Search Results (14)

The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami’s impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia’s recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions. Full article

Indonesia is situated on the Ring of Fire, which causes a lot of earthquakes. On the 28 September 2018, there was an earthquake in Palu, Sulawesi Island, Indonesia, which was one of the strongest shakings since 1980. Surprisingly, most traditional houses in Sulawesi survived. There has been some research on adapting traditional house structures to modern residential buildings. The limited availability of wood and complicated construction make adapting wood structures to current conditions challenging. The purpose of this study is to analyze space organization in ten traditional South Sulawesi house designs. A possible evacuation route can be found through the analysis as the first space for expeditiously escaping from an earthquake. In addition, modernizing the layout of a traditional South Sulawesi house and introducing it to local people was easy since they were familiar with the design. A deep analysis of spatial organization and its interrelations can help develop realistic designs, plans, and knowledge, thus improving the quality of residential projects. A descriptive qualitative method was used as a research method. Data were collected from field observations, brief interviews, and literature reviews. In order to analyz thee data, ORA-LITE was used to redraw the data and create the charts. It was found that different cultures have different evacuation spaces, in this case the Bugis tribe and the Toraja tribe. A corridor and kitchen were the most strategically located areas that could possibly be used for evacuation. Considering the differences in culture among tribes, designing evacuation spaces based on local culture was important. A recommendation based on this finding can also be made to the government of South Sulawesi in the design of residential houses. Full article

Despite the potentially catastrophic nature of disasters, survivors can be highly resilient. Resilience, the capacity to successfully adapt to adversity, is both individual and collective. Policymakers and academics have recently emphasised the importance of community resilience, but with little consideration of local survivors’ perspectives, particularly young survivors within low- and middle-income countries. Therefore, this exploratory study aims to give voice to disaster-affected caregivers, teachers and female adolescent students by examining their conceptualisations of community coping and priorities for resilient recovery following the 2018 Central Sulawesi earthquake and tsunami. A total of 127 survivors of the devastating disaster, including 47 adolescents, answered open-ended survey questions related to post-disaster resilience. A content analysis identified key constituents of community resilience. The results indicate that survivors highly value community cohesion and participation, drawing on the community’s intra-personal strengths to overcome post-disaster stressors. Student conceptualisations of and recommendations for a resilient recovery often differ from the views of important adults in their lives, for example, regarding the role played by the built environment, “trauma healing” and religiosity in the recovery process. These findings have implications for the design of disaster resilience interventions. Full article

The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with this event and assess the performance of the most widely used machine learning algorithms of logistic regression (LR) and random forest (RF). Eight controlling factors were considered, including elevation, hillslope gradient, aspect, relief, distance to rivers, peak ground velocity (PGV), peak ground acceleration (PGA), and lithology. To evaluate model uncertainty, training samples were randomly selected and used to establish the models 20 times, resulting in 20 susceptibility maps for different models. The quality of the landslide susceptibility maps was evaluated using several metrics, including the mean landslide susceptibility index (LSI), modelling uncertainty, and predictive accuracy. The results demonstrate that both models effectively capture the actual distribution of landslides, with areas exhibiting high LSI predominantly concentrated on both sides of the seismogenic fault. The RF model exhibits less sensitivity to changes in training samples, whereas the LR model displays significant variation in LSI with sample changes. Overall, both models demonstrate satisfactory performance; however, the RF model exhibits superior predictive capability compared to the LR model. Full article

The damage caused by natural disasters in rural areas differs in nature extent, landscape, and structure, from the damage caused in urban environments. Previous and current studies have focused mainly on mapping damaged structures in urban areas after catastrophic events such as earthquakes or tsunamis. However, research focusing on the level of damage or its distribution in rural areas is lacking. This study presents a methodology for mapping, characterizing, and assessing the damage in rural environments following natural disasters, both in built-up and vegetation areas, by combining synthetic-aperture radar (SAR) and optical remote sensing data. As a case study, we applied the methodology to characterize the rural areas affected by the Sulawesi earthquake and the subsequent tsunami event in Indonesia that occurred on 28 September 2018. High-resolution COSMO-SkyMed images obtained pre- and post-event, alongside Sentinel-2 images, were used as inputs. This study’s results emphasize that remote sensing data from rural areas must be treated differently from that of urban areas following a disaster. Additionally, the analysis must include the surrounding features, not only the damaged structures. Furthermore, the results highlight the applicability of the methodology for a variety of disaster events, as well as multiple hazards, and can be adapted using a combination of different optical and SAR sensors. Full article

The distribution of tsunami runup heights along the coast is studied both theoretically and experimentally using observation data of historical tsunami from 1992 to 2018. The physical mechanisms leading to the lognormal distribution of tsunami runup heights along the coast are discussed, and its statistical moments are calculated. It is shown that the lognormal distribution describes well the measurements of tsunami characteristics over the past 30 years. Special attention is paid to the multi-source 2018 Palu–Sulawesi tsunami, which was generated by an earthquake with magnitude 7.5 and numerous subsequent landslides. It is shown that even in this special case the lognormal distribution is a rather good approximation. Full article

Our understanding of pull-apart basins and their fault systems has been enhanced by analog experiments and simulations. However, there has been scarce interest to compare the faults that bound pull-apart basins with surface ruptures during earthquakes. In this study, we investigated the effects of a 2018 earthquake (Mw 7.5) on a pull-apart basin in the Palu–Koro fault system, Sulawesi Island, Indonesia, using geomorphic observations on digital elevation models and optical correlation with pre- and post-earthquake satellite images. A comparison of active fault traces determined by geomorphology with the locations of surface ruptures from the 2018 earthquake shows that some of the boundary faults of the basin are inactive and that active faulting has shifted to basin-shortcut faults and relay ramps. We also report evidence of lateral spreading, in which alluvial fan materials moved around the end of the alluvial fan. These phenomena may provide insights for anticipating the location of future surface ruptures in pull-apart basins. Full article

We obtained high-resolution (10 m) horizontal displacement fields from pre- and post-seismic Sentinel-2 optical images of the 2018 Mw7.5 Palu earthquake using subpixel image correlation. From these, we calculated the curl, divergence, and shear strain fields from the north-south (NS) and east-west (EW) displacement fields. Our results show that the surface rupture produced by the event was distributed within the Sulawesi neck (0.0974–0.6632°S) and Palu basin (0.8835–1.4206°S), and had a variable strike of 313.0–355.2° and strike slip of 2.00–6.62 m. The NS and EW displacement fields within the Palu basin included fine-scale displacements in both the near- and far-fault, the deformation patterns included a small restraining bend (localized shortening), a distributed rupture zone, and a major releasing bend (net extension) from the curl, divergence, and shear strain. Surface rupture was dominated by left-lateral strike-slip from initiation to termination, with a localized normal slip component peaking at ~3.75 m. The characteristics and geometric variation of the ruptured fault controlled both the formation of these surface deformation patterns and sustained supershear rupture. Full article

We use both Synthetic Aperture Radar (SAR) and Optical data to constrain the co-seismic ground deformation produced by the 2018 M_w 7.5 Sulawesi earthquake. We exploit data processing techniques mainly based on pixel cross-correlation approach, applied to Synthetic Aperture Radar (SAR) and optical images to estimate the North–South (NS) displacement component. This component is the most significant because of the NNW–SSE geometry of the fault responsible for the seismic event, i.e., the Palu-Koro fault, characterized by a strike-slip faulting mechanism. Our results show a good agreement between the different data allowing to clearly identify the surface rupture due to the fault slip. Moreover, we use SAR and optical intensity images to investigate several secondary phenomena generated by the seismic event such as tsunami, landslides, and coastal retreat. Finally, we discuss differences between SAR and optical outcomes showing strengths and disadvantages of each one according to the investigated phenomenon. Full article

On 28 September 2018, an Mw 7.4 earthquake, followed by a tsunami, struck central Sulawesi, Indonesia. It resulted in serious damage to central Sulawesi, especially in the Palu area. Two descending paths of the Advanced Land Observation Satellite 2 (ALOS-2) synthetic aperture radar (SAR) data were processed with interferometric synthetic aperture radar (InSAR) and pixel tracking techniques to image the coseismic deformation produced by the earthquake. The deformation measurement was used to determine the fault geometry and the coseismic distributed slip model with a constrained least square algorithm based on the homogeneous elastic half-space model. We divided the fault into four segments (named AS, BS, CS and DS, from the north to the south) in the inversion. The BS segment was almost parallel to the DS segment, the CS segment linked the BS and DS segments, and these three fault segments formed a fault step-over system. The Coulomb failure stress (CFS) change on the causative fault was also calculated. Results show that the maximum SAR line-of-sight (LOS) and horizontal deformation were −1.8 m and 3.6 m, respectively. The earthquake ruptured a 210-km-long fault with variable strike angles. The ruptured pattern of the causative fault is mainly a sinistral slip. Almost-pure normal characteristics could be identified along the fault segment across the Palu bay, which could be one of the factors resulting in the tsunami. The main slip area was concentrated at the depths of 0–20 km, and the maximum slip was 3.9 m. The estimated geodetic moment of the earthquake was 1.4 × 10²⁰ Nm, equivalent to an earthquake of Mw 7.4. The CFS results demonstrate that the fault step-over of 5.3 km width did not terminate the rupture propagation of the main shock to the south. Two M>6 earthquakes (the 23 January 2005 and the 18 August 2012) decreased CFS along CS segment and the middle part of DS segment of the 2018 main shock. This implies that the stress release during the previous two earthquakes may have played a vital role in controlling the coseismic slip pattern of the 2018 earthquake. Full article

We present the framework of the modeling package IonoSeis. This software models Global Navigation Satellite System (GNSS) derived slant total electron content (sTEC) perturbations in the ionosphere due to the interaction of the neutral atmosphere and charged particles in the ionosphere. We use a simplified model to couple the neutral particle momentum into the ionosphere and reconstruct time series of sTEC perturbations that match observed data in both arrival time and perturbation shape. We propagate neutral atmosphere disturbances to ionospheric heights using a three-dimensional ray-tracing code in spherical coordinates called Windy Atmospheric Sonic Propagation (WASP3D), which works for a stationary or non-stationary atmospheric models. The source of the atmosphere perturbation can be an earthquake or volcanic eruption; both couple significant amounts of energy into the atmosphere in the frequency range of a few Millihertz. We demonstrate the output of the code by comparing modeled sTEC perturbation data to the observed perturbation recorded at GNSS station BTNG (Bitung, Indonesia) immediately following the 28 September 2018, Sulawesi-Palu earthquake. With this framework, we provide a software to couple the lithosphere, atmosphere, and ionosphere that can be used to study post-seismic ionospherically-derived signals. Full article

The 28 September 2018 Mw 7.5 Palu earthquake occurred at a triple junction zone where the Philippine Sea, Australian, and Sunda plates are convergent. Here, we utilized Advanced Land Observing Satellite-2 (ALOS-2) interferometry synthetic aperture radar (InSAR) data together with broadband regional seismograms to investigate the source geometry and rupture kinematics of this earthquake. Results showed that the 2018 Palu earthquake ruptured a fault plane with a relatively steep dip angle of ~85°. The preferred rupture model demonstrated that the earthquake was a supershear event from early on, with an average rupture speed of 4.1 km/s, which is different from the common supershear events that typically show an initial subshear rupture. The rupture expanded rapidly (~4.1 km/s) from the hypocenter and propagated bilaterally towards the north and south along the strike direction during the first 8 s, and then to the south. Four visible asperities were ruptured during the slip pulse propagation, which resulted in four significant deformation lobes in the coseismic interferogram. The maximum slip of 6.5 m was observed to the south of the city of Palu, and the total seismic moment released within 40 s was 2.64 × 10²⁰ N·m, which was equivalent to Mw 7.55. Our results shed some light on the transtensional tectonism in Sulawesi, given that the 2018 Palu earthquake was dominated by left-lateral strike slip (slip maxima is 6.2 m) and that some significant normal faulting components (slip maxima is ~3 m) were resolved as well. Full article

This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets. Full article

A Mw 7.4 earthquake hit Donggala County, Central Sulawesi Province, Indonesia, on 28 September 2018, triggering a tsunami and liquefaction in Palu City and Donggala. Around 2101 fatalities ensued and 68,451 houses were damaged by the earthquake. In light of this devastating event, a post-earthquake map is required to establish the first step in the evacuation and mitigation plan. In this study, remote sensing imagery from the Landsat-8 and Sentinel-2 satellites was used. Pre- and post-earthquake satellite images were classified using artificial neural network (ANN) and support vector machine (SVM) classifiers and processed using a decorrelation method to generate the post-earthquake damage map. The affected areas were compared to the field data, the percentage conformity between the ANN and SVM results was analyzed, and four post-earthquake damage maps were generated. Based on the conformity analysis, the Landsat-8 imagery (85.83%) was superior to that of Sentinel-2 (63.88%). The resulting post-earthquake damage map can be used to assess the distribution of seismic damage following the Palu earthquake and may be used to mitigate damage in the event of future earthquakes. Full article