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Satellite Remote Sensing of High-Temperature Thermal Anomalies, Volume II

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 12229

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Special Issue Editors

Dr. Francesco Marchese

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Guest Editor

Istituto Di Metodologie Per L'analisi Ambientale, Tito Scalo, Italy
Interests: satellite remote sensing of volcanoes; fires; dust outbreaks; natural hazards
Special Issues, Collections and Topics in MDPI journals

Dr. Nicola Genzano

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Guest Editor

School of Engineering, University of Basilicata, via dell'Ateneo Lucano 10, 85100 Potenza, Italy
Interests: satellite remote sensing; natural hazards; earthquake risks, volcano monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Carolina Filizzola

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Guest Editor

Institute of Methodologies for Environmental Analysis, National Research Council, 85050 Potenza, Italy
Interests: monitoring and mitigation of forest fires; remote sensing of natural/anthropogenic risks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High-temperature thermal anomalies are of great interest to the scientific community. Hot features such as lava flows, forest fires and gas flares may have a significant impact on social and economic human activities. Efficient monitoring systems are then required to mitigate the effects of these features on population and environment. Satellite remote sensing plays from decades an important role to study, and monitor high-temperature thermal anomalies. New systems such as Unmanned Aerial Vehicle (UAV) have also shown a high potential in investigating hot targets, complementing ground and satellite observations.

This Special Issue focuses on innovative remote sensing techniques aiming at improving our capacity in detecting, analyzing and quantifying hot targets. The guest editors encourage the submission of manuscripts with particular reference to the:

Novel remote-sensing techniques for thermal anomaly investigation and characterization
Use of data from new generation satellite sensors;
Multi-sensor data fusion (e.g. thermal, microwave);
Uncertainty analysis related to the remote sensing of high-temperature anomalies

Dr. Francesco Marchese
Dr. Nicola Genzano
Dr. Carolina Filizzola
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Thermal anomalies
Satellite remote sensing
Natural/anthropogenic sources
New generation satellite sensors
Unmanned Aerial Vehicle

Published Papers (5 papers)

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Research

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24 pages, 25102 KiB

Open AccessArticle

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

by Mohammad Mansourmoghaddam, Iman Rousta, Hamidreza Ghafarian Malamiri, Mostafa Sadeghnejad, Jaromir Krzyszczak and Carla Sofia Santos Ferreira

Remote Sens. 2024, 16(3), 454; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16030454 - 24 Jan 2024

Cited by 2 | Viewed by 1431

Abstract

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change. Full article

(This article belongs to the Special Issue Satellite Remote Sensing of High-Temperature Thermal Anomalies, Volume II)

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19 pages, 13984 KiB

Open AccessArticle

Forest Fire Segmentation from Aerial Imagery Data Using an Improved Instance Segmentation Model

by Zhihao Guan, Xinyu Miao, Yunjie Mu, Quan Sun, Qiaolin Ye and Demin Gao

Remote Sens. 2022, 14(13), 3159; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133159 - 01 Jul 2022

Cited by 35 | Viewed by 3099

Abstract

In recent years, forest-fire monitoring methods represented by deep learning have been developed rapidly. The use of drone technology and optimization of existing models to improve forest-fire recognition accuracy and segmentation quality are of great significance for understanding the spatial distribution of forest fires and protecting forest resources. Due to the spreading and irregular nature of fire, it is extremely tough to detect fire accurately in a complex environment. Based on the aerial imagery dataset FLAME, this paper focuses on the analysis of methods to two deep-learning problems: (1) the video frames are classified as two classes (fire, no-fire) according to the presence or absence of fire. A novel image classification method based on channel domain attention mechanism was developed, which achieved a classification accuracy of 93.65%. (2) We propose a novel instance segmentation method (MaskSU R-CNN) for incipient forest-fire detection and segmentation based on MS R-CNN model. For the optimized model, the MaskIoU branch is reconstructed by a U-shaped network in order to reduce the segmentation error. Experimental results show that the precision of our MaskSU R-CNN reached 91.85%, recall 88.81%, F1-score 90.30%, and mean intersection over union (mIoU) 82.31%. Compared with many state-of-the-art segmentation models, our method achieves satisfactory results on forest-fire dataset. Full article

(This article belongs to the Special Issue Satellite Remote Sensing of High-Temperature Thermal Anomalies, Volume II)

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20 pages, 5862 KiB

Open AccessArticle

A Model for Expressing Industrial Information Based on Object-Oriented Industrial Heat Sources Detected Using Multi-Source Thermal Anomaly Data in China

by Caihong Ma, Jin Yang, Wei Xia, Jianbo Liu, Yifan Zhang and Xin Sui

Remote Sens. 2022, 14(4), 835; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040835 - 10 Feb 2022

Cited by 5 | Viewed by 1787

Abstract

Industrial heat sources have made a great contribution to Chinese economic development. However, it has also been found that emissions from industrial heat sources are the main contribution to regional air pollution. Therefore, the detection of industrial heat sources and the expression of related information is becoming important. In this paper, the detection of industrial heat sources was used to express industrial information, thus that the accuracy of the detection of industrial thermal anomalies could be improved and the problems of noise and missing parameters addressed. A model for expressing industrial information based on object-oriented industrial heat sources and using multi-source thermal anomaly data in China was, therefore, proposed. It was a new real-time, objective, and real way to describe the production operation status of industrial heat sources on a large-scale area. First, 4340 working industrial heat sources in mainland China were detected by applying an adaptive k-means algorithm to ACF (NPP VIIRS 375-m active fire/hotspot data) data from the period 19 January 2012 to 31 December 2020. Secondly, several features of working industrial heat sources were extracted from NPP VIIRS 375-m active fire/hotspot data (ACF), VIIRS Nightfire data (VNF), and the Fires product based on Landsat-8 AIRCAS (L8F) data. Areas containing working industrial heat sources were then identified based on these different types of fire data. Light, land-surface temperature, and CO₂ and N₂O emissions data related to the working industrial heat sources were also extracted. The results show that feature parameters extracted from the multi-source thermal anomaly data mostly have a good positive correlation with the other parameters. Full article

(This article belongs to the Special Issue Satellite Remote Sensing of High-Temperature Thermal Anomalies, Volume II)

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20 pages, 10527 KiB

Open AccessArticle

Mt. Etna Paroxysms of February–April 2021 Monitored and Quantified through a Multi-Platform Satellite Observing System

by Francesco Marchese, Carolina Filizzola, Teodosio Lacava, Alfredo Falconieri, Mariapia Faruolo, Nicola Genzano, Giuseppe Mazzeo, Carla Pietrapertosa, Nicola Pergola, Valerio Tramutoli and Marco Neri

Remote Sens. 2021, 13(16), 3074; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163074 - 05 Aug 2021

Cited by 19 | Viewed by 3700 | Correction

Abstract

On 16 February 2021, an eruptive paroxysm took place at Mt. Etna (Sicily, Italy), after continuous Strombolian activity recorded at summit craters, which intensified in December 2020. This was the first of 17 short, but violent, eruptive events occurring during February–April 2021, mostly at a time interval of about 2–3 days between each other. The paroxysms produced lava fountains (up to 1000 m high), huge tephra columns (up to 10–11 km above sea level), lava and pyroclastic flows, expanding 2–4 km towards East and South. The last event, which was characterised by about 3 days of almost continuous eruptive activity (30 March–1 April), generated the most lasting lava fountain (8–9 h). During some paroxysms, volcanic ash led to the temporary closure of the Vincenzo Bellini Catania International Airport. Heavy ash falls then affected the areas surrounding the volcano, in some cases reaching zones located hundreds of kilometres away from the eruptive vent. In this study, we investigate the Mt. Etna paroxysms mentioned above through a multi-platform satellite system. Results retrieved from Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), starting from outputs of the Robust Satellite Techniques for Volcanoes (RST_VOLC), indicate that the 17th paroxysm (31 March–1 April) was the most intense in terms of radiative power, with values estimated around 14 GW. Moreover, by the analysis of SEVIRI data, we found that the 5th and 17th paroxysms were the most energetic. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), providing shortwave infrared (SWIR) data at 20/30 m spatial resolution, enabled an accurate localisation of active vents and the mapping of the areas inundated by lava flows. In addition, according to the Normalized Hotspot Indices (NHI) tool, the 2nd (17–18 February) and 7th (28 February) paroxysm generated the largest thermal anomaly at Mt. Etna after April 2013, when Landsat-8 OLI data became available. Despite the impact of clouds/plumes, pixel saturation, and other factors (e.g., satellite viewing geometry) on thermal anomaly identification, the used multi-sensor approach allowed us to retrieve quantitative information about the 17 paroxysms occurring at Mt. Etna. This approach could support scientists in better interpreting changes in thermal activity, which could lead to future and more dangerous eruptions. Full article

(This article belongs to the Special Issue Satellite Remote Sensing of High-Temperature Thermal Anomalies, Volume II)

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