Land Surface Temperature Anomalies Detection for the Strong Earthquakes in 2018

Earthquake every year leads to human and material losses and unpredictability of it by now makes this natural disaster worsen. The objective of the current study was to determine the anomalies in land surface temperature (LST) in areas affected by earthquakes. In this research, three earthquakes (M >6) were studied. Moderate Resolution Imaging Spectroradiometer Aqua and Terra day and night LST data used from 2003 to 2018. The interquartile range (IQR) and mean ± 2σ methods utilized to select anomalies. As a result, based on the IQR method, no prior and after anomaly detected in selected cases and data. Based on mean ± 2σ, usually positive anomaly occurred during daytime. However, negative (or positive) anomaly occurred during the nighttime before the Mexico and Bolivia earthquakes. During 10 days after the earthquake, sometimes a negative anomaly detected

Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references

Assessment of Earthquake Site Amplification and Application of Passive Seismic Methods for Improved Site Classification in the Greater Vancouver Region, British Columbia

There is renewed interest to improve seismic microzonation mapping in Greater Vancouver, British Columbia (BC). We investigate local geology as the cause of observed variable ground shaking from the 2015 M 4.7 Vancouver Island earthquake. We observe high amplification at 4-6 Hz on thick sediment and the northern edge of the Fraser River delta, and disparities with current regional seismic microzonation mapping. Site amplification and shear-wave velocity (VS) are assessed from the first borehole earthquake recordings in BC. We also perform ambient vibration analyses at 13 new locations in southwest BC to highlight suitability of passive seismic methods for improving regional microzonation. We obtain well-resolved VS profiles from joint inversion of dispersion curves and horizontal to vertical spectral ratios (HVSRs). The corresponding National Building Code of Canada site classifications vary between D and C. This study is a notable contribution to public earthquake site assessments in the Greater Vancouver region

Sinkhole susceptibility mapping: A comparison between Bayes-based machine learning algorithms

Land degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human-induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors, and four different statistical approaches: naïve Bayes, Bayes net (BN), logistic regression, and Bayesian logistic regression. Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and receiver operating characteristic curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land use managers for designing hazard and land-degradation mitigation plans