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COMPARATIVE TEST OF SEVERAL RAINFALL ESTIMATION METHODS USING HIMAWARI-8 DATA
No full textIndonesian society needs information on potential hydrometeorological disasters, therefore the development of rainfall estimation methods becomes an important research activities to support disaster risk reduction. Central Kalimantan were selected as research location for comparative test of rainfall estimation methods based on Himawari-8 IR1 (11μm) data, because it has area with cloud cover fairly intensive throughout the year. Some rainfall estimation methods tested in this research are AE, CST, CSTM, IMSRA. Non Linear Relation, and Non Linear Inversion. Each of these methods tends to have a weakness in the value of accuracy, so this research aims to determine the most accurate method to be applied in Palangkaraya (27 meters above sea level) city and Muratewe (60 meters above sea level) district in Central Kalimantan. The experiment was conducted during the period of highest rainfall in January and February 2016 by converting the temperature data cloud tops (IR1) into a precipitation with AE, CST, CSTM, IMSRA, Non Linear Relation and Non Linear Inversion method. Based on the results of quantitative analysis, it was known that IMSRA was the best method which can be applied in rainfall estimation in Muarateweh’s and Palangka Raya’s winter period. The Accuracy of all estimation methods decreased when it was applied in Palangka Raya at afternoon and in Muarateweh at night until early morning. The estimation method with the lowest score was the AE with an average MSE value > 90 and the best estimation method was IMSRA with MSE value <12
Front Pages IJReSES Vol. 13, No. 1(2016)
No full textFront Pages IJReSES Vol. 13, No. 1(2016)
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*Note: This cover is a revision version of the Editorial Committee Preface section cover that was uploaded on May 26, 201
ANALYSIS OF CRITICAL LAND IN THE MUSI WATERSHED USING GEOGRAPHIC INFORMATION SYSTEMS
No full textCritical land is a land that is no longer functioning as a regulator of water, agricultural production elements and environmental protection elements. Owing to the fact that the analysis of critical land is usually carried out manually, the probability of errors in processing (human error) is very high. This research utilizes the Geographic Information System (GIS) technology to analyze critical area in protected forest area of Musi Watershed. The application of GIS technology, enables the analysis of critical land according to standard of critical land criteria. The results show that the very critical level area in protected forest area of Musi Watershed is 1.7%. The dominant level is in critical potential area (53.34%)
THE EFFECT OF HYDROLOGIC RESPONSE UNIT ON CI RASEA WATERSHED STREAMFLOW BASED ON LANDSAT TM
No full text. This paper discusses spatial pattern of Hydrologic Response Unit (HRU), which is a unit formed of hydrological analysis, including geology and soil type, elevation and slope, and also land cover in 2009. This paper also discusses the impact of HRU on streamflow of Ci Rasea watershed, West Java. Ci Rasea watershed is located at the upstream part of Ci Tarum watersheds in West Java Province, Indonesia. This research used SWAT (Soil and Water Assessment Tool) model to obtain spatial HRU and river flow. The method used Landsat TM data for land cover and daily rainfall for river flow modeling. The results have shown spatial pattern of HRU which was affected by land cover, soil type and slope. In 2009, accumulated surface runoff and streamflow changes were spatially affected by HRU changes. The large amount accumulation of river flow discharge happened in HRU with landcover paddy field, silty clay soil, and flat slope. While the low discharge of river flow happened in HRU with plantation, clay soil, and slightly steep slopes as HRU dominant. It was found that accumulation of surface runoff in Ci Rasea watershed can be reduced by changing the land cover type in some areas with clay and slightly steep slope to become plantation area and the areas with sandy loam soil and flat slope can be used for paddy fields. Beside affected by HRU, the river flow discharge was also affected by the distance of sub watershed to the outlet. By using NS model and statistical t-student for calibration and validation, it was obtained that the accuracy of river flow models with HRU was 70%. It meant that the model could better simulate water flows of the Ci Rasea watershed.
 
A TWO-STEPS RADIOMETRIC CORRECTION OF SPOT-4 MULTISPECTRAL AND MULTITEMPORAL FOR SEAMLESS MOSAIC IN CENTRAL KALIMANTAN
No full textThis 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
ESTIMATION OF GROSS PRIMARY PRODUCTION USING SATELLITE DATA AND GIS IN URBAN AREA, DENPASAR
No full textRemote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster
STUDY OF OCEAN PRIMARY PRODUCTIVITY USING OCEAN COLOR DATA AROUND JAPAN
No full textOcean primary production is an important factor for determining the ocean's role in global carbon cycle. In recent years, much more chlorophyll-a concentration data in the euphotic layer were derived from the satellite ocean color sensors. The primary productivity algorithms have been proposed based on satellite chlorophyll measurements (Piatt, 1988; Morel, 1991) and other environmental parameters such as sea surfacetemperature or mixed layer depth (Behrenfeld and Falkowski, 1997; Esaias, 1996; Asanuma, 2002). In order to estimate integrated primary productivity in the whole water column, the vertical distribution of chlorophyll concentration below the sea surface should be reconstructed based on satellite data. In this paper, the vertical profile data of chlorophyll-a (Chl-a) measured around Japan Islands from 1974 to 1994 were reanalyzed based on the shifted-Gaussian shape proposed by Piatt et al (1988). Using this statistical model (neural network) and the photosynthesis irradiance parameters from Asanuma (2002), the distribution of primary productivity and its seasonal variation around Japan islands were estimated from SeaWiFS data, and the results were compared with in situ data and the other two models estimated from VGPM and mixed layer depth model
CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS
No full textThis paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia
The Occurrence Of Geomagnetic Storm In Solar Cycles 23 And 24 And Their Correlation With Cycle Peaks
No full textThe 23rd solar cycle occurred from 1996 to 2008, while the 24th solar cycle occurred from 2009 to 2020. Throughout the cycles there were various solar activities that caused geomagnetic storm such as high speed stream (HSS), co-rotating interaction region (CIR), and coronal mass ejection (CME). By using Disturbance storm time (Dst) index, we identified 243 storms during cycle 23 and 149 storms during cycle 24. The distribution of geomagnetic storms corresponds to the distribution of the solar cycle based on sunspot numbers. The cycle 23 exhibited higher activity with 84 strong to extreme storms compare to cycle 24 which had 22 strong to very strong storms. In both cycles, 65% moderate geomagnetic storms were caused by the high speed stream, whereas 85% of strong geomagnetic storms were caused by CMEs. In this study, both cycles exhibit distinct characteristic in producing geomagnetic storms. The low or high maximum phase of a cycle is not associated with the frequency occurrence of strong to extreme geomagnetic storms; both cycles show comparable results in this regard. However a longer declining phase of solar cycle has more impact on production of more moderate storm