System Design And Development For Risk Assessment And Early Warning Prevention And Control Of Forest Fire

Forest have a series of hazards including air pollution damaging the ecological environment,endangering human health and property safety.Traditional forest fire risk assessment is mainly based on ground weather station data(temperature,humidity,wind force,wind direction,etc.),which lack of spatial continuity,and the warning effect is not ideal.With the rapid development of Earth observation technology and high-performance computing technology,the risk assessment and early warning prevention and control of large-scale forest fires have attracted the attention of experts and scholars at home and abroad.It also has become a research hotspot of forest remote sensing in recent years.Satellite remote sensing technology has characteristics of high speed and short period,and it is capable of obtain large-scale terrestrial image data.This technology has been widely used in agriculture,forestry,geology,oceans and other fields.However,with the continuous improvement of spatio-temporal resolution of remote sensing images,the data volume reached PB level already.Traditional serial data processing is not able to handle the storage of near-real-time data processing and analysis of massive remote sensing data.In the mean time,large-scale forest fire risk assessment and early-warning prevention and control require near-real-time,spatially continuous monitoring data and big data processing technologies.In response to the problem above,this thesis solves the problem of processing and storage of massive remote sensing data by using parallel computing and advanced big data technology.According to the needs of forest fire risk assessment and early warning prevention and control,this thesis finally designs and implements a convenient forest fire risk assessment and early warning prevention and control system by integrating remote sensing product data and computing resources.The main content of this thesis includes three parts:(1)We use golang programming to implement global FMC product parallel production by combine the Torque batch task scheduling framework with the vegetation canopy fuel content(FMC)product algorithm.Then,based on the Hadoop distributed file system(HDFS),Geotrellis raster maniputation tool and Accumulo,a key-value storage database,we implement distributed storage of FMC products which has rapid inquiry of remote sensing products.(2)This thesis uses the Spark MLlib machine learning tool to produce the vegetation combustion index(FI)products based on the logistic regression model.The near-realtime wildfire monitoring products are produced using weather satellite data based on the fire spot detection algorithm.Then,this thesis designs and implements a set of abovementioned product automated production scripts,including product download,preprocessing,missing data filling and algorithm parameter calculation and other common modules.(3)This thesis designed and implemented a forest fire risk assessment and early warning and prevention system platform based on the combine use of Linux highperformance distributed computing storage platform and forest fire data production.The system is composed of three layers include client layer,application layer and data layer.Combined with the production service of forest fire product,this system has become a production service system includes production,storage,processing and visualization process.Clients can submit orders,manage data and visualize forest fire risk assessment in a convenient and fast manner via Web.Compared with the traditional integrated complex system,the system is lightweight,extensible and easy to use.