Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
Abstract
:1. Introduction
2. Forest Fire Detection by the Satellite data in China
2.1. The NOAA Satellite
2.2. The Chinese Polar Orbiting Meteorological Satellite FY-1C/1D
2.3. The Chinese Geostationary Meteorological Satellite FY-2
2.4. The CBERS: China-Brazil Earth Resources Satellite
2.5. The EOS/MODIS Satellite
2.6. The ESA ENVISAT Satellite
3. New Techniques Used in Detection of Forest Fire in China
3.1. Estimation of Sub-Pixel Fire Burned Areas and Temperature
3.2. Auto-Identification of Forest Fire Hot Spots
3.3. Establishment of a New Fire Detection Channel Selection from Fire Experiment
3.4. A New Algorithm for Fire Burned Areas Identification
4. Forest Fire Emissions Estimation in China
4.1. Forest Biomass Simulation Based on BEPS Model and Satellite data
4.2. Quantifying Emission of Forest Fire in China using Satellite Data and Emission Model
5. Forest Fire Risk Prediction in China
5.1. The Fire Spread Behavior Model
5.2. Forest Fire Risk Prediction Based on Satellite Data and GIS
5.3. Forest Fuel Moisture Content Estimation Model
5.4. Comparison between the Fire Risk Rating Systems in China and Those Used in Other Regions
6. Conclusions and Remarks
Acknowledgments
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Channel | Wavelength (μm) | Resolution (km) | Dynamic range | Detecting sensitivity | Fire detection |
---|---|---|---|---|---|
1 | 0.58–0.68 | 1.1 | 0–90% | S/N ≥ 3 (ρ = 0.5%) | Burnt area |
2 | 0.84–0.89 | 1.1 | 0–90% | S/N ≥ 3 (ρ = 0.5%) | Burnt area |
3 | 3.55–3.95 | 1.1 | 190–340 K | NEΔT ≤ 0.4 K (300 K) | Hot-spot |
4 | 10.3–11.3 | 1.1 | 190–330 K | NEΔT ≤ 0.22 K (300 K) | Hot-spot |
5 | 11.5–12.5 | 1.1 | 190–330 K | NEΔT ≤ 0.22 K (300 K) | Burnt area |
Channel | Wavelength (μm) | Resolution (km) | Dynamic range | Temperature resolution (K) | S/N | Primary use |
---|---|---|---|---|---|---|
1 | 0.5–0.9 | 1.25 | 0–98% | 0.5ρ = 2.5% 95ρ = 95% | Burnt area | |
2 | 3.5–4.0 | 5 | 180–330 K | 0.6–0.5 | Hot-spot | |
3 | 6.3–7.6 | 5 | 180–280 K | 0.5–0.3 | Water vapor | |
4 | 10.3–11.3 | 5 | 180–330 K | 0.4–0.2 | Hot-spot | |
5 | 11.5–12.5 | 5 | 180–330 K | 0.4–0.2 | Burnt area |
Payload | CCD | IRMSS | WFI |
---|---|---|---|
Sensor Type | Push-broom | Electro-mechanic | Push-broom |
Visible and near infrared bands (μm) | 1: 0.45–0.52 2: 0.52–0.59 3: 0.63–0.69 4: 0.77–0.89 5: 0.51–0.73 | 6: 0.50–0.90 | 10: 0.63–0.69 11: 0.77–0.89 |
Shortwave infrared bands (μm) | 7: 1.55–1.75 8: 2.08–2.35 | ||
Thermal infrared bands (μm) | 9: 10.4–12.5 | ||
Resolution (m) | 19.5 | Band 6–8: 78 Band 9: 156 | 258 |
View angle | 8.32° | 8.80° | 59.6° |
Swath wide (km) | 113 | 119.5 | 890 |
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Zhang, J.-H.; Yao, F.-M.; Liu, C.; Yang, L.-M.; Boken, V.K. Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview. Int. J. Environ. Res. Public Health 2011, 8, 3156-3178. https://doi.org/10.3390/ijerph8083156
Zhang J-H, Yao F-M, Liu C, Yang L-M, Boken VK. Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview. International Journal of Environmental Research and Public Health. 2011; 8(8):3156-3178. https://doi.org/10.3390/ijerph8083156
Chicago/Turabian StyleZhang, Jia-Hua, Feng-Mei Yao, Cheng Liu, Li-Min Yang, and Vijendra K. Boken. 2011. "Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview" International Journal of Environmental Research and Public Health 8, no. 8: 3156-3178. https://doi.org/10.3390/ijerph8083156