Remote sensing active systems which is represented by C-band Synthetic Aperture Radar (SAR) enhance features in mapping inundation of coastal areas of Jakarta that are free of clouds/shadows. This study was conducted to assess the dynamical inundation of coastal areas in Jakarta based on multi-temporal data of C-band SAR Sentinel 1A. Data was analyzed using In-SAR and Radar Polarization analysis. Data processing was performed using SNAP 6.0 and QGIS Las Palmas 2.18.15 software. The results of this study indicate that the backscatter coefficient of the surface water is about - 19dB. The polarization analysis shows the appearance of water bodies and surface water mixed with other objects that was blue and cyan colors. VH polarization analysis showed more detection than VV polarization analysis. Dual polarization analysis reveals inundation changes in certain areas such as coastal dikes, reservoirs, mangrove ecosystems and built-up land in temporally and spatially. This study demonstrates an ability of rapid assessment in monitoring coastal inundation of tropical urban areas using InSAR and Radar Polarization analysis.
The Lancang Island waters have the potential of marine biological resources such as the crab (Portunus pelagicus). The crab is a species that eats suspended material. Remote sensing can estimate the parameters of Total Suspended Solid (TSS). The purpose of this study was to estimate the distribution and test the accuracy of TSS concentrations in Lancang Island waters extracted from Landsat 8 OLI images by field observations. Statistical test indicators that can be used for accuracy tests include; root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE). The results of the RMSE value was 11.5 showed that the size of the error based on the difference between the value of the image and field data. The smaller the RMSE value, means the results of the model estimation produced was more precise with those observations. The MAE value of 1.774 showed the simplest form of error size. The MAE results indicated that it could be seen that the prediction error of the distribution of TSS was too small. It means the prediction of the distribution of TSS in this study had high accuracy. The NMAE of 31.9% shows the error rate that is normalized and expressed in percent (%). The NMAE value below 30% that could be used as proof of the validity of image data. The high error value was caused by differences in the time taken by field data with the recording time of satellite images and the effect of thin cloud cover.
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