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Proceedings Paper

Forecasting method of national-level forest fire risk rating
Author(s): Xian-lin Qin; Zi-hui Zhang; Zeng-yuan Li; Hao-ruo Yi
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Paper Abstract

The risk level of forest fire not only depends on weather, topography, human activities, socio-economic conditions, but is also closely related to the types, growth, moisture content, and quantity of forest fuel on the ground. How to timely acquire information about the growth and moisture content of forest fuel and climate for the whole country is critical to national-level forest fire risk forecasting. The development and application of remote sensing (RS), geographic information system (GIS), databases, internet, and other modern information technologies has provided important technical means for macro-regional forest fire risk forecasting. In this paper, quantified forecasting of national-level forest fire risk was studied using Fuel State Index (FSI) and Background Composite Index (BCI). The FSI was estimated using Moderate Resolution Imaging Spectroradiaometer (MODIS) data. National meteorological data and other basic data on distribution of fuel types and forest fire risk rating were standardized in ArcGIS platform to calculate BCI. The FSI and the BCI were used to calculate the Forest Fire Danger Index (FFDI), which is regarded as a quantitative indicator for national forest fire risk forecasting and forest fire risk rating, shifting from qualitative description to quantitative estimation. The major forest fires occurred in recent years were taken as examples to validate the above method, and results indicated that the method can be used for quantitative forecasting of national-level forest fire risks.

Paper Details

Date Published: 24 November 2008
PDF: 7 pages
Proc. SPIE 7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 712317 (24 November 2008); doi: 10.1117/12.816205
Show Author Affiliations
Xian-lin Qin, Research Institute of Forest Resource Information Technology (China)
Zi-hui Zhang, State Forestry Administration (China)
Zeng-yuan Li, Research Institute of Forest Resource Information Technology (China)
Hao-ruo Yi, Research Institute of Forest Resource Information Technology (China)


Published in SPIE Proceedings Vol. 7123:
Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China
Qingxi Tong, Editor(s)

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