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International Journal of Remote Sensing and Earth Sciences (IJReSES)

ijreses
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Published by Lembaga Penerbangan dan Antariksa Nasional

ISSN : 02166739 EISSN : 2549516X DOI : -

Core Subject : Science,

Earth & Planetary Sciences

International Journal of Remote Sensing and Earth Sciences (IJReSES) is expected to enrich the serial publications on earth sciences, in general, and remote sensing in particular, not only in Indonesia and Asian countries, but also worldwide. This journal is intended, among others, to complement information on Remote Sensing and Earth Sciences, and also encourage young scientists in Indonesia and Asian countries to contribute their research results. This journal published by LAPAN.

Arjuna Subject : -

Articles 297 Documents

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Front Pages IJReSES Vol. 13, No. 2(2016) Editorial Journal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 2 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Front Pages IJReSES Vol. 13, No. 2(2016) *Note: This cover is a revision of the Peer Reviewers section of the cover that was uploaded on May 26, 2017

IDENTIFICATION OF SUITABLE AREA FOR SEAWEED CULTURE IN BALI WATERS BASED ON REMOTE SENSING SATELLITE DATA Sayidah Sulma; Anneke K.S.Manoppo; Maryani Hartuti
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 5,(2008)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Mariculture is a part of marine and fisheries sector that has important contribution to achieve fisheries production target. Area suitability for mariculture information is necessary for coastal development management. The information can be derived using remote sensing satellite data and Geographic Information System (GIS). The aim of this research is to identify area suitability for seaweed culture in Bali waters by considering several water physical parameters. Those parameters are bathymetry, water sheltered area. Sea Surface Temperature (SST) and Total Suspended Matter (TSM). The parameters are extracted from Landsat 7-ETM and ALOS data. High temporal resolution data such as NOAA and Aqua/Terra MODIS are also used to monitor the fluctuation of SST and TSM in particular period, so the occurrence of parameter fluctuation can be anticipated. The physical parameters generated algorithms are referred to the algorithms reported in the previous research. The result shows that remote sensing data can be used to produce area suitability for seaweed culture, and Bali has 3728.87 hectare of the area. Key words: area suitability, Bali Waters, mariculture, remote sensing, seaweed.

Front Pages IJReSES Vol. 14, No. 2(2017) Journal Editor
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Front Pages IJReSES Vol. 14, No. 2(2017)

VERTICAL DISTRIBUTION OF CHLOROPHYLL-A BASED ON NEURAL NETWORK TAKAHIRO OSAWA; CHAO FANG ZHAO; I WAYAN Nuarsa; I KETUT SWARDIKA; YASUHIRO SUGIMORI
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 2(2005)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

An algorithm of estimating Vertical distribution of Chlorophyll-a (Chl-a) was evaluated based on Artificial Neural Networks (ANN) method in Hokkaido field in the northwest of Pacific Ocean. The algorithm applied to the data of SeaWiFS on OrbView-2 and AVHRR on NOAA off Hokkaido, has been applied on September 24, 1998 and September 28, 2001. Ocean color sensor provides the information of the photosynthetic pigment concentration for the upper 22% of the euphotic zone. In order to model a primary production in the water column derived from satellite, it is important to obtain the vertical profile of Chl-a distribution, because the maximum value of Chl-a concentration used to lie in the subsurface region. A shifted Gaussian model has been proposed to describe the variation of the chlorophyll-a (Chl-a) profile which consists of four parameters, i.e. background biomass (B0), maximum depth of Chl-a (zm), total biomass in the peak (h), and a measurement of the thickness or vertical scale of the peak (cr). However, these parameters are not easy to be determined directly from satellite data. Therefore, in the present study, an ANN methodology is used. Using in-situ data from 1974 to 1994 around Japan Islands, the above four parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longitude, and Julian days. The total of 6983 profiles of Chl-a and temperature are used for ANN. The correlation coefficients of these parameters are 0.79 (B0), 0.73 (h), 0.76 (cr) and 0.79 (zm) respectively. A site called A-linc off Hokkaido is used to evaluate Chl-a concentration in each depth. After comparing with in-situ data and ANN model, the results show good agreement relatively. Therefore, the ANN method is applicable and available tool to estimate primary production and fish resources from the space. Keywords : Ocean color, Chlorophyll-a (Chl-a), Vertical structure, Artificial Neural Networks (ANN).

A TWO-STEPS RADIOMETRIC CORRECTION OF SPOT-4 MULTISPECTRAL AND MULTITEMPORAL FOR SEAMLESS MOSAIC IN CENTRAL KALIMANTAN . Kustiyo; Ratih Dewanti; Inggit Lolita Sari
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 11, No 2 (2014)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

This research analyzed the radiometric correction method using SPOT-4 imageries to produce the same reflectance for the same land cover. Top of Atmosphere (TOA) method was applied in previous radiometric correction approach, this TOA approach was upgraded with the reflectance effect from difference satellite viewing angle. The 250 scene of Central Kalimantan SPOT-4 imageries from 2006 until 2012 with varies viewing angle was used. This research applied two-step approaches, the first step is TOA correction, and the second step is normalization using a linear function of reflectance and satellite viewing angle. Gain and offset coefficient of this linear function was calculated using an iterative approach to producing the same reflectance in the forest area. The target of iterative processed is to minimize the standard deviation of a digital number from a forest area in the selected region. The result shows that the standard deviation of a digital number from a forest area in the two steps approach are 8.6, 16.5, and 16.8 for band 1, band 3 and band 4. These values are smaller compared with the standard deviation of digital number result from TOA approach are 15.0, 28,3 and 34.7 for band 1, band 3 and band 4. Decreasing the standard deviation shows the homogeneity of forest reflectance that could be seen in the seamless result. This algorithm can be applied for making seamless SPOT-4 mosaic whole of Indonesia.

COMPARISON RESULT OF DEM GENERATED FROM ASTER STEREO DATA AND SRTM Bambang Trisakti; Ita Carolita
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 4,(2007)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

This paper explains a method to generated DEM (Digital Elevation Model) from ASTER (Advanced Spaceborn Thermal Emission and Reflection Radiometer) stereo data and evaluates the generation of ASTER DEM and SRTM (Shuttle Radar Topography Mission) DEM with 90 m spatial resolution. ASTER DEM is generated from 3n (nadir looking) and 3b (backward looking) level 1b, with 10 ground control points (XYZ coordinate)derived from ASTER RGB 321 geometric-corrected image and SRTM DEM. Almost all tie points are collected automatically and several tie points is added manually. The triangulation and DEM extraction process are made automatically using ERDAS Imagine Software. DEM evaluation is carried out by comparing between ASTER DEM and SRTM DEM in the height distribution of vertical and horizontal transect lines and the height value of the whole DEM image. The process is continued by analyzing the height differences between ASTER DEM and SRTM DEM. The results shows thatASTER DEM has 15 m spatial resolution with height differnces less than 30 m for about 67 percent of total area, and absolute mean error is 27 m (compared with SRTM DEM) This absolute mean error is large enough, because the GCPs (Ground Control Point) used in this study are only in a small amount and most of study area is in the high terrain area (mountainous area) with dense vegetation coverage. Keyword: DEM, ASTER, height difference, GCP.

LINEAMENT DENSITY INFORMATION EXTRACTION USING DEM SRTM DATA TO PREDICT THE MINERAL POTENTIAL ZONES Udhi C. Nugroho; Arum Tjahjaningsih
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 1 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Utilization of remote sensing in geology is based on some identification of main parameters. They were the relief or morphology, flow patterns, and lineament. So it was necessary to study extraction method based on those parameters. This study aimed to obtain lineament density zone in the Geumpang area, Aceh, associated with mineral resource potential. Information of lineament density using remote sensing data was expected to help solve the problems that arised in the activities of early exploration, the difficulty of finding the prospect areas, so that the activities of pre-exploration always required a wide area and required a long time to determine the location of mineral prospect areas, it would have a direct impact on the financial of exploration activities. The used data was Landsat 8 and DEM SRTM of 30 m. The used method was processing of shaded relief on DEM data with the azimuth angle 0o, 45o, 90o, and 135o, then the result of hill shade process was done overlay, so DEM seen from all different azimuth angles. The results of the overlay were processed using the algorithm LINE with parameters such as the radius of the filter in pixels (RADI) 60, the threshold for edge gradient (GTHR) 120, the threshold for the curve length (LTHR) 100, the threshold for line fitting error (FTHR) 3, threshold for angular (ATHR) 30, and the threshold for linking distance (DTHR) 100. Vector lineament data from LINE algorithm process then performed density analysis to obtain lineament density zoning. Results from the study showed that the area has a high density lineament associated with mineral potency, so it was useful for exploration activities to minimize the survey area.

ANALYSIS OF SPOT-6 DATA FUSION USING GRAM-SCHMIDT SPECTRAL SHARPENING ON RURAL AREAS Danang Surya Candra
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 2 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Image fusion is a process to generate higher spatial resolution multispectral images by fusion of lower resolution multispectral images and higher resolution panchromatic images. It is used to generate not only visually appealing images but also provide detailed images to support applications in remote sensing field, including rural area. The aim of this study was to evaluate the performance of SPOT-6 data fusion using Gram-Schmidt Spectral Sharpening (GS) method on rural areas. GS method was compared with Principle Component Spectral Sharpening (PC) method to evaluate the reliability of GS method. In this study, the performance of GS was presented based on multispectral and panchromatic of SPOT-6 images. The spatial resolution of the multispectral (MS) image was enhanced by merging the high resolution Panchromatic (Pan) image in GS method. The fused image of GS and PC were assessed visually and statistically. Relative Mean Difference (RMD), Relative Variation Difference (RVD), and Peak Signal to Noise Ratio (PSNR) Index were used to assess the fused image statistically. The test sites of rural areas were devided into four main areas i.e., whole area, rice field area, forest area, and settlement. Based on the results, the visual quality of the fused image using GS method was better than using PC method. The color of the fused image using GS was better and more natural than using PC. In the statistical assessment, the RMD results of both methods were similar. In the RVD results, GS method was better then PC method especially in band 1 and band 3. GS method was better than PC method in PSNR result for each test site. It was observed that the Gram-Schmidt method provides the best performance for each band and test site. Thus, GS was a robust method for SPOT-6 data fusion especially on rural areas.

VARIABILITY AND VALIDATION OF SEA SURFACE TEMPERATURE ESTIMATED BY PATHFINDER ALGORITHM OF NOAA-AVHRR SATELLITE IN THE NORTH PAPUA WATERS Bisman Nababan; Bidawi Hasyim; Hilda I.N. Bada
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 8, (2011)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Variability and validation of sea surface temperatures (SST) in north Papua waters were conducted using SST estimated by Pathfinder algorithm of NOAA AVHRR satellite and SST measurements from TAO buoy in 2001-2009. Satellite data (SST Pathfinder) were daily, weekly, and monthly composite with 4x4 km2 resolution and downloaded from http://poet.jpl.nasa.gov. In situ data (SST measurement from buoy TAO) were measured at a depth of 1.5 m and recorded every hour (http://www.pmel.noaa.gov/tao_deliv). The in situ data then converted into daily, weekly, and monthly average data. In general, the SST values of both satellite and in situ SST in the north Papua waters ranged between 27.10 - 31.90 °C. During the east season (June-September), SST values (27.90-31.90 °C) were generally higher than the SST values ( 27.10-30.13 °C) during the west season (December-February). In general, the SST values both day-time and night-time from in situ and the satellite measurements showed no significant differences except in waters close to the shore. The results also showed that the coefficient of determination values (R2) between the satellite and the in situ SST measurements were relatively low (65%) and up to 5% of RMSE. The relatively low correlation between in situ dan satellite SST measurements may be due to high cloud coverage (90-96%) in the north Papua waters so that SST satellite data become less representative of the in situ data. These results also indicated that the Pathfinder algorithm can not be used as a valid estimate of SST NOAA AVHRR satellite for the north Papua waters. Keywords: SST Pathfinder, NOAA AVHRR, Validation, TAO buoy, North Papua Waters

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 (IJReSES) Vol 12, No 1 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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.

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