Using high-resolution UAV-borne thermal infrared

Remote Sensing Letters

ISSN: 2150-704X (Print) 2150-7058 (Online) Journal homepage: http://www.tandfonline.com/loi/trsl20

Using high-resolution UAV-borne thermal infrared imagery to detect coal fires in Majiliang mine, Datong coalfield, Northern China Feng Li, Weichao Yang, Xiaoyang Liu, Guangtong Sun & Jun Liu To cite this article: Feng Li, Weichao Yang, Xiaoyang Liu, Guangtong Sun & Jun Liu (2018) Using high-resolution UAV-borne thermal infrared imagery to detect coal fires in Majiliang mine, Datong coalfield, Northern China, Remote Sensing Letters, 9:1, 71-80, DOI: 10.1080/2150704X.2017.1392632 To link to this article: http://dx.doi.org/10.1080/2150704X.2017.1392632

Published online: 27 Oct 2018.

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Date: 29 October 2017, At: 05:14

REMOTE SENSING LETTERS, 2018 VOL. 9, NO. 1, 71–80 https://doi.org/10.1080/2150704X.2017.1392632

Using high-resolution UAV-borne thermal infrared imagery to detect coal fires in Majiliang mine, Datong coalfield, Northern China Feng Li

, Weichao Yang, Xiaoyang Liu, Guangtong Sun and Jun Liu

Department of Disaster Prevention Engineering, Institute of Disaster Prevention, Sanhe, China

Downloaded by [Feng Li] at 05:14 29 October 2017

ABSTRACT

Underground coal fires occur normally under inaccessible dangerous steep hills. As a light-weight and cost-effective equipment, unmanned aerial vehicles (UAVs)-based thermal infrared (TIR) imaging technique provides a choice to safely, timely and accurately map characteristics of coal fires which is difficult to realize using the conventional technologies. The colour images captured by UAV are used to generate a map of land cover types and estimate emissivity of ground features. TIR images are adopted to retrieve land surface temperature (LST) and thus generate orthophotos, which will be further used to recognize coal fire areas. The retrieved LST at night is validated with reference LST, and the results show yielding Root Mean Square Error (RMSE) is less than 1.03 K. The accuracy rate of identified coal fire areas at night time reaches as high as 92.78%, which is higher than that of daytime on 2nd October, 2016 and 5th October, 2016. In this research, the application of UAV-borne thermal imaging demonstrates a great potential to precisely and rapidly describe features of coal fires.

ARTICLE HISTORY

Received 29 April 2017 Accepted 4 October 2017

1. Introduction Coal fires, which are usually triggered via spontaneous combustion of coal mixed with air, pose great threats to non-renewable resources, local environment, climate, human health and safety (Song and Kuenzer 2014). Remote sensing has the advantage of flexible data acquisition on a large scale with a long history of application in the investigation of coal fires over nearly 53 years. Detection of coal fires from satellite data has been implemented by researchers using several space borne sensors like NOAA-AVHRR (National Oceanic and Atmospheric and Administration–Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) in various coalfields around the world. However, images from NOAA-AVHRR and MODIS can determine coal fires in sub-pixel levels due to their very coarse spatial size with 1km resolution (Agarwal et al. 2006). Landsat TM and ETM+ images with medium-resolution are usually used to detect CONTACT Feng Li [email protected] Institute of Disaster Prevention, No. 465 Xueyuan Street, Yanjiao Development Zone, Sanhe City 065201, Hebei Province, P.R. China © 2017 Informa UK Limited, trading as Taylor & Francis Group


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changes in coal fires based on single-channel land surface temperature (LST) retrieval algorithm and natural-breaks extraction method of coal fire areas (Jiang et al. 2017). Huo et al. (2015) adopted a single-window method to retrieve LST from TM/ETM+ thermal data, identified coal fire areas using a threshold technique and predicted the spreading directions of coal fires in Wuda coal field in China. Multi-temporal ASTER sensor data with higher thermal sensitivity was also applied to detect coal fires and assess firefighting effects by Temperature and Emissivity Separation (TES) algorithm as well as selfadaptive gradient-based thresholding (SAGBT) algorithm (Li et al. 2016). The predominant advantage of satellite thermal infrared (TIR) methods is the capability of detecting coal fires over a large area, but detection precision of coal fires is not very high because of lower spatial resolution and complex atmospheric conditions. Airborne thermal infrared remote sensing technology has the advantage of fast data acquisition with high spatial resolution and the ability of covering a large area. Wang, Deng, and Yan (2004) reported that the 107th band (9236.7 nm) pixel values of airborne OMIS1 (Operative Modular Imaging Spectrometer) in early morning had a better linear relationship with reference LST surveyed in fieldwork, which is more suitable for generating maps of LST. Zhang et al. (2004) extracted thermal anomalies induced by coal fires from airborne images by density slicing approach, and concluded that band 8–12.5 μm at night time had the highest detection accuracy for low-temperature thermal anomalies induced by coal fires while daytime band 3–5 μm had the highest detection accuracy for sub-pixel high temperature induced by coal fires. Airborne thermal infrared remote sensing data requires higher flight and processing costs than satellite images, although it has satisfying spatial resolution and the capacity of covering a large area. Recent developments in Unmanned Aerial Vehicles (UAVs) equipped with global positioning system (GPS) and different measuring devices have reduced the cost of collecting images. UAVs provide a complementary tool for remote sensing technologies and are better suited for producing large scale maps as a result of having smaller dimensions, light weight, flexibility, reliably flight performance at low altitudes (Nishar et al. 2016). Vasterling and Meyer (2013) discovered that UAV thermal imagery could provide a more detailed temperature distribution map using manual image-mosaicing method for inaccessible coal mines by characterized with steep hillsides. Wang et al. (2015) determined the locations of underground coal fires by adopting high-resolution RGB images to identify the ground fissures. However, it is fairly difficult to identify all underground coal fires merely according to land surface fissures from colour imagery. Therefore, in this study we demonstrate a high quality workflow for detecting coal fires using retrieved LST by associating UAV TIR imagery with natural colour imagery.

2. Study site and datasets Majiliang coal mine is located on the northwestern edge of Datong coalfield in Shanxi Province, China, and is located approximately 30 km from western downtown Datong. The experimental area is bounded by the latitude 40°02ʹ44ʹ’ N and 40°03ʹ02ʹ’ N and the longitudes 112°57ʹ33ʹ’ E and 112°58ʹ01ʹ’ E, where the elevation varies from 1200 m to 1400 m. This coal mine exploits coal seams of Jurassic Datong formation which include 11 workable seams, where No. 2 and No. 3 coal seams were already mined out by many small coal mines and the remaining No. 7, No. 11–2, No. 14–2 and No. 14–3 coal seams


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were merely available to Majiliang coal field. Coal fires were triggered by expediting air circulation between underground coal seams which air entered into through ground surface fissures. These fissures were induced by shallow mining and weak roof structure during illegal and small-scale coal mining activities. In this study, a quadcopter UAV platform was developed to carry a payload with visible camera and thermal infrared camera for remote sensing operations. The UAV weighs 2.6 kg including battery and camera, and has flight duration of approximately 20 min. A Sony ILCE-6000 mounted below the quadcopter is used to capture RGB images and a FLIR Tau2 324 camera is used to capture thermal infrared videos. The Sony camera is measured as 23.5 mm×15.6 mm with focal length of 20 mm and pixel size of 3.9 μm. The FLIR sensor is a focal plane array based on uncooled microbolometers with thermal sensitivity of less than 50 mK at the rate of 9 Hz, providing 13 mm lens, 324 × 256 pixels with a pixel size of 25 μm, 14-bit at-sensor uncalibrated radiance and with spectral response in the range of 7.5–13.5 μm. During the flight mission, the UAV was controlled according to an autopilot system which offered automatic GPS navigation with the flight speed of 5 m s−1 and fixed at relative altitude of 200 m using planned waypoints. The RGB colour and thermal imagery were respectively collected at sun angles at 17:20 and 16:08, local time, on 2nd October, 2016 under the weather conditions of a breezy and bright sunny day. Another TIR imagery acquisition campaign was conducted at 20:00 local time, on 3rd October, 2016 with strong wind on a clear night. The third operation of acquisition TIR image data was carried out at 12:25 local time on 5th October, 2016 with a strong breeze on a sunny day. A portable weather station (Model Kestrel 4500) was used to measure local atmospheric parameters such as air temperature, barometric pressure and relative humidity of three-day flight missions. The 16 reference LST values were measured by thermal imager (Fluke Ti400) with a spectral response of 7.5–14 μm at night on 3rd October, 2016 and 180 obvious fire spots or smoke spots with widths from 0.5 cm to 250 cm, which included 46 spots for the1st time, 61 spots for the 2nd time and 73 spots for the 3rd time, were measured with a handheld GPS (Unistrong G128) in three-times surveying activities.

3. Methodology To explicitly describe the approach for detection of coal fire areas, a flowchart showed in Figure 1 is designed to help people to get a clear picture of the process of detecting coal fire areas through using methods based on UAVs RGB and TIR images.

3.1. Imagery mosaic and DTM generation The pix4Dmapper software was applied to search and compare homonymy feature points in overlapping images in terms of bundle block adjustment algorithm based on computer visioning theory. The numbers of over 6000 tie points per image were extracted whereas only 20 tie points traditionally would be selected for matching per image. The software computed interior orientation parameters of camera such as focal length, principle point and lens distortion, and adjusted iteratively for positions and orientations of images (Bollard-Brren et al. 2015). After refining georeference of image


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Figure 1. Flowchart which shows how to use UAVs images to detect coal fire areas.

planes by applying known GCPs, a digital surface model (DSM) was generated with 3D point clouds for each image pixel using cross-correlation technique. A digital terrain model (DTM) was created by filtering non-ground points from 3D point clouds using adaptive TIN algorithm (Axelsson 2000). A normalized digital surface model (nDSM) was derived by substracting DTM from DSM. Subsequently, orthomosaic was also generated in the light of computed 3D information. In this study, a colour orthophoto with the ground resolution of 0.04 m, a DSM, a DTM and a nDSM were generated based on the 58 colour images acquired on 2nd October, 2016 while three TIR orthophotos with the ground resolution of 0.37 m were respectively generated using thermal images acquired on 2nd, 3rd, and 5th October, 2016.

3.2. Imagery classification and emissivity estimation Thermal anomalies from underground coal fires might occur at any place of ground surface and thus distinguishing diverse land cover types helps to analyze the temperature differences of object surfaces. In order to assign emissivity to different ground features by matching image classification results to TIR images, water, vegetation, manmade surface and bare land classes of ground features need to be determined using a rule-based approach and object-based image analysis (OBIA). The visible orthomosaic and DEM were resampled into the resolution of 0.37 m, exactly the same as all TIR orthomosaics. And then all three TIR orthomoaics were respectively conducted image co-registrations to the same visible orthomosaics with registration errors of less than 0.4 pixels. A multi-resolution segmentation algorithm was executed to image input layers



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including blue, green, red, TIR, DEM and nDSM bands respectively allocated with weight values of 1.0, 1.0, 1.0, 0.6, 0.8 and 0.8 using Trimble eCognition software. The segmentation was also impacted by homogeneity criteria of shape to colour with 0.2 to 0.8 as well as of compactness to smoothness with 0.7 to 0.3. To reduce confusion between road and bare soil, we first manually extracted road class taking advantage of road vectorization method. The next step, we utilized Ratio Green of less than 0.039 and mean DEM of less than1245.9 m to derive water class. Then a large number of shadows caused by houses and trees were distinguished from unclassified objects on the basis of Maximum difference (Max. diff.) among all band values. About over 90% residential houses of the village in the Majiliang coal field have been abandoned owing to the surface subsidence or emission of toxic gases, even some roofs have been demolished. Because few people are active around dilapidated houses, a lot of weeds grow vigorously not only in the surrounding of houses and inside of abandoned roofless houses, but also under most trees. Therefore, these shadows were classified into vegetation I. By collecting a sequence of training samples of each class, a nearest neighbour (NN) classifier was adopted to distinguish bare land, building and vegetation II classes based on image objects acquired from image segmentation (Kraaijenbrink et al. 2016). The building class was eventually confirmed by means of elevation of nDSM whereas bare land and vegetation II involving lots of green plants were improved and determined using slight manual editing operations. The combination of vegetation I and vegetation II made up vegetation class. Likewise, road class and building class were merged into man-made surface class owing to their similar properties. We chose 301 reference samples to indicate the accuracy of this classification with the results of overall accuracy of 95.68% and Kappa coefficient for the confusion matrix of 0.94. As a result, we considered, in the range of 7.5 to 13.5 μm atmosphere window, emissivity of water, vegetation, man-made surface, and bare land classes were respectively to be 0.99, 0.98, 0.95, and 0.97, and then we matched all material types of each class with those in ASTER spectral library version 2.0, and through averaging them, we got these reflectivity values, and finally we used 1 to subtract these values, and the emissivity of each class could be derived.

3.3. LST retrieval and coal fire detection The DN values of an original TIR image were converted into thermal radiance of TIR sensor after the FLIR thermal camera was radiometrically calibrated in the laboratory using blackbodies sources. The thermal infrared camera in this study offers an isolated channel in the spectral range 7.5–13.5 μm, hence radiative transfer Equation (1) as one of single-band atmospheric calibration approaches was utilized to retrieve the LST (Berni et al. 2009). BT ¼ ½εBS þ ð1 εÞL# τ þ L"

(1)

where BT and BS are thermal radiance received by TIR sensor and blackbody radiance, ε is emissivity of ground objects, τ denotes atmospheric transmissivity, L↑ and L↓ denote upwelling and downwelling thermal radiation, respectively. The calibrated radiances of TIR imagery were used as BT while τ, L↑ and L↓ were estimated by using MODTRAN 4.0 radiative transfer software considering sensor altitude,

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atmospheric pressure, air temperature and relatively humidity. After BS was computed in terms of Equation (1), the inverse function of Planck’s law (2) was used to calculate the temperature of terrestrial surface.


T¼

λ ln

h

c2

c1 λ5 BS

i þ1

(2)

where T and λ signify retrieved LST (Unit is K) and wavelength of thermal sensor (Unit is μm), constants c1 and c2 are 1.19104 × 108 w μm4 m−2 sr−1 and 1.43877 × 104 μm K. In identified approaches of coal fires, mean temperature plus the standard deviation (σt) of temperature values merely approximately indicates the segment threshold of temperature for coal fires. Therefore, we employed self-adaptive gradient based threshold (SAGBT) method to detect boundaries of coal fires, which was demonstrated to be more accurate in recognizing coal fires (Du et al. 2015). This approach first generated a temperature gradient image in terms of LST image using improved Sobel operator, then the gradient image was segmented into 11 potential high gradient buffer maps with a pair of lower and upper bounds ranging from [0.5σg, 1.5σg] to [1.5σg, 3.2σg], where σg is the standard deviation of gradient. A mathematical morphology thinning algorithm was adopted to create high gradient lines containing one pixel from 11 potential high gradient buffer maps. The next step was to use mean LST and σt to exclude some high gradient lines with low temperature. The average temperature of the remaining high gradient lines from 11 potential high gradient buffer maps was used as the criterion of coal fires identification, and initial coal fire areas were extracted by the segment threshold of temperature. However, some electric heating areas, such as electric light and transformer areas, which are formed during the process of converting electric energy into thermal energy, tended to be identified as false positives of coal fire areas because of apparent higher temperatures than its neighbourhood during the night. Then a mask of electric heating areas could be derived from common sections between initial coal fire areas and building class areas. Final coal fire map was generated by eliminating electric heating areas with the help of the mask of electric heating areas.

4. Results and analysis 4.1. The accuracy of retrieved LST A flight mission equipped with TIR sensor was carried out at night on 3rd October, 2016 by surveying simultaneously 16 reference LST values with Fluke Ti400 thermal imager over water, vegetation, bare land and man-made surface. Reference LST and retrieved LST from thermal images are listed in Table 1. Table 1 indicates the minimum, maximum, average and Root Mean Square Error (RMSE) of the two different LST values (ΔT) are respectively −1.85 K, 1.72 K, 0.36 K and 1.03 K. Analysis shows a good linear relationship between LST and retrieved LST from thermal imagery with correlation coefficient of 0.81. As a result, these temperature data present a good accuracy for retrieved LST of thermal imagery.

4.2. Analysis of coal fires detection accuracy In order to verify the accuracy of coal fires detection using TIR technology, 180 distinct fire spots or smoke spots which came from fissures or holes were located by a handheld


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Table 1. Reference LST obtained in the field and LST retrieved from thermal imagery. Surface type vegetation vegetation vegetation vegetation vegetation bare land bare land bare land

Reference LST (K) 287.69 287.56 287.62 286.06 286.26 287.05 291.61 287.45

Retrieved LST (K) 287.05 286.98 285.97 284.46 284.84 288.9 291.45 285.73

ΔT (K) 0.64 0.58 1.65 1.6 1.42 −1.85 0.16 1.72

Surface type bare land man-made surface man-made surface man-made surface man-made surface man-made surface water water

Reference LST (K) 288.92 289.97 289.39 288.65 290.18 288.95 288.36 286.3

Retrieved LST (K) 289.66 290.1 289.83 289.52 290.46 287.9 288.11 285.38

ΔT(K) −0.74 −0.13 −0.44 −0.87 −0.28 1.05 0.25 0.92


Root Mean Square Error of retrieved LST is 1.03 K.

Figure 2. (a). Extracted coal fire areas (purple areas) using TIR LST map on 2nd October, 2016. (b). Extracted coal fire areas (pink areas) using TIR LST map on 3rd October, 2016. (c). Extracted coal fire areas (coral areas) using TIR LST map on 5th October, 2016.

GPS device with the position accuracy of 3–5 m. As Figure 2 shows, purple, pink and coral areas signify recognized coal fire areas while blue, green, red crosses respectively express surveyed locations of the 1st, 2nd, 3rd time for surveying fire spots or smoke spots. And yellow areas numbered from 1 to 11 were applied to describe situations of coal fires in each area. The dense grass and steep hills are too abundant to discover these fire spots. Therefore, three-times surveys are sufficient to determine the positions of these obvious fire spots or smoke spots. In this case, the surveyed spot is thought to be overlaid with the coal fire areas if the distance of two is less than 3 m which is the minimum position error of the handheld GPS. The overlap ratios (true positive) of


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obvious fire spots and detected fire areas separately are 78.33%, 92.78% and 83.89% on TIR images of 2nd, 3rd, 5th October, 2016. Additionally, detected potential coal fire areas underground also need to be checked to ascertain whether fire is present. Taking coal fire areas of 3rd October, 2016 as an example, No. 1 fire areas including roads in Figure 2(b) were determined by local villagers, who claimed No. 1 fire area was once located over several pitheads of illegal small mines; a house shape from the centre of No. 2 area indicated an abandoned temple. This was also confirmed by local villagers who described the fact that coal fires from cracks ignited this temple; the thermal anomalies of high temperature from No. 3, No. 4 and No. 9 fire areas were validated using Fluke Ti400 thermal imager. Some false positives such as electric light (No. 5, No. 6 and No. 7 areas) and a transformer (No. 8 areas) from residential sites at night on 3rd October, 2016 had been removed according to electric heating mask. Most undetected fire areas such as tiny fire holes enclosed by red circles from No.2 area were more likely to be induced by weak thermal anomalies on the land surface. The diameters of these fire holes are usually much smaller than the resolution of collected thermal imagery (37 cm). To sum up, it is reasonable to infer that the detection accuracy of fire areas in Figure 2(b) reaches 92.78%. As shown in Figure 2(a), 26 surveyed fire spots in the afternoon of 2nd October, 2016 don’t coincide with identified fire areas in the northern part of No. 2 area, whereas identified fire areas in the south part have better overlaps with field surveyed spots with exception of 6 spots in the three red circles. Compared to that of Figure 2(b), No. 1 and No. 3 areas contain a few detected fire areas; No. 5, No. 6 and No. 8 areas don’t include any coal fire areas; No. 2, No. 4, No.7, No.9 and No. 10 areas increase more false positive fire areas. All these abnormal detected fire areas were possibly disturbed by solar irradiation-caused LST increment in daytime. In this case, the accuracy of detected coal fire areas in Figure 2(a) is probably less than 78.33%. As can be seen from Figure 2(c), similar to Figure 2(a), 24 surveyed fire spots on 5th October, 2016 and identified fire areas appear not to have any overlap in the north of No.2 area, while except for 5 points in two red circles, the majority of surveyed spots match with recognized fire areas in the southern part. Moreover, contrasted with Figure 2(b), No. 2, No. 7 and No. 9 areas increase more false positives resulting from the influence of solar heating land surface; No. 4 area and No.1 pavement area lost a great many fire areas; No. 3, No. 5 and No. 11 areas add few tiny fire areas; No. 6, No, 8 and No. 10 area don’t reserve any detected fire areas. Besides, contrasting to the previous two graphs, it’s found that detected fire areas tend to stretch in the southeast direction due to the influence of a northwest wind at the speed of 4.1 m s−1. Consequently, the detection accuracy of fire areas in Figure 2(c) should be lower than 83.89%.

5. Conclusions This article demonstrates an approach of identifying coal fire areas by using UAVs for TIR and colour imagery captured during different times over 3 days in Majiliang mine, Datong coalfield, northern China. Depending on reference LST at night on 3rd October, 2016, the estimation of LST from thermal imagery of 37 cm resolution by mean of radiance calibration and atmospheric correction indicates successful LST


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calculation results with the RMSE of 1.03 K. By comparing different coal fire areas detected from two-daytime and one-nighttime TIR images, the results show that recognized coal fire areas from nighttime thermal image achieve better identification accuracy of 92.78% in the condition of eliminating solar effects while detection accuracy of coal fire areas in the other two day times apparently is below 78.33 and 83.89% owing to interference of increasing surface temperature from solar irradiance. We also find that removing false fire areas caused by electric facilities at night would improve the precision of detection of coal fire areas. We furthermore conclude that improved resolution of TIR imagery helps to obtain more precise identification of weak and small coal fire areas.

Disclosure statement


No potential conflict of interest was reported by the authors.

Funding This work was supported by the Innovation Team Program for Fundamental Research Funds of Central Universities [Grant No. ZY20160102].

ORCID Feng Li

http://orcid.org/0000-0001-7885-9016

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