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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 9 Documents
 1 
Search results for , issue "Vol. 17 No. 2 (2020)" : 9 Documents clear
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.
DETECTION AND ANALYSIS OF SURFACE URBAN COOL ISLAND USING THERMAL INFRARED IMAGERY OF SALATIGA CITY, INDONESIA Bayu Elwantyo Bagus Dewantoro; Panji Mahyatar; Wafiq Nur Hayani
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.a3387

Abstract

The detection and monitoring of the dynamics of urban micro-climatesneeds to be performedeffectively, efficiently, consistently and sustainably inan effort to improve urban resilience to suchphenomena. Thermal remote sensing posesses surface thermal energy detection capabilities which can be converted into surface temperatures and utilised to analyse the urban micro-climate phenomenon overlarge areas, short periods of time, and at low cost. This paper studies the surface urban cool island (SUCI) effect, the reverse phenomenon of the surface urban heat island (SUHI) effect, in an effort to provide cities with resistance to the urban microclimate phenomenon.The study also aims to detect urban micro-climate phenomena, and to calculate the intensity and spatial distribution of SUCI. The methods used include quantitative-descriptive analysis of remote sensing data, including LST extraction, spectral transformation, multispectral classification for land cover mapping, and statistical analysis. The results show that the urban micro-climate phenomenon in the form of SUHI in the middle of the city of Salatiga is due to the high level of building density in the area experiencing the effect, which mostly has a normal surface temperature based on the calculation of the threshold, while the relative SUCI occurs at the edge of the city. SUCI intensity in Salatiga ranges between -6.71°C and0°C and is associated with vegetation.
MONITORING CHANGES IN CORAL REEF HABITAT COVER ON BERALAS PASIR ISLAND USING SPOT 4 AND SPOT 7 IMAGERY FROM 2011 AND 2018 Rosaria Damai; Viv Djanat Prasita; Kuncoro Teguh Setiawan
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.a3428

Abstract

Beralas Pasir is part of the Regional Marine Conservation Area (KKLD), which was established by the Bintan Regency Government with Bintan Regent Decree No. 261 / VIII / 2007. Water tourism activities undertaken by tourists on the island have had an impact on the condition of the coral reefs, as have other factors, such as bauxite, granite and land sand mining activities around the island. This research aims to determine changes in the coral reef habitat cover and the condition of the coral reefs around Beralas Pasir Island with a remote sensing function, using SPOT 4 imagery acquired on June 1, 2011 and SPOT 7 imagery from April 5, 2020. Data collection of environmental parameters related to the coral reefs was also made. The image processing used the Lyzenga algorithm to simplify the image classification process. The percentage of coral live cover around the island ranges from 26% -53%; this has experienced a significant change, from 67,560 hectares in 2011 to 38,338 hectares in 2018, a total decrease in the area of 29,222 hectares. Some of the natural factors found in the research which have caused damage to the reefs were Drupella snails, the abundance of Caulerpa racemosaalgae, and sea urchins. The majority of the coral reef types consist of Non-Acropora: Coral Massive, Coral, Coral Foliose, Coral Encrusting, Acropora: Acropora Tabulate, Acropora Encrusting, and Acropora Digitate
MULTITEMPORAL ANALYSIS FOR TROPHIC STATE MAPPING IN BATUR LAKE AT BALI PROVINCE BASED ON HIGH-RESOLUTION PLANETSCOPE IMAGERY Rahma Nafila Fitri Sabrina; Sudaryatno
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.a3381

Abstract

Remote sensing data for analyzing and evaluating trophic state ecosystem problems seen in Batur Lake isan approach that is suitable for water parameters that cannot be observed terrestrially. As the multitemporal spatial data used in this study were extensive, it was necessary to consider the effectiveness and efficiency of the processing and analysis, therefore R Studio was used as a data processing tool. Theresearch aims to(1) map the trophic state of Batur Lake multitemporally usingPlanetScope Imagery;(2) assess the accuracy of the trophic state model and applyitto anothertemporal data as a SpatialBigData;and (3) understand the trophic state impacton the water quality of Batur Lake based on physical factors andthelake’s chemical concentration (sulfur concentration). Theresearch showsthatthetrophic state of Batur Lake isin good condition,with an ultraoligotrophic state as the majority class,based on the mean Trophic State Index (TSI) value of9.49. The standard errorsof each trophic state parameter were0.010 for total phosphor, 0.609 for chlorophyll-a, and 0.225 for Secchi Disk Transparency (SDT). The multitemporal model demonstratesthat the correlation between the increase oftrophic state and mass fish death cases in Batur Lake is existent.
MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2 Samsul Arifin; Tatik Kartika; Dede Dirgahayu; Gatot Nugroho
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.a3385

Abstract

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (ΔNDVI) and open land index (ΔNBR).The study found that the best thresholds to detect revegetation were ΔNDVI > 0.0309 and ΔNBR < 0.0176 and to detect devegetation ΔNDVI < -0.0206 and ΔNBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.
INTERSEASONAL VARIABILITY IN THE ANALYSIS OF TOTAL SUSPENDED SOLIDS(TSS) IN SURABAYA COASTAL WATERS USING LANDSAT-8 SATELLITE DATA Bela Karbela; Pingkan Mayestika Afgatiani; Ety Parwati
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.a3418

Abstract

The spatial and temporal capabilities of remote sensing data are very effective for monitoring the value of total suspended solids(TSS) in water using optical sensors. In this study,TSS observations were conductedin the westseason, transition season 1, east season, and transition season 2 in 2018 and 2019. Landsat 8 image data wereused,extracted into TSS values using a semi-analytic model developed in the Mahakam Delta, East Kalimantan, Indonesia. The TSS data obtained were then analysed for distribution patterns in each season. The sample points were randomly scattered throughout the study area. The TSS distribution pattern in the west season showeda high concentration spread over the coastal area to theoff sea, while the pattern in the east season only showeda high concentration inthecoastal areas. Transitional seasons1 and 2 showed different patterns of TSS distribution in 2018 and 2019, with more varied values. The distribution of TSS is strongly influenced by the season. Observation of each cluster resultedin the conclusion thathuman activity and the rainfall rate can affect the concentration of TSS.
A COMPARISON OF RAINFALL ESTIMATION USING HIMAWARI-8 SATELLITE DATA IN DIFFERENT INDONESIAN TOPOGRAPHIES Nadine Ayasha
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.a3441

Abstract

The Himawari-8 satellite can be used to derive precipitation data for rainfall estimation. This study aims to test several methods for suchestimation employing the Himawari-8 satellite. The methods are compared in three regions with different topographies, namely Bukittinggi, Pontianak and Ambon. The rainfall estimation methods that are tested are auto estimator, IMSRA, non-linear relation and non-linear inversion approaches. Based on the determination of the statistical verification(RMSE, standard deviation and correlation coefficient) of the amount of rainfall, the best method in Bukittinggi and Pontianak was shown to be IMSRA, while for the Ambon region was the non-linear relations. The best methods from each research area were mapped using the Google Maps Application Programming Interface (API).
SHORELINE CHANGES AFTER THE SUNDA STRAIT TSUNAMI ON THE COAST OF PANDEGLANG REGENCY, BANTEN Fandi Dwi Julianto; Cahya Riski Fathurohman; Salsabila Diyah Rahmawati; Taufiq Ihsanudin
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.a3390

Abstract

The Sunda Strait tsunami occurred on the coast of west Banten and South Lampung at 22nd December 2018, resulting in 437 deaths, with10 victims missing. The disaster had various impacts on the environment and ecosystem, with this area suffering the greatest effects from the disaster. The utilisation of remote sensing technology enables the monitoring of coastal areas in an effective and low-cost manner. Shoreline extraction using the Google Earth Engine, which is an open-source platform that facilitates the processing of a large number of data quickly. This study used Landsat-8 Surface Reflectance Tier 1 data that was geometrically and radiometrically corrected, with processing using the Modification of Normalized Difference Water Index (MNDWI) algorithm. The results show that 30.1% of the coastline in Pandeglang Regency occurred suffered abrasion, 20.2% suffered accretion,while 40.7% saw no change. The maximum abrasion of 130.2 meters occurred in the village of Tanjung Jaya. Moreover, the maximum shoreline accretion was 43.3 meters in the village of Panimbang Jaya. The average shorelinechange in Pandeglang Regencywas 3.9 meters.
BATHYMETRIC EXTRACTION USING PLANETSCOPE IMAGERY (CASE STUDY: KEMUJAN ISLAND, CENTRAL JAVA) Asih Sekar Sesama; Kuncoro Teguh Setiawan; Atriyon Julzarika
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.a3445

Abstract

Bathymetry refers to the depth of the seabed relative to the lowest water level. Depth information is essential for various studies of marine resource activities, for managing port facilities and facilities, supporting dredging operations, and predicting the flow of sediment from rivers into the sea. Bathymetric mapping using remote sensing offers a more flexible, efficient,and cost-effective method and covers a largearea. This study aims to determine the ability of Planet Scope imagery to estimate and map bathymetry and to as certain its accuracy using the Stumpf algorithm on the in-situ depth data. PlanetScope level 3B satellite imagery and tide-corrected survey dataare employed; satellite images are useful in high-precision bathymetry extraction.The bathymetric extraction method used the Stumpf algorithm. The research location was Kemujan Island, Karimunjawa Islands, Central Java. The selection of this region wasbased on its water characteristics, which have a reasonably high variation in depth. Based on the results of the data processing, it was found that the PlanetScope image data were able to estimate depths of up to 20 m. In the bathymetric results, the R2 accuracy value was 0.6952, the average RMSE value was 2.85 m,and the overall accuracy rate was 71.68%.

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