## 2 Forest Fire Detection by the Satellite data in China
### The EOS/MODIS Satellite  
A key instrument of the NASA's EOS Terra and Aqua missions, the MODIS was designed to measure the reflected and emitted radiation in multiple channels including VIS, NIR, MIR, and TIR.  
The MODIS data provide good temporal coverage (four time daily), high spectral resolution (36 bands total) and a medium spatial resolution (250 m, 500 m, and 1000 m). The MODIS has a higher spatial resolution (250 m) than that of AVHRR for Band 1 (0.620-0.670 \(\upmu\)m) and Band 2 (0.841-0.876 \(\upmu\)m). Additionally, the images and high level products are freely accessible. The four time daily MODIS images greatly enhanced fire monitoring on a global scale with a 1 km spatial resolution for MODIS channels at 4 \(\upmu\)m (Ch. 21, Ch. 22) with high saturation level at 500K and 331K, and 11 \(\upmu\)m (Ch. 31) with high saturation level at approximately 400 K for the Terra MODIS and 340K for the Aqua MODIS, respectively [22,42,43]. The MODIS Ch. 1 and Ch. 2 are usually applied to detect the burned areas and the smoke; and the Ch. 1, Ch. 2 Ch. 6, Ch. 7, Ch. 20- Ch. 25, Ch. 31 and Ch. 32 are used to detect active fire [22,29]. The MODIS active fire algorithm was developed by NASA [22,42], and have been used in detecting forest fire in many regions in the world [43-47].Chinese researchers began to detect forest active fires using MODIS active fire algorithm developed by [PERSON] _et al._ from 2001 [48-51], and forest active fire can be detected accurately in different region of China, except that a few low temperature hot spots might be missed, depending on the differences of regions or seasons [52]. [PERSON] _et al._ reported that the MODIS band 7 is sensitive to high temperature, not sensitive to medium temperature, and this can be used to verify fire intensity [53]. We developed a MODIS three channels active fire algorithm. Figure 5 shows fire hot spots detected using the EOS/MODIS data in Northern China. In this case, we used Ch. 20 (3.66-3.84 \(\upmu\)m), Ch. 2 (0.84-0.87 \(\upmu\)m) and Ch. 1 (0.62-0.67 \(\upmu\)m) (shown in the left image of Figure 5) and Ch. 7 (2.10-2.13 \(\upmu\)m), Ch. 2 and Ch. 1 (shown in the right image of Figure 5) to form some false-color composites to detect forest fire hot spots and smoke plumes.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Payload** & **CCD** & **IRMSS** & **WFI** \\ \hline Sensor Type & Push-broom & Electro-mechanic & Push-broom \\  & 1: 0.45–0.52 & & \\ Visible and near & 2: 0.52–0.59 & 6: 0.50–0.90 & 10: 0.63–0.69 \\ infrared bands & 3: 0.63–0.69 & & 11: 0.77–0.89 \\ (\(\upmu\)m) & 4: 0.77–0.89 & & \\  & 5: 0.51–0.73 & & \\ Shortwave infrared & & 7: 1.55–1.75 & \\ bands (\(\upmu\)m) & & 8: 2.08–2.35 & \\ Thermal infrared & & 9: 10.4–12.5 & \\ bands (\(\upmu\)m) & & & \\ Resolution (m) & 19.5 & Band 6–8: 78 & 258 \\  & & Band 9: 156 & \\ View angle & 8.32\({}^{\circ}\) & 8.80\({}^{\circ}\) & 59.6\({}^{\circ}\) \\ Swath wide (km) & 113 & 119.5 & 890 \\ \hline \end{tabular}
\end{table}
Table 3: The CBERS payloads, sensors and channels characteristics.  
Figure 5 presents an example of forest fire hot spot detection using the EOS/MODIS in Northern China on 14 October, 2001. The left image was mapped using MODIS Ch. 20 (3.66-3.84 \(\upmu\)m, R), Ch. 2 (0.84-0.87 \(\upmu\)m, G) and Ch. 1 (0.62-0.67 \(\upmu\)m, B); and right image was mapped using MODIS Ch. 7 (2.10-2.13 \(\upmu\)m, R), Ch. 2 (G) and Ch. 1 (B) through the false-color composite analysis to detect forest fire hot spot (red colour) and smoke plumes. For MODIS, the smoke can be much more easily identified.