多源遥感森林火情监测的进展与展望

Progress and prospect of forest fire monitoring based on the multi-source remote sensing data

掌握森林火情时空特征及其演变态势对于灾害防控具有重要意义。随着大量新一代亚米级星载平台和传感器投入使用、轻小型无人机系统的飞速发展、智能化遥感反演方法的不断优化,当前多源遥感技术已具备低成本、近实时、多尺度、宽覆盖、高精度的火情监测能力,协同多源遥感数据对森林火灾进行监测、分析和持续跟踪,能够为森林火灾提供有效预测和评估,进一步为防灾减灾决策提供科学依据。针对多源遥感技术的快速发展,本文综述了其在森林火情灾前、灾中、灾后的多元化应用,系统分析了当前火险评估、可燃物参数反演、火点检测、火灾行为分析、火烧迹地识别、火烧烈度评价及植被恢复监测等方向的发展现状,评述了火情遥感在数据、方法上的发展过程并对后续的研究趋势进行了探讨。总的来讲,多源遥感技术为开展局部、区域和全球范围的火灾研究提供了一种低成本、多时相、高效率的手段,未来研究预计将继续以多源遥感技术协同为基础,通过改进并整合新的遥感分析方法进一步理解火灾模式,提升火情监测能力。

In the past decades, remote sensing methods of forest fire monitoring were mainly ground patrol, visual interpretation of aerial images, and remote sensing satellite observation with low spatial and temporal resolution. Nowadays, mobile measurement backpack system, light and small UAV, image fusion technology with high spatial and temporal resolution, and near real-time data sharing platform are driving remote sensing into broader forest fire application scenarios. The spatiotemporal-spectral resolution of a single data source is difficult to improve simultaneously as restricted by satellite orbit, observation mode, and sensor performance. The monitoring results may also be constrained by environmental factors such as cloud and rain. This condition leads to reduced monitoring accuracy and inability to collect reliable and detailed fire data to meet the emergency needs of fire location and continuous monitoring. Determining the spatial and temporal characteristics of forest fires is important for disaster prevention and control. A large number of new-generation sub-meter satellite platforms and sensors are currently used, and intelligent remote sensing inversion methods are constantly optimized. With the support of these technologies, the current fire monitoring capability based on multi-source remote sensing methods has the advantages of low cost, near real-time performance, multi-scale, wide coverage, and high precision. The monitoring, analysis, and continuous tracking of forest fires with multi-source remote sensing data can provide effective prediction and evaluation for forest fires.In general, in pre-fire, based on traditional fire risk factors such as meteorological, topographical, and human factors, multi-source remote sensing data and inversion optimization algorithm of fuel parameters are used to provide more accurate three-dimensional characteristic information of vegetation, including fuel moisture content, canopy height, and forest biomass. In during-fire, the accuracy and timeliness of a single remote sensing data source need to be improved due to spatial and temporal heterogeneity of ground objects. Matching the spatial and temporal domains between polar-orbiting meteorological satellite fire detection results and the geostationary satellite fire intensity monitoring results can make up for the shortcomings of a single remote sensing data source and realize dynamic monitoring of forest fires with high spatial and temporal resolution. Satellite monitoring is limited by revisit cycles and dense cloud cover in some cases. This problem can be effectively solved by data complementation, fusion, or using airborne or ground platform monitoring. Fire intensity monitoring results can also be used as dynamic input data for biomass burning in atmospheric dispersion models, which provides the basis for fire emission dispersion simulation. In post-fire, optical, radar, and LiDAR data can be combined to improve the ability to gauge environmental changes caused by fire.For the rapid development of multi-source remote sensing technology, this study summarizes current fire risk assessment, fuel parameter inversion, fire detection, fire behavior analysis, burned area identification, fire intensity evaluation, and vegetation recovery monitoring. In general, future research is expected to be based on the synergy of multi-source remote sensing technologies. This synergy can be made through the optimization and integration of new remote sensing analysis methods to further understand fire patterns and improve fire monitoring ability.