Paper
25 October 2010 A new model for fire forecast
Author Affiliations +
Abstract
In the last ten years, with the help of satellite remote sensing, we build up a huge database of fire points in China. The remote sensing data that we used to do the fire monitoring include NOAA, FY-1, FY-3 and MODIS. In this paper, we present a new model for fire forecast base on the former database and NCEP reanalysis data of last ten years. As we know, the reason of land surface fire can be divided to two groups: subsurface property and meteorological factors. Both of them are very complicated. For subsurface property, there are many factors that relational to wild fire, such as land surface type and combustible material. For meteorological factors, they also strongly impact to the fire occur. There are four factors of meteorological should be pay attention in the fire forecast, they are wind speed, precipitation, temperature and humidity. For the former two groups of reasons of fire's taken place, we build a two-part model to do the fire forecast. For the first part, corresponding to the subsurface factors, we used the ten years fire points monitoring database to describe it. We do the statistics on the database by five days (overlapping, 366 periods totally) and 0.5625 degree grid (according to NCEP). In each grid and each period of days, the average number of fire points describes the fire status corresponding to the average meteorological conditions and subsurface condition at that grid and at that time period. For the second part, firstly, we average the four meteorological factors into five days periods and 0.5625 degrees grids; secondly we evaluate the different of the four factors from the average value in the target day (forecast day).
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Shengli Wu and Cheng Liu "A new model for fire forecast", Proc. SPIE 7831, Earth Resources and Environmental Remote Sensing/GIS Applications, 78310O (25 October 2010); https://doi.org/10.1117/12.865533
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Cited by 1 scholarly publication.
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KEYWORDS
Meteorology

Databases

Atmospheric modeling

Remote sensing

Data modeling

Environmental sensing

Humidity

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