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This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes, and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over 50 % or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.

SUMMARYOn the 2021 January 15 (local date), an MW 6.2 earthquake struck the Mamuju and Majene regions of West Sulawesi, Indonesia. This event killed more than 100 inhabitants, leaving at least 30 000 people displaced from their homes, and damaged almost 8000 buildings within a radius of ∼30 km from the main shock's epicentre location (as shown on our damage proxy map). This event was generated by an active fault that continues to the Makassar Strait Thrust (MST) offshore West Sulawesi. The hazard potential of this fault remains poorly understood. In this study, we use seismic and Global Positioning System (GPS) data to investigate the source characteristics of the main shock. The results suggest that the main shock partially ruptured one segment of the MST, activated a secondary fault structure, and likely brought the updip unruptured section of the MST segment closure to failure. Our analysis of interseismic GPS velocities indicates that the Mamuju and Majene regions have a higher crustal strain rate than other nearby regions. The results (partial rupture of the MST segment, the updip unruptured section of the MST and high strain rate in the Mamuju and Majene regions) together suggest a significant seismic hazard potential in West Sulawesi, particularly in the Mamuju and Majene areas.

This article presents a framework for semi-automated building damage assessment due to earthquakes from remote sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over fifty percent or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.

The usage of aqueous lubricants in eco-friendly bio-medical friction systems has attracted significant attention. Several bottle-brush polymers with generally ionic functional groups have been developed based on the structure of biological lubricant lubricin. However, hydrophilic nonionic brush polymers have attracted less attention, especially in terms of wear properties. We developed bottle-brush polymers (BP) using hydrophilic 2-hydroxyethyl methacrylate (HEMA), a highly biocompatible yet nonionic molecule. The lubrication properties of polymer films were analyzed in an aqueous state using a ball-on-disk, which revealed that BPHEMA showed a lower aqueous friction coefficient than linear poly(HEMA), even lower than hyaluronic acid (HA) and polyvinyl alcohol (PVA), which are widely used as lubricating polymers. Significantly, we discovered that the combination of HA, PVA, and BPHEMA is demonstrated to be essential in influencing the surface wear properties; the ratio of 1 : 2 (HA : BPHEMA) had the maximum wear resistance, despite a slight increase in the aqueous friction coefficient.

While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospatial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate field surveys with spatially exhaustive, though uncertain, damage data from forecasts or remote sensing. The framework can be implemented after an earthquake to produce a spatially distributed estimate of damage and, importantly, its uncertainty. An example application with real data collected after the 2015 Nepal earthquake illustrates how regression kriging can combine a diversity of datasets—and downweight uninformative sources—reflecting its ability to accommodate context-specific variations in data type and quality. Through a sensitivity analysis on the number of field surveys, we demonstrate that with only a few surveys, this method can provide more accurate results than a standard engineering forecast.

Shaking from the 6 July 2019 Mw 7.1 Ridgecrest, California, mainshock was strongly felt through southern California, but generated relatively minimal structural damage in Ridgecrest. We consider the extent to which a damage proxy map (DPM) generated from satellite-based Synthetic Aperture Radar images can detect minor damage throughout the town of Ridgecrest. The DPM does not, as expected, detect all minor structural damage to individual structures, nor can it distinguish between structural damage and earthquake-related movement that is not consequential. However, the DPM does confirm many instances of minor structural damage to larger structures and groups of smaller structures and in some instances suggests minor structural damage that is not apparent upon visual inspection. Although ambiguous identification of minor damage may not be useful to guide earthquake response, the identification of minor, possibly hidden damage is potentially useful for other purposes. Overall, the DPM confirms that structural damage was commensurate with modified Mercalli intensity no higher than 7 throughout Ridgecrest. We consider both instrumental and intensity data to explore further the distribution of near-field ground motions over the frequency range of engineering concern. Peak ground accelerations and peak ground velocities estimated from “Did You Feel It?” intensity data using the Worden et al. (2012) ground-motion intensity conversion equation (GMICE) are consistent with recorded instrumental data. Both instrumental and estimated mainshock peak accelerations are further consistent with predictions from both the Boore et al. (2014) ground-motion prediction equation (GMPE), but lower than predicted by the Atkinson and Wald (2007) and Atkinson et al. (2014) intensity prediction equations (IPEs). A GMPE such as Boore et al. (2014), which is constrained by a large global dataset, together with a well-constrained GMICE, may thus characterize expected shaking intensities for large earthquakes better than an IPE based on more limited intensity data.

A series of four Mw>6 earthquakes struck the northern region of Lombok, eastern Indonesia, in a span of three weeks from late July to mid-August 2018. The series was thought to be associated with the Flores thrust, but the exact mechanism causing the unusual earthquake series has remained elusive. Our Interferometric Synthetic Aperture Radar analysis, combined with insights from seismology, indicates that the events originated at different hypocenter depths with differing fault geometries, which may explain the cascading behavior of the events, and indicates that better imaging of active fault geometry might provide some insight into future rupture behavior on other similar thrust systems. Our static stress change calculations suggest that the earlier events in the sequence played a role in promoting the later events. In addition, the second event brought the most significant impact on a nearby volcano, by causing volumetric expansion at its shallow magma plumbing system and unclamping its magma ascent zone, which may potentially have an impact on its future eruptive activity. However, no volcanic activity has so far occurred after the earthquakes. Finally, our damage proxy maps suggest that the second event caused the greatest damage to buildings.

Dysregulation of histone deacetylase 6 (HDAC6) can lead to the pathologic states and result in the development of various diseases including cancers and inflammatory diseases. The objective of this study was to elucidate the regulatory role of microRNA-22 (miR-22) in HDAC6-mediated expression of proinflammatory cytokines in lipopolysaccharide (LPS)-stimulated macrophages. LPS stimulation induced HDAC6 expression, but suppressed miR-22 expression in macrophages, suggesting possible correlation between HDAC6 and miR-22. Luciferase reporter assays revealed that 3'UTR of HDAC6 was a bona fide target site of miR-22. Transfection of miR-22 mimic significantly inhibited LPS-induced HDAC6 expression, while miR-22 inhibitor further increased LPS-induced HDAC6 expression. LPS-induced activation of NF-κB and AP-1 was inhibited by miR-22 mimic, but further increased by miR-22 inhibitor. LPS-induced expression of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 was inhibited by miR-22 mimic, but further increased by miR-22 inhibitor. Taken together, these data provide evidence that miR-22 can downregulate LPS-induced expression of proinflammatory cytokines via suppression of NF-κB and AP-1 axis by targeting HDAC6 in macrophages. [BMB Reports 2020; 53(4): 223-228].

The death toll and economic impact of an earthquake can be greatly exacerbated if seismic ground shaking triggers landslides. Earthquake-triggered landslides typically occur in two different contexts: localized failure of steep slopes and resulting landslides that pose a major threat to life in areas below; and lateral spreading of nearly flat sediment plains due to shaking-induced liquefaction, which can damage large areas of critical infrastructure. Unexpected catastrophic landsliding triggered by the 28 September 2018 earthquake at Palu, Indonesia did not occur in either typical context, but produced both destructive outcomes. Here, we show that alluvial ground failure in the Palu Valley was a direct consequence of irrigation creating a new liquefaction hazard. Aqueduct-supported cultivation, primarily of wet rice, raised the water table to near ground level, saturating sandy alluvial soils that liquefied in response to strong ground shaking. Large-displacement lateral spreads occurred on slopes of 1°. Slopes steeper than 1.5° sourced long-runout landslides and debris flows that swept through villages occupying the gentler slopes below. The resulting damage and loss of life would probably not have occurred in the absence of a raised water table. Earthquake-triggered landsliding of gentle, irrigated alluvial slopes is an under-recognized, but avoidable, anthropogenic hazard. Aqueduct-supported cultivation of rice resulted in liquefaction of the alluvial soils that led to the landslides triggered by the Palu 2018 earthquake, according to satellite analyses.

In volcanology, remote sensing provides a global observation framework that is becoming increasingly valuable at multiple spatio-temporal scales. Satellite observations have improved monitoring for even the most remote volcanoes, provided a large-scale context to extrapolate field-based observations, and is now routinely used to characterize and monitor volcanic systems and their hazards. However, remote sensing remains underutilized to characterize volcanic impacts that result from the interaction of volcanic processes and societal assets. This chapter first provides a review of applications of remote sensing for impact assessment in volcanology, both in pre- and post-event contexts. Two case studies are used to illustrate the application of recent remote-sensing methods in impact assessments within the field of volcanology. First, the impact to the built environment caused by the 2014 eruption of Kelud volcano (Indonesia) is assessed using automated damage proxy maps produced from interferometric synthetic-aperture radar data. This automated approach is compared, validated and discussed against an impact assessment performed from the manual inspection of high-resolution satellite images. Second, Google Earth Engine is used to illustrate how simplified access to remote-sensing data offered by cloud-based platforms can provide a global and systematic framework to revisit the impacts of eruptions We illustrate the application of Google Earth Engine to various tasks, such as change detection and time series analyses, using the 2010 eruption of Merapi volcano (Indonesia). Very few volcanic impact assessments fully utilize remote sensing, but as such data become more easily available, stored and processed, there is the exciting potential for new methods and tools that can better capture, characterize and ultimately forecast volcanic impacts.

Satellite remote sensing is playing an increasing role in the rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth's surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth's surface. In this study, we propose a novel approach to damage mapping, combining deep learning with the full time history of SAR observations of an impacted region in order to detect anomalous variations in the Earth's surface properties due to a natural disaster. We quantify Earth surface change using time series of Interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local behavior of coherence through time before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with observed damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy., Comment: 18 pages, 7 figures, plus supplementary materials

Satellite radar imaging from SAR (Synthetic Aperture Radar) is a remote sensing technology that captures ground surface level changes at a relatively high resolution. This technology has been used in many applications, one of which is the estimation of damages after natural disasters, such as wildfire, earthquake, and hurricane events. An efficient and accurate assessment of damages after natural catastrophe events allows public and private sectors to quickly respond in order to mitigate losses and to better prepare for disaster relief. Advances in machine learning and image processing techniques can be applied to this dataset to survey large areas and estimate property damages. In this paper, we introduce a machine learning-based approach for taking satellite radar images and geographical data as inputs to classify the damage status of individual buildings after a major wildfire event. We believe the demonstration of this damage estimation methodology and its application to real world natural disaster events will have a high potential to improve social resilience.

We analyzed Synthetic Aperture Radar (SAR) images from Copernicus Sentinel-1A and 1B satellites operated by the European Space Agency and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by the Japan Aerospace Exploration Agency and Global Navigation Satellite System (GNSS) data from the Network of the Americas for the 4 July 2019 Mw 6.4 and 5 July (local; 6 July UTC) Mw 7.1 Ridgecrest earthquakes. We integrated geodetic measurements for the 3D vector field of coseismic surface deformation for the two events, using SAR data from Sentinel-1 and ALOS-2 satellites. We combined less precise large-scale displacements from SAR images by pixel offset tracking or matching, including the along-track component, with the more precise SAR interferometry (Interferometric Synthetic Aperture Radar [InSAR]) measurements in the radar line of sight (LoS) direction and intermediate-precision along-track InSAR to estimate all three components of the surface displacement for the two events together. We also estimated the coseismic deformation for the two earthquakes from time-series processing of continuous Global Navigation Satellite System data stations in the area. InSAR coherence and coherence change maps the surface disruptions due to fault ruptures reaching the surface. Large slip in the Mw 6.4 earthquake was on a NE-striking fault that intersects with the NW-striking fault that was the main rupture in the Mw 7.1 earthquake. The main fault bifurcates towards the southeast ending 3 km from the Garlock Fault. The Garlock fault had triggered slip of about 20 mm in the radar LoS along a short section directly south of the main rupture. About 3 km northwest of the Mw 7.1 epicenter, the surface fault separates into two strands that form a pull-apart with about 1 m of down-drop. Further northwest is a wide zone of complex deformation.

Damage mapping using Synthetic Aperture Radar (SAR) imagery has been studied in recent decades to support rapid response to natural disasters. Many researches have been developing coherent and incoherent change detection. However, their performances can vary depending on the types of the damages, the characteristics of the scatterers and the corresponding capability of algorithms. In particular, the coherence-based methods have been used as promising detectors over urban areas where high coherences are observed, but their detection accuracies still remain controversial over the area where low coherences are mainly observed such as the 2018 Hokkaido landslides. In order to understand the characteristics of landslide (damage) detectors for low-coherence areas and find an alternative and complementary method, we designed the coherence difference, coherence normalized difference, log-ratio, intensity correlation difference, and normalized differences of the intensity correlation assuming limited availability of dataset, and also developed multi-temporal algorithms using the coherence, intensity, and intensity correlation. They were tested and evaluated using multiple polygons extracted from aerial photos. We were able to observe that the multi-temporal intensity correlation method has the potential to detect the landslides over the low coherence region and all types of land uses.

Sudden breaching of a saddle dam on July 23, 2018, on the perimeter of the Xe Nammoy hydroelectric-power reservoir, recently constructed in the Mekong basin, southern Laos, caused catastrophic flooding that resulted in fatalities and displaced thousands of individuals. This study aims to (1) assess the cause of the dam breach, and (2) understand the hydrologic and geomorphic impact of the flood. Our analysis shows that the collapse of the dam occurred as the reservoir was nearly full, and the water level was rising against the inner slope of the earthen dam. As the water discharged through the breach, we calculate that the reservoir fell ~22 m and lost ~350 million m3, more than a third of the total stored volume on July 23. Inspection of publicly available photographs of the failed dam confirms our interpretation that the failure was structural, involving both piping and rotational slumping, and not due to overtopping of the dam. Failure to properly process the local materials before construction of the earth-core of the dam is the likely cause of failure. Our analysis of satellite imagery, bolstered by field observations, reveals that the flooding inundated an area of ~46 km2 of villages and rural land in the Vang Ngao River, a tributary of the Mekong River basin. By using 1D and 2D hydrological modeling, we calculate a peak flood discharge of ~8500 m3/s, about half the mean discharge of the Mississippi River. The dam failure and concomitant losses are consistent with common inadequacies in this region in assessing potential hazards, challenges of geomorphological-geotechnical engineering when using tropical materials for construction, and emergency planning and warning. Thus, this analysis has important implications for the safer design and construction of the myriad of other dams that are either operating or planned within the multi-national Mekong River basin. We acknowledge ASF and NASA for providing Sentinel-1 imagery and GPM data, respectively. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. EP would like to acknowledge support by the SUG-NAP (3/19) by National Institute of Education at NTU. We thank Lum Shawn Kaihekulani Yamauchi for the botanical identifications, Pavel Adamek for his assistance with the manuscript style, and Abang M.S. Nugraha for his assistance in XRD analyses. We thank Thomas Dunne for his suggestions at the early stages of this research. We specially thanks MoE and NRF for their sustained support to the Earth Observatory of Singapore-EOS. We thank as well two anonymous reviewers and Editor Zhongyuan Chen for their critical reviews and suggestions.

This paper presents a new damage-mapping algorithm based on coherence images estimated from multitemporal polarimetric–interferometric synthetic aperture radar (SAR) data. The interferometric coherence has been restricted in the conventional damage-mapping approaches because the decorrelation sources are too complicated to interpret accurately and temporal decorrelation effects caused by slowly occurring natural changes and disaster events are often coupled together. To overcome these limitations, we formulate a coherence model that accounts for temporal decorrelation in two simplified layers, ground and volume layers, for long-temporal repeat-pass scenarios with zero spatial baseline. The model parameters include: 1) ground-to-volume ratio, a factor to determine the relative scattering contribution of ground and volume layers; 2) temporally correlated change, which captures the exponentially decaying behavior of coherence with time; and 3) temporally uncorrelated change, which is associated with random temporal changes. We estimate the model parameters in three steps: coherence optimization, interferometric pair-invariant parameter estimation, and interferometric pair-variant parameter estimation. To isolate the effects of disaster events from background natural changes, we calculate the probability density functions of historical change pixel by pixel and produce a probability map of damage. We tested the algorithm with uninhabited aerial vehicle data acquired from 2009 to 2015 for mapping the area damaged by the 2015 Lake Fire in California. Based on performance evaluation using receiver operating characteristic curves for optimized coherences and averaged probability maps, the proposed method reduced the false alarm from 0.25 to 0.07 when the probability of detection was 0.85 compared to coherence products alone.

Space-based radar observations have transformed our understanding of Earth over the last several decades. Driven by increasingly complex science questions, space radar missions have grown ever more sophisticated with costs rising often to hundreds of millions of dollars. At the other end of the cost and complexity spectrum, CubeSats have emerged in recent years as a disruptive innovation in the satellite sector and are now considered a means to address targeted science questions in a rapid and affordable manner. CubeSats enable new kinds of constellation-based Earth science observations not previously affordable with traditional spacecraft. Constellations of low-cost sensors provide both global spatial and high temporal coverage. As such, CubeSats are not only viable platforms to address current Earth science goals, but they also open a new realm of possibilities for science advancement and unique applications. Radar instruments have often been regarded as unsuitable for small satellite platforms due to their traditionally large size, weight, and power (SWaP). Burgeoning missions such as Radar in a CubeSat (RainCube) and CubeSat Imaging Radar for Earth Science (CIRES), being developed by Jet Propulsion Laboratory and SRI International, respectively, and funded by NASA’s Earth Science Technology Office (ESTO), are slated to dispel this notion. The key to the simplification and miniaturization of the radar subsystems in a manner that still offers compelling science and applications is 1) component technological advancement; and 2) an integrated instrument architecture and mission design that exploits the capabilities offered by CubeSat platforms. This paper reviews the state-of-the-art and future developments of CubeSat radar missions for Earth remote sensing and the implications for NASA’s current and future Earth Science program. The key enabling technologies for radio frequency (RF), digital, and antennas are surveyed, as well as the evolution of the CubeSat avionics, in the aspects that mostly impact radar development, namely power, volume, and attitude control and knowledge and precision orbit determination (POD). We investigate various radar applications that could benefit from low-cost CubeSat platforms, such as altimetry, sounding, precipitation profiling, scatterometry, synthetic aperture radar (SAR), and interferometric SAR (InSAR). We also explore the science motivation and impact of future missions that are based on these technological advancements.

We developed a terpyridine-functionalized microgel (tpy-mG) for its supramolecular assembly. Tpy-mG was synthesized by amidation between 3-(4-([2,2':6',2 ''-terpyridin]-4'-yl)phenoxy) propan-1-amin ...