Study On Forest Fire Monitoring Based On Multi-source Remote Sensing Data And Inversion Of Canopy Water Content In Fire Scars

Forest fire seriously affects the stability of forest ecosystem and threatens the safety of human life and property.Real-time forest fire monitoring and post-disaster recovery monitoring are very important.Compared with traditional methods,remote sensing can accurately detect fires in a larger area continuously.Besides,it improves detection efficiency.The monitoring of vegetation restoration is also significant.Canopy water content is an important index for monitoring forest health,which can be used as a quantitative index for vegetation restoration in the burned areas.Radiative transfer models have potential advantages in terms of transferability compared to empirical models.Currently,most researches about inverting vegetation water content by physical models focus on crops.There are few researches about forests and fire scars.Hence,in the research about real-time monitoring of forest fires.In this study,multi-sensor remote sensing data were combined to monitor the forest fire spots in Muli Tibetan Autonomous County of Sichuan Province since March 30,2019.First,we located the fire spots and their time of occurrence using the Chinese Gaofen-4(GF-4)satellite.Second,we computed the spectral difference between burning and unaffected forest stands using Sentinel-2.Third,we classified the fire scars on the basis of Sentinel-2using the OTSU algorithm to set the threshold of differenced Normalized Burn Ratio(d NBR).Finally,we used the synthetic aperture radar data from Sentinel-1A to relate Normalized Difference Vegetation Index(NDVI)to Polarization Ratio(PR).In the research about monitoring the restoration of vegetation in fire scars,based on the INFORM model,this study used the look-up table to invert the canopy water content of the fire scars in Genhe county.Additionally,Google Earth Engine and Mann-kendall model were used to analysis the temporal change of canopy water content in fire scars.In the end,the map of forest canopy water content in August 2018 of Genhe county was made.The results of multi-source remote sensing data show that:(1)The location of fire spots can be accurately determined using the IRS and PMS data from GF-4.The burning time is March 30 using the PMS data from GF-4.(2)A difference is observed in the spectral curves of among the different fire scars of Sentinel-2 data.(3)The total area of damaged fire scars is 41.56 hm~2,with 94.67%accuracy using the d NBR of 0.35 as the threshold from Sentinel-2 derived d NBR map.Lightly damaged fire scars are also classified(66.56 hm~2,90.94%).(4)The PR from Sentinel-1A data increases from 6.6 d B to 10.8 d B after burning.NDVI is linearly related to PR(R~2=0.58 for fitting and 0.50 for verification).The inversion results of canopy water content show that:(1)The inversion results of canopy water content with higher accuracy are obtained,with R~2 of 0.79 and normalized RMSE of 0.52.(2)The overall inversion of CWC and NDWI shows an exponential growth relationship(R~2 is 0.77).(3)The temporal change research of canopy water content in fire scars shows that CWC decreases significantly after the fire,and recovery rates in various plots are obtained.(4)The map of CWC in August 2018 is drawn,which is useful for forest fire and forest pest warning in this region.(5)The temporal and spatial changes before and after the fire were obtained by inversion in 2015 plots.This study provides an efficient method that can ignore the influence of cloud and rain and other complex environments to monitor forest fires.This study provides a reference for the identification of small fire disasters after their occurrences.The research methods and results can provide technical support for forest fire emergency.This research proves that it is universal and efficient in inverting CWC and monitoring canopy water content of burned areas by INFORM model and GEE.The research method and results can provide technical supports for forest fire protection and forest pest control.In this study,a monitoring method for forest fire and vegetation restoration from pre-fire to post-fire was finally determined through collaborative monitoring.