scholarly journals Volcanic Hot-Spot Detection Using SENTINEL-2: A Comparison with MODIS–MIROVA Thermal Data Series

2020 ◽  
Vol 12 (5) ◽  
pp. 820 ◽  
Author(s):  
Francesco Massimetti ◽  
Diego Coppola ◽  
Marco Laiolo ◽  
Sébastien Valade ◽  
Corrado Cigolini ◽  
...  
Keyword(s):  
Hot Spot ◽  
Detection Algorithm ◽  
Distinct Pattern ◽  
Data Series ◽  
Infrared Observation ◽  
Spot Detection ◽  
Wide Range ◽  
Volcanic Processes ◽  
Moderate Resolution ◽  
Sentinel 2

In the satellite thermal remote sensing, the new generation of sensors with high-spatial resolution SWIR data open the door to an improved constraining of thermal phenomena related to volcanic processes, with strong implications for monitoring applications. In this paper, we describe a new hot-spot detection algorithm developed for SENTINEL-2/MSI data that combines spectral indices on the SWIR bands 8a-11-12 (with a 20-meter resolution) with a spatial and statistical analysis on clusters of alerted pixels. The algorithm is able to detect hot-spot-contaminated pixels (S2Pix) in a wide range of environments and for several types of volcanic activities, showing high accuracy performances of about 1% and 94% in averaged omission and commission rates, respectively, underlining a strong reliability on a global scale. The S2-derived thermal trends, retrieved at eight key-case volcanoes, are then compared with the Volcanic Radiative Power (VRP) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) and processed by the MIROVA (Middle InfraRed Observation of Volcanic Activity) system during an almost four-year-long period, January 2016 to October 2019. The presented data indicate an overall excellent correlation between the two thermal signals, enhancing the higher sensitivity of SENTINEL-2 to detect subtle, low-temperature thermal signals. Moreover, for each case we explore the specific relationship between S2Pix and VRP showing how different volcanic processes (i.e., lava flows, domes, lakes and open-vent activity) produce a distinct pattern in terms of size and intensity of the thermal anomaly. These promising results indicate how the algorithm here presented could be applicable for volcanic monitoring purposes and integrated into operational systems. Moreover, the combination of high-resolution (S2/MSI) and moderate-resolution (MODIS) thermal timeseries constitutes a breakthrough for future multi-sensor hot-spot detection systems, with increased monitoring capabilities that are useful for communities which interact with active volcanoes.

Volcanic Hot-Spot detection using SENTINEL-2: results from the comparison with MODIS-MIROVA thermal signals.

2020 ◽  
Author(s):  
Francesco Massimetti ◽  
Diego Coppola ◽  
Marco Laiolo ◽  
Sébastien Valade ◽  
Corrado Cigolini ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Distinct Pattern ◽  
Pixel Resolution ◽  
Spot Detection ◽  
Wide Range ◽  
Volcanic Processes ◽  
Sentinel 2

<p>In the satellite thermal remote sensing, the high-spatial resolution sensors may improve thermal constraining of volcanic phenomena, with direct implications on the comprehension of volcanic processes and monitoring purposes. Here we present a new hot-spot detection algorithm, developed for SENTINEL 2 (S2) data, which combines contextual spectral and spatial analysis, applied on the 8a-11-12 SWIR bands with 20 meters/pixel resolution. The algorithm is able to detect and count the number of hotspot-contaminated pixels (S2Pix), in a wide range of environments and for several types of volcanic activities. The S2-derived thermal trends, retrieved at different worldwide key-cases volcanoes, are than compared with the Volcanic Radiative Power (VRP) from MODIS images processed by the MIROVA system during the period 2016-2019. Dataseries showed an overall excellent correlation between the two imagery suites, enhancing the higher sensitivity of SENTINEL-2 to detect small size and subtle, low-temperature thermal signals. Results outline a relation between the S2Pix and VRP ratios and the volcanic processes (i.e. lava flows, domes, lakes, open-vent activity) producing a distinct pattern in terms of size and intensity of the thermal anomaly. Moreover, the high-spatial resolution of S2 imagery potentiality let to decrypt which is the thermal contribution of the different active volcanic portions, and to understand their evolution in terms of intensity and persistence. Our analysis indicates how the combination of high- (S2) and moderate- (MODIS) resolution thermal timeseries represent an improvement in the space-based volcano monitoring that can be useful for monitoring applications and communities which relate with active volcanoes.</p>

scholarly journals RSTVOLC implementation on MODIS data for monitoring of thermal volcanic activity

2011 ◽  
Vol 54 (5) ◽  
Author(s):  
Teodosio Lacava ◽  
Francesco Marchese ◽  
Nicola Pergola ◽  
Valerio Tramutoli ◽  
Irina Coviello ◽  
...  
Keyword(s):  
Volcanic Activity ◽  
Hot Spots ◽  
Hot Spot ◽  
Mount Etna ◽  
Satellite Monitoring ◽  
Modis Data ◽  
Spot Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

An optimized configuration of the Robust Satellite Technique (RST) approach was developed within the framework of the ‘LAVA’ project. This project is funded by the Italian Department of Civil Protection and the Italian Istituto Nazionale di Geofisica e Vulcanologia, with the aim to improve the effectiveness of satellite monitoring of thermal volcanic activity. This improved RST configuration, named RSTVOLC, has recently been implemented in an automatic processing chain that was developed to detect hot-spots in near real-time for Italian volcanoes. This study presents the results obtained for the Mount Etna eruption of July 14-24, 2006, using the Moderate Resolution Imaging Spectroradiometer (MODIS) data. To better assess the operational performance, the RSTVOLC results are also discussed in comparison with those obtained by MODVOLC, a well-established, MODIS-based algorithm for hot-spot detection that is used worldwide.

scholarly journals Potential Application of Hough Transform to Extract Hot Spot Delineated Boundaries using Landsat-8 Satellite Images

2020 ◽  
Vol 9 (3) ◽  
pp. 3550-3557
Keyword(s):  
Hough Transform ◽  
Hot Spots ◽  
Large Scale ◽  
Hot Spot ◽  
Satellite Image ◽  
Detection Algorithm ◽  
Landsat 8 ◽  
Spot Detection ◽  
Canny Edge ◽  
Satellite Image Analysis

Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines everywhere throughout the globe. Aimless mining over a period of past 100 years has prompted large scale damages to the ecosystem of the earth. For example, debasement in nature of water, soil, air, vegetation dissemination and variations in land topography have caused degradation. Research is needed to be more attentive on developing the prospective use of the satellite image analysis for hot spot detection because ground-based hot spots monitoring is time-taking, complex, cumbrous and very expensive. In this paper, a two-stage model has been developed to extract the hot spot delineated boundaries in Jharia coal field (JCF) region. In the first stage, contextual thresholding (CT) technique has been used to classify the hot spot and non-hot spot regions. After thorough processing, hot spots regions have been retrieved and for performance evaluation sensitivity and specificity are calculated, which suggest that hot spots were detected accurately in successful and efficient way. In second stage, the Canny edge detection algorithm is applied to detect the edges of the hot spot regions and then the binary image is generated, which is later converted into a vector image. Finally Hough transform is implemented on the obtained vector images for delineating hot spot boundaries. In future, delineated hot spot boundaries may be used to obtain the expansion or shrinking information of hot spot regions and it can be used for area estimation also.

scholarly journals Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 129 ◽  
Author(s):  
Gaia Piazzi ◽  
Cemal Tanis ◽  
Semih Kuter ◽  
Burak Simsek ◽  
Silvia Puca ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Large Scale ◽  
Complex Topography ◽  
Snow Properties ◽  
High Resolution Imagery ◽  
Wide Range ◽  
Moderate Resolution ◽  
Sentinel 2

Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10—Snow detection (SN-OBS-1) and H12—Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances.

scholarly journals Study on Hot Spot Detection Algorithm based on K-Means

2021 ◽  
Vol 793 (1) ◽  
pp. 012017
Author(s):  
ChenghaoDeng ◽  
YuShen ◽  
KanjianZhang
Keyword(s):  
Hot Spot ◽  
Detection Algorithm ◽  
Spot Detection

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

scholarly journals Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 917
Author(s):  
Limengnan Zhou ◽  
Hongyu Han ◽  
Hanzhou Wu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Hot Spot ◽  
Data Embedding ◽  
Histogram Shifting ◽  
Secret Data ◽  
Content Selection ◽  
Wide Range ◽  
Map Generation ◽  
Content Adaptive

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.

A parametric DFM solution for analog circuits: Electrical driven hot spot detection, analysis and correction flow

2011 ◽  
Author(s):  
Rami F. Salem ◽  
Ahmed Arafa ◽  
Sherif Hany ◽  
Abdelrahman ElMously ◽  
Haitham Eissa ◽  
...  
Keyword(s):  
Analog Circuits ◽  
Hot Spot ◽  
Detection Analysis ◽  
Spot Detection

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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