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.

No abstract

No abstract

Conventional variogram has been widely applied to study spatial variability of geochemical data. In case of data is not normally distributed, the conventional estimator is biased. In this study, Cressie variogram and Moran correlogram were used to identify the degree of spatial variabilty of Cu content using 1341 stream sediment samples in Jiurui copper mining area. Cressie variogram was applied to reduce the influences of high values in identifying spatial variability in different directions. Moran correlogram was employed to study spatial correlation at different distances and the influences of data distribution on the results in quantitative ways. It was found that Cressie variogram yields stable robust estimates of the variogram with the maximum spatial variability of 12km for all directions; Moran correlogram provided more information, directly viewed and stable than variogram. Moran correlogram identified a strong positive spatial correlation at distances below 6km for the raw data and a strong positive spatial correlation at distances below 11km for Box-Cox transformed data.

In this study, vegetation coverage changes over a 30-year period for the Tuy Duc and Dak R’lap districts,Dak Nong province (central highland of Vietnam) were assessed using remote sensing and Geographic Information Systems (GIS) techniques. 03 Landsat satellite images,including Landsat TM February 13, 1990, Landsat TM February 22, 2005 and Landsat 8 January, 15 2020 were used to calculate the normalized difference vegetation index (NDVI), then assessed the changes in vegetation coverage density. The NDVI differencing method is also used as a change detection method and provides detailed information for monitoring changes in land cover in periods 1990 – 2005, 2005 – 2020 and 1990 – 2020. Analysis of the obtained results showed that the vegetation coverage declined sharply during 1990 – 2005 period,then the vegetation coverage has begun to recover in period 2005 – 2020. From the findings of this study, it can be easily concluded that the Tuy Duc and Dak R’lap areas has lost its valuable vegetation cover both qualitatively and quantitatively.