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On 28 September 2018, a large earthquake of magnitude 7.5 occurred at 72 km away from the city of Palu, Central Sulawesi, Indonesia. Following the main shock, a series of tsunami attacked the eastern coasts of central Sulawesi and the severe damages were reported along the coast of Palu Bay.In the aftermath of the event, since the rupture was occurred along Palu-Koro strike-slip fault, the tsunami impact was expected to be relatively low compared with the other past tsunami events caused by the thrust fault rupture. However, the reported damage, more than 70,000 houses were reported to be damaged, was much more severe than expected because of the combination of strong ground motion, landslides, mudflows and tsunamis.After the event occurred, as a part of the Indonesian government's reconnaissance, we conducted a post-tsunami field survey to identify the tsunami impact. The survey was conducted on 21 and 22 October, 2018, and aimed to measure the extent of tsunami inland penetration with RTK-GPS, flow depths and to inspect the structural damage. The survey also aimed to collect the ground truth information for satellite remote sensing and survivor video analysis to understand how the tsunami attacked and devastated the coastal areas of central Palu.From the survey at the central Palu, we found that the major tsunami impact was concentrated within about 200 m inland from the shoreline. The spatial distribution of tsunami flow depths was scattered in the narrow range of tsunami inundation zone of the central Palu coast; 1-3m flow depths in average, 6 m the maximum at the central Palu.Some survivor videos provided important information on tsunami attack, especially on the time series of the first and second tsunami attacks. From the video analysis, we found that the second tsunami hit Palu Grand Mall with the splash height of almost 8 m above the sea level, and the difference of 1st. and 2nd. tsunami attack was likely to be 2 minutes. This implies that these tsunamis were generated by a phenomenon with short time scale or different sources. [ABSTRACT FROM AUTHOR]

We developed tsunami fragility functions using three sources of damage data from the 2018 Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS), (ii) a visual interpretation of optical satellite images (VI), and (iii) a machine learning and remote sensing approach utilized on multisensor and multitemporal satellite images (MLRS). Tsunami fragility functions are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific tsunami intensity measure, in this case obtained from the interpolation of multiple surveyed points of tsunami flow depth. We observed that the FS approach led to a more consistent function than that of the VI and MLRS methods. In particular, an initial damage probability observed at zero inundation depth in the latter two methods revealed the effects of misclassifications on tsunami fragility functions derived from VI data; however, it also highlighted the remarkable advantages of MLRS methods. The reasons and insights used to overcome such limitations are discussed together with the pros and cons of each method. The results show that the tsunami damage observed in the 2018 Sulawesi event in Indonesia, expressed in the fragility function developed herein, is similar in shape to the function developed after the 1993 Hokkaido Nansei-oki tsunami, albeit with a slightly lower damage probability between zero-to-five-meter inundation depths. On the other hand, in comparison with the fragility function developed after the 2004 Indian Ocean tsunami in Banda Aceh, the characteristics of Palu structures exhibit higher fragility in response to tsunamis. The two-meter inundation depth exhibited nearly 20% probability of damage in the case of Banda Aceh, while the probability of damage was close to 70% at the same depth in Palu. [ABSTRACT FROM AUTHOR]

Change detection between images is a procedure used in many applications of remote sensing data. Among these applications, the identification of damaged infrastructures in urban areas due to a large-scale disaster is a task that is crucial for distributing relief, quantifying losses, and rescue purposes. A crucial consideration for change detection is that the images must be co-registered precisely to avoid errors resulting from misalignments. An essential consideration is that some large-magnitude earthquakes produce very complex distortions of the ground surface; therefore, a pair of images recorded before and after a particular earthquake cannot be co-registered accurately. In this study, we intend to identify changes between images that are not co-registered. The proposed procedure is based on the use of phase correlation, which shows different patterns in changed and non-changed areas. A careful study of the properties of phase correlation suggests that it is robust against misalignments between images. However, previous studies showed that, in areas with no-changes, the signal power in the phase correlation is not concentrated in a single component, but rather in several components. Thus, we study the performance of the ℓ 1 -regularized logistic regression classifier to identify the relevant components of phase correlation and learn to detect non-changed and changes areas. An empirical evaluation consisting of identifying the changes between pre-event and post-event images corresponding to the 2018 Sulawesi Indonesia earthquake-tsunami was performed for this purpose. Pairs of visible and near-infrared (VNIR) spectral bands of medium-resolution were used to compute the phase correlation to set feature space. The phase correlation-based feature space consisted of 484 features. We evaluate the proposed procedure using a damage inventory performed from visual inspection of optical images of 0.5-m resolution. A third-party provided the referred inventory. Because of the limitation of medium-resolution imagery, the different damage levels in the damage inventory were merged into a binary class: "changed" and "non-changed". The results demonstrate that the proposed procedure efficiently reproduced 85 ± 6% of the damage inventory. Furthermore, our results identified tsunami-affected areas that were not previously identified by visual inspection. • Phase correlation and sparse model to identify changes in urban areas • The procedure does not require image registration. • Designed for areas with very complex ground deformation due to earthquakes • The procedure achieved an averaged overall accuracy of 85%. [ABSTRACT FROM AUTHOR]