Analysis of coal fire dynamics in the Wuda syncline impacted by fire-fighting activities based on in-situ observations and Landsat-8 remote sensing data

Song, Zeyang; Kuenzer, Claudia; Zhu, Hongqing; Zhang, Zhen; Jia, Yaorong; Sun, Yulin; Zhang, Jianzhong

doi:10.1016/j.coal.2015.03.008

Cited by 86 publications

(19 citation statements)

References 60 publications

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“…Further research work is needed on the use of remotely sensed images acquired in different seasons such as summer and spring. Moreover, more experiments on typical coal fields presented in previous studies such as India's Jharia coal field (Pal et al, 2016;Roy et al, 2015a;Roy et al, 2015b;Singh et al, 2017) and China's Wuda (Song et al, 2015) and Rujigou coal fields (Huo et al, 2014a;Huo et al, 2014b) are necessary to test the effectiveness of HSA in UCF detection.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to the high cost involved in data acquisition by these airborne scanners, available free orbital images such as Landsat-5 TM, -7 ETM+, -8 TIRS, ASTER and NOAA-9 AVHRR have been widely used to detect coal fires by many authors (Abbas et al, 2015;Du et al, 2015a;Du et al, 2015b;Jiang et al, 2011;Singh et al, 2017;Song and Kuenzer, 2017). Among these images, Landsat-8 TIRS is a high resolution thermal infrared data which has been successfully used to extract surface and UCF information in many coal fields around the world such as China's Wuda (Song et al, 2015) and Rujigou (Huo et al, 2014a;Huo et al, 2014b) and India's Jharia (Pal et al, 2016;Roy et al, 2015a;Roy et al, 2015b;Singh et al, 2017). Therefore, the Landsat-8 TIRS data was used to detect UCFs in this study.…”

Section: Introductionmentioning

confidence: 99%

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Use of Hot Spot Analysis to Detect Underground Coal Fires from Landsat-8 TIRS Data: A Case Study in the Khanh Hoa Coal Field, North-East of Vietnam

Nguyễn¹,

Vu²

2019

Environ. Nat. Resour. J.

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Underground coal fire (UCF) detection from remotely sensed data plays an important role in controlling and preventing the effects of coal fires and their environmental impact. The limitation of commonly used methods does not take into account spatial autocorrelation among observations. For solving this limitation, a method for UCF detection was proposed using hot spot analysis (HSA). Based on the radiative transfer equation (RTE), land surface temperatures (LSTs) were firstly retrieved from the Landsat-8 TIRS data. The degree of spatial clustering among these LSTs was measured using HSA. UCF areas were then delineated based on 99 percent confidence level of hot spot areas. These fires were finally validated using known UCF sites and cross-validated with the results extracted from the ASTER TIR image. It was found from a case study in the Khanh Hoa coal field (North-East of Vietnam); (i) UCFs were strongly correlated with known coal fires and were highly consistent with those obtained from the ASTER TIR data; (ii) a total fire area of 197 hectares was detected, of which the fire areas of low, medium, high and extremely high levels were 37.3, 47.3, 53.2 and 59.3 hectares respectively; (iii) these fires were mainly detected in the central area and at coal ash dump sites of the southern coal field. The results show HSA can be used to effectively detect UCFs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Hot Spot Analysis to Detect Underground Coal Fires from Landsat-8 TIRS Data: A Case Study in the Khanh Hoa Coal Field, North-East of Vietnam

Nguyễn¹,

Vu²

2019

Environ. Nat. Resour. J.

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“…1.3. In the context of LST it is mainly the AVHRR, MODIS (Frey and Kuenzer 2014;Frey et al 2012), AATSR, and Landsat sensors (Song et al 2015) which have been employed for time series analysis. However, in general, the science community analyzing multiannual up to multidecadal LST patterns and extracting hot spots and anomalies in the context of urban heart islands, long-burning underground coal fires, and other phenomena requiring long term monitoring is relatively small.…”

Section: Earth Observation Sensors For Time Series Analysismentioning

confidence: 99%

Remote Sensing Time Series Revealing Land Surface Dynamics: Status Quo and the Pathway Ahead

Kuenzer

Dech

Wagner

2015

Remote Sensing Time Series

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The face of our planet is changing at an unprecedented rate. Forest ecosystems diminish at alarming speed, urban and agricultural areas expand into the surrounding natural space, aquaculture is spreading, sea level rise leads to changes in coastal ecosystems, and even without obvious land cover change, land use intensity may change and complex ecosystems may undergo transient changes in composition. Satellite based earth observation is a powerful means to monitor these changes, and especially time series analysis holds the potential to reveal long term land surface dynamics. Whereas in past decades time series analysis was an elaborate undertaking mostly performed by a limited number of experts using coarse resolution data, attention shifts nowadays to open source tools and novel techniques for analyzing time series and the utilization of the same for numerous environmental applications. The reasons are the pressing call for climate-relevant, long term data analyses and value added products revealing past land surface dynamics and trends, the growing demand for global data sets, and the opening up of multidecadal remote sensing data archives, all at a time of considerablyimproved hardware power, computer literacy, and a general trend towards cloud solutions and available open source algorithms and programming languages. This chapter presents a comprehensive overview of time series analysis. We introduce currently orbiting optical, radar, and thermal infrared sensors and elucidate which of them are suitable for long term monitoring tasks based on remote sensing time

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“…In comparison with traditional optical bands, thermal-infrared bands can be better used for detecting coal spontaneous combustion (Mansor et al 1994;Zhang et al 2004;Gangopadhyay et al 2006). Landsat satellite series have been the commonly used data sources for monitoring spontaneous combustion, due to their advantages in temporal, spatial, and spectral resolutions as well as wide swath (Saraf et al 1995;Mishra et al 2011;Song et al 2015). In our study, three-period Landsat-5 TM imagery of 2007, 2009, 2010 and a SPOT-5 high spatial resolution image of 2009 are selected as the primary data sources (table 1).…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

Use of Landsat thermal imagery for dynamically monitoring spontaneous combustion of Datong Jurassic coalfields in China

Xue

Li²,

Shang³

et al. 2018

J Earth Syst Sci

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It is highly helpful and necessary to investigate and monitor the status of coal seam. Fortunately, remote sensing has facilitated the identification and dynamical monitoring of spontaneous combustion for a large area coal mining area, especially using the time series remotely-sensed datasets. In this paper, Datong Jurassic coal mining area is used as the study area, China, and an exclusion method and a multiplefactor analysis method are jointly used to identify the spontaneous combustion, including land surface temperature (LST), burnt rocks, and land use and land cover change (LUCC). The LST is firstly retrieved using a single-window algorithm due to a thermal infrared band of Landsat-5 TM (Thematic Mapper). Burnt rocks is then extracted using a decision-tree classification method based on a high-resolution SPOT-5 image. The thermal anomaly areas are identified and refined by the spatial overlay analysis of the above affecting factors. Three-period maps of coal fire areas are obtained and dynamically analyzed in 2007, 2009 and 2010. The results show that a total of 12 coal fire areas have been identified, which account for more than 1% of the total area of the study area. In general, there is an increasing trend yearly and a total of 771,970 m 2 is increased. The average annual increase is 257,320 m 2 , the average annual growth rate is 3.78%, and the dynamic degree is 11.29%.

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Analysis of coal fire dynamics in the Wuda syncline impacted by fire-fighting activities based on in-situ observations and Landsat-8 remote sensing data

Cited by 86 publications

References 60 publications

Use of Hot Spot Analysis to Detect Underground Coal Fires from Landsat-8 TIRS Data: A Case Study in the Khanh Hoa Coal Field, North-East of Vietnam

Use of Hot Spot Analysis to Detect Underground Coal Fires from Landsat-8 TIRS Data: A Case Study in the Khanh Hoa Coal Field, North-East of Vietnam

Remote Sensing Time Series Revealing Land Surface Dynamics: Status Quo and the Pathway Ahead

Use of Landsat thermal imagery for dynamically monitoring spontaneous combustion of Datong Jurassic coalfields in China

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