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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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DETECTING THE AREA DAMAGE DUE TO COAL MINING ACTIVITIES USING LANDSAT MULTITEMPORAL (Case Study: Kutai Kartanegara, East Kalimantan) Suwarsono; Nanik Suryo Haryani; Indah Prasasti; Hana Listi Fitriana; M. Priyatna; M. Rokhis Khomarudin
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/j.ijreses.2017.v14.a2851

Abstract

Coal is one of the most mining commodities to date, especially to supply both national and international energy needs. Coal mining activities that are not well managed will have an impact on the occurrence of environmental damage. This research tried to utilize the multitemporal Landsat data to analyze the land damage caused by coal mining activities. The research took place at several coal mine sites in East Kalimantan Province. The method developed in this research is the method of change detection. The study tried to know the land damage caused by mining activities using NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), NDWI (Normalized Difference Water Index) and GEMI (Global Environment Monitoring Index) parameter based change detection method. The results showed that coal mine area along with the damage that occurred in it can be detected from multitemporal Landsat data using NDSI value-based change detection method. The area damage due to coal mining activities can be classified into high, moderate, and low classes based on the mean and standard deviation of NDSI changes (ΔNDSI). The results of this study are expected to be used to support government efforts and mining managers in post-mining land reclamation activities.
DETECTING THE AFFECTED AREAS OF MOUNT SINABUNG ERUPTION USING LANDSAT 8 IMAGERIES BASED ON REFLECTANCE CHANGE Suwarsono; Hidayat; Jalu Tejo Nugroho; Wiweka; Parwati; M. Rokhis Khomarudin
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 1 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2672

Abstract

The position of Indonesia as part of a "ring of fire" bringing the consequence that the life of the nation and the state will also be influenced by volcanism. Therefore, it is necessary to map rapidly the affected areas of a volcano eruption. Objective of the research is to detect the affected areas of Mount Sinabung eruption recently in North Sumatera by using optical images Landsat 8 Operational Land Imager (OLI). A pair of Landsat 8 images in 2013 and 2014, period before and after eruption, was used to analysis the reflectance change from that period. Affected areas of eruption was separated based on threshold value of reflectance change. The research showed that the affected areas of Mount Sinabung eruption can be detected and separated by using Landsat 8 OLI images based on the change of reflectance value band 4, 5 and NDVI. Band 5 showed the highest values of decreasing and band 4 showed the highest values of increasing. Compared with another uses of single band, the combination of both bands (NDVI) give the best result for detecting the affected areas of volcanic eruption.
MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND Aulia Ilham; Marza Ihsan Marzuki
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/j.ijreses.2017.v14.a2820

Abstract

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for wide data observation. In remote sensing, machine learning method could be used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.
SYNERGY APPROACH FOR IMPLEMENTING THE POLICY ON HIGH RESOLUTION IMAGERY TO ACCELERATE BASIC AND THEMATIC GEOSPATIAL INFORMATION Sukendra Martha; Aris Poniman; Hartono
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 1 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2601

Abstract

Presidential Order no. 6/2012 mentioned explicitly to use ortho-rectifed image for the purposes of national program done by all Indonesian governmental agencies. Policy of uses, control quality, processing and distribution of high resolution of satellite data are regulated by this Order. There are some advantages of implementing this Order particularly in accelerating the national geospatial data and information, however, without synergy use of high resolution imagery (including integration, coordination and harmonization), in the present condition so far some obstacles have been discovered. Without synergic actions or approaches, the Order will not provide optimal impact as the main objectives to make more efficient in using the national budget. This article describes the needs of synergy approach to implement the Presidential Order no. 6/2012 concerning the uses, distribution of high remotely sensed imageries.
Back Pages IJReSES Vol. 14, No. 2(2017) Journal Editor
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

Abstract

Back Pages IJReSES Vol. 14, No. 2(2017)
CHLOROPHYLL-A CONCENTRATIONS ESTIMATION FROM AQUA-MODIS AND VIIRS-NPP SATELLITE SENSORS IN SOUTH JAVA SEA WATERS Rayhan Nuris; Jonson Lumban Gaol; Teguh Prayogo
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 1 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2673

Abstract

This study aimed to estimate the concentration of chlorophyll-a from satellite imagery of National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) in the south Java Sea waters and compare it to the concentrations of chlorophyll-a estimation result from the MODIS-Aqua satellite. NPP satellite had Visible/Infrared Imager Radiometer Suite (VIIRS) sensors which performance was same as Moderate Resolution Imaging Spectroradiometer (MODIS) sensor with a better spatial resolution. This study used daily satellite imagery of VIIRS-NPP for the period of September 2012 to August 2013. The algorithm that was used to estimate the concentration of chlorophyll-a was Ocean Color 3-band ratio (OC-3). The results showed that the spatial distribution pattern of chlorophyll-a concentration between VIIRS - NPP sensor and MODIS had the same pattern, but the estimation of chlorophyll-a concentration from the MODIS sensor was higher than VIIRS -NPP sensor. The concentration of chlorophyll-a showed that there were spatial and temporal variation in the south Java Sea waters. Generally, concentrations of chlorophyll-a was higher in East monsoon than West monsoon.
DETECTING THE SPATIAL DISTRIBUTION OF SETTLEMENTS ON VOLCANIC REGION USING IMAGE LANDSAT-8 OLI IMAGERY Suwarsono; M. Rokhis Khomarudin
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 1 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2602

Abstract

Geologically, Indonesia region is on track ring of fire, brings the consequence that the danger of volcanic eruption could occur at any time. Information sites where the settlement is located in the affected areas on emergency response process is needed in quick time. The availability of up to date data is important because it illustrates the actual condition of the region. Active volcanic landforms ranging from the crater to footslope in general is prone area to volcanic eruption, either by the threat of lava flows, pyroclastic falls, or lahars. This study aims to detect the spatial distribution of the settlement on volcanic region using Landsat-8 OLI. Parameters used for the detection of settlements is Normalized Difference Build-up Index (NDBI). Research methods include radiometric correction, delineation of the boundaries of volcanic landforms, NDBI value extraction, extraction of settlement areas, as well as the accuracy assesment.  Study area is Sinabung Volcano region located in the province of North Sumatera. Recently, the volcano experienced a devastating and catastrophic eruption. The results showed that the spatial distribution of settlements on volcanic landforms can be detected quickly from Landsat-8 OLI based on NDBI parameters with a sufficient degree of accuracy.
Front Pages IJReSES Vol. 14, No. 1(2017) Editorial Journal
International Journal of Remote Sensing and Earth Sciences Vol. 14 No. 1 (2017)
Publisher : BRIN

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Abstract

Front Pages IJReSES Vol. 14, No. 1(2017)
MONITORING OF LAKE ECOSYSTEM PARAMETER USING LANDSAT DATA (A CASE STUDY: LAKE RAWA PENING) Bambang Trisakti; Nana Suwargana; Joko Santo Cahyono
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 1 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2674

Abstract

Most lakes in Indonesia have suffered (decrease in quality) caused by land conversion in the catchment area, soil erosion, and water pollution from agriculture and households. This study utilizes remote sensing data to monitor several parameters used as ecosystem status assessors in accordance with the guidelines of Lake Ecosystem Management provided by the Ministry of Environment. The monitoring was done at Lake Rawa Pening using Landsat TM/ETM+ satellite data over the period of 2000-2013. The data standardization was done for sun angle correction and also atmospheric correction by removing dark pixels using histogram adjustment method. RGB color composites (R: NIR + SWIR, G: NIR, B: NIR-RED) were used for water hyacinth identification; thus, the lake water surface area can be delineated. Further samples were collected for water hyacinth and water classification with Maximum Likelihood method. Total Suspended Matter (TSM) by Doxaran model and the water clarity from field measurement was correlated to build water clarity algorithm. The results show that Lake Rawa Pening was deterioting in term of quality during the period of 2000-2013; it can be seen from the dynamic rate of the shrinkage and the expansion of the lake water surface area, the uncontrolled distribution of water hyacinth which it covered 45% of the lake water surface area in 2013, the increased of TSM concentration, and the decreased of water clarity. Most parts of Rawa Pening’s water have clarity less than 2.5 m which indicated that the thropic status is hypertrophic class.
APPLICATION OF VAN HENGEL AND SPITZER ALGORITHM FOR INFORMATION ON BATHYMETRY EXTRACTION USING LANDSAT DATA Kuncoro Teguh Setiawan; Syifa Wismayati Adawiah; Takahiro OSAWA; I. Wayan Nuarsa
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 1 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2603

Abstract

Remote sensing technology provides an opportunity for effective and efficient bathymetry mapping, especially in areas which level of depth changes quickly. Bathymetry information is very useful for hydrographic and shipping safety. Landsat medium resolution satellite imagery can be used for the extraction of bathymetry information. This study aims to extract information from the Landsat bathymetry by using Van Hengel and Spitzer rotation algorithm transformation (1991) in the water of Menjangan Island, Bali. This study shows that Van Hengel and Spitzer rotation algorithm transformation (1991) can be used to extract information on the bathymetry of Menjangan Island. Extraction of bathymetric information generated from Landsat TM imagery data in March 19, 1997 had shown the depth interval of (-0.6) m to (-12.3) m and R2 value of 0.671. While Data LANDSAT ETM + dated June 23, 2000 resulted in depth interval of 0 m to (-19.1) m and R2 value of 0.796. Furthermore, data LANDSAT ETM + dated March 12, 2003 resulted in depth interval of 0 m to (-22.5) m and R2 value of 0.931.

Page 19 of 33 | Total Record : 327

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