Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang
"> Figure 1
<p>Sketch map of the study area: (<b>a</b>) Location and topography of the study area; (<b>b</b>) Sentinel-2 true-color image of the study area; (<b>c</b>) In-situ photo of the study area; (<b>d</b>) Ground feature classification map.</p> "> Figure 2
<p>Ground subsidence results: (<b>a</b>) persistent scatter interferometry (PSI); (<b>b</b>) distributed scatterer interferometry (DSI) method.</p> "> Figure 3
<p>Flow chart of strength and weakness constraint method (SWCM). D represents the result value of normalized difference vegetation index (NDVI), temperature, and subsidence. D<sub>0</sub> represents the threshold values of a thermal anomaly, subsidence anomaly, and NDVI anomaly.</p> "> Figure 4
<p>Schematic of filtering correction: (<b>a</b>) Preliminary filtering result; (<b>b</b>) Filtering correction result (do not meet threshold); (<b>c</b>) Filtering correction result (meet threshold).</p> "> Figure 5
<p>Flow chart of the NDVI filtering result (NFR) correction.</p> "> Figure 6
<p>Ground temperature inversion results of the study area from 2015 to 2017.</p> "> Figure 7
<p>Thermal anomaly extraction results: (<b>a</b>) Extraction result based on the fixed threshold method [<a href="#B34-remotesensing-13-01141" class="html-bibr">34</a>]; (<b>b</b>) Extraction result based on the self-adaptive gradient-based thresholding method (SAGBT).</p> "> Figure 8
<p>Results of strong constraint process (SCP) (The base map is sentinel-2 optical image): (<b>a</b>) subsidence filtering result (SFR); (<b>b</b>) subsidence filtering correction result (SFCR); (<b>c</b>) NFR; (<b>d</b>) NDVI filtering correction result (NFCR).</p> "> Figure 9
<p>Final coal fire areas in the study region.</p> "> Figure 10
<p>Comparison of the real coal fire areas and coal fire areas identified by different methods: (<b>a</b>) thermal infrared method; (<b>b</b>) nighttime thermal infrared technology (NTIT); (<b>c</b>) DSI method; (<b>d</b>) thermal infrared and InSAR (interferometric synthetic aperture radar) joint method (TIIJ); (<b>e</b>) the proposed SWCM.</p> "> Figure 11
<p>Coal fire detection based on different methods in the study area. The percentages on the vertical axis are the ratio of omission error, commission error, and correctly identified coal fire areas to the real coal fire areas.</p> "> Figure 12
<p>Spatio-temporal changes in the area of coal fires.</p> "> Figure 13
<p>Profile analysis of average land surface temperature (LST) and subsidence rate in coal fire areas.</p> "> Figure 14
<p>Distribution of in-situ coal fire points and interpolation result of the subsidence rate in the study area.</p> "> Figure 15
<p>Relationship between the temperature and subsidence rate of coal fire points in the study area: (<b>a</b>) Relationship between the measured temperature and subsidence rate of coal fire points; (<b>b</b>) Relationship between time-series average temperature and subsidence rate of coal fire points.</p> "> Figure 15 Cont.
<p>Relationship between the temperature and subsidence rate of coal fire points in the study area: (<b>a</b>) Relationship between the measured temperature and subsidence rate of coal fire points; (<b>b</b>) Relationship between time-series average temperature and subsidence rate of coal fire points.</p> "> Figure 16
<p>Fukang fire area identified by TIIJ and SWCM5.2. Reasons for commission errors and omission errors of coal fire areas.</p> ">
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Datasets
3. Methods
3.1. Temperature Inversion and Thermal Anomaly Extraction
3.2. NDVI Inversion
3.3. Subsidence Information Inversion
3.4. Joint Processing of Three Remote Sensing Parameters
3.4.1. Strong Constraint Process
- Reasonable thresholds are established to extract thermal anomaly, subsidence anomaly, and NDVI anomaly areas. The thresholds corresponding to thermal, subsidence, and NDVI anomalies are obtained by the self-adaptive gradient-based thresholding (SAGBT) method [32,35], the standard deviation of subsidence based on distributed scatterer interferometry (DSI) [36,37,38], and the sum of the mean and standard deviation of NDVI, respectively.
- A subsidence filter is prepared to filter the thermal anomaly area for obtaining the subsidence filtering result (SFR), which is then corrected to attain the subsidence filtering correction result (SFCR).
- NDVI filter is prepared to perform NDVI filtering on SFCR to obtain the NDVI filtering result (NFR), which is then corrected to derive the NDVI filtering correction result (NFCR).
3.4.2. Weak Constraint Process
- According to the risk level of suspected coal fires, the preliminary coal fire areas and the suspected coal fire areas are determined, and the weak constraint determination area is obtained. Specifically, the NFCR in the high/extremely high suspected coal fire area indicates the preliminary coal fire areas, and the NFCR in the other risk levels of suspected coal fire areas indicates the suspected coal fire areas. Then, the areas of the high/extremely high suspected coal fire, except the preliminary coal fire areas, are judged as weak constraint areas.
- The temperature, subsidence, and NDVI information in the weak constraint areas are statically analyzed. Further, according to the anomaly threshold calculation method described in Section 3.4.1, the threshold values of thermal anomaly, subsidence anomaly, and NDVI anomaly are obtained. The areas that meet any two kinds of anomaly thresholds indicate the omission errors of coal fire areas, otherwise, they are judged as non-coal fire areas.
- The initial coal fire areas and the omission errors of coal fire areas are the final coal fire areas.
4. Results and Analysis
4.1. Temperature Inversion and Thermal anomaly Extraction
4.2. Coal Fire Areas Based on Strong and Weak Constraints
4.3. Coal Fire Detection Accuracy
4.4. Spatio-temporal Evolution of Coal Fires
4.5. Relationship between Temperature and Subsidence of Coal Fires
5. Discussion
5.1. Feasibility and Effectiveness of the Proposed Strong and Weak Constraints Method (SWCM)
5.2. Relationship between Surface Temperature and Subsidence in Coal Fire Areas
6. Conclusions
- The results demonstrate that the proposed method has a good performance on the commission error rate, omission error rate, and identification accuracy of the coal fire area over the study area. Specifically, these three parameters are 9%, 37%, and 91%, respectively. Compared with the four conventional methods, the average commission error rate and omission error rate are decreased by 70.4 %, 30.6%, respectively. The average identification accuracy of the coal fire area is increased by 30.6%.
- Spatio-temporal changes of coal fires have been analyzed. The coal fire tended to spread to the southeast from 2015 to 2017, and its reduction range was 2.8 times that of its increase range, so the overall combustion areas showed a decreasing trend over the study area.
- The relationship between the temperature and subsidence of coal fire areas is investigated. The time-series average temperature of the coal fire areas has a strong negative correlation with the ground subsidence rate, and the correlation coefficient was 0.82 from 2015 to 2017. Nevertheless, more precise relationships between them over the coal fire areas need further investigation.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Period | Track Number | Cloud Amount (%) |
---|---|---|
20151021 | 143/29 | 0.26 |
20151030 | 142/30 | 2.38 |
20161007 | 143/29 | 0.17 |
20161016 | 142/30 | 7.07 |
20171120 | 142/30 | 8.05 |
20171206 | 142/30 | 7.07 |
Index | Classification | Quantized Value | Index | Classification | Quantized Value |
---|---|---|---|---|---|
TLRSF | 0–20% | 5 | ARTS | 0–20% | 1 |
21–40% | 4 | 21–40% | 2 | ||
41–60% | 3 | 41–60% | 3 | ||
61–80% | 2 | 61–80% | 4 | ||
81–100% | 1 | 81–100% | 5 |
Serial Number | TLRSF (%) | ARTS (%) | Total Score |
---|---|---|---|
A | 81.6 | 4 | 2 |
B | 6.25 | 36 | 7 |
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Liu, J.; Wang, Y.; Yan, S.; Zhao, F.; Li, Y.; Dang, L.; Liu, X.; Shao, Y.; Peng, B. Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang. Remote Sens. 2021, 13, 1141. https://doi.org/10.3390/rs13061141
Liu J, Wang Y, Yan S, Zhao F, Li Y, Dang L, Liu X, Shao Y, Peng B. Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang. Remote Sensing. 2021; 13(6):1141. https://doi.org/10.3390/rs13061141
Chicago/Turabian StyleLiu, Jinglong, Yunjia Wang, Shiyong Yan, Feng Zhao, Yi Li, Libo Dang, Xixi Liu, Yaqin Shao, and Bin Peng. 2021. "Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang" Remote Sensing 13, no. 6: 1141. https://doi.org/10.3390/rs13061141