Regional Thermal Radiation Characteristics of FY Satellite Remote Sensing Based on Big Data Analysis

It is of great significance to study the thermal radiation anomalies of earthquake swarms in the same area in terms of selecting abnormal characteristic determination parameters, optimizing and determining the processing model, and understanding the abnormal machine. In this paper, we investigated short-term and long-term thermal radiation anomalies induced by earthquake swarms in Iran and Pakistan between 2007 and 2016. The anomalies were extracted from infrared remote sensing black body temperature data from the China Geostationary Meteorological Satellites (FY-2C/2E/2F/2G) using the multiscale time-frequency relative power spectrum (MS T-FRPS) method. By analyzing and summarizing the thermal radiation anomalies of series earthquake groups with consistency law through a stable and reliable MS T-FRPS method, we first obtained the relationship between anomalies and ShakeMaps from USGS and proposed the anomaly regional indicator (ARI) to determine seismic anomalies and the magnitude decision factor (MDF) to determine seismic magnitude. In addition, we explored the following discussions: earthquake impact on regional thermal radiation background and the relationship between thermal anomalies and earthquake magnitude and the like. Future research directions using the MS T-FRPS method to characterize regional thermal radiation anomalies induced by strong earthquakes could help improve the accuracy of earthquake magnitude determination.

Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning

The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection.

Influence of the Atlantic Ocean thermal anomaly on the Longsnout seahorse Hippocampus reidi in a Brazilian estuary

One of the consequences of climate change is an increase in the temperature of the oceans, which is considered to be one of the greatest impacts on biodiversity. Fish may respond to this impact in several ways, including shifts in their patterns of occurrence. The present study investigated the variation in the structure of a H. reidi population between 2015 and 2017 in the northern Guaíba Island area, highlighting a possible relationship to thermal anomaly associated with the El Niño phenomenon. The seahorse population monitoring was performed monthly, recording sex ratio, abundance, juvenile and adult proportion, depth of occurrence, total length and the holdfast which the seahorse were found attached. The influence of the El Niño event on the study population was evaluated by the correlation of the thermal anomaly data reported for the Tropical South Atlantic Index. Seahorse density on northern Guaíba island was positively and significantly correlated with water temperature, but the sex ratio and number of juveniles were not. The diversity of holdfasts used increased over the study period and was inversely proportional to the thermal anomaly. These results suggest that the thermal anomalies caused by the El Niño in the South Atlantic might trigger migration behaviour in the study species, providing a large aggregation during that period in Guaíba island.

Unusual Surface Latent Heat Flux Variations and Their Critical Dynamics Revealed before Strong Earthquakes

We focus on the possible thermal channel of the well-known Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism to identify the behavior of thermal anomalies during and prior to strong seismic events. For this, we investigate the variation of Surface Latent Heat Flux (SLHF) as resulting from satellite observables. We demonstrate a spatio-temporal variation in the SLHF before and after a set of strong seismic events occurred in Kathmandu, Nepal, and Kumamoto, Japan, having magnitudes of 7.8, 7.3, and 7.0, respectively. Before the studied earthquake cases, significant enhancements in the SLHF were identified near the epicenters. Additionally, in order to check whether critical dynamics, as the signature of a complex phenomenon such as earthquake preparation, are reflected in the SLHF data, we performed a criticality analysis using the natural time analysis method. The approach to criticality was detected within one week before each mainshock.

Geochemical signatures of benthic foraminiferal shells from a heat-polluted shallow marine environment provide field evidence for growth and calcification under extreme warmth

Shallow marine calcifiers play an important role as marine ecosystem engineers and in the global carbon cycle. Understanding their response to warming is essential to evaluate the fate of marine ecosystems under global change scenarios. A rare opportunity to test the effect of warming acting on natural ecosystems is by investigation of heat-polluted areas. Here, we study growth and calcification in benthic foraminifera that inhabit a thermally polluted coastal area in Israel, where they are exposed to elevated temperatures reaching up to ~42°C in summer. Live specimens of two known heat-tolerant species Lachlanella sp. 1 and Pararotalia calcariformata were collected over a period of 1 year from two stations, representing thermally polluted and undisturbed (control) shallow hard bottom habitats. Single-chamber element ratios of these specimens were obtained using laser ablation, and the Mg/Ca of the most recently grown final chambers were used to calculate their calcification temperatures. Our results provide the first direct field evidence that these foraminifera species not only persist at extreme warm temperatures but continue to calcify and grow. Species-specific Mg/Ca thermometry indicates that P. calcariformata precipitate their shells at temperatures as high as 40°C and Lachlanella sp. 1 at least up to 36°C, but both species show a threshold for calcification at cold temperatures: calcification in P. calcariformata only occurred above 22°C and in Lachlanella sp. 1 above 15°C. Our observations from the heat-polluted area indicate that under future warming scenarios, calcification in heat-tolerant foraminifera species will not be inhibited during summer, but instead the temperature window for their calcification will be expanded throughout much of the year. The observed inhibition of calcification at low temperatures indicates that the role of heat-tolerant foraminifera in carbonate production will most likely increase in future decades.