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

Abstract

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.

Community Citizen Science for Risk Management of a Spontaneously Combusting Coal‐Mine Waste Heap in Ban Chaung, Dawei District, Myanmar

“…From 2014 on, in every season (Figures S5 to S9 in SI and Figure 5), a permanent hot spot has been present at the coal-mine waste heap. The surface temperature elevation of approximately 5°C to 10°C at the minewaste heap hot spot is consistent with the underground spontaneous combustion observed in other cases (Nguyen & Vu, 2019;Saraf et al, 1995). This confirms that spontaneous combustion inside the waste heap as well as low-temperature oxidation and occasional smoldering on the surface of the waste heap have been ongoing since 2014.…”

Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

“…Anselin (1995) indicates that testing for the significance of spatial autocorrelation statistics such as the global and local Moran's I, and Getis-Ord's G * i can be carried out based on an assumption of a normal distribution. However, these statistics are very sensitive to a strongly skewed distribution (Hoang et al 2017;Nguyen et al 2014;Nguyen 2018;Nguyen et al 2016;Nguyen and Vu 2019a;Nguyen and Vu 2019b) due to the existence of a high and very high number of COVID-19 cases in some provinces or cities. Wherefore, in this study, testing for the significance of these spatial autocorrelation statistics was carried out by a randomization test which recalculates the statistic many times to generate a reference distribution (Anselin 2005).…”

“…Several studies (Alves et al 2021;Liu et al 2021;Nguyen and Vu 2019b) have computed the Getis-Ord's G * i statistic with the help of ArcGIS software using Getis z-scores defined in a study by Mitchel (2005). If provinces/cities with 1.65<Getis z-scores<1.…”

Landsat Time-series Images-based Urban Heat Island Analysis: The Effects of Changes in Vegetation and Built-up Land on Land Surface Temperature in Summer in the Hanoi Metropolitan Area, Vietnam

“…The first step involves the conversion of the DN data (Q cal ) to top of atmosphere (ToA) radiance (L ToA,λ ) using inflight sensor calibration parameters in the metadata file. The conversion of (Q cal )-to-(L ToA,λ ) for Landsat-5 TM data (Chander et al, 2009;Vu and Nguyen, 2018a) and Landsat-8 OLI/TIRS data (Nguyen and Vu, 2019;Zanter, 2016) are performed using Equation (1) and(2), respectively:…”