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首页> 外文期刊>International Journal of Environmental Research and Public Health >Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview
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Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

机译：过去三个十年中利用卫星传感器和模拟模型对中国森林火灾进行检测，排放估算和风险预测的概述

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摘要

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction，have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.
机译：森林火灾对生态系统产生重大影响，并极大地影响大气中温室气体和气溶胶的数量。本文概述了过去三十年来中国利用卫星图像，气候数据和各种模拟模型进行的森林火灾探测，排放估算和火灾风险预测。自1980年代以来，许多卫星（例如，NOAA / AVHRR，FY系列，MODIS，CBERS和ENVISAT）获取的遥感数据已被广泛用于检测中国的森林火灾热点和烧伤地区。一些发达的算法已被用于检测亚像素级别的森林火灾热点。关于对森林燃烧排放进行建模，开发了遥感数据驱动的净初级生产力（NPP）估计模型，用于估计森林生物量和燃料。为了改善我国森林火灾的风险建模，采用了地面温度，相对湿度，风速和风向等实时气象数据作为模型输入，以改善森林火灾发生及其行为的预测。卫星传感器的短波红外（SWIR）和近红外（NIR）通道已用于检测活燃料含水量（FMC），而归一化差水指数（NDWI）用于评估森林植被状况及其水分状况。

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