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Yulianto F.,Bogor Agricultural University | Yulianto F.,Indonesian National Institute of Aeronautics and Space LAPAN | Tjahjono B.,Bogor Agricultural University | Anwar S.,Bogor Agricultural University
Arabian Journal of Geosciences | Year: 2015

Volcanic eruption hazard mapping is very important to fulfill information needs to prepare for emergency situations. Rapid mapping is one of the steps necessary for emergency response in disaster mitigation effort. Limitations of time, data, and knowledge mapping techniques can be a problem when performing the operational work. In this research, the combinations of the Monte Carlo algorithm and energy cone model have been applied to reproduce the probability of block-and-ash type of pyroclastic flows of the 2010 eruption of Merapi volcano. These approaches are applied as an alternative method of rapid, objective, and reproducible for hazard mapping of pyroclastic flows. In addition, the method of Interferometry Synthetic Aperture Radar (InSAR) has been used in this research to update the digital elevation model (DEM) data. The availability of DEM data updates was required as input of topography, which determines the pyroclastic flows. This research has produced DEM PALSAR 2010 pre-eruption of Merapi volcano, with a spatial resolution of 30 m. The result of the vertical accuracy calculations was performed using the root mean square error (RMSE) approach, which show the value of RMSE at 9.08 m. There are four eruptive phases, which have been used for the simulation scenarios, namely: phase 1 (period 26–29 October 2010), phase 2 (period 30 October–3 November 2010), phase 3 (period 4–5 November 2010), and phase 4 (period 6–23 November 2010). The results of the Monte Carlo algorithm to reproduce the effects of the 2010 eruption of Merapi volcano, has show that the height correction (hc) on the DEM data gives effect to the probability distribution of pyroclastic flows. At the hc = 1, 2, 3, 4, and 5 m, the value of overall accuracy based on cross-correlation matrix of the reference map are 76.38, 77.38, 77.00, 77.75, and 77.25 %, respectively. In these scenarios, the hc = 4 m can give the best accuracy. Meanwhile, the results of the comparison of the results of the difference of the average run out on the energy cone model obtained from the reference map is 843 m. © 2014, Saudi Society for Geosciences.

Adipranata R.,Petra Christian University | Budhi G.S.,Petra Christian University | Setiahadi B.,Indonesian National Institute of Aeronautics and Space LAPAN
International Journal of Multimedia and Ubiquitous Engineering | Year: 2013

The sun is the unlimited energy source for life on the earth. However, besides as the energy source, the sun also gives disruptions to the universe around the earth and also to the life on the earth. Sources of the disruptions from the sun are flares and Coronal Mass Ejection/CME. Both of those disruptions in general come from group of sunspots. With the growing of dependency of human life with modern technology, either facility on the surface of the earth or in universe around the earth, the disruptions from the sun should be anticipated. In order to know the complexity level of sunspot groups and their activity, Modified-Zurich sunspot classification is used. Image of sunspots can be taken using the Michelson Doppler Imager instrument (MDI) Continuum/SOHO (Solar and Heliospheric Observatory). This research was conducted on the automatic classification of sunspot group that can be used to analyze the space weather conditions and provide information to the public. There are two stages to classify sunspot groups namely feature extraction and pattern recognition. For feature extraction, we used digital image processing to get features of sunspot group, and for pattern recognition, we used artificial neural network. We compared 3 methods of artificial neural networks to get the best result of classification namely backpropagation, probabilistic and combination between self-organizing map and k-nearest neighbor. Among three of them, probabilistic neural network gave the best classification result.

Julzarika A.,Indonesian National Institute of Aeronautics and Space LAPAN
ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings | Year: 2015

The scarcity of height models is one of the important issues in Indonesia. X SAR, SRTM C, and Aster GDEM are free available global height models. ALOS Palsar height model is the low cost data. Four data can be integrated the height models. Integration takes advantage of each characteristic data. The spatial resolution uses ALOS Palsar. Aster GDEM have minimal height error in the lowland but requires a high pass filter on the plateau or highland. SAR has the advantages of minimal error in the plateau or highland and need a low pass filter on the lowland. DSM uses X-SAR. Characteristics and penetration to vegetation objets can be seen from the wavelength type of SAR data. The aim of this study is to make integration of height models in order to get the vertical accuracy better than vertical accuracy of global height models and minimum height error. The study area is located in Tabalong, South Kalimantan. The first process is to crop the height models data into 50 km2 area. Then, the next process is geoid undulation correction. It uses EGM 2008 for the correction. then geoid undulation correction used with EGM 2008. The next step is to detect the pits and spires by using radius value 1000 m and depth +2σ (+5 m) with uncertainty 95,45%. The next process is to generate HEM and to display the height error of the height model. If four height models data have been completed and height error has been corrected, the next process is to integrate the height models. This integration use 11 scenario of height model combination. To know the accuracy of the 11 integration height model, 80 reference point measured using GNSS and similar point observed on the integration height model are selected. The accuracy test covers RMSE, accuracy (z), and the height difference test. The result of this study shows that the combination of the SRTM C-ALOS Palsar is an optimal height model integration with a vertical accuracy in 1.32 m. In addition, the combination of X-SAR-SRTM C-ALOS Palsar is the second best combination. This combination has a vertical accuracy of 1.96 m. Integration of height models can be used for mapping scale 1: 25000-1: 50000.

Candra D.S.,Indonesian National Institute of Aeronautics and Space LAPAN
34th Asian Conference on Remote Sensing 2013, ACRS 2013 | Year: 2013

Image fusion is a process to generate higher spatial resolution multispectral images by the 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 agriculture. The aim of this study is to evaluate the performance of SPOT-6 data fusion using Gram-Schmidt Spectral Sharpening (GS) method on agriculture land. Comparing GS method with Principle Component Spectral Sharpening (PC) method was done to evaluate the reliability of GS method. In this study, the performance of GS is presented based on multispectral and panchromatic of SPOT-6 image. The spatial resolution of the multispectral (MS) image is enhanced by merging the high resolution Panchromatic (Pan) image in GS method. The fused image of GS and PC were assessed visually and stastically. 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 agriculture land 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 is better than using PC method. The color of the fused image using GS is better and more natural than using PC. In the statistical assessment, the RMD results of both methods are similar. In the RVD results, GS method is better then PC method especially in band 1 and band 3. GS method is better than PC method in PSNR result for each test site. It is observed that the Gram-Schmidt method provides the best performance for each band and test site. Thus, GS is robust method for SPOT-6 data fusion especially on agriculture land.

Setiyoko A.,Indonesian National Institute of Aeronautics and Space LAPAN
34th Asian Conference on Remote Sensing 2013, ACRS 2013 | Year: 2013

Spatial interpolation is the estimation the value of properties at unsampled sites within the area covered by existing observations. In all the different techniques of DEM (digital elevation model) generation; accuracy of generated DEM is dependent also on spatial interpolation techniques. In this research work, the study and analysis of DEM interpolation techniques are conducted. The middle resolution satellite that has capability in acquisition of stereo images through across the track is IRS-1C. In this project, point map contained height information were generated from IRS-1C PAN stereo data and from geodetic single frequency GPS in differential mode. Different interpolation techniques were applied on these data sets with different combination within these data sets. The interpolation techniques were applied in this research are: IDW, global polynomial, local polynomial, RBF, ordinary kriging, simple kriging, universal kriging, disjunctive kriging, ordinary cokriging, simple cokriging, universal cokriging, disjunctive cokriging. The accuracy of generated DEMs through different interpolation techniques were evaluated with ground point data collected from geodetic single frequency GPS in differential mode. Based on interpolation methods that have been used in this work, kriging interpolation techniques gave less error than other interpolation techniques. The range error of IRS-1C DEMs are between 26.28 m to 40.17 m. Interpolation method with the least error is universal kriging and interpolation method with the highest error is global polynomial. This work shows level of confidence which interpolation techniques can generate better-interpolated continuous surface.

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