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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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ASSESSMENT OF THE ACCURACY OF DEM FROM PANCHROMATIC PLEIADES IMAGERY (CASE STUDY: BANDUNG CITY. WEST JAVA) Rian Nurtyawan; Nadia Fiscarina
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.a3329

Abstract

Pleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000.
SPATIAL DISTRIBUTION OF GREEN OPEN SPACES AND RELATION TO LAND SURFACE TEMPERATURE IN BANDAR LAMPUNG CITY Rizky Cahaya Meikatama; 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.a3795

Abstract

Bandar Lampung City, the capital city of Lampung Province in Indonesia, became the number three city on the island of Sumatra, with enormous population growth from 2000 to 2015. Population growth resulted in increasing built-up land affecting several aspects, one of which was the increase in surface temperature in urban areas. This study aims to determine changes in green open space, land surface temperature (LST), and the spatial pattern of changes in Bandar Lampung City. Data processing uses Landsat 8 imagery for green space and Google Earth Engine for LST. The results of this study indicate that the distribution of changes in green open space the east to west experienced a change in green open space to non-green open space which resulted in an increase in temperature in the east, southeast, and west, from 25-30oC the temperature increased to >30oC. The change in green open space in the west and some areas found that a change from non-RTH to a public or private green open space resulted in a decrease in temperature starting from 25-30oC decreased to 20-25oC. The spatial pattern of changes in green open space in Bandar Lampung City has a clustered pattern in the west and east of the area following the topography (100-500 masl). At the same time, the land surface temperature pattern (LST) in Bandar Lampung City has a clustered pattern at temperatures <20oC, 20-25oC (found at an altitude of 100-500 masl), and >30oC (following an altitude of 25-100 masl) while for temperatures 25-30oC has a scattered pattern (following an altitude of 25-100 masl) in Bandar Lampung City.
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.
BIOMASS ESTIMATION MODEL AND CARBON DIOXIDE SEQUESTRATION FOR MANGROVE FOREST USING SENTINEL-2 IN BENOA BAY, BALI A. A. Md. Ananda Putra Suardana; Nanin Anggraini; Kholifatul Aziz; Muhammad Rizki Nandika; Azura Ulfa; Agung Dwi Wijaya; Abd. Rahman As-syakur; Gathot Winarso; Wiji Prasetio; Ratih Dewanti
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.a3797

Abstract

Remote sensing technology can be used to find out the potential of mangrove forests information. One of the potentials is to be able to absorb three times more CO2 than other forests. CO2 absorbed during the photosynthesis process, produces organic compounds that are stored in the mangrove forest biomass. Utilization of remote sensing technology is able to detect mangrove forest biomass using the density level of the vegetation index. This study focuses on determining the best AGB model based on the vegetation index and the ability of mangrove forests to absorb CO2. This research was conducted in Benoa Bay, Bali Province, Indonesia. The satellite image used is Sentinel-2. Classification of mangroves and non-mangroves using a multivariate random forest algorithm. Furthermore, the mangrove forest biomass model using a semi-empirical approach, while the estimation of CO2 sequestration using allometric equations. Mean Absolute Error (MAE) is used to evaluate the validation of the model results. The classification results showed that the detected area of Benoa Bay mangrove forest reached 1134 ha (OA: 0.98, kappa: 0.95). The best AGB estimation result is the DVI-based AGB model (MAE: 23,525) with a value range of 0 to 468.38 Mg/ha. DVI-based AGB derivatives are BGB with a value range of 0 to 79.425 Mg/ha, TAB with a value range of 0 to 547.8 Mg/ha, TCS with a value range of 0 to 257.47 Mg/ha, and ACS with a value range of 0 to 944.912 Mg/ha.
SPATIAL AND TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE CHANGE ON NEW BRITAIN ISLAND Rafika Minati Devi; Tofan Agung Eka Prasetya; Diah Indriani
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.a3342

Abstract

Land Surface Temperature (LST) is a parameter to estimate the temperature of the Earth’s surface and to detect climate change. Papua New Guinea is a tropical country with rainforests, the greatest proportion of which are located on the island of New Britain. Hectares of rainforests have been logged and deforested because of infrastructure construction. This study aims to investigate the change in land surface temperatures on the island from 2000 to 2019. The temperature data were taken from National Aeronautics and Space Administration (NASA) Terra satellites and were analysed using two statistical models: spatial and temporal. The spatial model used multivariate regression, while the temporal one used autoregression (AR). In this study, a cubic spline fitted curve was employed because this has the advantage of being smoother and providing good visuals. The results show that almost all the sub-regions of New Britain have experienced a significant increase in land surface temperature, with a Z value of 7.97 and a confidence interval (CI) of 0.264 – 0.437. The study only investigated land surface temperature change on New Britain Island using spatial and temporal analysis, so further analysis is needed which takes into account other variables such as vegetation and land cover, or which establishes correlations with other variables such as human health.
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
Back Pages IJReSES Vol. 19, No. 1 (2022) Journal Manager
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.a3865

Abstract

Back Pages IJReSES Vol. 19, No. 1 (2022)
Front Pages IJReSES Vol. 16, No. 2 (2019) Journal Editor
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3343

Abstract

Front Pages IJReSES Vol. 16, No. 2 (2019)
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.
ROLLING MOSAIC METHOD TO SUPPORT THE DEVELOPMENT OF POTENTIAL FISHING ZONE FORECASTING FOR COASTAL AREAS Komang Iwan Suniada; Eko Susilo; Wingking Era Rintaka Siwi; Nuryani Widagti
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3252

Abstract

The production of the Indonesian Institute for Marine Research and Observation’s mapping of forecast fishing areas (peta prakiraan daerah penangkapan ikan or PPDPI) based on passive satellite imagery is often constrained by high-cloud-cover issues, which lead to sub-optimal results. This study examines the use of the rolling mosaic method for providing geophysical variables, in particular, seasurface temperature (STT) together with minimum cloud cover, to enable clearer identification of oceanographic conditions. The analysis was carried out in contrasting seasons: dry season in July 2018 and rainy season in December 2018. In general, the rolling mosaic method is able to reduce cloud cover for sea-surface temperature (SST) data. A longer time range will increase the coverage percentage (CP) of SST data. In July, the CP of SST data increased significantly, from 15.3 % to 30.29% for the reference 1D mosaic and up to 84.19 % to 89.07% for the 14D mosaic. In contrast, the CP of SST data in December tended to be lower, from 4.93 % to 13.03% in the 1D mosaic to 41.48 % to 51.60% in the14D mosaic. However, the longer time range decreases the relationship between the reference SST data and rolling mosaic method data. A strong relationship lies between the 1D mosaic and 3D mosaics, with correlation coefficients of 0.984 for July and 0.945 for December. Furthermore, a longer time range will decrease root mean square error (RMSE) values. In July, RMSE decreased from 0.288°C (3D mosaic) to 0.471°C (14D mosaic). The RMSE value in December decreased from 0.387°C (3D mosaic) to 0.477°C (14D mosaic). Based on scoring analysis of CP, correlation coefficient and RMSE value, results indicate that the 7D mosaic method is useful for providing low-cloud-coverage SST data for PPDPI production in the dry season, while the 14D mosaic method is suitable for the rainy season.

Page 9 of 33 | Total Record : 327

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