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RSS FeedsRemote Sensing, Vol. 11, Pages 134: Analysis of Thermal Anomalies in Volcanic Areas Using Multiscale and Multitemporal Monitoring: Vulcano Island Test Case (Remote Sensing)

 
 

11 january 2019 15:00:23

 
Remote Sensing, Vol. 11, Pages 134: Analysis of Thermal Anomalies in Volcanic Areas Using Multiscale and Multitemporal Monitoring: Vulcano Island Test Case (Remote Sensing)
 


Surface temperatures derived by 208 ASTER and L8 satellite imagery were analysed to test multiscale and multitemporal capability through available sets of thermal data to support the volcanic monitoring of Vulcano Island in Italy. The analysis of thermal historical series derived by ASTER and L8 shows that two are the main thermally active areas: La Fossa crater and the mud pool of Fangaia. In this work we aimed to assess the correlation between the satellite-retrieved temperatures with those measured during the daytime ground field campaign conducted within the same time period and, in particular cases, simultaneously. Moreover, nighttime data acquired by an airborne and field campaign were processed with the same methodology applied to satellite data for a multiscale approach verification. Historical meteorological data acquired from a weather station were also considered. Statistically significant correlations were observed between nighttime acquisitions and meteorological data. Correlations were also significant for temperature measured during the airborne campaign, while differences up to 50% with daytime acquisition during the ground field campaigns were observed. The analysis of the results suggests that within nighttime data acquisition, differences between satellite-derived temperatures and ground temperature measurements are considerably reduced; therefore nighttime data acquisition is recommended to detect thermal anomalies.


 
76 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 135: Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network (Remote Sensing)
Remote Sensing, Vol. 11, Pages 133: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China (Remote Sensing)
 
 
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