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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. 10 No. 1 (2013)" : 8 Documents clear
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.
DEVELOPMENT OF LAND MOISTURE ESTIMATION MODEL USING MODIS INFRARED, THERMAL, AND EVI TO DETECT DROUGHT AT PADDY FIELD Dede Dirgahayu Domiri
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.a1842

Abstract

The drought phenomena often occurs in summer season at paddy field of Java island. The drought phenomena causes decrease in rice production. This research was aimed to develop a model of land moisture (LM) estimation at agricultural field, especially for paddy field based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data which has seven reflectance and two thermal bands. The method used in this study included data correction, advance processing of MODIS data (land indices transformation), extraction of land indices value at location of field survey, and regression analysis to make the best model of land moisture estimation. The result showed that reflectance of 2nd channel (NIR) and rasio of Enhanced Vegetation Index (EVI) with Land Surface Temperature (LST) had high correlation with surface soil moisture (% weight) at 0 – 20 cm depth with formula: LM = 15.9*EVI/LST – 0.934*R2 – 16.8 (SE=9.6%; R2 =76.2%). Based on the model, land moisture was derived spatially at the agricultural field, especially at paddy field to detect andmonitor drought events. Information of land moisture can be used as an indicator to detect drought condition and early growing season of paddy crop
DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN Tatik Kartika; Inggit Lolita Sari; Bambang Trisakti
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.a1843

Abstract

Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal classification in Sumatera Island using Landsat imagery in 25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was redetermined to obtain the final stratification zone. The indices was generated by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each zone described forest as 100, non-forest as 0, and uncertain forest between 50-99. The result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification.
UTILIZATION OF MULTI TEMPORAL SAR DATA FOR FOREST MAPPING MODEL DEVELOPMENT Bambang Trisakti; Rossi Hamzah
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.a1844

Abstract

Utilization of optical satellite data in tropical region was limited to free cloud cover. Therefore, Synthetic Aperture Radar (SAR) becomes an alternative solution for forest mapping in Indonesia due to its capability to penetrate cloud. The objective of this research was to develop a forestmapping model based on multi temporal SAR data. Multi temporal ALOS PALSAR data for 2007 and 2008 were used for forest mapping, and one year mosaic LANDSAT data in 2008 was used as references data to obtain training sample and to verify the final forest classification. PALSAR processing was done using gamma naught conversion and Lee filtering. Samples were made in forest and water area, and the statistical values of the each object were calculated. Some thresholds were determined based on the average and standard deviation, and the best threshold was selected to classify forest and water in 2008. It was assumed that forest could not change in 1-2 years period. The classification of forest, water, and the change were combined to produce final forest in 2008, and then it was visually verified with mosaic LANDSAT in 2008. The result showed that forest, water, and the change could be well classified using threshold method. The forest derived from PALSAR was visually consistent with forest appearance in LANDSAT and forest produced from INCAS. It has better performance than forest derived from INCAS for separating oil palm plantation from the forest.

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