## 6 Conclusions and Remarks  
This paper has provided an overview of the fire detection, emission estimation and risk prediction via operational satellite sensors, simulated models and climate information in China in the past three decades. Overall, in the last 30 years, China has developed its meteorological and environmental satellite sensors to detect forest fires across the nation and the surrounding areas. The second generation of China's polar orbiting meteorological satellite (Fengyun-3) with substantially advanced multi-spectral imaging systems was launched on 28 May, 2008. The disaster and environment monitoring and forecast small satellite constellation was launched on 6 September, 2008, and comprised two satellites (HJ-1A and HJ-1B). There are three kinds of optical remote-sensing sensors on board, multi-spectral imager (CCD) with 360 km swath and 30 m spatial resolution, infrared imager (IRS) with 720 km swath and 150 m spatial resolution, and hyper-spectral imager (HIS) with 50 km swath and 100 m spatial resolution, and 110 channels range 0.45-0.90 \(\upmu\)m [116]. The CCD (0.43-0.9 \(\upmu\)m) and IRS data (3.5-3.9 \(\upmu\)m) work well for the hot-spot detection and forest fires assessment [116-118]. Along with other currently operational satellites such as NOAA/AVHRR, FY-series, EOS/MODIS, ENVISAT and CBERS, a more accurate and efficient operational system in fire detection, emission estimation and risk prediction can be established and operated before, during and after a fire season. However, for the fire risk management one needs to take into account and assess the fire risk potentials.  
A forest fire can be a real ecological disaster regardless of whether it is caused by natural forces or human activities. It is possible to map forest fire risk zones to minimize the frequency of fires and avert damage. As climate changes and extreme climatic events become more prevalent, the frequency and intensity of forest fire occurrence will likely increase in China and the surrounding areas. It is important to develop a new fire danger and risk model to forecast the forest fire potential. Accurate forecasting of the El Nino event occurrence is critical.  
Forest fires have significant impact on vegetation dynamics and deplete timber resources, so that forest fires monitoring is a critical aspect of sustainable forest management. Forest fires have direct and important effects on the global carbon cycle, atmospheric chemistry, regional radiation and climate and in regulating terrestrial ecosystems and biodiversity [119]. Uncertainty over the effects of future climate change upon fire regimes, and the importance of vegetation-atmosphere feedbacks has fostered increased effort to develop coupled models of vegetation and fire to understand these future changes. To reach a closure in carbon balance including some greenhouse gases as CO\({}_{2}\) and CH\({}_{4}\), _etc._, and to understand smoke aerosols produced from burning and which influence global climate and atmospheric chemistry, the forest fire emissions estimates based on satellite sensors is very important and complex. Additional studies should be carried out to accurately detect forest burned areas, fragmentation of burned scars, fuel loading and biomass based on the multi-sensors. Further investigation, analysis, and computations of the fraction of fuel consumed by forest fire, and emission factors for the different vegetation types are necessary to quantitatively evaluate and verify Chinese forest fires emissions in the future.