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.

Analyzing correlations between GNSS retrieved precipitable water vapor and land surface temperature after earthquakes occurrence.

Earthquake is a common and destructive natural disaster. The enormous amount of energy released from seismic events can result in anomalous land surface temperature (LST) and catalyze the accumulation of water vapor in the atmosphere. The majority of previous works are not consensual concerning precipitable water vapor (PWV) and LST after the earthquake. Here, we utilized multi-source data to analyze the changes of PWV and LST anomaly after three Ms 4.0-5.3 crustal earthquakes at low depth (8-9 km) that occurred in Qinghai-Tibet Plateau. Firstly, PWV retrieval using Global Navigation Satellite System (GNSS) technology is performed, showing that its root mean square error (RMSE) is less than 1.8 mm against radiosonde (RS) data or European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) PWV data. The PWV change derived from the nearest GNSS stations around the hypocenter during the earthquakes shows anomalies, and the results reveal that PWV anomalies occurred after the earthquakes, mainly obeying a trend of increasing first and then decreasing. In addition, LST increases three days before PWV peak with a thermal anomaly of 12 °C higher than that of previous days. Robust Satellite Technique (RST) algorithm and ALICE index on Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are introduced to analyze the correlation between the abnormality of LST and PWV. Based on ten-year background field data (2012-2021), the results show that LST during the earthquake has more thermal anomaly occurrences than in previous years. The more severe the LST thermal anomaly is, the higher the probability of a PWV peak occurring.

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.

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.

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.

Relationship between sea surface temperature and the nesting of the Olive Ridley sea turtle Lepidochelys olivacea (Testudines: Cheloniidae) in Gorgona Island, Colombian Pacific

Introduction: Lepidochelys olivacea is the most abundant sea turtle in the Colombian Pacific and oceanographic conditions at the Eastern Tropical Pacific may have an effect on its reproductive behavior Objective: To assess the relationship between reproductive aspects of L. olivacea nesting at Gorgona island and sea surface temperature at local and regional scales. Methods: Monthly mean data of reproductive attributes associated with nesting females, nests, eggs and hatchlings were established using records from Gorgona's National Natural Park nesting monitoring and were correlated (cross-correlations) with SST of Gorgona island and Panama Bight, and with the temperature variability of the El Niño 1+2, El Niño 3, El Niño 3.4 and El Niño 4 regions, their thermal anomalies, and the ONI and SOI indexes. Significant correlations were included in a Generalized Additive Model. Results: Highest inverse correlation since maximum correlation is 1 was established between visits to the beach, number of nesting females, number of nests, clutch size, incubation days and hatching success and Niño 1+2 region with a lag of 12 months, whereas egg diameter, egg weight, straight carapace length and width and weight of hatchlings were correlated with Niño 4 anomaly, SOI and ONI indexes. It was also found that there is a non-linear tendency to decrease number of nesting females, number of nests, clutch size, egg diameter and egg weight with regional positive anomalies. Conclusions: Nesting attributes of L. olivacea in Gorgona correlate mainly with variations in regional SST, being low at high sea surface temperatures the year prior to nesting (12-month lag).

Introducción: Lepidochelys olivacea es la tortuga marina más abundante del Pacífico colombiano y las condiciones oceanográficas en el Pacífico Oriental Tropical podrían tener un efecto en su comportamiento reproductivo. Objetivo: Evaluar la relación entre los aspectos reproductivos de L. olivacea anidantes en isla Gorgona y la temperatura superficial del mar a escala local y regional. Métodos: Se estableció la información promedio mensual de los atributos asociados con las hembras anidantes, nidos, huevos y neonatos utilizando los registros del monitoreo de anidación del Parque Natural Nacional Gorgona y se correlacionaron (correlación cruzada) con la temperatura superficial del mar de la Ensenada de Panamá y las aguas de Gorgona, y con la variabilidad en la temperatura de las regiones El Niño 1+2, El Niño 3, El Niño 3.4 y El Niño 4, sus anomalías térmicas y con los índices ONI y SOI. Las correlaciones significativas se incluyeron en modelos aditivos generalizados. Resultados: Las correlaciones inversas más altas, considerando que la máxima correlación es 1 se establecieron entre las visitas a la playa, el número de hembras anidantes, el número de nidos, el tamaño de las nidadas, los días de incubación y el éxito de eclosión y la región Niño 1+2 con un rezago de 12 meses, mientras que el diámetro y el peso de los huevos, el largo y ancho del caparazón y peso de los neonatos se correlacionaron con la anomalía de Niño 4 y los índices SOI y ONI. También se encontró que existe una tendencia no lineal a disminuir el número de hembras anidantes, número de nidos, tamaño de nidadas, diámetro y el peso de los huevos con anomalías regionales positivas. Conclusiones: Los atributos de anidación de L. olivacea en Gorgona se correlacionan principalmente con las variaciones en la temperatura superficial del mar regional, siendo bajos a temperaturas altas en el año anterior a la anidación (rezago de 12 meses).

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.