| Phyllostachys Pubescens in China is distributed most widely,the largest area of bamboo species with characteristics of fast growing and high yield,extensive use,strong regeneration ability,economic value high and sustainable renewal.According to incomplete statistics,the area of Phyllostachys Pubescens in China is about 3,868,300 ha,accounting for about 70% of the total area of bamboo forest,accounting for 20% of the world's bamboo forest area,in the maintenance of ecological balance play a significant role.By means of remote sensing technology on Phyllostachys Pubescens biomass will provide the basic data for study of carbon sequestration capacity of Phyllostachys Pubescens.Forward LiDAR remote sensing technology can obtain high precision and high density 3D coordinate data,and can be constructed three-dimensional model of vegetation,and then back to the vegetation biomass.The application of LiDAR technology in Phyllostachys Pubescens biomass estimation will provide more means for the future of Phyllostachys Pubescens forest biomass estimation.In this paper,based on the research on the feasibility of the ALS data and Airborne Hyperspectral Data to retrieve the aboveground biomass of the Phyllostachys Pubescens forest.Choose Huangshan City as the flight area,access to ALS data and Airborne Hyperspectral Data;in the flight track survey 50(effective 44)of the pieces of the Phyllostachys Pubescens plot and calculated biomass.The characteristic variables of different remote sensing data were extracted as independent variables,and the sample plots were used as the dependent variables,and the inversion model was established based on different remote sensing data sources.The reasons for the accuracy of the two models are compared and analyzed.The main conclusions are as follows:(1)ALS data after normalization processing to eliminate the influence of terrain factors;point cloud classification using software classification and manual editing method,to distinguish the pastry and vegetation points and noise points;point cloud statistics defined above 2m above the ground point for Phyllostachys Pubescens forest reflection point,to avoid the miscellaneous shrub vegetation,such as the impact.It can be considered that the point of all of the variables used to extract the variables is the reflection point of Phyllostachys Pubescens.(2)ALS data after pre processing in ENVI IDL module programming statistical information of a point cloud as independent variables,ground survey to obtain Phyllostachys Pubescens biomass as dependent variables using SPSS 22 software for multiple linear regression analysis to establish inversion model f1:InW=5.024+0.101×Inh50+0.226×Inhmax-0.318×Ind15+0.582×Inc,the model can explain changes in biomass 64.13%.This indicates that it is feasible to use ALS technique to estimate the biomass of aboveground of Phyllostachys Pubescens forest.(3)Airborne Hyperspectral Data is processed,the variable position and area variables,vegetation index and original bands are drawn using the envi software,and the introduction of the ground sample to average height as independent variables,the ground survey,to obtain the biomass as the dependent variable,use SPSS 22 software the method of multiple linear regression analysis was used to establish inversion model f2:W2=1.514+0.765×Dr+4.324×SDr-1.602×VI2+0.937 hmean,and the model can be explaining movements in Phyllostachys Pubescens forest biomass 58.3%.It shows that it is feasible to use Airborne Hyperspectral technology to estimate the biomass of aboveground parts of the Phyllostachys Pubescens forest.(4)ALS data and Airborne Hyperspectral Data extraction characteristic variables with the ground survey data to establish the Phyllostachys Pubescens forest biomass inversion model is feasible,by comparing their model with R2?Ra2?RMSE and DW found ALS data to establish the inversion model accuracy is higher than that of Airborne Hyperspectral Data to establish the inversion. |
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