Study On Retrieval And Application Of Forest Canopy Height Based On Spaceborne Large-footprint LiDAR Data

LiDAR (Light Detection and Ranging) is one of the research focuses on remote sensing, Earth observation, forestry investigation in recent years, and forest survey and mapping in China and abroad have a wide range of applications. The recent studies concentrate on the estimation of forest canopy height and forestry biomass. Therefore, the paper take Jilin province's Wangqing Forestry Bureau as the study area, where is located in the low mountain in the Changbai Mountains. The first chapter reviews of the status and trends on LiDAR technology applications in forestry at home and abroad, and analysis of its research problems, main contents and expected objectives in this study; Then, the paper has analysis of ICESat-GLAS (the Ice, Cloud, and land Elevation-Geoscience Laser Altimeter System) return waveforms preprocessing and extraction parameter method, and also studies on factors (plot slope, etc.) that effect on forest canopy height estimation. Finally, to verify the relationship between ICESat-GLAS return waveform parameters and the mean forest canopy height and forest aboveground biomass, the research combines with field survey data to construct the mean forest canopy height and forest aboveground biomass retrieval model based on ICESat-GLAS return waveform parameters. Meanwhile, the research also uses the GIS spatial analysis techniques to make further improvement on the predicted accuracy on forest mean canopy height and aboveground biomass. The main results are as follows:1) Respectively using Wavelet Transform method and Gaussian Filter to obtain the ICESat-GLAS return waveforms denoising, the paper extracts the denoised return waveform parameters for estimation of the forest mean canopy height and forest biomass. Its results show that compared with the Gaussian Filter, Wavelet Transform makes the Root Mean Square Error (RMSE) of ICESat-GLAS return waveforms decreased 0.7188, Signal to Noise Ratio (SNR) of that increased 16.17 dB; Wavelet Transform betters than Gaussian Filter in retaining the useful information and suppressing "overlay" problem of the ICESat-GLAS return waveforms.2) The research uses multivariate statistical regression method to analysis the correlation relationship between ICESat-GLAS return waveform parameters(Extent,R20,R50 etc.) and the measured plot mean canopy height, measured forest aboveground biomass. And then according to the correlation coefficient of each other, the paper calculates the forest mean canopy height, abovegroud biomass estimation equation. From the fitting results, it shows that these coefficients of multiple correlation are 0.801,0.710 respectively, these predicted accuracy are 82.59%,80.56%respectively.3) The different spatial resolution of Eigital Elevation Model (DEM) data has a certain impact on estimating forest mean canopy height. The forest measured mean canopy height of 77 test plots is 21.30 meters. The estimated mean canopy height from forest Canopy Height Model (CHM) with different resolutions (20m,30m,90m) is 15.6 meters,18.9 meters,17.4 meters, the predicted accuracy is 73.23%,88.73%, respectively. The moving window-difference Filter can eliminate the impact of slope on estimating forest mean canopy height. When the spatial resolution of filtered CHM is 30 meters, the predicted forest mean canopy height is 19.2 meters; and its predicted accuracy is 90.14%.4) By anglysis of the relationship between the plot slope and estimated forest canopy height value, its regression equation shows that:when the plots slope is less than 10°, the effect of plot slope on forest mean canopy height estimation is weak and its effection is ignored. When the plot slope is more than 30°or more, the slope of the forest canopy height estimation is larger and the waveform broadening associated with the plot slope shapes in exponential growth, showing significant "overlap" problems within the return waveforms.5) By GIS software to extract a forest canopy height and aboveground biomass raster data with a spatial resolution of 30m, the results show that the, the estimated forest mean canopy height value of whole study area is 18.7 meters. The measured forest mean canopy height value of 77 test plots is 19.2 meters, its predicted accuracy is 90.14%. Removal of farmland, buildings, roads, rivers and other non-forest land, the estimated forest mean canopy height value of the study area is 23.1 meters. The estimated overall forest aboveground biomass is 91.017 ton/hectare. Removal of non-forestry land, the estimated overall forest aboveground biomass is 104.561 ton/hectare. The total forest aboveground biomass of study area is 5,747, 996 tons.The research results provide a new method of ICESat-GLAS waveforms data processing and also provide scientific references to estimate forest mean canopy height and forest above-ground biomass based on large-footprint waveform data.