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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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RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA I Wayan Nuarsa; Fumihiko Nishio
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1221

Abstract

Rice is an agriculture plants that has the specific characteristic in the life stage due to the growth stage having different proportion of vegetation, water, and soil. Vegetation index is one of the satellite remote sensing parameter that is widely used to monitor the global vegetation cover. The objective of the study is to know the spectral characteristic of rice plant in the life stage and find the relationship between the rice growth parameters and the remote sensing data by the Landsat ETM data using the correlation and regression analysis. The result of study shows that the spectral characteristic of the rice before one month of age is defferent comparing after one month. All of the examined vegetation index has close linear relationship with rice coverage. Difference Vegetation Index (DVI) is the best vegetation index which estimates rice coverage with equation y = 1.762x + 2.558 and R degree value was 0.946. Rice age has a high quadratic relationship with all of evaluated vegetation index. Transformed Vegetation Index (TVI) is the best vegetation to predict the age of the rice. Formula y = 0.013x - 1.625x + 145.8 is the relationship form between the rice age and the TVI with R = 0.939. Peak of the vegetation index of rice is in the rice age of 2 months. This period is the transition of vegetative and generative stages.
PRESENT UNDERSTANDING OF ACEH TSUNAMI (APPLICATIONS OF DATA FROM FIELD TO SATELLITE OBSERVATIONS) I Gede Hendrawan; Bambang Sukresno; Yasuhiro Sugimori
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1222

Abstract

Application of data from field to satellite observation and simulation has been made as present understanding of Aceh tsunami. Tsunami has attracted attention after struck Aceh in December 26th 2004, generated by a strong eartquake with magnitude Mw=9.0. The eatrhquake triggered giant tsunami waves that propagated throughout the Indian Ocean, causing extreme inundation and destruction along the northern and western coast of Sumatra. Within hours, the tsunami devastated the distant shores of Thailand to east as well as Sri Lanka, India and Maldives to the west. The tsunami also caused deaths, and destruction in Somalia and other nations of East Africa. The tsunami was recorded on tidal stations throughout the Indian Oceans in worldwide. Unlike the Pacific, the Indian Ocean does not yet have a network of deep-ocean pressure sensors, and so coastal tide gauges provide the only direct measurement of Indian Ocean stunami amplitudes. We had many lessons and basic knowledge which had already been learned from this tragic event in the Indian Ocean. Many more lessons should be learned in the near future as this tragedy unfolds and reverals many failures to value and protect human life in this neglected region of the world.
THE USE OF SATELLITE REMOTE SENSING (ALOS SATELLITE DATA) I Wayan Gede Astawa Karang; I Gede Hendrawan
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1223

Abstract

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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.
Full Paper IJReSES Vol. 15, No. 2(2018) LAPAN
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v15i2.14434

Abstract

Full Paper IJReSES Vol. 15, No. 2(2018)
IJReSES Vol. 14 No. 2 December 2017 LAPAN
International Journal of Remote Sensing and Earth Sciences Vol. 14 No. 2 (2017)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v14i2.14439

Abstract

IJReSES Vol. 14 No. 2 December 2017

Page 33 of 33 | Total Record : 327

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