Monitoring Loss Of Aboveground Biomass Caused By Logging In Forest Areas Using Remote Sensing Dataset

The carbon cycle of forest ecosystems plays a very important role in the carbon exchange between the biosphere and the atmosphere.Forests are easily disturbed by natural and human factors,such as logging,wind,fire,pests,and droughts and so on.Monitoring the loss of aboveground biomass caused by forest disturbances is important for researches on carbon cycling of forest ecosystems.Currently,the method for the accurate estimation of forest biomass in large-scale is still immature,and there is a great uncertainty in current estimation results.Classical remote sensing monitoring methods mainly focus on the Vegetation Index(VI)or radar backscatter coefficients.The sensitivity of these remote sensing signals to forest biomass is always saturated when it reaches a certain level.The accurate monitoring of biomass loss caused by forest disturbances should rely on the direct detection of forest spatial structure.This study focuses on the monitoring of biomass loss caused by logging in the Amazon forested area through the synergy of C-band radar interferometry,optical multi-angle stereo observations,and LiDAR data.The research content is divided into the following four parts:(1)Evaluate the terrain slope information.The Light Detection and Ranging(LiDAR)can directly detect forest spatial structure,which has been successfully applied to the monitoring of Aboveground Biomass(AGB)in flat forest areas.However,the waveform will be broadened by terrain.The solution to the problem of waveform broadening needs the information of terrain slope.Current free global terrain data include SRTM(Shuttle Radar Topographic Mission)DEM generated using C-band radar interference data and ASTER(Advanced Spaceborne Thermal Emission and Reflection Radiometer)GDEM generated using spaceborne photogrammetry data.In this study,we used point cloud data to extract the terrain topographic information with high accuracy through the combination of different filtering algorithms.Based on the slope information with high accuracy,we evaluate the slope information of SRTM DEM and ASTER GDEM.The results show that the slope information of SRTM DEM is superior to ASTER GDEM.(2)Retrieval of forest canopy heights by using large-footprint waveform data assisted by the LiDAR model.Although existing researches have made obvious progress for using LiDAR waveforms on slopes lower than 15°,the effect of terrain slope on LiDAR waveforms is still a great challenge to the accurate mapping of forest canopy height on slopes larger than 15°.Based on the known terrain slope information and using the LiDAR model,retrieval of forest Canopy heights by using large-footprint waveform data assisted by the LiDAR model over Hillsides.Based on the simulated waveform data,the method can effectively correct the impact of the slope within 0°-40°.(3)Slope-adaptive waveform metrics of large footprint LiDAR for estimation of forest aboveground biomass.Retrieval of forest canopy heights assisted by LiDAR model in the previous section needs to rely on the LiDAR model,and the practicality of the algorithm has been limited.This section,the LiDAR waveform of bare ground is modeled as a function of terrain slope,footprint size and lase pulse length which are determined according to the LiDAR waveform of a forest stand.We defined Slope-adaptive waveform metrics,the newly defined waveform metrics are used as independent variables to predict forest AGB.Based on the simulated LiDAR waveform data footprint size of 25m and 70m,we compare the accuracy of estimate forest AGB using three different methods.Over the full dynamic range of terrain slopes of 0°–40°,the PIPE method gave the best estimation of forest AGB with R~2=0.92 and RMSE=16.45 Mg/ha for footprint size of 70 m.The estimation of forest AGB using GLAS data and field measurements confirms the conclusion from simulated LiDAR waveforms.The PIPE method gave the best estimation of forest AGB with R~2=0.84 and RMSE=35.07 Mg/ha(4)Monitoring of forest biomass loss in deforestation areas based on SRTM DEM,ASTER stereo imagery and LiDAR data.Because of the weak penetration ability of C-band radar interferometric data on the forest canopy.In the Amazon rainforest zone,the SRTM DEM obtained by using C-band radar interferometric data mainly describes the elevation of the top of the forest canopy in 2000.The ASTER stereo imagery for the production of ASTER GDEM has been released,which provides the elevation of ground after deforestation.Therefore,combining the SRTM DEM with the ASTER DSM data,the forest height can be obtained in the logging area after 2000.With the LiDAR data and forest canopy height data,the loss of forest biomass caused by deforestation can be monitored.We use the existing Global Forest Change product to obtain the disturbance area.The disturbance area was confirmed by the ASTER DSM data and SRTM DEM data of this time period.Then,we get the relationship between the heights metric of SRTM DEM-ASTER DSM and the height metric from GLAS data.Due to the lack of field data,we estimate AGB using exised models.Finally,we estimate the AGB using the height metric;we get the change of AGB through the change of height.The result showed that the difference between the SRTM DEM and the ASTER DSM before and after the disturbance was highly correlated with the forest canopy height provided by the LiDAR(R~2=0.96,RMSE=1.65m),and the estimated biomass loss is highly correlated with the point-type data from LiDAR,the R~2 was 0.66.The results show that the monitoring algorithm has certain feasibility.