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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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GROUNDWATER LEVEL ESTIMATION MODEL ON PEATLANDS USING SAR SENTINEL-1 DATA IN PART OF RIAU, INDONESIA Ardila Yananto; Junun Sartohadi; Hero Marhaento; Awaluddin
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.a3618

Abstract

The character of peatlands has the ability to store large amounts of water, but the surface of the peatlands dries quickly and easy to burn during the dry season. Research aims to build a model to estimate groundwater level of peatland. Statistical analysis of Karl Pearson Product Moment correlation test was used to determine the relationship between the back scatter values and the Surface Soil Moisture (SSM) values from the Sentinel-1 SAR data processing with the groundwater level values measured using the Sipalaga instrument. Regression analysis was used to determine the model that could be used to estimate the groundwater level of peatlands in the study area based on the results of Sentinel-1 SAR data processing. The results showed that the Sentinel-1 SAR data with the Sigma_0 format in decibel (db) units with VV polarization had the highest correlation value with the groundwater level data of peatlands measured using the Sipalaga instrument, with a value of r -0.648 (moderate correlation). Model to estimate water level of peatlands was Y = -101.629 + (-7.414 x), where 'Y' was the groundwater level of peatlands in the study area and 'x' was the Sentinel-1 SAR data with Sigma_0 format in decibel (db) units with VV polarization. The spatial and temporal patterns of peatlands groundwater level in the study area from Sentinel-1 SAR data showed peatlands that to survive at a water level <40 cm was in the area around of the Rokan River and also in plantation areas, especially Acacia plantations, where canals were made to irrigate and land management.
COMPARISON OF MACHINE LEARNING MODELS FOR LAND COVER CLASSIFICATION Bambang H. Trisasongko; Dyah R. Panuju; Nur Etika Karyati; Rizqi I’anatus Sholihah
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3786

Abstract

Land cover data remain one of crucial information for public use. Â With rapid human-associated land alteration, this information needs to be frequently updated. Remotely-sensed data provide the best option to construct land cover maps with numerous methods available in the literature. While disagreement exists to select the robust one, further exploration should be made to extend the understanding on the behavior of machine learners, in particular, for classification problems. This article discusses performance of pixel-based machine learning algorithms, frequently used in research or implementation. Five popular algorithms were evaluated to distinguish five rural land cover classes, i.e. built-ups, crops, mixed garden, oil palm plantations and rubber estates, from Sentinel-2 data. This research found that the benchmark, classification and regression tree, was unable to differentiate woody vegetation, although the overall accuracy was sufficiently moderate. This suggested that overall accuracy cannot be seen as the only measure for assessing the quality of the thematic output. Meanwhile, support vector machines and random forest competed to yield the highest accuracy and class detection capability, although the latter was in favor with 98% accuracy level. A newly developed model, like extreme gradient boosting, achieved a similar level of accuracy. This research implies that modern machine learning approaches would be invaluable for land cover classification; hence, access to these modeling toolkits is substantial.
ESTIMATION OF OIL PALM PLANT PRODUCTIVITY USING SENTINEL-2A IMAGERY AT CIKASUNGKA PLANATION PTPN VIII, BOGOR, WEST JAVA Afifah Nur Rahmasari; Supriatna; Andry Rustanto
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3775

Abstract

Palm oil is one of the commodities that is growing well in Indonesia with a high commercial value which makes the demand for processed palm oil products increase, it is necessary to have data and technology to estimate the productivity of oil palm plantations more efficiently. Remote sensing technology is one of the technologies that can be used to decision problems spatially and accurately, efficiently, and dynamically. One of them is remote sensing using Sentinel-2A imagery. This study aims to analyze the distribution and the accuracy of the NDVI and ARVI algorithms for the estimation of oil palm productivity at the Cikasungka Plantation PTPN VIII. The estimated productivity of oil palm plantations at Cikasungka Plantation varies in each block with an estimated productivity of oil palm plantations of 35,061 Kg/Ha/Month using the algorithm NDVI and ARVI algorithm is 35,431 Kg/Ha/Month. Oil palm productivity was regressed by vegetation index and plant age to generate a model. Based on modeling with these two algorithms, the accuracy of the ARVI algorithm model has a lower RMSE value than NDVI, so it can be said that it is better in estimation of oil palm plant productivity at the Cikasungka Plantation.
ANALYSIS OF POTENTIAL FISHING ZONES IN COASTAL WATERS: A CASE STUDY OF NIAS ISLAND WATERS Anang Dwi Purwanto; Teguh Prayogo; Sartono Marpaung; Argo Galih Suhada
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 1 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3298

Abstract

The need for information on potential fishing zones based on remote sensing satellite data (ZPPI) in coastal waters is increasing. This study aims to create an information model of such zones in coastal waters (coastal ZPPI). The image data used include GHRSST, SNPP-VIIRS and MODIS-Aqua images acquired from September 1st-30th, 2018 and September 1st-30th, 2019, together with other supporting data. The coastal ZPPI information is based on the results of thermal front SST detection and overlaying this with chlorophyll-a. The method of determining the thermal front sea surface temperature (SST) used Single Image Edge Detection (SIED). The chlorophyll-a range used was in the mesotropic area (0.2-0.5 mg/m3). Coastal ZPPI coordinates were determined using the polygon centre of mass, while the coastal ZPPI information generated was only for coastal areas with a radius of between 4-12 nautical miles and was divided into two criteria, namely High Potential (HP) and Low Potential (LP). The results show that the coastal ZPPI models were suitable to determine fishing locations around Nias Island. The percentage of coastal ZPPI information generated was around 90% information monthly. In September 2018, 27 days of information were produced, consisting of 11 HP sets of coastal ZPPI information and 16 sets of LP information, while in September 2019 it was possible to produce 29 days of such information, comprising 11 sets of HP coastal ZPPI information and 18 LP sets. The use of SST parameters of GHRSST images and the addition of chlorophyll-a parameters to MODIS-Aqua images are very effective and efficient ways of supporting the provision of coastal ZPPI information in the waters of Nias Island and its surroundings.
COMPARISON OF DATA ASSIMILATION USING SURFACE OBSERVATION, UPPER AIR, AND SATELLITE RADIATION DATA ON RAINFALL PREDICTION IN THE JAMBI REGION (CASE STUDY OF HEAVY RAIN OCTOBER 20TH, 2020) Saveira Fairuz I.; Nindya Pradita; Danurahni Aryashta; Gandhi Mahendra
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3758

Abstract

Weather Research and Forecasting (WRF) is a mesoscale numerical weather prediction model that can provide good rainfall prediction information. The accuracy of the initial conditions and the accuracy of the parameterization scheme used in the WRF model affect the quality of the resulting rainfall prediction. Therefore it is necessary to assimilate to optimize the accuracy of the initial conditions in the model using the Three Dimensional Variational (3DVAR) assimilation technique. The purpose of this study was to determine the effect of applying the 3DVAR assimilation technique with the surface, upper air, and satellite radiation observations in predicting the occurrence of heavy rain on October 20th, 2020, in the Jambi region by first conducting a parameterization test of the cumulus and microphysical schemes. In this study, four experimental schemes were used, namely no assimilation (NON), observation data assimilation (OBS), satellite radiation data assimilation (SAT), and satellite radiation and observation data assimilation (BOTH). Each experimental model result was then verified statistically and spatially to determine the effect of the applied data assimilation. The results of this study indicate that the combination of Grell-3D and Thompson scheme shows the best performance in predicting rainfall. Then based on the spatial analysis of the SAT experiment, it is known that it can improve the model's initial conditions on the temperature and pressure parameters. Meanwhile, based on statistical verification, the SAT experiment improved the accuracy of rainfall predictions with a better forecast skill score than other experiments tested.
HAIL IDENTIFICATION BASED ON WEATHER FACTOR ANALYSIS AND HIMAWARI 8 SATELLITE IMAGERY (CASE STUDY OF HAIL ON 2ND MARCH 2021 IN MALANG INDONESIA) Marinda Nur Auliya; Aditya Mulya
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.a3712

Abstract

A hail phenomenon occurred in Malang, Sumbermanjing Wetan District (8°6’S and 112°24’E) on March 2, 2021. According to the Regional National Disaster Management Agency, it was accompanied by heavy rain and strong winds, which caused several trees to fall, resulting in damage to people's houses (BNPBD, 2021). Hail is precipitation in the form of ice, usually an irregular round shape produced by cumulonimbus convective clouds (AMS, 2019). The research was conducted by examining global, regional, and local weather factors and analysing the cloud characteristics from satellite image data during hail events. Based on the analysis, it was found that ENSO, sea surface temperature anomalies, and MJO had no effect on the incidence of the hail. The streamline map showed the presence of shearlines and tropical cyclones around the Malang area, and the temperature significantly decrease from 07.00 UTC to 08.00 UTC of 4.4°C and from 08.00 UTC to 09.00 UTC of 3.6°C with significant increase in humidity from 07.00 UTC to 08.00 UTC of 10%. The cloud top temperature was analysed to be at the ripe stage at 07.40 UTC and 8.40 UTC, at -68.2°C.
CARBON MONOXIDE SPATIAL PATTERN BASED ON VEHICLE VOLUME DISTRIBUTION IN TANGERANG CITY Arfani Priyambodo; Adi Wibowo; M. Dadang Basuki
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3789

Abstract

Air pollution conditions in urban areas continue to increase due to the volume of vehicles every year. This volume increases sources of pollution such as motor vehicles which account for 60-70% of pollution. This study aims to analyze the distribution of vehicle volume and spatial pattern of CO in Tangerang City and see the relationship. The analysis used is descriptive and statistical spatial analysis. The results showed the distribution of vehicle volume in the morning ranged from <800-1600 vehicles on primary collector roads, while in the afternoon, there were 800 to >2000 vehicles on primary arterial roads. The spatial pattern of CO that formed on primary and collector arterial roads with residential land uses, industrial areas, and warehouses, then the CO concentration tends to be high. Meanwhile, other primary collector roads have low to moderate CO concentrations. The Spearman test and linear regression results showed a significant effect between vehicle volume on the Tangerang City CO pattern, with a strength value of 0.689 and an R Square of 0.476.
ANALYSIS OF WATER PRODUCTIVITY IN THE BANDA SEA BASED ON REMOTE SENSING SATELLITE DATA Sartono Marpaung; Rizky Faristyawan; Anang Dwi Purwanto; Wikanti Asriningrum; Argo Galih Suhadha; Teguh Prayogo; Jansen Sitorus
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 1 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3280

Abstract

This study examines the density of potential fishing zone (PFZ) points and chlorophyll-a concentration in the Banda Sea. The data used are those on chlorophyll-a from the Aqua MODIS satellite, PFZ points from ZAP and the monthly southern oscillation index. The methods used are single image edge detection, polygon center of mass, density function and a Hovmoller diagram. The result of the analysis show that productivity of chlorophyll-a in the Banda Sea is influenced by seasonal factors (dry season and wet season) and ENSO phenomena (El Niño and La Niña). High productivity of chlorophyll-a  occurs during in the dry season with the peak in August, while low productivity occurs in the wet season and the transition period, with the lowest levels in April and December. The variability in chlorophyll-a production is influenced by the global El Niño and La Niña phenomena; production increases during El Niño and decreases during La Niña. Tuna conservation areas have as lower productivity of chlorophyll-a and PFZ point density compared to the northern and southern parts of the Banda Sea. High density PFZ point regions are associated with regions that have higher productivity of chlorophyll-a, namely the southern part of the Banda Sea, while low density PFZ point areas are associated with regions that have a low productivity of chlorophyll-a, namely tuna conservation areas. The effect of the El Niño phenomenon in increasing chlorophyll-a concentration is stronger in the southern part of study area than in the tuna conservation area. On the other hand, the effect of La Niña phenomenon in decreasing chlorophyll-a concentration is stronger in the tuna conservation area than in the southern and northern parts of the study area.
VARIABILITY OF SEA SURFACE TEMPERATURE AT FISHERIES MANAGEMENT AREA 715 IN INDONESIA AND ITS RELATION TO THE MONSOON, ENSO AND FISHERY PRODUCTION Komang Iwan Suniada
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 2 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3370

Abstract

Sea surface temperature (SST) is one of the important oceanographic and climateparameters. Its variability and anomalies often influence the environment and organisms, both in theoceans and on land. This study aims to identify the variability of SST and help the fisheriescommunity to understand how climate phenomena such as ENSO and monsoonal phases (representedby wind speed) are related to SST and fishery production in Fisheries Management Area (FMA) 715.SST was measured at Parimo, which represents conditions in the western part of the area insideTomini Bay, and at Bitung, which represents SST in the open ocean, with a more exposuredgeographical position. SST was derived from MODIS satellite imagery, downloaded from the oceancolordatabase (https://oceancolor.gsfc.nasa.gov/) with a 4 km spatial resolution, from January 2009 toDecember 2018. Wind speed data, historical El Niño or La Niña events, and fish production data werealso used in the study. Pearson’s correlation (Walpole, 1993) was used to test the relationshipbetween SST variability or anomaly and ENSO and monsoons. The results show that the SSTcharacteristics and variability of the Parimo and Bitung waters are very different, although they bothlie in the same FMA 715. SST in Parimo waters is warmer, but with lower variability than in Bitungwaters. SST in Parimo has a low correlation with ENSO (r=0.06, n=66), low correlation with windspeed (r=-0.29, n=120), with also a low correlation between SST anomaly and ENSO (r=0.05, n=66).SST in Bitung has a higher, but inverse, correlation with ENSO (r=-0.53, n=66), high correlation withwind speed (r=-0.60, n=119), with also a high correlation between SST anomaly and ENSO (r=-0.74,n=66). Unlike in other parts of Indonesia, fishery production in Parimo, or the western part insideTomini Bay, is not affected by ENSO events.
LOCAL CLIMATE ZONE (LCZ) IN BANDAR LAMPUNG CITY Farhan Anfasa Putra; Adi Wibowo; Iqbal Putut Ash Sidiq
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3790

Abstract

The rapid growth of the population in Bandar Lampung has led to a change in the land's usage from vegetation to built-up land. In the end, less vegetation will be present, which also results in higher temperatures in urban. This study intends to identify the state of the city's building density, vegetation density, land surface temperature, and Local Climate Zone (LCZ) in Bandar Lampung. Local Climate Zone (LCZ) maps can provide information on the physical structure of urban planning based on building density, and vegetation density, and are useful in the mitigation and public monitoring of increasing urban temperatures. The data was collected using images from Landsat 8 OLI/TIRS and high-resolution satellite imagery from Maxar Technologies downloaded using Google Earth Pro. Additionally, a field survey was used to measure the air temperature. The LCZ Generator WUDAPT is used to process LCZ data. The findings revealed that Bandar Lampung was dominated by medium-density buildings in the city's canter and medium-density vegetation in its western. The highest LST in residential areas is 35°C, while forest areas have the lowest LST at 15,68°C. There are 14 LCZ classifications, covering seven building types and seven land cover types. The dense tree zone has the highest vegetation density, the open low-rise zone has the highest land surface temperature, and the compact low-rise zone has the highest building density.

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