Babag Purbantoro

Tiling is a common process for remote sensing image data. Tiling the remote sensing image data is beneficial, especially for streamlining many further processes. However, shifting may occur during the tiling process. Although the shift occurs only in subpixels, it is visually noticeable and might cause analysis errors in multitemporal data. In general, image registration is carried out between the data to be registered and the data that is used as a reference (one-to-one or one-to-many image registration). This approach is possible when there is definitive data that can be used as a reference. However, there is no definitive data that can be used as a reference when tiled data shifts, since all of the data are affected by the tiling process. In this study, many-to-many image registration is proposed. Multitemporal data are used to calculate the average of the pixel distances from paired tiles. The calculation result becomes the reference value for shifting the tiles so that the tiles are aligned. Landsat-8 scenes that were acquired in 2019 are tiled. Then the pixel distance between tiles in the same location is calculated. The shifts are calculated using the average of the pixel distances. Synthetic data are used to perform the initial assessment, where some tiles were shifted by some subpixels values and then shifted back using the proposed method. The same assessment method is then carried out for all the tiles generated from the Landsat-8 scenes. The results show the average effectivity of synthetics data is higher than 76%, while on the overall data test, the effectivity is higher than 51%.

Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors.

Precise evaluation of cloud types is indispensable for the detailed analysis of the Earth’s radiation budget. The split window algorithm (SWA) is an algorithm that has been widely employed for cloud type classification from meteorological satellite imagery. In this study, we apply the SWA to analyze the clouds that appear in the Japan area using the imagery of Himawari-8 meteorological satellite. The brightness temperature (BT) information from band 13 (BT13, 10 µm) and band 15 (BT15, 12 µm) are employed with the BT difference (BTD) between these two bands (BTD13-15). For daytime analysis, the albedo of band 1 (0.47 µm) is also used to discriminate the cloudy and cloud-free areas. The validation of the resulting cloud type (SWA13-15), which includes ten classes including cloud-free condition, is carried out using the space-borne lidar data concurrent with the satellite observations. In addition, two different classifiers, namely, the sequential minimal optimization (SMO) and Naïve Bayes (NB) classifiers are tested with the results of SWA. When about 10% of 2 million data points are used for training the classifiers, the test results reveal that the correctly classified points are 97.0% and 89.5% for the first dataset (observed in July 2015) and 97.4%, and 92.1% for the second dataset (July 2016) for SMO and NB, respectively.

Synthetic Aperture Radar (SAR) is a very powerful tool in microwave remote sensing due to its capability of all-weather and day-to-night time operation [1]. Carl Wiley invented SAR in 1951 to overcome the poor azimuth resolution in conventional Side-Looking Airborne Radar (SLAR), followed by a patent filed in 1954 [2]. Unlike SLAR, using the forward motion of the platform and the principle of Doppler beam sharpening, the azimuth resolution of a SAR is equal to half the antenna length and is independent of the range distance [3]. Since the invention of SAR, steered by the breakthrough in science and technology, many advanced SAR techniques have been proposed and realized. These techniques include, but are not limited to, spotlight SAR for a finer image resolution [4], scan SAR for a wider swath coverage [5], and the remarkable polarimetric [6] and interferometric [7] SAR techniques using multichannel SAR system [8], [9], [10] for advanced remote sensing applications. Over the years, SAR has been widely used in different types of application, particularly in Earth observation such as disaster damage assessment [11], land deformation observation [12], oceanography [13], terrain classification [14], target detection [15], and so on. The diversified applications of SAR have encouraged the rapid development of airborne and spaceborne SAR sensors.

Mt Sinabung has been erupting and spewing fumes many times in the recent year after inactive for four centuries. This paper investigates the ground deformation due to the Mt Sinabung eruption in February 2018 using Differential Interferometry Synthetic Aperture Radar Technique (DInSAR). The deformation observed and extracted from Sentinel 1A satellite data in ascending orbit that provided by Europe Space Agency (ESA). The result shows eruption direction and depth vulcanic fumes in millimeters units. This study will improve our mitigating and understanding to predict future eruption effect.