Study On The Estimation Method Of Forest Canopy Parameters Using LiDAR Data

Forest ecosystem is a very important part of the terrestrial ecosystem and plays a crucial role in global climate change.Forest structural parameters are key input to forest ecosystem process models,which can reflect the productivity of terrestrial ecosystems to a certain degree.Thus forest parameters estimation is very important for studying and understanding global climate change.Several traditional methods,such as field measurements,have been employed for accurately measuring forest canopy parameters.However,these methods are labor-intensive and time-consuming,making it impract ica l in large regions.With the rapid development of remote sensing techniques,optical imagery,Synthetic Aperture Radar(SAR),and Light Detection and Ranging(Li DAR)have been widely used in obtaining forest structural parameters.However,optical images only reflect the information about horizontal canopy structure due to their low penetration capability,which greatly affects the estimation accuracy of forest vertical structural parameters.Although SAR can penetrate the forest canopy to some degree,it is still heavily affected by terrain features.In contrast,Li DAR is an active remote sensing technique,capable of accurately obtaining the information about forest canopy and ground at the same time.Therefore,Li DAR has a major advantage over other remote sensing methods in retrieving forest canopy parameters.This paper aims at estimating forest canopy parameters using Li DAR data.There are three specific objectives: 1)to investigate methods for airborne Li DAR data processing and forest biomass estimation by combing airborne discrete-return and fullwaveform data;2)to effectively process Ice,Cloud,and land Elevation Satellite/ Geoscience Laser Altimeter System(ICESat/GLAS)data,and propose new methods for accurately obtaining ground elevation and forest canopy height over mountaino us vegetated areas;3)to propose algorithms for processing photon-counting Li DAR data,and explore the possibility of photon-counting Li DAR data in estimating forest canopy height and above-ground biomass.The main findings are as follows:1)The study proposed an improved progressive TIN(Triangulated Irregular Network)densification(PTD)method for better filtering of airborne Li DAR data.Compared with the traditional PTD method,the revised PTD method has two major improvements:(1)an additional step is introduced to build an improved TIN for better approximation of the real terrain surface,and(2)improvements in iterative judgment criterions.The results suggest that our revised PTD method is capable of reducing Type I,Type II,and total errors by 10.26%,0.79% and 8.07% respectively.2)Airborne discrete-return LiDAR-derived metrics(DR-metrics)and fullwaveform Li DAR-derived metrics(FW-metrics)were first obtained from airborne discrete-return and ful-waveform Li DAR data.The nonlinear combination of DR-and FW-metrics was also evaluated for above-ground biomass estimation.This research concluded that the synergistic use of DRand FW-metrics can provide better above-ground biomass estimates in coniferous forests.3)This research proposed a new method based on continuous wavelet transform(CWT)for better estimation of ground elevation from GLAS data over mountainous vegetated areas.CWT was first applied to each GLAS waveform for peak detection,then ground peaks were determined using rules derived from auxiliary digital elevation models(DEMs),and thereafter ground elevation was calculated based on the ground peaks.Compared with the traditional methods based on Gaussian decomposition,the CWT method can improve the estimation accuracy of ground elevation over mountaino us vegetated areas.4)Previous terrain correction methods only consider the effect of terrain slope and footprint size when estimating forest canopy height using GLAS data.In fact the measurement accuracy of forest canopy height is often affected by footprint size,shape,orientation,terrain slope and aspect.An improved terrain correction model was proposed in this study to remove the effect of allaforementioned factors when estimating canopy height over sloped terrains.The revised method was found significa ntly better than the traditional ones according to the canopy heights derived from small footprint Li DAR data in a test area in China.The revised method was able to reduce the RMSE of the canopy height estimates by up to 1.2 m.5)To explore the possibility of photon-counting Li DAR data for measuring forest canopy parameters.The noise photons were first reduced by a noise filter ing algorithm based on a multi-step mathematical and statistical signal extraction process.Then a new iterative classification method was proposed to separate the canopy photons and ground photons.Finally,photons feature parameters were calculated from classified photon-counting data,and compared with points feature parameters calculated from airborne Li DAR data.Results indicated that photon-counting Li DAR can be an important data source for retrieving forest canopy parameters.This study also provides a methodology guideline for estimating forest canopy parameters from new generation Space-borne Li DAR data.