Establishment Of Forest Fire Spread Model Based On MODIS Data

This paper demonstrate how to establish exponent model of forest fire spreading with Gray System modeling theory and MODIS remote sensing data as original data source.The key point of this paper is to establish the technology system of acquiring remote sensing data, polising data,processing data,establishing forest fire spreading model and simulating the spreading model with the modern survey technology,to provide data support for the forest fire police based on the established fire spreading model.This paper also try to explore new way for the study of forest fire spreading regulations.The background of this paper is the forest fire which took place in Gala Mountain of NenJiang county,HeiHe city of HeiLongJiang province in 2006,and the researching data is the high time-resolution MODIS remote sensing image.On the comparasion of different remote sensing data,the MODIS that has the anti-fire channels is choosen to be the data source of this researching .I get the MODIS remote sensing image dated from May 21^th to May 29^th.The remote sensing image has a spectrum from visible light to infrared and the resolution include 250 metre,500 metre and 1000 metre.I try to dine the forest fire spreading model based on those dynamic MODIS data.In the process of modeling,the first step is the pretreatment of remote sensing image.Then process the data fusion work for the image to add the fire information to the image.Next the image need to be vectorized using the ArcGIS software so as to get some data information which is necessary to the forest fire spreading model establishment.In the process of vectorizing, The determination of the gravity center of polygon of forest fire spreading is the first problem to be solved.The octree is imported into this process to make the work meet the custom requirement.On the basis of the characteristic of the data,the ER algorithm and LIR algorithm are used to ensure the precision of this forest fire spreading model.From the model I set up, the forecasting relative error which is smaller than 1% make up 50% of the whole relative error, while the relative error smaller than 5% make up 25%,and the error between 5% and 10% make up 50%. In order to test and verify the precision,the forest fire data from Kandu River,Miandu River and Russia are used and it turns out that the precision meet the model requirement.GM(1,N) model , which could provide a new way to reasearch the relatively forest fire factors.It is the fist time to put forward gray forest fire spreading model and CA model in the computer simulation and computer expression, then make the two model together .and achieve advantage complementary in it. at the same time, we also use ArcEngine to open up forest fire forecast system, which could successfully load remote sensing image, tracking the forest fire's borderline ,modeling data and getting data automatically, forecasting forest fire spreading borderline automatically, and could also make a compare for load remote sensing image.The main originality of this paper include:(1)Set up the forest fire spreading model using the remote sensing data for the first time.Thus this paper propose a new way for forecasting the spread of forest fire and establish a new technology system for forest fire spreading model establishment.(2)In the course of modeling,the method of robust estimation is imported and the ER algorithm and LIR algotirhm are proposed according to the characteristic of the modeling data.(3)The Gray Theory which is imported into the modeling research make the model simple,clear and visual with high precision.At the same time this model is in common use and easy to be implementde by computer to achieve auto-forecasting.(4)In the simulation on computer,the idea of improving the CA model on the basis of gray forest fire spreading model is proposed.Establish technic system for gray forest fire spreading model which base on MODIS data is really a complementarity to traditionally forest fire spreading model, after make a conclusion of past experience it make the data choosing and model theory together, in this way, we could see a bright future in establishing forest fire spreading model.