Estimation of forest canopy density based on ICESat-GLAS waveform data
Using the Wangqing forestry area in Jilin Province as a study area,the potential ability of GLAS waveform for estimating forest canopy density was explored in this study.The GLAS waveform was smoothed and fitted by Gaussian low pass filters,and then waveform parameters (i.e.I and ec) were extracted from the smoothed GLAS waveform.The single-variable model and multi-variable model were developed with the two waveform parameters,respectively.The results showed that the single-variable model built with I were superior to that developed with ec.The accuracy of multi-variable models was better than that of the single-variable models.For broad-leaf forest,the R2adj and RMSE (σRMsE) were 0.72 and 0.07,respectively,and the validation results were R2adj =0.74 and σRMSE =0.06.The model accuracy of coniferous forest was the highest (R2adj =0.80,σRMSE =0.10) with validation results of R2adj =0.76 and σRMSE =0.11.The model accuracy of the mixed forest was higher than the broad-leaf forest,but lower than the coniferous forest.The values of R2adj and RMSE were 0.75 and 0.09,respectively,with the validation results of R2adj =0.71 and σRMSE =0.07.The results demonstrate that GLAS waveforms have the capability to estimate the forest canopy density,and the estimation accuracy can be improved by combining I and ec.
LiDARGLAS waveformforest canopy densityecho energy
国家科技期刊平台
NSTL
万方数据
维普
