scholarly journals Composite Polymer for Hybrid Activity Protective Panel in Microwave Generation of Composite Polytetrafluoroethylene -Rapana Thomasiana

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2432
Author(s):  
Ionel Dănuț Savu ◽  
Daniela Tarniță ◽  
Sorin Vasile Savu ◽  
Gabriel Constantin Benga ◽  
Laura-Madalina Cursaru ◽  
...  
Keyword(s):  
Ceramic Composite ◽  
Microwave Sintering ◽  
Original Data ◽  
Plasma Discharge ◽  
Beach Sand ◽  
Burned Area ◽  
Beam Power ◽  
Burned Areas ◽  
Microwave Beam ◽  
Similar Solutions

During the microwave sintering of a polymer-ceramic composite plasma discharge is experienced. The discharge could occur failure of the power source. The solution proposed by the paper is original, no similar solutions being presented by the literature. It consists of using a polymer-ceramic composite protective panel, to stop the plasma discharge to the entrance of the guiding tunnel. Six composites resulted by combining three polymers, Polytetrafluoroethylene (PTFE), STRATITEX composite and Polyvinylchloride (PVC) with two natural ceramics containing calcium carbonate: Rapana Thomasiana (RT) sea-shells and beach sand were used to build the protective panel.Theoretical balance of the power to the panel was analysed and the thermal field was determined. It was applied heating using 0.6-1.2-1.8-2.4-3.0 kW microwave beam power. The panels were subjected to heating with and without material to be sintered. It was analyzed: RT chemical (CaCO3 as Calcite and Aragonite), burned area (range: 200–4000 mm2) and penetration (range: 1.6–5.5 mm), and thermal analysis of the burned areas comparing to the original data. PTFE-RT composite proved the lowest penetration to 0.6 and 1.2 kW. Other 1.2 kW all composites experienced vital failures. Transformation of the polymer matrix of composite consisted of slightly decreasing of the phase shifting temperature and of slightly increasing of the melting start and liquidus temperature.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA

Fire ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 26
Author(s):  
Casey Teske ◽  
Melanie K. Vanderhoof ◽  
Todd J. Hawbaker ◽  
Joe Noble ◽  
John Kevin Hiers
Keyword(s):  
Prescribed Fire ◽  
Spatial Data ◽  
Fire History ◽  
Fire Management ◽  
Fire Behavior ◽  
Fire Risk ◽  
Behavior Modeling ◽  
Burned Area ◽  
Burned Areas ◽  
Burn Probability

Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.

scholarly journals Estimates of biomass burning emissions in tropical Asia based on satellite-derived data

2010 ◽  
Vol 10 (5) ◽  
pp. 2335-2351 ◽  
Author(s):  
D. Chang ◽  
Y. Song
Keyword(s):  
Biomass Burning ◽  
Atmospheric Chemistry ◽  
Crop Residue ◽  
Fire Season ◽  
Burned Area ◽  
Published Data ◽  
Tropical Asia ◽  
Fire Emissions ◽  
Burned Areas ◽  
In Fire

Abstract. Biomass burning in tropical Asia emits large amounts of trace gases and particulate matter into the atmosphere, which has significant implications for atmospheric chemistry and climatic change. In this study, emissions from open biomass burning over tropical Asia were evaluated during seven fire years from 2000 to 2006 (1 March 2000–31 February 2007). The size of the burned areas was estimated from newly published 1-km L3JRC and 500-m MODIS burned area products (MCD45A1). Available fuel loads and emission factors were assigned to each vegetation type in a GlobCover characterisation map, and fuel moisture content was taken into account when calculating combustion factors. Over the whole period, both burned areas and fire emissions showed clear spatial and seasonal variations. The size of the L3JRC burned areas ranged from 36 031 km2 in fire year 2005 to 52 303 km2 in 2001, and the MCD45A1 burned areas ranged from 54 790 km2 in fire year 2001 to 148 967 km2 in 2004. Comparisons of L3JRC and MCD45A1 burned areas using ground-based measurements and other satellite data were made in several major burning regions, and the results suggest that MCD45A1 generally performed better than L3JRC, although with a certain degree of underestimation in forest areas. The average annual L3JRC-based emissions were 123 (102–152), 12 (9–15), 1.0 (0.7–1.3), 1.9 (1.4–2.6), 0.11 (0.09–0.12), 0.89 (0.63–1.21), 0.043 (0.036–0.053), 0.021 (0.021–0.023), 0.41 (0.34–0.52), 3.4 (2.6–4.3), and 3.6 (2.8–4.7) Tg yr−1 for CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10, respectively, whereas MCD45A1-based emissions were 122 (108–144), 9.3 (7.7–11.7), 0.63 (0.46–0.86), 1.1 (0.8–1.6), 0.11 (0.10–0.13), 0.54 (0.38–0.76), 0.043 (0.038–0.051), 0.033 (0.032–0.037), 0.39 (0.34–0.47), 3.0 (2.6–3.7), and 3.3 (2.8–4.0) Tg yr−1. Forest burning was identified as the major source of the fire emissions due to its high carbon density. Although agricultural burning was the second highest contributor, it is possible that some crop residue combustion was missed by satellite observations. This possibility is supported by comparisons with previously published data, and this result may be due to the small size of the field crop residue burning. Fire emissions were mainly concentrated in Indonesia, India, Myanmar, and Cambodia. Furthermore, the peak in the size of the burned area was generally found in the early fire season, whereas the maximum fire emissions often occurred in the late fire season.

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

scholarly journals A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2422
Author(s):  
Lisa Knopp ◽  
Marc Wieland ◽  
Michaela Rättich ◽  
Sandro Martinis
Keyword(s):  
Deep Learning ◽  
Network Performance ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Economic Consequences ◽  
Validation Dataset ◽  
Burned Area ◽  
Burned Areas ◽  
Spectral Bands ◽  
Sentinel 2

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.

scholarly journals Effects of Climatic Warming and Wildfires on Recent Vegetation Changes in the Lake Baikal Basin

Climate ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 57 ◽  
Author(s):  
Alexander N. Safronov
Keyword(s):  
Lake Baikal ◽  
Fold Increase ◽  
Heavy Rain ◽  
Burned Area ◽  
Vegetation Changes ◽  
Baikal Basin ◽  
Burned Areas ◽  
Global Circulation ◽  
Lake Baikal Basin ◽  
Rain Season

The vegetation changes in the area of the Russian part of the Lake Baikal water basin for the period 2010–2018 were investigated using MCD12C1 land cover. The decline in swamp systems area began in 2012 and continued until 2015, after which it partially recovered during the heavy rain season in 2018. During the period of 2010–2018, the area covered by forests did not exceed 20.3% of the Baikal basin of the total portion of the Baikal basin under study. Deforestation began in 2013 and continued until 2017. Over 2013–2018, the forest level decreased by 12.1% compared to the forest state in 2013. The analysis of summer rainfalls and aridity indexes was performed by using CRU TS and GPCC climatic datasets. It is shown that the interannual variations of precipitation and aridity changes are determined by the variability of the global circulation of moist air masses. The MCD64A1 (burned area) and MCD14ML (active fires) MODIS products were used for investigation of the influence of wildfires on vegetation changes. The spatial hotspot distributions and burned areas in general correspond to aridity zones, but they cannot explain the 20-fold increase in the number of wildfires. Most of the hotspot locations are away from settlements, roads, and loggings, in difficult-to-access mountainous areas, as well as in the low-inhabited areas of Siberia. We assume that the nature of such ignitions includes dry thunderstorms, pyrocumulus lightning, or remote impact.

scholarly journals Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

2019 ◽  
Vol 11 (22) ◽  
pp. 2695
Author(s):  
Peng Wang ◽  
Lei Zhang ◽  
Gong Zhang ◽  
Benzhou Jin ◽  
Henry Leung
Keyword(s):  
Objective Function ◽  
Multispectral Imaging ◽  
Accurate Determination ◽  
Super Resolution ◽  
Burned Area ◽  
Landsat 8 ◽  
Random Walker ◽  
Burned Areas ◽  
Burned Area Mapping ◽  
Super Resolution Mapping

Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

scholarly journals MAPPING OF BURNED AREA USING PRESENCE AND BACKGROUND LEARNING FRAMEWORK ON THE GOOGLE EARTH ENGINE PLATFORM

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 37-41
Author(s):  
D. Attaf ◽  
K. Djerriri ◽  
D. Mansour ◽  
D. Hamdadou
Keyword(s):  
Remote Sensing ◽  
Classification Problem ◽  
Google Earth ◽  
The Other ◽  
Burned Area ◽  
Support Vector ◽  
Classification Techniques ◽  
Burned Areas ◽  
Google Earth Engine ◽  
One Class Classification

<p><strong>Abstract.</strong> Mapping of burned areas caused by forest fires was always a main concern to researchers in the field of remote sensing. Thus, various spectral indices and classification techniques have been proposed in the literature. In such a problem, only one specific class is of real interest and could be referred to as a one-class classification problem. One-class classification methods are highly desirable for quick mapping of classes of interest. A common used solution to deal with One-Class classification problem is based on oneclass support vector machine (OC-SVM). This method has proved useful in classification of remote sensing images. However, overfitting problem and difficulty in tuning parameters have become the major obstacles for this method. The new Presence and Background Learning (PBL) framework does not require complicated model selection and can generate very high accuracy results. On the other hand the Google Earth Engine (GEE) portal provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. Therefore, this study mainly aims to investigate the possibility of using the PBL framework within the GEE platform to extract burned areas from freely available Landsat archive in the year 2015. The quality of the results obtained using PBL framework was assessed using ground truth digitized by qualified technicians and compared to other classification techniques: Thresholding burned area spectral Index (BAI) and OC-SVM classifiers. Experimental results demonstrate that PBL framework for mapping the burned areas shows the higher classification accuracy than the other classifiers, and it highlights the suitability for the cases with few positive labelled samples available, which facilitates the tedious work of manual digitizing.</p>

scholarly journals Impact of large wildfires on PM<sub>10</sub> levels and human mortality in Portugal

2021 ◽  
Vol 21 (9) ◽  
pp. 2867-2880
Author(s):  
Patricia Tarín-Carrasco ◽  
Sofia Augusto ◽  
Laura Palacios-Peña ◽  
Nuno Ratola ◽  
Pedro Jiménez-Guerrero
Keyword(s):  
Fire Season ◽  
Burned Area ◽  
Substantial Impact ◽  
Burned Areas ◽  
The North ◽  
Mainland Portugal ◽  
Large Fires ◽  
European Forest ◽  
Air Quality Monitoring Stations ◽  
June July

Abstract. Uncontrolled wildfires have a substantial impact on the environment, the economy and local populations. According to the European Forest Fire Information System (EFFIS), between 2000 and 2013 wildfires burned up to 740 000 ha of land annually in the south of Europe, Portugal being the country with the highest percentage of burned area per square kilometre. However, there is still a lack of knowledge regarding the impacts of the wildfire-related pollutants on the mortality of the country's population. All wildfires occurring during the fire season (June–July–August–September) from 2001 and 2016 were identified, and those with a burned area above 1000 ha (large fires) were considered for the study. During the studied period (2001–2016), more than 2 million ha of forest (929 766 ha from June to September alone) were burned in mainland Portugal. Although large fires only represent less than 1 % of the number of total fires, in terms of burned area their contribution is 46 % (53 % from June to September). To assess the spatial impact of the wildfires, burned areas in each region of Portugal were correlated with PM10 concentrations measured at nearby background air quality monitoring stations. Associations between PM10 and all-cause (excluding injuries, poisoning and external causes) and cause-specific mortality (circulatory and respiratory) were studied for the affected populations using Poisson regression models. A significant positive correlation between burned area and PM10 was found in some regions of Portugal, as well as a significant association between PM10 concentrations and mortality, these being apparently related to large wildfires in some of the regions. The north, centre and inland of Portugal are the most affected areas. The high temperatures and long episodes of drought expected in the future will increase the probabilities of extreme events and therefore the occurrence of wildfires.

scholarly journals ANALISIS KARAKTERISTIK TEMPERATUR AREA TERBAKAR (BURNED AREA) MENGGUNAKAN DATA LANDSAT-8 TIRS DI KALIMANTAN

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
. Suwarsono ◽  
Any Zubaidah ◽  
. Parwati ◽  
M. Rokhis Khomarudin
Keyword(s):  
Brightness Temperature ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Burned Area ◽  
Landsat 8 ◽  
Burnt Area ◽  
Burned Areas ◽  
Good Ability ◽  
Infra Red ◽  
D Value

Biomass burning in an area will leave traces of fire such as charcoal, ash, and outcrop of land in the area known as the burned area. The burnt area is thought to have a relatively higher temperature than the surrounding area were not burned. This study aims to determine the characteristics of the temperature of the burned area using remote sensing data of Landsat-8 TIRS (Thermal Infra Red Sensor). The selected research locations are parts of Central Kalimantan and South Kalimantan incoming Landsat scene-8 path / row 118/062. The research method is a data processing Landsat-8 TIRS (channels 10 and 11) to produce an image of the brightness temperature as well as data analysis includes a statistical analysis of central tendency of the values of the brightness temperature of the sample (calculation of mean and standard deviation) as well as distance calculation (D-value). The results showed that the brightness temperature data either channel 10 or channel 11 Landsat-8 TIRS has good ability in separating the burned area and bare soil, but has a low ability to separate the burned areas and settlements. Thus, the brightness temperature parameter cannot be used as a single variable for the extraction of burned areas in a scene image of a single acquisition. Abstrak Peristiwa kebakaran biomassa pada suatu daerah akan menyisakan bekas-bekas kebakaran seperti arang, abu, serta singkapan tanah pada daerah tersebut yang dikenal dengan burned area. Daerah bekas kebakaran tersebut diduga memiliki temperatur yang relatif lebih tinggi dibandingkan dengan daerah sekitarnya yang tidak terbakar. Penelitian ini bertujuan untuk mengetahui karakteristik temperatur burned area menggunakan data penginderaan jauh Landsat-8 Thermal Infra Red Sensor (TIRS). Lokasi penelitian yang dipilih adalah sebagian wilayah Kalimantan Tengah dan Kalimantan Selatan yang masuk scene Landsat-8 path/row 118/062. Metode penelitian yang dilakukan adalah pengolahan data Landsat-8 TIRS (kanal 10 dan 11) untuk menghasilkan citra suhu kecerahan serta analisis data yang meliputi analisis statistik tendensi sentral dari nilai-nilai suhu kecerahan dari sampel (perhitungan rerata dan standar deviasi) serta perhitungan jarak (D-value). Hasil penelitian menunjukkan bahwa data suhu kecerahan baik kanal 10 maupun kanal 11 Landsat-8 TIRS memiliki kemampuan yang baik dalam memisahkan burned area dan lahan terbuka, namun memiliki kemampuan yang rendah untuk memisahkan burned area dan permukiman. Dengan demikian, parameter suhu kecerahan belum bisa dipergunakan sebagai variabel tunggal untuk ekstraksi burned area pada suatu scene citra perekaman tunggal.

Sign in / Sign up

Export Citation Format

Share Document