Analysis Results Optimization of the Acid Catalyst Concentration for Synthesis of Anti‐Cancer Agent Gamavuton‐0 by Using Mathematical and Statistical Software

439 The next process is refinement and reflattening which aimed to eliminate two phases: topography and noise phases. t is necessary to choose Ground Control Points GCPs first as references for phase unwrapping and filtered interferograms. The GCPs must meet the following criteria: . GCP must lie on the residual orbital fringes . GCP should not be located on the topography or shift fringes . GCP should be located in areas with high coherence bright area . GCP should be distributed well in the area Refinement and reflattening are very important steps for correcting the phase information that has been unwrapped or shifted before converted to height values. These two results are shown in Fig. a and Fig. b , respectively. a b Figure 6. a Re‐flattened interferogram; b Re‐flattened unwrapped phase The next process is converting the phase to the heightshift value and geocoding. The values of the absolute phase is calibrated and combined with the unwrapped phase of synthetic phase and converted into height profiles. The data is the geocoded and projected into a map projection. This step is almost the same as previous geocoding procedure, except that the Range‐Doppler equation is applied simultaneously to the two antennas, making it possible to get not only the height of each pixel, but also the location Easting, Northing in the geodetic reference system as shown in Fig. . Figure 7. Result of conversion to height value Mapping the interferometry is performed to generate Digital Elevation Model DEM data by radar interferometry. The shifting information is obtained by combining both SAR imageries. nformation about the rate deformation of ground surface area on 440 Mount Merapi by using the ALOSPALSAR data on October , 8 before eruption and February , after eruption is shown in Fig. 8. Figure 8. Result of DInSAR Mt. Merapi land deformation As seen in Fig. 8, the rate of deformation is diverse and the area around the Mount Merapi is subsided as shown in green area, or negative values of land deformation in the figure. A negative value, or green to blue grades in the color scale means that the surface moving away from the satellite position, while yellow to red areas mean the positive deformation that the surface is moving toward the satellite position. We found that the land deformation reached about + . meters that occur around the crater of the volcano, as seen as orange color from the image. While in the foot of the mountain, there is a negative deformation about ‐ . 8 meters as marked in green area. This deformation caused by inflation which is generally caused by the pressure of magma in volcanoes pressed toward the surface of the Earth. This phenomenon happened not so long before the eruption of the Merapi. On the other hand, deflation occurred after the eruption where magma has been pressed towards the surface so that there are no longer objects to press on the volcano. Since the vacancy occurs on the volcano, the soil in the area is subsided at a certain level. The deformation result still contain noise in the form of black spots or holes in the image caused by the imperfect filtering results.

4. Conclusions

The simulation results showed that the land deformation on Mount Merapi and its surroundings between 8‐ is about . meters in the area around the crater. But in the foot of the mountain there is a land subsidence up to ‐ . 8 meters. To increase the accuracy of the deformation level, DnSAR method should be improved using more accurate filters and increasing the number of image data to get a model active volcano deformation during the shorter period. References Agustan . Ground deformation detection based on ALOS‐PALSAR data utilizing DnSAR technique in ndonesia, Dissertation, Nagoya University, Japan. Burgmann, R., et al. . Synthetic Aperture Radar nterferometry to measure Earth’s surface topography and its deformation. Annu. Rev. Earth Planet. Sci. . 8: – CCRS, Canada Centre for Remote Sensing . Fundamentals of Remote Sensing. http:www.nrcan.gc.caearth‐ sciencesgeomaticssatellite‐imagery‐air‐photossatellite‐imagery‐productseducational‐resources . Last accessed June . CGAR‐CS . SRTM data selection options. http:srtm.csi.cgiar.orgSELECTONinputCoord.asp. Last accessed June . Chaussard, E., et al. . Characterization of open and closed volcanic systems in ndonesia and Mexico using nSAR time series. J. of Geophysical Research: Solid Earth, Vol. 8, pp. ‐ , doi: . jgrb. 88, . JAXA . About ALOS‐PALSAR. http:www.eorc.jaxa.jpALOSenaboutpalsar.htm. Last accessed June . 441 JAXA . ALOSPALSAR Level . . product Format description. NEB‐ B. http:www.eorc.jaxa.jpALOSendocfdataPALSAR_x_Format_EL.pdf. Last accessed June . Pallister, J.S., et al. . Merapi eruption—Chronology and extrusion rates monitored with satellite radar and used in eruption forecasting, J. Volcanol. Geotherm. Res., – , doi: . j.jvolgeores. . . . Pamungkas, A.M. et.al . Monitoring of Merapi Volcano Deformation Using nterferometry Synthetic Aperture Radar nSAR Technique. J. of Environment Vol. , pp. ‐ . Simkin, T., and L. Siebert . Global Volcanism Program. Smithsonian nstitution, Global Volcanism Program Digital nformation Series, GVP‐ . http:www.volcano.si.edueducationquestions. Last accessed Oct. , Surono, J.P., et al. . The explosive eruption of Java’s Merapi volcano—A ‐year’ event, J. Volcanol. Geotherm. Res., ‐ C , – , doi: . j.jvolgeores. . . 8. Voight, B., et.al . istorical eruptions of Merapi volcano, central Java, ndonesia, 8– 8. Journal of Volcanology and Geothermal Research, , ‐ 8. Joint Scientific Symposium IJJSS 2016 Chiba, 20‐24 November 2016 442 Analyzing Land Use and Land Cover using Combined Landsat 8 and ALOS‐2PALSAR‐2 Data‐Case Study: Bandung Regency Dodi Sudiana a , Retno Wigajatri Purnamaningsih a , Sulistiyaningsih a , Bambang Setiadi b , Josaphat Tetuko Sri Sumantyo b a Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok 16424, Indonesia b Josaphat Microwave Remote Sensing Laboratory, Center for Environmental Remote Sensing, Chiba University, 1‐33 Yayoi‐cho, Inage‐ku, Chiba 263‐8522, Japan Abstract Bandung regency is one of the biggest regency in ndonesia with large number of population and rapid utilization of land. Land cover monitoring is necessary to prevent any land misuses and natural disasters. A way to monitor land cover is to classify the land cover uses remote sensing technique. To achieve better accuracy level, we propose active and passive satellite data fusion to monitor the land cover changes during ‐year period. We combined active ALOS‐ PALSAR‐ and passive Landsat 8 satellite imageries using maximum likelihood to produce land cover changes with less atmospheric disruption. Maximum likelihood is a supervised classification method using reference training sample data and probability of a pixel to be clustered in a specific class. The use of joint processing data resulted in better accuracy comparing to ALOS‐ PALSAR‐ only . Residential area, barren land, and paddy fieldsplantations during ‐year period and are increasing by . , . , and 88. 8, respectively. While forest is decreased by . in the same period. Keywords Remote Sensing, Land Use Land Cover, ALOS‐ PALSAR‐ , Landsat 8, Maximum Likelihood

1. Introduction

Bandung regency is one of the biggest regency in Java island. As the capital of West Java Province, it has , . km area with population of , Million. The large number of population ensures that land uses in this regency change dynamically every year. Badan Pusat Statistik BPS of West Java reported that paddy fields in Bandung regency is ,8 a in and increased to , a in BPS, . Natural resource utilizations in this regency, in fact is often excessive and violate the government’s rules and can be harmful to the population themselves. For example, some people burnt forest to open new paddy fields or plantations, or built new houses in an area prone to flood and landslide. As reported by Bapedas Citarum forest areas in this regency were damaged by , 8 . a in 8 and , . a in Bapedas, . Therefore, land use monitoring is necessary to observe the changing of