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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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DEVELOPING TROPICAL LANDSLIDE SUSCEPTIBILITY MAP USING DINSAR TECHNIQUE OF JERS-1 SAR DATA Ilham Alimuddin; Luhur Bayuaji; Haeruddin C. Maddi; Josaphat Tetuko Sri Sumantyo; Hiroaki Kuze
International Journal of Remote Sensing and Earth Sciences Vol. 8 (2011)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2011.v8.a1739

Abstract

Comprehensive information in natural disaster area is essential to prevent and mitigate people from further damage that might occur before and after such event. Mapping this area is one way to comprehend the situation when disaster strikes. Remote sensing data have been widely used along with GIS to create a susceptibility map. The objective of this study was to develop existing landslides susceptibility map by integrating optical satellite images of Landsat ETM and ASTER with Japanese Earth Resource Satellites (JERS-1) Synthetic Aperture Radar (SAR) data complemented by ground GPS and feature measurement into a Geographical Information Systems (GIS) platform. The study area was focused on a landslide event occurred on 26 March 2004 in Jeneberang Watershed of South Sulawesi, Indonesia. Change detection analysis was used to extract thematic information and the technique of Differential SAR Interferometry (DInSAR) was employed to detect slight surface displacement before the landslide event. The DInSAR processed images would be used to add as one weighted analysis factor in creating landslide susceptibility map. The result indicated that there was a slight movement of the slope prior to the event of landslide during the JERS-1 SAR data acquisition period of 1993-1998. Keywords: Optical Images, JERS-1 SAR, DInSAR, Tropical Landslide, GIS, Susceptibility Map 1. Introduction Recently, natural disasters increased in terms of frequency, complexity, scope, and destructive capacity. They have been particularly severe during the last few years when the world has experienced several large-scale natural disasters such as the Indian Ocean earthquake and tsunami; floods and forest fires in Europe, India and China, and drought in Africa (Sassa, 2005). Mapping such natural disaster areas is essential to prevent and mitigate people from further damage that might occur before and after such event. In Indonesia in p.articular, in these recent years natural disasters occurred more frequently compared to the last decade (BNPB, 2008). Once within a month in 2011, in three different islands, Indonesia was stricken by earthquake, tsunami, flash floods, and volcanic eruptions with severe fatalities to the people and environment. It was obvious that Indonesia was prone to natural disaster due to its position of being squeezed geologically by three major world plates and this fact makes Indonesia one of the most dangerous
IDENTIFICATION OF LAND SURFACE TEMPERATURE DISTRIBUTION OF GEOTHERMAL AREA IN UNGARAN MOUNT BY USING LANDSAT 8 IMAGERY Udhi C. Nugroho; Dede Dirgahayu Domiri
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 2 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2708

Abstract

Indonesia located at the confluence of Eurasian tectonic plate, Australian tectonic plate and the Pacific tectonic plate. Therefore, Indonesia has big geothermal potential. One of the areas that has geothermal potential is Ungaran Mount. Remote sensing technology can have a role in geothermal exploration activity to map the distribution of land surface temperatures associated with geothermal manifestations. The advantages of remote sensing are able to get information without having to go directly to the field with a large area, and it takes quick, so that the information can be used as an initial reference exploration activities. This study aimed to obtain the distribution of land surface temperature as a regional analysis of geothermal potential. The method of this research was a correlation of brightness temperature (BT) Landsat 8 with land surface temperature (LST) MODIS. The results of correlation analysis showed the R2 value was equal to 0.87, it shows that between BT Landsat 8 and LST MODIS has a very high correlation. Based on Landsat 8 LST imagery correction, the average of fumarole temperature and hot spring is 240C. Fumarole and hot spring are located in dense vegetation land which has average temperature around 26.90C. Land surface temperature Landsat 8 can not be directly used to identify geothermal potential, especially in the dense vegetation area, due to the existence of dense vegetation which can absorb heat energy released by geothermal surface feature.
DETECTION OF FOREST FIRE, SMOKE SOURCE LOCATIONS IN KALIMANTAN DURING THE DRY SEASON FOR THE YEAR 2015 USING LANDSAT 8 FROM THE THRESHOLD OF BRIGHTNESS TEMPERATURE ALGORITHM Kustiyo; Ratih Dewanti; Inggit Lolitasari
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 2 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2692

Abstract

Almost every dry season, there are large forest/land fires in several regions in Indonesia, especially in Kalimantan and Sumatra in the dry season of August to September 2015 a forest fire in 6 provinces namely West Kalimantan, Central Kalimantan, South Kalimantan, Riau, Jambi, and South Sumatra. Even some parties proposed that the Government of Indonesia declares them as a national disaster. The low-resolution remote sensing data have been widely used for monitoring the occurrence of forest/land fires (hotspots), and mapping of burnt scars. The hotspot detection was done by utilizing the data of NOAA-AVHRR and MODIS data which have a lower spatial resolution (1 km). In order to increase the level of detail and accuracy of product information, this research is done by using Landsat 8 TIRS (Thermal Infrared Sensor) band which has a greater spatial resolution of 100 m. The purpose of this research is to find and to determine the threshold value of the brightness temperature of the TIRS data to identify the source of fire smoke. The data used is the Landsat 8 of several parts of Borneo during the period of 24 August to 18 September 2015 recorded by the LAPAN's receiving station. Landsat - 8 TIRS band was converted into brightness temperature in degrees Celsius, then dots in a region that is considered the source of the smoke if the temperature of each pixel in the region > 43oC, and given the attributes with the highest temperatures of the pixels in the region. The source of the smoke was obtained through visual interpretation of the objects in the multispectral Natural Color Composite (NCC) and True Color Composite (TCC) images. Analysis of errors (commission error) is obtained by comparing the temperature detected by TIRS band with a visual appearance of the source of the smoke. The result of the experiment showed that there were detected 9 scenes with high temperatures over 43oC from the 27 scenes Kalimantan Landsat 8 data, which include 153 sites. The accuracy (commission error) of identification results using temperature ≥ 51°C is 0%, temperature ≥ 47°C is 10%, and temperature ≥ 43°C is 30.5%.
Back Pages IJReSES Vol. 12, No. 2(2015) Editorial Journal
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 2 (2015)
Publisher : BRIN

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

Abstract

Back Pages IJReSES Vol. 12, No. 2(2015)
DEM GENERATION FROM STEREO ALOS PRISM AND ITS QUALITY IMPROVEMENT Bambang Trisakti; Atriyon Julzarika
International Journal of Remote Sensing and Earth Sciences Vol. 8 (2011)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2011.v8.a1740

Abstract

Digital elevation mode (DEM) is important data for supporting many activities. One of DEM generation methods is photogrametry of optical stereo data based on image matching and collinear correlation. The problem of DEM from optical stereo data is bullseye due to low contrast in relatively flat area and cloud cover. The research purpose is to generate DEM from ALOS PRISM stereo data level 1B2R and improve the quality of the DEM. DEM was generated using Leica Photogrametry Suite (LPS) software. The study area is located in Sragen district and its vicinity. The process needed three dimension of Ground Control Point (GCP) XYZ, as input data for collinear correlation. Ground measurement was conducted using differential GPS to collect 30 GCPs that used for input (21 GCPs) and for accuracy evaluation (9 GCPs). The generated DEM has good detail (10 m), but it has bullseye which mostly occurred in relatively flat area. The quality improvement was carried out by combining the DEM with SRTM DEM (30 m) using DEM fusion method. Both DEMs were processed for geoids correction (EGM 2008), co-registration and histogram normalization. The fusion method was conducted by considering height error map (HEM) of each DEM. The quality of fused DEM was evaluated by comparing HEM, the number of bullseye, and vertical accuracy before and after the fusion. The result shows that DEM fusion can preserve detail information of the DEM and significantly reduce the bullseye (decreasing more than 66% of bullseye). It also shows the improvement (from 7.6 m to 7.3 m) of vertical accuracy.
DERIVING INHERENT OPTICAL PROPERTIES FROM MERIS IMAGERY AND IN SITU MEASUREMENT USING QUASI-ANALYTICAL ALGORITHM Wiwin Ambarwulan; Widiatmaka; Syarif Budhiman
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1835

Abstract

The paper describes inherent optical properties (IOP) of the Berau coastal waters derived from in situ measurements and Medium Resolution Imaging Spectrometer (MERIS) satellite data. Field measurements of optical water, total suspended matter (TSM), and chlorophyll-a (Chl-a) concentrations were carried out during the dry season of 2007. During this periode, only four MERISdata were coincided with in situ measurements on 31 August 2007. The MERIS top-of-atmosphere radiances were atmospherically corrected using the MODTRAN radiative transfer model. The in situ optical measurement have been processed into apparent optical properties (AOP) and sub surface irradiance. The remote sensing reflectance of in situ measurement as well as MERIS data were inverted into the IOP using quasi-analytical algorithm (QAA). The result indicated that coefficient of determination (R 2) of backscattering coefficients of suspended particles (bbp) increased with increasing wavelength, however the R2 of absorption spectra of phytoplankton (aph) decreased with increasing wavelength.
LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE Katmoko Ari Sambodo; Novie Indriasari
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1836

Abstract

Land cover classification is one of the extensive used applications in the field of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because its capability to penetrate through clouds, haze, and smoke. This study showed on an alternative method for land cover classification of ALOS-PALSAR data using Support Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order to minimize the presence of outliers in the training samples and to increase inter-class separabilities, prior to classification, a training sample selection and evaluation technique by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes discriminated: forest, rubber plantation, mangrove & shrubs with trees, oilpalm & coconut, shrubs, cropland, bare soil, settlement, and water. Overall accuracy of 87.79% was obtained, with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%.
MULTITEMPORAL LANDSAT DATA TO QUICK MAPPING OF PADDY FIELD BASED ON STATISTICAL PARAMETERS OF VEGETATION INDEX (CASE STUDY: TANGGAMUS, LAMPUNG) I Made Parsa; Dede Dirgahayu
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1838

Abstract

Paddy field has unique characteristics that distinguish it from other plants. Before it planting, paddy field is always flooded so that the appearance is dominated by water (aqueous phase). Within the growth of rice, field conditions will be increasingly dominated by greenish rice plants.While at the end, the rice plants will turn yellow indicating for harvesting. During flooding stage, the normalized difference vegetation index (NDVI) of pady field is negative. The negative value of NDVI of paddy field will ultimately increase to the maximum value at the maximum vegetative growth. TheNDVI of paddy field will decrease from generative phase until harvest and after harvest. The objective of this study was to perform the vegetation index analyses for multitemporal Landsat imagery of paddy field. The results showed that the difference of vegetation index values (maximum - minimum)of paddy field were greater than the difference of vegetation index values of other land uses. Such differences values can be used as indicator to map land for rice. The evaluation results with reference data showed that the mapping accuracy (overall accuracy) was of 87.4 percent.
FISHPOND AQUACULTURE INVENTORY IN MAROS REGENCY OF SOUTH SULAWESI PROVINCE Yennie Marini; Emiyati; Teguh Prayogo; Rossi Hamzah; Bidawi Hasyim
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1839

Abstract

Currently, fishpond aquaculture becomes an interesting business for investors because of its profit, and a source of livelihood for coastal communities. Inventory and monitoring of fishpond aquaculture provide important baseline data to determine the policy of expansion and revitalization of the fishpond. The aim of this research was to conduct an inventory and monitoring of fishpond area inMaros regency of South Sulawesi province using Satellite Pour l’Observation de la Terre (SPOT -4) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Apeture Radar (PALSAR). SPOT image classification process was performed using maximum likelihood supervised classification method and the density slice method for ALOS PALSAR. Fishpond area from SPOT data was 9693.58 hectares (ha), this results have been through the process of validation and verification by the ground truth data. The fishponds area from PALSAR was 7080.5 Ha, less than the result from SPOT data. This was due to the classification result of PALSAR data showing someobjects around fishponds (dike, mangrove, and scrub) separately and were not combined in fishponds area calculation. Meanwhile, the result of SPOT -4 image classification combined object around fishponds area.
GROWTH RATE AND PRODUCTIVITY DYNAMICS OF ENHALUS ACOROIDES LEAVES AT THE SEAGRASS ECOSYSTEM IN PARI ISLANDS BASED ON IN SITU AND ALOS SATELLITE DATA Agustin Rustam; Dietriech Geoffrey Bengen; Zainal Arifin; Jonson Lumban Gaol; Risti Endriani Arhatin
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1847

Abstract

Enhalus acoroides is the largest population of seagrasses in Indonesia. However, growth rate and productivity analyses of Enhalus acoroides and the use of satellite data to estimate its the productivity are still rare. The goal of the research was to analyze the growth rate, productivity rate,seasonal productivity of Enhalus acoroides in Pari island and its surroundings. The study was divided into two phases i.e., in situ measurments and satellite image processing. The field study was conducted to obtain the coverage percentage, density, growth rate, and productivity rate, while the satellite image processing was used to estimate the extent of seagrass. The study was conducted in August 2011 toJuly 2012 to accommodate all four seasons. Results showed that the highest growth rate andproductivity occurred during the transitional season from west Monsoon to the east Monsoon of 5.6cm/day and 15.75 mgC/day, respectively. While, the lowest growth rate and productivity occurred during the transition from east Monsoon to the west Monsoon of 3.93 cm/day and 11.4 mgC/day, respectively. Enhalus acoroides productivity reached its maximum during the west Monsoon at 1081.71 mgC/day/m2 and minimum during east Monsoon with 774.85 mgC/day/m2 . Based on ALOS data in 2008 and 2009, total production of Enhalus acoroides in the proximity of Pari islands reached its maximum occur during the west Monsoon (48.73 – 49.59 Ton C) and minimum during transitional season (16.4-16.69 Ton C). Potential atmospheric CO2 absorption by Enhalus acoroides in Pari island was estimated at the number 60.14 – 181.82 Ton C.

Page 23 of 33 | Total Record : 327

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