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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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COMBINATION OF SPECKLE DIVERGENCE AND NEIGHBORHOOD ANALYSIS TO CLASSIFY SETTLEMENT FROM TERASAR-X DATA M. Rokhis Khomarudin; Agung Indrajit
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 1 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1820

Abstract

Abstract. The objectives of this research were to develop and improve methods for determination of settlements area with focus on synthetic aperture radar (SAR) data. Remote sensing settlement classification has made great progress, both for optical and radar data as well for their fusion. Yet, in radar imagery, settlement classification still contains some problems. Several studies on application of radar imagery have been conducted using techniques such as textural analysis, multi-temporal analysis, statistical model, spatial indexes, and object-based classification. Most of the development methods have several problems in the specific area especially in the tropical country. Several studies also showed that settlement classification accuracies were just below 60%. This was not sufficient enough to classify settlement areas using SAR imagery. Therefore, in this research, we proposed a new method i.e., the combination of the speckle divergence and the neighborhood analysis. The proposed method was applied to classify settlement area in Cilacap and Padang Districts of Indonesia. The results showed that the proposed method produced a good accuracy i.e., 85.5% for Cilacap Districts and 78.1% for Padang Districts.
ANALYSIS OF SEA SURFACE HEIGHT ANOMALY CHARACTERISTICS BASED ON SATELLITE ALTIMETRY DATA (CASE STUDY: SEAS SURROUNDING JAVA ISLAND) Sartono Marpaung; Wawan K. Harsanugraha
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 2 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2611

Abstract

Sea surface height anomaly is a oceanographic parameter that has spatial and temporal variability. This paper aims to determine the characters of sea surface height anomaly in the south and north seas of Java Island. To find these characters, a descriptive analysis of monthly anomaly data is performed spatially, zonally and temporally. Based on satellite altimetry data from 1993 to 2010, the analysis shows that the average of sea surface height anomaly varies, ranging from -15 cm to 15 cm. Spatially and zonally, there are three patterns that can be concidered as sea surface height anomaly characteristics: anomaly is higher in coastal areas than in open seas, anomaly is lower in coastal areas than in open seas and anomaly in coastal area is almost the same as in open seas. The first and second patterns occur in the south and north seas of Java Island. The third pattern occurs simultaneously in south and north seas of Java Island. Characteristics of temporal anomaly have a sinusoidal pattern in south and north seas of Java Island.
LAND COVER CLASSIFICATION ALOS AVNIR DATA USING IKONOS AS REFERENCE Bambang Trisakti; Dini Oktavia Ambarwati
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 1 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1822

Abstract

Abstract. Advanced Land Observation Satellite (ALOS) is a Japanese satellite equipped with 3 sensors i.e., PRISM, AVNIR, and PALSAR. The Advanced Visible and Near Infrared Radiometer (AVNIR) provides multi spectral sensors ranging from Visible to Near Infrared to observe land and coastal zones. It has 10 meter spatial resolution, which can be used to map land cover with a scale of 1:25000. The purpose of this research was to determineclassification for land cover mapping using ALOS AVNIR data. Training samples were collected for 11 land cover classes from Bromo volcano by visually referring to very high resolution data of IKONOS panchromatic data. The training samples were divided into samples for classification input and samples for accuracy evaluation. Principal component analysis (PCA) was conducted for AVNIR data, and the generated PCA bands were classified using Maximum Likehood Enhanced Neighbor method. The classification result was filtered and re-classed into 8 classes. Misclassifications were evaluated and corrected in the post processing stage. The accuracy of classifications results, before and after post processing, were evaluated using confusion matrix method. The result showed that Maximum Likelihood Enhanced Neighbor classifier with post processing can produce land cover classification result of AVNIR data with good accuracy (total accuracy 94% and kappa statistic 0.92). ALOS AVNIR has been proven as a potential satellite data to map land cover in the study area with good accuracy.
UTILIZATION OF SAR AND EARTH GRAVITY DATA FOR SUB BITUMINOUS COAL DETECTION Atriyon Julzarika; Kuncoro Teguh Setiawan
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 2 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2612

Abstract

Remote sensing data can be used for geological and mining applications, such as coal detection. Coal consists of five classes of Anthracite, Bituminous, Sub-Bituminous, Lignite coal and Peat coal. In this study, the type of coal that is discussed is Sub bituminous, Lignite coal, and peat coal. This study aims to detect potential sub bituminous using Synthetic Aperture Radar (SAR) data, and earth gravity. One type of remote sensing data to detect potential sub bituminous, lignite coal and peat coal are SAR data and satellite data Geodesy. SAR data used in this study is ALOS PALSAR. SAR data is used to predict the boundary between Lignite coal with Peat coal. The method used is backscattering. In addition to the SAR data is also used to make height model. The method used is interferometry. Geodetic satellite data is used to extract the value of the earth gravity and geodynamics. The method used is physical geodesy. Potential sub-bituminous coal can be known after the correlation between the predicted limits lignite coal-peat coal by the earth gravity, geodynamics, and height model. Volume predictions of potential sub bituminous can be known by calculating the volume using height model and transverse profile test. The results of this study useful for preliminary survey of geological in mining exploration activities.
GEOSTATISTICAL TEST USING LEAST SQUARE ADJUSTMENT COMPUTATION TO OBTAIN THE REDUCTION PARAMETER FOR DSM TO DEM CONVERSION (Study of Case: Cilacap, Indonesia) Atriyon Julzarika
International Journal of Remote Sensing and Earth Sciences Vol. 7 No. 1 (2010)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2010.v7.a1538

Abstract

Abstract. ALOS satellite is one of the natural resources satellites that can be used for 3Dmodel applications. The problems of 3D model generation based on satellite imagery arethe model always in Digital Surface Model (DSM), not in Digital Elevation Model (DEM).The reference system of 3D model that are produced by ALOS satellite image is still assurface for z axis, whereas x axis and y axis has been closed to 2D reference system insome certain datum and system of map projection. Therefore, it needs a research to observethe accuracy and the precision of ALOS satellite data using a least square adjustment inparameter methods. The results of this research will be used as a reference for next researchto find a way for changing DSM from ALOS satellite image to be DEM automatically.
COMPARISON OF THE VEGETATION INDICES TO DETECT THE TROPICAL RAIN FOREST CHANGES USING BREAKS FOR ADDITIVE SEASONAL AND TREND (BFAST) MODEL Yahya Darmawan; Parwati Sofan
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 1 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1823

Abstract

Remotely sensed vegetation indices (VI) such as the Normalized Difference Vegetation Index (NDVI) are increasingly used as a proxy indicator of the state and condition of the land cover/vegetation, including forest. However, the Enhanced Vegetation Index (EVI) on the outcome of forest change detection has not been widely investigated. We compared the influence of using EVI and NDVI on the number and time of detected changes by applying Breaks for Additive Seasonal and Trend (BFAST), a change detection algorithm. We used MODIS 16-day NDVI and EVI composite images (April 2000-April 2012) of three pixels (pixels 352, 378, and 380) in the tropical peat swamp forest area around the flux tower of Palangka Raya, Central Kalimantan. The results of BFAST method were compared to the Normalized Difference Fraction Index (NDFI) maps and the maps were validated by the hotspot of the Infrastructure and Operational MODIS-Based Near Real-Time Fire(INDOFIRE). Overall, the number and time of changes detected in the three pixels differed with both time series data because of the data quality due to the cloud cover. Nonetheless, we found that EVI is more sensitive than NDVI for detecting abrupt changes such as the forest fires of August 2009-October 2009 that occurred in our study area and it was verified by the NDFI and the hotspot data. Our results demonstrated that the EVI for forest monitoring in the tropical peat swamp forest area which is covered by intense cloud cover is better than that NDVI. Nonetheless, further research with improving spatial resolution of satellite images for application of NDFI is highly recommended.
AN EFFECTIVE INFORMATION SYSTEM OF DROUGHT IMPACT ON RICE PRODUCTION BASED ON REMOTE SENSING Rizatus Shofiyati; Wataru Takeuchi; Soni Darmawan; Parwati Sofan
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 2 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2613

Abstract

Long droughts experienced in the past are identified as one of the main factors in the failure of rice production. In this regard, special attention to monitor the condition is encouraged to reduce the damage. Currently, various satellite data and approaches can withdraw valuable information for monitoring and anticipating drought hazards. MODIS, MTSAT, AMSR-E, TRMM and GSMaP have been used in this activity. Meteorological drought index (SPI) of the daily and monthly rainfall data from TRMM and GSMaP have analyzed for last 10-year period. While, agronomic drought index has been studied by observing the character of some indices (EVI, VCI, VHI, LST, and NDVI) of sixteen-day and monthly MODIS, MTSAT, and AMSR-E data at a period of 4 years. Network for satellite data transfer has been built between LAPAN (data provider), ICALRD (implementer), IAARD Cloud Computing, University of Tokyo (technical supporter), and NASA. Two information system have been developed: 1) agricultural drought using the model developed by LAPAN, and 2) meteorological drought developed by Takeuchi (University of Tokyo).The accuracy study using quantitative method for LAPAN model uses VHI is 60% (Kappa 0,44), while that of for University of Tokyo model uses qualitative model with KBDI value 500-600 shows an early indication of drought for paddy field. This will help the government or field officers in rapid management actions for the indicated drought area.This paper describes the implementation and dissemination of drought impact monitoring model on the area of rice production center using an integrated information system satellite based model. The two developed information systems are effective for spatially dissemination of drought information.
Back Pages IJReSES Vol. 11, No. 2(2014) Editorial Secretariat
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 2 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Back Pages IJReSES Vol. 11, No. 2(2014)
INDENTIFYING PATTERNS OF SATTELITE IMAGERY USING AN ARTIFICIAL NEURAL NETWORK Iskhaq Iskandar; Azhar K. Affandi; Dedi Setiabudidaya; Muhammad Irfan; Wijaya Mardiansyah; Fadli Syamsuddin
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 1 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1824

Abstract

An artificial neural network analysis based on the self-organizing map (SOM) was used to examine patterns of satellite imagery. This study used 3 × 4 SOM array to extract patterns of satellite-observed chlorophyll-a (chl-a) along the southern coast of the Lesser Sunda Islands from 1998 to 2006. The analyses indicated two characteristic spatial patterns, namely the northwest and the southeast monsoon patterns. The northwest monsoon pattern was characterized by a low chl-a concentration. In contrast, the southeast monsoon pattern was indicated by a high chl-a distributed along the southern coast of the Lesser Sunda Islands. Furthermore, this study demonstrated that the seasonal variations of those two patterns were related to the variations of winds and sea surface temperature (SST). The winds were predominantly southeasterly (northwesterly) during southeast (northwest) monsoon, drived offshore (onshore) Ekman transport and produced upwelling (downwelling) along the southern coasts of the Lesser Sunda Islands. Consequently, upwelling reduce dSST and helped replenish the surface water nutrients, thus supporting high chl-a concentration. Finally, this study demonstrated that the SOM method was very useful for the identifications of patterns in various satellite imageries.
ESTIMATION OF RADIOMETRIC PERFORMANCE OF ELEKCTRO-OPTICAL IMAGING SENSOR OF LOW EARTH EQUATORIAL ORBIT LAPAN SATTELITE Ahmad Maryanto; Andy Indradjad; Dinari Nikken Sulastrie Sirin; Ayom Widipaminto
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 1 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1825

Abstract

Study of spectro-radiometric performance of electro-optical imager which is planned to be launched on low earth equatorial orbit LAPAN satellite was conducted through simulative calculation of image irradiance and its associated generated voltage at the image detector output. Simulative calculation was applied to three scenarios of selected spectral bands. Those spectral bands were selected spectra (1), which consisted of spectral bands B = (390-540 and 790-900) nm, G = (470-610 and 700-900 ) nm, and R = (590-650 and 650-900) nm; selected spectra (2) consisted of B1 = (390-540) nm, G1 = (470-610) nm, and R1 = (590-650) nm; and selected spectra (3) consisted of B1(Green) = (525-605) nm, B2(Red) = (630-690) nm, and B3(NIR) = (750-900) nm, on three scenarios of optical aperture or f-number (N) 2.8, 4.0, and 5.6. Green grasses, dry grasses, and conifers were examples of the imaged target, chosen as representation of vegetations. Kodak KLI-8023 was used as the optical detector. The integration time was assumed 3 miliseconds which correspond to about 20 m ground sampling distance (GSD). Solar zenith angle were varying from 90ï‚° (early morning) to 0ï‚° (solar noon). The results showed that option (3) of selected spectra, as proposed for pushbroom imager of LAPAN satellite, was relatively accepted to be implemented and complemented with f-number 4.0 of optical system used. Whereas simulation RGB color displayed composed by R = B2(Red), G = B3(NIR), B = B1(Green) also showed a greenish color sense as expected for vegetation imaged target.

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