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