Processing Thermal Infrared Imagery Time-Series from Volcano Permanent Ground-Based Monitoring Network. Latest Methodological Improvements to Characterize Surface Temperatures Behavior of Thermal Anomaly Areas

In this technical paper, the state-of-art of automated procedures to process thermal infrared (TIR) scenes acquired by a permanent ground-based surveillance system, is discussed. TIR scenes regard diffuse degassing areas at Campi Flegrei and Vesuvio in the Neapolitan volcanic district (Italy). The processing system was developed in-house by using the flexible and fast processing Matlab© environment. The multi-step procedure, starting from raw infrared (IR) frames, generates a final product consisting mainly of de-seasoned temperatures and heat fluxes time-series as well as maps of yearly rates of temperature change of the IR frames. Accurate descriptions of all operational phases and of the procedures of analysis are illustrated; a Matlab© code (Natick, Massachusetts, U.S.A.) is provided as supplementary material. This product is ordinarily addressed to study volcanic dynamics and improve the forecasting of the volcanic activity. Nevertheless, it can be a useful tool to investigate the surface temperature field of any areas subjected to thermal anomalies, both of natural and anthropic origin.

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Surface Temperature Multiscale Monitoring by Thermal Infrared Satellite and Ground Images at Campi Flegrei Volcanic Area (Italy)

Remote Sensing ◽

10.3390/rs11091007 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1007 ◽

Cited By ~ 4

Author(s):

Teresa Caputo ◽

Eliana Bellucci Sessa ◽

Malvina Silvestri ◽

Maria Fabrizia Buongiorno ◽

Massimo Musacchio ◽

...

Keyword(s):

Time Series ◽

Surface Temperature ◽

Land Surface ◽

Thermal Infrared ◽

Environmental Research ◽

Campi Flegrei ◽

Landsat 8 ◽

Volcanic Area ◽

Thermal Anomalies ◽

Surface Temperatures

Land Surface Temperature (LST) from satellite data is a key component in many aspects of environmental research. In volcanic areas, LST is used to detect ground thermal anomalies providing a supplementary tool to monitor the activity status of a particular volcano. In this work, we describe a procedure aimed at identifying spatial thermal anomalies in thermal infrared (TIR) satellite frames which are corrected for the seasonal influence by using TIR images from ground stations. The procedure was applied to the volcanic area of Campi Flegrei (Italy) using TIR ASTER and Landsat 8 satellite imagery and TIR ground images acquired from the Thermal Infrared volcanic surveillance Network (TIRNet) (INGV, Osservatorio Vesuviano). The continuous TIRNet time-series images were processed to evaluate the seasonal component which was used to correct the surface temperatures estimated by the satellite’s discrete data. The results showed a good correspondence between de-seasoned time series of surface ground temperatures and satellite temperatures. The seasonal correction of satellite surface temperatures allows monitoring of the surface thermal field to be extended to all the satellite frames, covering a wide portion of Campi Flegrei volcanic area.

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The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

Engineering Proceedings ◽

10.3390/engproc2021005005 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Malvina Silvestri ◽

Federico Rabuffi ◽

Massimo Musacchio ◽

Sergio Teggi ◽

Maria Fabrizia Buongiorno

Keyword(s):

Time Series ◽

Land Surface Temperature ◽

Satellite Data ◽

Land Surface ◽

Urban Areas ◽

Temperature Time Series ◽

Campi Flegrei ◽

Thermal Anomalies ◽

Pca Method ◽

Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

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Spectral analysis of ground thermal image temperatures: what we are learning at Solfatara volcano (Italy)

Advances in Geosciences ◽

10.5194/adgeo-52-55-2020 ◽

2020 ◽

Vol 52 ◽

pp. 55-65

Author(s):

Teresa Caputo ◽

Paola Cusano ◽

Simona Petrosino ◽

Fabio Sansivero ◽

Giuseppe Vilardo

Keyword(s):

Time Series ◽

Time Series Data ◽

Earth Tides ◽

Campi Flegrei ◽

Series Data ◽

Daily Maximum ◽

Thermal Anomalies ◽

Thermal Cameras ◽

Infrared Cameras ◽

Tidal Constituents

Abstract. The Solfatara volcano in the Campi Flegrei caldera (Italy), is monitored by different, permanent ground networks handled by INGV (Istituto Nazionale di Geofisica e Vulcanologia), including thermal infrared cameras (TIRNet). The TIRNet network is composed by five stations equipped with FLIR A645SC or A655SC thermal cameras acquiring at nightime infrared scenes of portions of the Solfatara area characterized by significant thermal anomalies. The dataset processed in this work consists of daily maximum temperatures time-series from 25 April 2014 to 31 May 2019, acquired by three TIRNet stations (SF1 and SF2 inside Solfatara crater, and PIS near Pisciarelli boiling mud pool), and also consists of atmospheric pressure and air temperature time-series. Data pre-processing was carried out in order to remove the seasonal components and the influence of the Earth tides to the selected time-series. By using the STL algorithm (Seasonal Decomposition of Time Series by Loess), the time-series were decomposed into three components (seasonal, trend and remainder) to find seasonality and remove it. Then, a harmonic analysis was performed on the de-seasonalized signals in order to identify and remove the long-period tidal constituents (mainly fortnightly and monthly). Finally, Power Spectral Density was calculated by FFT Matlab algorithm, after applying an acausal Butterworth filter, focusing on the [15–120] d band, to check if characteristic periodicities exist for each site. The reliability and significance of the spectral peaks were proved by statistical and empirical methods. We found that most of the residual periodicities are ascribable to ambient factors, while 18.16 d for Pisciarelli site and 88.71 d for Solfatara have a possible endogenous origin.

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Surface Temperature Monitoring by Satellite Thermal Infrared Imagery at Mayon Volcano of Philippines, 1988-2019

10.5194/egusphere-egu2020-1412 ◽

2020 ◽

Author(s):

Hai-Po Chan ◽

Kostas Konstantinou

Keyword(s):

Remote Sensing ◽

Time Series ◽

Surface Temperature ◽

Spatial Resolution ◽

Satellite Remote Sensing ◽

Thermal Infrared ◽

Active Volcano ◽

Infrared Imagery ◽

Mayon Volcano ◽

Thermal Infrared Imagery

<p>Mayon Volcano on eastern Luzon Island is the most active volcano in the Philippines. It is named and renowned as the "perfect cone" for the symmetric conical shape and has recorded eruptions over 50 times in the past 500 years. Geographically the volcano is surrounded by the eight cities and municipalities with 1 million inhabitants. Currently, its activity is daily monitored by on-site observations such as seismometers installed on Mayon's slopes, plus, electronic distance meters (EDMs), precise leveling benchmarks, and portable fly spectrometers. Compared to existing direct on-site measurements, satellite remote sensing is currently assuming an essential role in understanding the whole picture of volcanic processes. The vulnerability to volcanic hazards is high for Mayon given that it is located in an area of high population density on Luzon Island. However, the satellite remote sensing method and dataset have not been integrated into Mayon&#8217;s hazard mapping and monitoring system, despite abundant open-access satellite dataset archives. Here, we perform multiscale and multitemporal monitoring based on the analysis of a nineteen-year Land Surface Temperature (LST) time series derived from satellite-retrieved thermal infrared imagery. Both Landsat thermal imagery (with 30-meter spatial resolution) and MODIS (Moderate Resolution Imaging Spectroradiometer) LST products (with 1-kilometer spatial resolution) are used for the analysis. The Ensemble Empirical Mode Decomposition (EEMD) is applied as the decomposition tool to decompose oscillatory components of various timescales within the LST time series. The physical interpretation of decomposed LST components at various periods are explored and compared with Mayon&#8217;s eruption records. Results show that annual-period components of LST tend to lose their regularity following an eruption, and amplitudes of short-period LST components are very responsive to the eruption events. The satellite remote sensing approach provides more insights at larger spatial and temporal scales on this renowned active volcano. This study not only presents the advantages and effectiveness of satellite remote sensing on volcanic monitoring but also provides valuable surface information for exploring the subsurface volcanic structures in Mayon.</p>

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Tracking the Endogenous Dynamics of the Solfatara Volcano (Campi Flegrei, Italy) through the Analysis of Ground Thermal Image Temperatures

Atmosphere ◽

10.3390/atmos12080940 ◽

2021 ◽

Vol 12 (8) ◽

pp. 940

Author(s):

Paola Cusano ◽

Teresa Caputo ◽

Enza De Lauro ◽

Mariarosaria Falanga ◽

Simona Petrosino ◽

...

Keyword(s):

Time Series ◽

Soil Temperature ◽

Large Scale ◽

Spectral Power ◽

Thermal Infrared ◽

Atmospheric Temperature ◽

Campi Flegrei ◽

Atmospheric Effect ◽

Spatio Temporal ◽

Temperature Time

In the last decades, thermal infrared ground-based cameras have become effective tools to detect significant spatio-temporal anomalies in the hydrothermal/volcanic environment, possibly linked to impending eruptions. In this paper, we analyzed the temperature time-series recorded by the ground-based Thermal Infrared Radiometer permanent network of INGV-OV, installed inside the Solfatara-Pisciarelli area, the most active fluid emission zones of the Campi Flegrei caldera (Italy). We investigated the temperatures’ behavior in the interval 25 June 2016–29 May 2020, with the aim of tracking possible endogenous hydrothermal/volcanic sources. We performed the Independent Component Analysis, the time evolution estimation of the spectral power, the cross-correlation and the Changing Points’ detection. We compared the obtained patterns with the behavior of atmospheric temperature and pressure, of the time-series recorded by the thermal camera of Mt. Vesuvius, of the local seismicity moment rate and of the CO2 emission flux. We found an overall influence of exogenous, large scale atmospheric effect, which dominated in 2016–2017. Starting from 2018, a clear endogenous forcing overcame the atmospheric factor, and dominated strongly soil temperature variations until the end of the observations. This paper highlights the importance of monitoring and investigating the soil temperature in volcanic environments, as well as the atmospheric parameters.

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CHARACTERIZATION OF THERMAL ANOMALIES IN A TUNDRA RIVER USING THERMAL INFRARED IMAGERY

10.1130/abs/2019am-340733 ◽

2019 ◽

Author(s):

Tyler V. King ◽

◽

Bethany T. Neilson

Keyword(s):

Thermal Infrared ◽

Infrared Imagery ◽

Thermal Anomalies ◽

Thermal Infrared Imagery

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THERMAL ANOMALY DETECTION BASED ON SALIENCY ANALYSIS FROM MULTIMODAL IMAGING SOURCES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-1-2021-55-2021 ◽

2021 ◽

Vol V-1-2021 ◽

pp. 55-64

Author(s):

A. Sledz ◽

C. Heipke

Keyword(s):

Anomaly Detection ◽

Information Fusion ◽

Evidence Theory ◽

Thermal Anomaly ◽

Thermal Infrared ◽

Surface Model ◽

Salient Region Detection ◽

Thermal Anomalies ◽

Salient Region ◽

Optical Images

Abstract. Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy.

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