scholarly journals Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

2021 ◽  
Vol 13 (6) ◽  
pp. 1141
Author(s):  
Jinglong Liu ◽  
Yunjia Wang ◽  
Shiyong Yan ◽  
Feng Zhao ◽  
Yi Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Identification Accuracy ◽  
Omission Error ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Commission Error ◽  
Thermal Anomalies ◽  
Coal Fire ◽  
Coal Fires ◽  
Underground Coal Fires

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R2 reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.

scholarly journals Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

2021 ◽  
Vol 10 (7) ◽  
pp. 449
Author(s):  
Yanyan Gao ◽  
Ming Hao ◽  
Yunjia Wang ◽  
Libo Dang ◽  
Yuecheng Guo
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Fire Detection ◽  
False Alarms ◽  
Landsat 8 ◽  
Traditional Methods ◽  
Coal Fire ◽  
Contour Model ◽  
Coal Fires ◽  
Underground Coal Fires

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.

The Risk of Coal Fires And Land Subsidence in Jharia Coalfields, India, Analysed Using Remote Sensing Techniques

2021 ◽  
Author(s):  
Vamshi Karanam ◽  
Shagun Garg ◽  
Mahdi Motagh ◽  
Kamal Jain
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Risk ◽  
Land Subsidence ◽  
Coal Mines ◽  
Geographical Information ◽  
Residential Areas ◽  
Persistent Scatterer Interferometry ◽  
Coal Fire ◽  
Coal Fires

<p>Coal fires, land subsidence, roof collapse, and other life-threatening risks are a predictable phenomenon for the mineworkers and the neighbourhood population in coalfields. Jharia Coalfields in India are suffered heavily from land subsidence and coal fires for over a century. In addition to the loss of precious coal reserves, this has led to severe damage to the environment, livelihood, transportation, and precious lives.</p><p>Such incidents highlight the dire need for a well-defined methodology for risk analysis for the coalfield. In this study, we regenerated a Land Use Land Cover map prepared using Indian Remote Sensing satellite imagery and ground survey. Persistent Scatterer Interferometry analysis using Sentinel -1 images was carried out to study the land subsidence phenomenon between Nov 2018 and Apr 2019. For the same study period, coal fire zones were identified with Landsat – 8 thermal band imagery. Integration of coal fire maps, subsidence velocity maps, and land use maps was further implemented in a geographical information background environment to extract the high-risk zones. These high-risk areas include residential areas, railways, and mining sites, requiring immediate attention.</p><p>The results show that the coal mines are affected by subsidence of up to 20 cm/yr and a temperature anomaly of nearly 20<sup>o</sup>C is noticed. A high-risk zone of almost 18 sq. km. was demarcated with Kusunda, Gaslitand, and West Mudidih collieries being the most critically affected zones in the Coal mines. The study demonstrates the potential to combine data from multiple satellite sensors to build a safer ecosystem around the coal mines.  </p>

scholarly journals Study on inversion of coal seam temperature in mining area --Pingshuo mining area of Shanxi Province

2020 ◽  
Vol 165 ◽  
pp. 03014
Author(s):  
Yuexin Chen ◽  
Shunbao Liao ◽  
Dahui Qin
Keyword(s):  
Surface Temperature ◽  
Temperature Inversion ◽  
Mining Area ◽  
Shanxi Province ◽  
Vegetation Coverage ◽  
Inversion Method ◽  
Landsat 8 ◽  
Threshold Method ◽  
Coal Fire ◽  
Coal Fires

Landsat 8 is widely used in the extraction of surface temperature, but the data of surface temperature and abnormal area in Pingshuo mining area is vacant based on Landsat 8 in recent years, and there is no standard optimal algorithm to follow. In order to explore the possibility of underground coal fire in Pingshuo mining area of Shanxi Province in the future, based on the Landsat 8 satellite data, the temperature inversion method is used to observe the temperature distribution of the mining area, and three commonly used algorithms of temperature inversion processing are used to compare and analyze the SC algorithm as the best data processing method. The artificial threshold method and NDVI threshold method are used to extract the temperature anomaly area and vegetation coverage area, and calculate the area and proportion of coal fire potential area. According to a series of the data and result charts analysis, it shows that: the highest vegetation index of Pingshuo mining area is 0.79, the vegetation coverage is low, and the surface temperature is more than 41.44 ℃, which may lead to the spontaneous combustion of underground coal mines. However, the area prone to underground coal fires is small and controllable. According to the area of potential coal fires in the mining area, the local relevant departments can take relevant measures to prevent coal fire through the distribution map of potential coal fires.

scholarly journals Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery

2021 ◽  
Vol 14 (1) ◽  
pp. 45
Author(s):  
Zewei Wang ◽  
Pengfei Yang ◽  
Haotian Liang ◽  
Change Zheng ◽  
Jiyan Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Satellite Data ◽  
Detection Efficiency ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Wildfire Smoke ◽  
Fire Smoke ◽  
Remote Sensing Satellite

Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke.

scholarly journals Desarrollo de una interfaz para el cálculo de los niveles de profundidad en cuerpos de agua a través de batimetría (TOLTECA)

2019 ◽  
pp. 15-21
Author(s):  
Felipe Astudillo-Montenegro ◽  
Israel Yañez-Vargas ◽  
Josué López-Ruíz ◽  
Ramón Parra-Michel ◽  
Deni Torres-Román
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Spatial Scales ◽  
Precise Determination ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Rivers And Lakes ◽  
Depth Water

Bathymetry is a method of quantifying depths to study the topography of water bodies, including oceans, seas, rivers and lakes. The measurement of bathymetry by means of satellite images is one of the fundamental investigations in the field of remote sensing (RS) of marine environment, which has a lot of applications for the coastal environment and its monitoring. The precise determination of depth water is essential for various purposes, such as the monitoring of underwater topography, the movement of deposited sediments and the production of maritime maps for navigation. Remote sensing allows the bathymetry modeling at spatial scales that are impossible to achieve with traditional methods. Bathymetry can be estimated using RS with several techniques, each with its own capacity for depth detection, accuracy, error, strengths, advantages, disadvantages and the best application environment. Before that, a GUI interface is developed in Matlab that contains enough data to be able to compute the bathymetry in multispectral images from the satellite LANDSAT 8, with the intention of being able to analyze how flooded an area will be.

scholarly journals DETECTION AND DELINEATION OF SUBSURFACE COAL MINE FIRE FROM SPACEBORNE THERMAL INFRARED DATA IN JHARIA COALFIELD, DHANBAD, INDIA

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 65-69
Author(s):  
S. S. Biswal ◽  
A. K. Gorai
Keyword(s):  
Satellite Data ◽  
Land Subsidence ◽  
Thermal Infrared ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Kinetic Temperature ◽  
Jharia Coalfield ◽  
Coal Fire ◽  
Coal Fires ◽  
Infrared Data

<p><strong>Abstract.</strong> Coal fire has been found to be a major problem worldwide in coal mining areas. The surrounding areas get hugely affected, and significant amount of reserves are wasted due to the burning of coal. This severely affects the environment condition, which leads to a rise in temperature of the region, which is a major reason for climate change. Greenhouse gases like CO2 SO2 NO CH4 are also emitted from the cracks and fissures. Large masses of the burning of coal also causes land subsidence and collapse. Underground coal fires ignited by natural causes or human error leads to atmospheric pollution, acid rain, land subsidence, and increased coronary and respiratory diseases. They consume a valuable energy resource, destroy floral and faunal habitats, and promote human suffering because of heat, subsidence, and pollution. Jharia Coalfield, Jharkhand, India, is well known for being the storehouse of prime coking coal and for accommodating the maximum number of coal fires among all the coalfields in the country. In this paper, some of the important issues of coal fire mapping from satellite thermal infrared data have been addressed in particular reference to Jharia Coalfield. Namely, these are: retrieval of spectral radiance from raw digital satellite data using scene-specific calibration coefficients of the detectors from metadata, thermal emissivity of surface to obtain kinetic temperature at each ground resolution cell of satellite data, field-based modelling of pixel-integrated temperature for differentiating surface and subsurface fire pixels in Landsat 8 thermal IR data, identification of surface coal fire locations from infrared data and lateral propagation of coal fire.</p>

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