The aim of the CFQ algorithm

The ASTER / ETM CFQ algorithm is designed to compute coal fire, radiative energy releases (CFRE), from potential coal fire pixels of respective satellite image data. The coal fire quantification is performed via the TIR method (description in chapter 7), using ETM band 6 or ASTER band 10 respectively. The CFQ algorithm does not include a coal fire detection element and may thus be seen as a post-processing step to an ASTER and ETM data based coal fire detection procedure.

Zhang (2004) recently presented the first non-interactive TIR satellite data based, coal fire detection algorithm. The coal fire detection is performed here via a statistical method, using a moving window technique to extract thermal anomalies in large areas. For each window a set of statistical tests is performed including, e.g., histogram-based, dynamic threshold tests to identify potential coal fire related thermal anomalies.

Based on the fact that coal fires often occur on, or in the vicinity of coal-bearing strata and coal surfaces like coal storage and coal waste piles, Kuenzer (personal communication, 2004) is developing an automated, coal fire area demarcation algorithm. The algorithm mainly aims at the automated extraction of coal surfaces from multi-spectral ETM and Aster daytime data, applying a knowledge- based test sequence and partial unmixing techniques to calibrated and atmospherically corrected data. The extracted coal surfaces are then buffered to define a zone in which the occurrence of coal fires is likely. These demarcated regions are further modified taking vegetation density and the occurrence of pyrometamorphic rocks into account. A first application of the statistically based thermal anomaly detection method, combined with coal fire area demarcation on ETM data from two previously non- studied areas, has demonstrated that coal fires can be reliably detected using this approach (Zhang et al., submitted).

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

At the DLR, the algorithms of Zhang (2004) and Kuenzer (personal communication, 2004), and the presented CFQ algorithm are currently being combined into an automated, coal fire detection and quantification algorithm, suitable to detect and quantify coal fires in northern China on a routine basis. 10.2 CFQ algorithm description

10.2.1 Algorithm outline

The CFQ algorithm presented here mainly performs the different processing steps described in detail in chapter 8, sections 8.1.1 (ETM data processing) and 8.3.1 (ASTER data processing). The CFQ algorithm requires ASTER channel band 10 data or ETM channel band 6 data, and a coal fire mask (= binary file including detected coal fire pixels) as input. The output includes two different CFRE images as well as an ASCII file containing the computed CFRE values and a quality assessment.

Figure 10-1: Outline of QFC algorithm.

In a first processing step the input data sets are calibrated and atmospherically corrected. The atmospheric correction is adapted to dry-desert climatic conditions, which are considered to be suitable for the semi-arid to arid regions in northern China. Following the results of the theoretical study (chapter 6) and the two case studies (chapters 8 and 9), neighbouring fire pixels are then grouped together to form continuous fire clusters, in order to minimise computation errors, induced by background signal variations. The TIR method (description in chapter 7) is applied to each individual coal fire cluster to calculate the respective CFRE. Prior to the preparation of the output data, a quality assessment is performed for each computed CFRE value.

MODULE 1

calibration of satellite data and atmospheric correction

MODULE 2

clustering of adjacent image pixel computation of CFRE values computation of CFRE via the TIR method

MODULE 3

preparation of output

ASTER / ETM data DEM (optional) coal fire mask

coal fire statistics CFRE images

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

The ASTER / ETM CFQ algorithm can be logically dived into three main modules (figure 10-1). The first module performs the calibration and atmospheric correction of the ASTER and ETM data sets. The second module clusters adjacent coal fire pixels into coal fire clusters and computes respective CFRE for each coal fire cluster via the TIR method, while the last module creates the output CFRE images and the tabulated results including the computed CFRE values and a quality assessment.

10.2.2 Module 1: Calibration and atmospheric correction

This module includes the calibration and atmospheric correction of the ASTER and ETM data. The calibration of the ASTER and ETM TIR data is performed via equation 3-6 and the calibration parameters listed in tables 10-1 and 10-2. Different calibration parameters are applied for ASTER band 10, ETM channel 6, high-gain data and ETM channel 6 low-gain data. Due to the fact that a bias of

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-launch calibration of the ETM band 6 data that was processed before the 20th December 2000, ETM data sets processed before this date are corrected for this bias. The configuration parameters are stored in a respective file and can be adapted by the user if required.

Low Gain High Gain

Lmin [W / m² sr 0 m] Lmax [W / m² sr 0 m] Lmin [W / m² sr 0 m] Lmax [W / m² sr 0 m] 0.0 17.04 3.2 12.65

Table 10-1: Radiances at the maximum and minimum digital number of the ETM TIR channel, used to

transform ETM level 1b DN values to spectral radiances (Landsat 7 Science Data Users Handbook: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html) Lmin [W / m² sr 1 m] Lmax [W / m² sr 1 m] 0.0 32.7

Table 10-2: Radiances at the maximum and minimum digital number of the ASTER TIR channel, used

to transform ETM level 1b DN values to spectral radiances (http://asterweb.jpl.nasa.gov/).

The atmospheric correction of the ASTER and ETM data was optimised to dry desert conditions. The atmospheric correction parameters are calculated via the MODTRAN code (Berk et al., 1989), as with the data processing described in chapter 8, sections 8.1.1 and 8.3.1. The atmospheric correction is performed via equation 3-7 and the atmospheric parameters are listed in tables 10-3 and 10-4. In cases where the user provides a digital elevation model (DEM), an atmospheric correction is performed for each image pixel in order to account to the changes in atmospheric thickness due to actual topographic

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

elevation. If no DEM is available, all pixel values are corrected using atmospheric parameters corresponding to a user-defined, mean terrain elevation. The parameterised atmospheric constants are stored in external look-up tables, which can be modified by the user.

high level [km] Lpath [mW / m2

trans 0.0 2445.3 0.7080 6803.7 0.5 2159.1 0.7320 5932.5 1.0 1891.7 0.7557 5141.4 1.5 1640.8 0.7793 4423.7 2.0 1408.9 0.8028 3768.7 2.5 1201.3 0.8262 3176.3

Table 10-3: Path radiance (Lpath), atmospheric ground-to-sensor transmittance (T) and downwelling flux of atmosphere (F) used for the atmospheric correction of the ETM channel 6.

high level [km] Lpath[mW / m2 sr m] trans Flux [mW / m

2 m] 0.0 1267.2 0.7814 5817.4 0.5 1118.9 0.8079 5072.5 1.0 980.3 0.8341 4396.1 2.0 850.3 0.8602 3782.4 1.5 730.1 0.8861 3222.4 2.5 622.5 0.9119 2715.9

Table 10-4: Path radiance (Lpath), atmospheric ground-to-sensor transmittance (T) and downwelling flux of atmosphere (F) used for the atmospheric correction of the ASTER channel 10.

10.2.3 Module 2: Clustering of adjacent coal fire pixels and computation of CFRE

In module 2 adjacent fire clusters are aggregated into continuous fire clusters. After the clustering, a two-dimensional distance map is calculated, which contains the distance to the nearest background pixel for each coal fire cluster. Each element in this distance map is assigned a distance corresponding to the number of pixels to be visited when travelling from the current fire cluster to the neighbouring background pixel. Based on this distance map, the ten closest neighbours to the fire cluster are selected in order to compute the mean background radiance, as well as the respective standard deviation of the background radiance. Based on the computed background radiances, CFRE values are computed in a next processing step for each coal fire cluster using equations 7-1 (ETM data) and 7-2 (ASTER data).

10.2.4 Module 3: Preparation of output

In this module a colour-coded CFRE image is created that includes the mean CFRE value for each coal fire cluster. Furthermore, a tabulated output file is generated containing the CFRE estimates and the quality measures for each cluster. This allows visual assessment of the results as well as the use in further processing steps.

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