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| 1 | Forest canopy height mapping over China using GLAS and MODIS data显示文摘The Geoscience Laser Altimeter System(GLAS)accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysical parameters of forest ecosystems.Since the GLAS footprints are discontinuously distributed with a relativity low density,continuous vegetation height distributions cannot be mapped with a high accuracy using GLAS data alone.The MODIS BRDF product provides more forest structural information than other optical remote sensing data.This study aimed to map forest canopy heights over China from the GLAS and MODIS BRDF data.Firstly,the waveform characteristic parameters were extracted from the GLAS data by the method of wavelet analysis,and the terrain index was calculated using the ASTER GDEM data.Secondly,the model reducing the topographic influence was constructed from the waveform characteristic parameters and terrain index.Thirdly,the final canopy height estimation model was constructed from the neural network combining the canopy height estimated with the GLAS point and the MODIS BRDF data,and applied to get the continuous canopy height map over China.Finally,the map was validated by the measured data and the airborne Li DAR data,and the validation results indicated that forest canopy heights can be estimated with high accuracy from combined GLAS and MODIS data. | YANG Ting WANG Cheng LI GuiCai LUO SheZhou XI XiaoHuan GAO Shuai ZENG HongCheng | 2015 | Science China Earth Sciences2015，58，1： | 8 |
| 2 | Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data显示文摘Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data. | Cheng Wang Shezhou Luo Xiaohuan Xi Sheng Nie Dan Ma Youju Huang | 2020 | Forest Ecosystems2020，7，3： | 2 |
| 3 | Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots显示文摘Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs. | Jie Zou Wei Hou Ling Chen Qianfeng Wang Peihong Zhong Yong Zuo Shezhou Luo Peng Leng | 2020 | Forest Ecosystems2020，7，4： | 0 |
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