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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 8 Documents
 1 
Search results for , issue "Vol. 21 No. 2 (2024)" : 8 Documents clear
THE EVOLUTION OF AGRICULTURAL LAND AROUND THE SAND MINING AREA FROM 2011 – 2021 IN LELES DISTRICT, GARUT REGENCY, WEST JAVA Damayanti, Astrid; Fachrizal, Helmi Rahmat; Kintan Maulidina
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.6566

Abstract

Mining activities can alter land use patterns, including converting agricultural land. The transformation of agricultural land is occurring at varying rates, whether rapid or gradual. The evolution of agricultural land may be observed in terms of its shape, area, and land function. This paper was prepared to determine the evolution of agricultural land due to the expansion of limestone mining in Leles District from 2011 to 2021 to support further research in this area. The data collection process employed a combination of historical Google Earth images, secondary data, and land farm surveys conducted in Leles District, Garut Regency, from 2011 to 2021. The images were then assessed to identify land shape, area, and function changes. Subsequently, the data were subjected to analysis and comparison with existing literature. The study results demonstrate the evolution of the site and the utilization of agricultural land as a consequence of mining development. The expansion of the mining area has resulted in the transformation of the surrounding land into agricultural land. During the observation period, the mining area increased by 98%, while the farming area decreased by 27%.
MARINE CRIME IN INDONESIA: A SPATIO-TEMPORAL ASSESSMENT OF EMERGING TRENDS AND HOTSPOTS Kurniawan, Rahmad; Manessa, Masita Dwi Mandini; Golkariansyah; Wiratama, Eska Yosep; Budiman, Asep
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.7089

Abstract

Indonesia, with its vast maritime domain, faces significant challenges related to maritime crime, including illegal, unreported, and unregulated (IUU) fishing, piracy, human trafficking, and smuggling. The country’s strategic position, bordering key shipping routes like the Strait of Malacca and the Sunda Strait, exacerbates its vulnerability to transnational crimes. This study provides a spatio-temporal assessment of emerging trends and hotspots of marine crime in Indonesia during the period of 2022-2023. Through an analysis of crime incidents, the research identifies key areas of concern, such as the Java Sea, Sumatra, and Eastern Indonesia, where illegal activities have shown persistent and intensifying patterns. The Strait of Malacca and Aceh emerged as critical zones, with increased incidents of piracy and human trafficking, partly linked to the Rohingya refugee crisis. Additionally, the study highlights the environmental impact of illegal activities in ecologically sensitive regions, such as Papua and the Coral Triangle, where illegal logging, mining, and destructive fishing practices threaten marine ecosystems. The analysis also reveals seasonal trends, with the highest concentration of incidents occurring between July and September, coinciding with peak fishing activities. Despite efforts by the Indonesian government, including the Sinking of Foreign Vessels Policy and regional cooperation initiatives like ReCAAP, enforcement gaps remain, particularly in remote regions. The study identifies critical gaps in maritime security, including the need for improved technological surveillance and enhanced community engagement in enforcement efforts. The findings underscore the importance of spatial-temporal monitoring to inform targeted law enforcement and policy responses, thereby protecting Indonesia’s marine resources and enhancing national security.
ASSESSMENT OF GROUND SURFACE DEFORMATION IN BENGKULU CITY INDUCED BY EARTHQUAKES USING DINSAR-BASED REMOTE SENSING IMAGE ANALYSIS Utama, Ferzha Putra; Vatresia, Arie; Zalbuin Mase, Lindung; Faris, Ahmad
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.8915

Abstract

In 2007, Bengkulu city, Indonesia and its surrounding areas experienced a significant earthquake with a magnitude of 8.6 Mw, resulting in extensive damage. Between 2014 and 2022, Bengkulu Province encountered a total of 3469 earthquakes, signifying a heightened frequency of seismic activity in the region. This escalated seismic activity in Bengkulu elicits concerns regarding potential ground surface deformation. To address this, the study utilized DInSAR (Differential Interferometry SAR) technology, employing a blend of satellite images to quantify land surface deformation. Notably, the research made use of three pairs of satellite images for analysis. One pair of ALOS-PALSAR images dated between January 29 and September 16, 2007, was employed to investigate ground surface deformation following the September 12, 2007 earthquake. Additionally, observations were made using two pairs of Sentinel-1 satellite images covering periods from November 3 to 27, 2014, and from June 30 to July 24, 2022, to monitor land surface deformation resulting from earthquakes on November 10, 2014 and July 20, 2022. The study findings depicted uplift deformation reaching 53.4 mm and the highest subsidence measuring -12.8 mm in the ALOS-PALSAR image pair. In the Sentinel-1 image pair between November 3 and 27, 2014, the most notable observed uplift amounted to 38.9 mm, while the greatest subsidence was recorded at -34.8 mm. Lastly, the image pair dated between June 30 and July 24, 2022, exclusively exhibited uplift, with values peaking at 45.2 mm.
ASSESSING FUTURE SPATIAL DISTRIBUTION OF THE SEASONAL RAINFALL IN BINTAN ISLANSD USING THE DOWNSCALED CORDEX-SEA MODELS Narulita, Ida; Dwita Sutjiningsih; Eko Kusratmoko; Muhamad Rahman Djuwansah; Faiz Rohman Fajary; Widya Ningrum
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.9068

Abstract

Sustainable water resource management must consider climate change to minimize climate disasters. The water resources of Bintan island are limited, although rainfall is quite high, but the small catchment area and the component rocks of the island of Bintan have low water retention capacity. Currently, Bintan Island is experiencing an increase in water needs due to population growth and economic activities. Therefore, understanding changes in seasonal rainfall in the future is important on this island. This paper aims to study the chances of future seasonal rainfall variability using long-term projection climate modeling. Currently, a high-resolution climate model is available for historical and future periods, namely CORDEX-SEA for the Southeast Asia region. Because the study area is a small island with an area of around 1170 km2, the resolution of the CORDEX-SEA projection climate model data is insufficient. This study uses a statistical downscaling method with quantile mapping to detail the spatial resolution. The results of the analysis show that rainfall on Bintan Island is likely to change in the future due to climate change. Rainfall in Bintan Island in the future will likely be below normal rainfall in all seasons, except in the northern part of Bintan in the SON season. The greatest posibility of rainfall is below normal rainfall in the JJA season. The analysis results show that the eastern part of Bintan Island is a suitable area to build a water reservoir for managing water shortages in Bintan island caused by potentially decreasing rainfall in the future.
ASSESSING THE PERFORMANCE OF DETERMINISTIC PRECIPITATION NOWCASTING ALGORITHMS WITH WEATHER RADAR DATA AND MULTIMETRIC VERIFICATION Ali, Abdullah
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.11217

Abstract

This study assesses the performance of four deterministic radar-based nowcasting algorithms—LINDA, SPROG, ANVIL, and Extrapolation—using C-band weather radar data located at Tangerang. Forecasts were generated with the pysteps library and verified up to +96 minutes using spatial inspection, ROC analysis, and Taylor/Target diagrams. At short lead times (+8 to +24 minutes), all algorithms achieved high discrimination skill (AUC > 0.90), with SPROG and LINDA reaching peak AUC values of 0.96 and 0.95, respectively. Beyond +56 minutes, LINDA maintained the highest AUC (0.64), while SPROG and Extrapolation dropped below 0.60. Statistical verification showed that LINDA consistently preserved rainfall structure with correlation coefficients ≥ 0.80 at short range and ~0.65 at +80 minutes. Target diagrams indicated low bias (< ±0.1) and uRMSD stability for LINDA, while SPROG exhibited increasing overdispersion and structural error. Spatially, LINDA captured convective growth and peak intensities more realistically than other methods. These results demonstrate that LINDA offers the most balanced and skillful performance across metrics, especially in maintaining accuracy during medium-range forecasts. The findings support its operational suitability for nowcasting convective rainfall in tropical regions.
GLOBAL HORIZONTAL IRRADIANCE ESTIMATION IN TROPICAL TERRAIN USING SEMI-EMPIRICAL APPROACH: A SEASONAL ASSESSMENT IN WEST JAVA, INDONESIA Garniwa, Pranda Mulya; Azzahra, Rifdah Octavi; Dimyati, Muhammad
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.11405

Abstract

Accurate estimation of solar irradiance is essential for optimizing solar energy planning, particularly in tropical regions like Indonesia, where observational infrastructure is limited and atmospheric conditions are highly variable. This study addresses the challenge by applying the Perez semi-empirical model to estimate Global Horizontal Irradiance (GHI) across West Java, a topographically diverse province with seasonal weather dynamics. The model integrates satellite-based reflectance data from the GK2A satellite and atmospheric parameters from AERONET, using a spatial resolution of 0.5 km. GHI estimation was conducted for four tropical seasonal phases: the rainy season, transition to dry, dry season, and transition to rainy. Model validation was performed using hourly GHI measurements from two BMKG stations in Indramayu. The Perez model showed strong performance, with RMSE ranging from 146.96 to 163.52 W/m² and relative RMSE below 38%. The results indicate that the model reliably captures both seasonal and spatial variations of solar radiation under tropical atmospheric conditions. Spatial analysis reveals a consistent pattern: lowland and coastal areas receive significantly higher GHI compared to highland regions, which are affected by cloud formation and orographic effects. These findings confirm the model’s suitability for tropical solar forecasting and offer valuable insights for identifying high-potential zones for photovoltaic development.
ILLEGAL OIL MINING DETECTION THROUGH REMOTE SENSING IN MUSI BANYUASIN REGENCY, SOUTH SUMATRA, INDONESIA Setiadi, Restu; Supriatna; Dimyati, Muhammad; Arsyad, Ibrahim
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.13244

Abstract

Illegal oil mining activities present significant environmental, economic, and regulatory challenges, particularly in resource-abundant regions that are difficult to monitor such as Musi Banyuasin Regency in South Sumatra. This study applied an integrated method that combines drone-based remote sensing, visual interpretation, and spatial statistical analysis to detect, map, and evaluate the spatial distribution of illegal shallow oil wells. High-resolution aerial imagery was acquired using DJI Phantom 4 Pro drones, processed into orthomosaic images, and interpreted visually to identify suspected well locations. A total of 2664 illegal oil wells were identified and georeferenced. The results of spatial autocorrelation analysis using Moran’s I indicated a clustered distribution pattern, with significant concentrations found in subdistricts such as Lawang Wetan, Batang Hari Leko, and Tungkal Jaya. The Moran’s I index value of 0.652075 confirmed a statistically significant spatial clustering. Ground validation was conducted through direct field surveys, which verified the presence of the wells and provided supporting photographic documentation and GPS coordinates. The dataset was also compared with official records of legal oil wells to ensure accuracy and distinction between legal and illegal infrastructure. The findings demonstrate that unmanned aerial vehicle-based spatial analysis offers a reliable and scalable solution for monitoring unregulated extraction activities. This approach supports data-driven enforcement, enhances environmental oversight, and informs the development of more effective regulatory policies in regions impacted by informal oil production.
RANDOM FOREST CLASSIFICATION FOR MANGROVE CANOPY COVER SPATIAL ANALYSIS IN BENOA BAY – BALI, INDONESIA Nanin; Noverita Dian Takarina; Ratih Dewanti Dimyati; Dwi Nowo Martono; Evi Frimawaty; Rahmadi; A. A. Md. Ananda Putra Suardana
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.13466

Abstract

Mangroves play a crucial role in maintaining the stability of coastal ecosystems by providing habitats for diverse species, protecting shorelines from erosion, and acting as a carbon sink. The importance of conserving and developing mangrove areas can be effectively monitored using remote sensing data and classification methods, such as Random Forest (RF), ensuring an accurate assessment and management of these vital ecosystems. This research aims to develop and evaluate an RF classification model to produce accurate spatial information on mangrove canopy cover. The research area, Benoa Bay in Bali, Indonesia, is known for its dynamic and ecologically complex mangrove habitats. The inputs for RF classification are bands on Sentinel-2A satellite imagery, Mangrove Vegetation Index (MVI), Normalized Difference Vegetation Index (NDVI), Enhanced Mangrove Index (EMI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and the Normalized Difference Salinity Index (NDSalI), along with topographic variables such as elevation and slope. Model validation was conducted using high-resolution imagery from Google Earth Pro and cross-referenced with the 2024 National Mangrove Map. The classification of coastal land cover is divided into water bodies, mangroves, open land, built-up land, and non-mangrove vegetation, with an overall accuracy of 0.98 and a kappa statistic of 0.98. In contrast, the accuracy of the classification of mangrove canopy cover concerning the national mangrove map produces an overall accuracy of 0.97 and a kappa value of 0.86. These findings demonstrate the robustness of the RF model and its potential for supporting data-driven coastal management practices.

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