Estimation of Forest Canopy Height by Integrating GLAS and FY3A-MERSI Data in Changbai Mountain

Space-borne LiDAR has the direct measurement capability with high precision on the vertical structure of forest,and optical remote sensing is an effective way to obtain bio-physiological parameters of regional scale forest.So,the highest regional inversion of forest canopy height by integrating the high precision sample data of LiDAR and other grid remote sensing data will greatly enhance the measurement accuracy of forestry.The processing of waveform data of large footprint LiDAR GLAS and algorithm for forest canopy height in different terrain condition have been implemented.The GLAS + MERSI joint inversion model for forest canopy height of a regional scale in different forest types have been established.And the map of canopy height of Changbai Mountain forest has been produced.Overall,the results of canopy height estimated by GLAS has very high accuracy and the GLAS + MERSI joint inversion model of needle-leaf forest has highest accuracy,that of broadleaf forest has higher accuracy.By analysis,we can find that the GLAS + MERSI joint inversion model,which considering of optical remote sensing of biophysical parameters have higher accuracy,and the results are consistent with land cover data in the spatial distribution.