## A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery
###### Abstract  
The study of the effects of the radiation budget on the Earth's ecosystems and climate, as it drastically alters the landscape and biological structure, and emits large amounts of greenhouse gases and aerosol particles [1]. Smoke aerosols may interact with cloud droplets and considerably change the earth's radiation budget [2-3]. It is important to recognize that changes in climate can affect changes in fire regime, which is a combination of the type of fire which occurs in a given region, the frequency at which fires occur, and the seasonality of burning. Consequently, there is the potential for substantial feedback between fire and the environment. Global fire detection is thus a very important issue for humans and environment. Whether fire detection can be effective, timely and accurate or not, will correlate directly with the security of human life and the degree of destruction of the environment. Remote sensing is able to monitor the fire activity over a wide range. An increasing number of programs have been established with the goal of obtaining information about and determining the fire regime by satellite, e.g., the International Geosphere-Biosphere Program, Data and Information System's (IGBP-DIS) Global Fire Product initiative [4], the ASTR World Fire Atlas [5-6] and the MODerate Resolution Imaging Spectro-radiometer (MODIS) Fire Product [7]. Different sensors have both advantages and limitations for global and regional fire detection. Geostationary satellites can acquire data every 15 to 30 minutes over a given area, but any given one of them may only cover a portion of the Earth's surface. Further, they generally provide very low spatial resolution (4 Km pixel size or larger). On the other hand, current satellite sensors with high spatial resolution such as the Landsat Thematic Mapper (TM), which has 60 m resolution in the thermal band, often cannot cover the global area quickly, and only provide a 16 days revisit period for most parts of the Earth. The Advanced Very High Resolution Radiometer (NOAA-AVHRR) has a long history in fire detection. It offers a spatial resolution of 1 km and covers most of the Earth's surface every day. However, its Mid-infrared (MIR) channel has a low saturation of 320 k, which limits the use for fire detection [2; 8]. MODIS on Terra and Aqua can cover most of the world at least four times a day (twice during the day and twice during the night). Meanwhile, MODIS enhances the saturation brightness temperature to 500 K and 340 K for the MIR and TIR channels, respectively [7; 9-10]. MODIS is expected to play an important role in global fire detection and monitoring, but its nadir spatial resolution of 1 km may not be fully sufficient for the early detection of small fires, resolution of individual fire fronts and fire intensity estimation [7]. For smaller fires, MODIS' spatial resolution limits its ability to detect active fires and the determination of effective fire temperature and effective fire area for smaller fires are impossible with MODIS, because their TIR signals are around the same level as the background variation [11]. The Bispectral InfraRed Detection (BIRD) small satellite mission, launched on 22 October 2001, was developed by the German Aerospace Center (DLR), and continued to make observations until the beginning of 2004. The principal BIRD imaging payload includes the Hotspot Recognition System HSRS with channels in the Mid-Infrared (MIR,3.4-4.2 \(\upmu\)m)and the Thermal Infrared (TIR 8.5-9.3 \(\upmu\)m) spectral ranges and the Wide-Angle Optoelectronic Stereo Scanner WAOSS-B with a nadir channel in Near-Infrared (NIR:0.84-0.90 \(\upmu\)m). The ground resolution of the BIRD nadir channels is 185 m in the NIR and 370 m in the MIR and TIR. However, all three channels have the same sampling step of 185 m due to a factor of 2 oversampling of the MIR and TIR data. BIRD can detect hot targets as small as 1/7 of a MODIS or AVHRR pixel [12-13].