Description of required input for the CFQ algorithm, and its output products

10.4.1 Inputs to the CFQ algorithm

Satellite data

The CFQ algorithm has been developed especially for ASTER channel 10 and ETM channel 6 data. It has been implemented in such a way that it automatically handles both ETM channel 6 high-gain data and ETM channel 6 low-gain data. The ETM and ASTER data, being ingested into the algorithm, are sensor-calibrated and geometrically corrected level 1b data sets, with pixel values in digital numbers (DN).

Digital elevation model

A digital elevation model (DEM) can be optionally provided to the algorithm. The DEM has to have the same spatial resolution as the corresponding satellite data and must also cover a similar area. DEM pixel values have to be assigned in meters above sea level. In the case where no DEM is available, the user can specify a mean sea level height that can then be used as a reference height for the atmospheric correction.

Coal fire pixel mask

A coal fire pixel mask is mandatory for the CFQ algorithm. The mask must be derived from the ETM or ASTER satellite data, prior to running the quantification algorithm. This can be done either by using

10 An ASTER and ETM satellite data based, automated coal fire quantification algorithm

an automated, coal fire detection algorithm (e.g. Zhang, 2004), or by visually interpreting the thermal anomalies of the corresponding satellite scene. The coal fire mask has to be stored as a binary array, with coal fire values set to one (1) and no coal fire values set to zero (0).

10.4.2 CFQ algorithm output products

The output of the coal fire quantification algorithm consists of four data sets, described below:

Main output

The main output consists of two TIFF files containing a visualisation of the computed mean CFRE values. The first file includes colour-coded, mean CFRE values for each fire clusters, scaled logarithmically from 0.1 to 10 MW and 0.1 MW to 100 MW, respectively. The different scaling is performed, on the one hand to highlight energy variations of fire clusters with low corresponding CFRE values, and on the other, to avoid saturation of fire clusters with high corresponding CFRE values. An example of the main output from the CFQ algorithm is displayed in figure 10-2.

Figure 10-2: An example of the CFQ algorithm main output: CFRE images from the Ruqigou and

Gulaben coalfields, derived from ETM night-time TIR data. The output includes two CFRE images representing mean, computed CFRE estimates for entire fire clusters, scaled logarithmically, left) from 0.1 to 10 MW and right) from 0.1 to 100 MW.

Tabulated output (ASCII file)

Beside the main output, an ASCII file is provided, which contains a fire identifier/cluster number (in the first column), the upper left x and y coordinates of each fire cluster (second and third column), the total size of the fire cluster (fourth column), the computed mean CFRE value of each fire cluster (fifth column), the computed maximum CFRE value (sixth column) and the computed minimum CFRE value (seventh column). Table 10-5 includes an example of the CFQ output ASCII file.

10 An ASTER and ETM satellite data based, automated coal fire quantification algorithm

Table 10-5: An example of the CFQ output ASCII file.

The upper left x and y coordinates refer to pixel positions and not to geographical coordinates. The cluster size is given in m² and refers to the area coverage of the detected coal fire cluster. Due to the fact that a coal fire pixel always contains both fire and background information, as described in chapter 5, the cluster size does not refer to the actual size of the coal fire surface anomaly. The mean,

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computation of the mean, max and min CFRE is explained in section 10.3 (quality assessment). 10.5 Limitations and transferability of the CFQ algorithm

The atmospheric correction included in the CFQ algorithm is adapted to dry-desert atmospheric conditions. In the TIR spectral range, the ground-to-sensor signal is strongly influenced by atmospheric water vapour content. The dry desert model used here assumes a very low atmospheric water vapour content, limiting the application of the current CFQ algorithm to arid and semi-arid regions. The CFQ algorithm can however easily be adapted by the user to other atmospheric conditions, simply by replacing the respective look-up tables.

A major limitation of the CFQ algorithm that occurs as a result of the implemented TIR band based, coal fire quantification approach is, as described in chapter 7, that the TIR method cannot be applied to large and hot CFSA anomalies. ‘Hot’ in this case refers to average surface temperatures of more than 600 K. Nevertheless CFSA in the three coalfields are in average significantly cooler than 600 K, and similarly low, coal fire related, surface temperatures have been reported from other coalfields in northern China (see chapter 2, section 2.3). As this algorithm, however, was only tested in three coalfields yet, it will be subject of further work to verify the wider applicability of the CFQ algorithm for general quantification of coal fires in northern China.

An additional limitation of the TIR method is it’s high sensitivity to background temperature variations (description in chapter 7). The CFQ main output (CFRE images) should thus only be interpreted in combination with the quality assessment figures along with the tabulated output.

cluster number UL x UL y cluster size mean CFRE max CFRE min CFRE

1 238 141 25200 1.0003818 1.2221395 0.7880989 2 245 174 136800 7.7151241 10.9264536 4.9836359 3 186 178 14400 0.5775985 0.6897060 0.4684574 4 181 184 32400 0.8871909 1.3476485 0.4675472 5 22 186 21600 0.9915951 1.3235970 0.6804683 … … … …

10 An ASTER and ETM satellite data based, automated coal fire quantification algorithm

Despite the limitations induced by the atmospheric correction and the TIR method, the accuracy of the CFRE determination strongly depends on the accuracy of the input coal fire pixel mask. As mentioned in the introduction to this chapter, the research of Zhang et al. (submitted) and Zhang (2004) has outlined that coal fires can be detected, in previously unknown areas, using ETM data. However, an additional, visual interpretation of the automatically detected anomaly pixels, achieved by combing geological field information, and the knowledge of local coal fire experts, will definitely improve coal fire detection. Additional input information should therefore be considered whenever it is available.

As discussed before, the CFQ algorithm here presented is specifically developed for ASTER and ETM TIR band data. It can however, be easily adapted to other high-resolution TIR data (for example to high-resolution airborne TIR data) by changing the atmospheric lookup tables and the calibration parameters in the configuration file. Thus, the presented algorithm can, in cases where the coal fire surface temperatures are lower than 600 K, be regarded as a robust tool for a remote coal fire analysis task.