequency and forecast probability is represented by a reliability diagram to evaluate nowcast reliability and sharpness. Qualitative analysis of the results showed that the STEPS ensemble produces smoothed reflectivity fields that cannot capture extreme values in an observed quasi-linear convective system (QLCS), but that the algorithm achieves good accuracy under the threshold used, up to 40 minutes lead time. The ROC shows a curved upper left-hand corner, and the reliability diagram is an almost perfect nowcast diagonal line.
EFFECT OF LOW PASS FILTER ON BATHYMETRIC DETECTION IN PULAU PUTRI SHALLOW SEA, KEPULAUAN SERIBU USING PLANETSCOPE SATELLITE IMAGERY Alberto Junior Hutagaol; Kuncoro Teguh Setiawan; Muhammad Sulaiman Nur Ubay; Hastuadi Harsa
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3897

Abstract

Sea depth measurements are usually only carried out at locations that can be passed by ships, so measurements in shallow waters are often not possible. Along with the development of remote sensing technology, shallow water bathymetry mapping can now be done using satellite imagery. The Stumpf method is a ratio model that compares two bands in order to reduce the effect of water albedo. The purpose of this research is to study the processing of satellite imagery data for the detection of bathymetry in shallow sea waters, to determine the effect of the low pass filter, and to find out the methods for obtaining detection results with high accuracy. In this study, the primary data used was PlanetScope imagery from the NICFI program. Bathymetry detection of shallow marine waters was carried out around the waters of Putri Island, Seribu Islands Regency. The results of the accuracy test for the detection of shallow sea bathymetry without the application of a low pass filter using the confusion matrix method and the RMSE calculation have higher accuracy with an overall accuracy value of 94.17% and an RMSE value of 1.61
DETECTING SURFACE WATER AREAS AS ALTERNATIVE WATER RESOURCE LOCATIONS DURING THE DRY SEASON USING SENTINEL-2 IMAGERY (CASE STUDY: LOWLAND REGION OF BEKASI-KARAWANG, WEST JAVA PROVINCE) Jalu Tejo Nugroho; Suwarsono; Galdita Aruba Chulafak; Atriyon Julzarika; R Johannes Manalu; Sri Harini; Argo Suhadha; Sayidah Sulma
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 2 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3626

Abstract

In Indonesia, drought is a type of disaster that often occurs, especially during the dry season. What is most needed at such times is the availability of sufficient water sources to meet shortages. Therefore, water source locations are vital during the dry season in order to meet needs. To meet this information need, remote sensing data offer a precise solution. This research proposes a rapid method of detecting surface water areas based on remote sensing image data. It focuses on the use of remote sensing satellite imagery to detect objects and the location of surface water sources. The purpose of the study is to rapidly identify objects and locate surface water sources using Sentinel-2 MSI (MultiSpectral Instrument), one of the latest types of remote sensing satellite data. Several water index (WI) methods were applied before deciding which was most suitable for detecting surface water objects. The lowland region of Bekasi-Karawang, a drought prone area, was designated as the research location. The results of the research show that by using Sentinel-2 MSI imagery, MNDWI (Modified Normalized Water Index) is the appropriate parameter to detect surface water areas in the lowland region of Bekasi-Karawang, West Java Province, Indonesia, during times of drought. The method can be employed as an alternative approach based on remote sensing data for the rapid detection of surface water areas as alternative sources of water during the dry season. The existence of natural water sources (swamps, marshes, ponds) that remain during this time can be used as alternative water resources. Further research is still needed which focuses on different geographical conditions and other regions in Indonesia.
SPATIAL ANALYSIS OF LAND USE AND LAND COVER VARIATIONS AFFECTING TEA PRODUCTION IN GUNUNGMAS PLANTATION THROUGH REMOTE SENSING TECHNIQUES Elok Lestari Paramita; Masita Dwi Mandini Manessa; Mangapul Parlindungan Tambunan
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3888

Abstract

Tea is a manufactured beverage that is popular around the world. In value chain analysis to increase efficiency, remote sensing technology can be developed to monitor the phenomenon of land use land cover (LULC) change and vegetation health conditions. This study aims to identify LULC in tea plantations, identify the health condition of tea plantations, then analyze spatial trends of changes in tea productivity in Gunungmas Afdeling-1 due to changes in tea area or tea vegetation health condition. Identification of changes in LULC in tea plantations can be carried out using remote sensing technology and machine learning, in this study, Google Earth Engine (GEE) LULC identification was generated using a supervised classification with the random forest algorithm on the GEE. Tea productivity trends decreased from 2019 to 2020, but increased from 2020 to 2021. They show that the trend of changes in the area of tea plantation classification is decreasing. According to the NDVI result, most of the reduced area of tea plantations is in areas with healthy vegetation. The trends in tea productivity changes are not in line with changes in the LULC area of tea plantation classification class and tea vegetation health condition.
ESTIMATION OF ABOVEGROUND CARBON STOCK USING SAR SENTINEL-1 IMAGERY IN SAMARINDA CITY Bayu Elwanto Bagus Dewanto; Retnadi Heru Jatmiko
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 1 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3609

Abstract

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.
ASSESSING THE POSSIBILITY OF LAND SUBSIDENCE DUE TO GEOTHERMAL PRODUCTION IN SARULLA GEOTHERMAL FIELD USING SENTINEL-1 Mochamad Iqbal; Panggea Ghiyats Sabrian
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 2 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3843

Abstract

Sarulla geothermal field is one of the largest geothermal fields in the world which has a 330 MW installed capacity. The field consists of three areas, namely Namora Langit (NIL)-1, NIL-2, and Silangkitang (SIL) which operated from 2017 and 2018. It is situated precisely at the Sarulla graben which is an active tectonic area composed of Quaternary Toba tuff and intermediate lava and extrusive felsic pyroclastic Toru. This study aims to see whether land subsidence may emerge in the Sarulla geothermal field and its environs in addition to determining whether the geothermal activity or anthropogenic is responsible for the deformation. We used the persistent scatterer (PS) interferometry synthetic aperture radar (InSAR) method to calculate the rate of subsidence in the area. 30 ascending images from Sentinel-1 were gathered from 5 January to 18 December 2020 with a separation of 12 days to run the analysis. The results demonstrate that Sarulla is undergoing subsidence occurring at NIL and SIL with a velocity of 0 to -32.9 mm/year. Although negative deformation occurs in the geothermal area, there is no solid evidence indicating geothermal fluid extraction is the cause of subsidence.
SPECTRAL CHARACTERISTICS OF FLASH FLOOD AREAS FROM MEDIUM SPATIAL OPTICAL IMAGERY Muhammad Priyatna; Muhammad Rokhis Khomarudin; Galdita Aruba Chulafak; Sastra Kusuma Wijaya
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 2 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3666

Abstract

This study aims to investigate surface reflectance changes over flash flood areas in Nusa Tenggara Timur, Indonesia. Fifteen sample points from Sentinel-2 satellite imagery were used to analyse the differences in reflectance of areas before and after flash flood events. The method used involved analysis of the significant differences in the dreflectance values of each Sentinel-2 channel. The analysis results show that channels 6, 7, and 8A displayed significant differences compared to the others with regard to reflectance before and after flooding, for both settlements and shrubs. The results could be used for further research in building a reflectance index for the rapid detection of affected areas, with a focus on these channels.

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