Background: Trunk volume(Vt) is an essential parameter for estimating forest stand volume, biomass, and carbon sequestration potential. As the dominant tree species in desert riparian forests, Euphrates poplar(Populus...Background: Trunk volume(Vt) is an essential parameter for estimating forest stand volume, biomass, and carbon sequestration potential. As the dominant tree species in desert riparian forests, Euphrates poplar(Populus euphratica) has a high proportion of irregularly shaped tree trunks along the Tarim River, NW China, where the habitat is very fragile owing to long-term water stress. This causes uncertainty in estimation accuracy as well as technical challenges for forest surveys. Our study aimed to acquire P. euphratica Vtusing terrestrial laser scanning(TLS) and to establish a species-specific Vtprediction model.Methods: A total of 240 individual trees were measured by TLS multiple-station in 12 sampling plots in three sections along the lower reaches of the Tarim River. Vtwas calculated by a definite integration method using trunk diameters(Di) at every 0.1-m tree height obtained from TLS, and all data were split randomly into two sets:70% of data were used to estimate the model parameter calibration, and the remaining 30% were used for model validation. Sixteen widely used candidate tree Vtestimation models were fitted to the TLS-measured Vtand tree structural parameter data, including tree height(H), diameter at breast height(DBH), and basal diameter(BD). All model performances were evaluated and compared by the statistical parameters of determination coefficient(R^(2)),root mean square error(RMSE), Bayesian information criterion(BIC), mean prediction error(ME), mean absolute error(MAE), and modeling efficiency(EF), and accordingly the best model was selected.Results: TLS point cloud reflection intensity(RI) has advantageous in the extraction of data from irregular tree trunk structures. The P. euphratica tree Vtvalues showed obvious differences at the same tree height(H). There was no significant correlation between Vtand H(R^(2)=0.11, P < 0.01), which reflected the irregularity of P. euphratica trunk shape in the study area. Among all the models, model(14): Vt=0.909DBH1.184H0.487BD0.836(R^(2)=0.97, RMSE=0.14) had the best prediction capability for irregularly shaped Vtwith the highest R^(2), BIC(-37.96), and EF(0.96), and produced a smaller ME(0.006) and MAE(1.177) compared to other models. The prediction accuracy was 93.18%.Conclusions: TLS point cloud RI has a potential for nondestructively measuring irregularly shaped trunk structures of P. euphratica and developed Vtprediction models. The multivariate models more effectively predicted Vtfor irregularly shaped trees compared to one-way and general volume models.展开更多
Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information belo...Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 32260285, 31860134, 32160367, 31800469)the Third Xinjiang Scientific Expedition and Research Program (Nos2022xjkk0301, 2021xjkk14002)+1 种基金the Youth Innovation Promotion Association of the Chinese Academy of Sciences (No. 2022445)the Tianchi Doctor Program of Xinjiang Autonomous Region (No.Y970000362)。
文摘Background: Trunk volume(Vt) is an essential parameter for estimating forest stand volume, biomass, and carbon sequestration potential. As the dominant tree species in desert riparian forests, Euphrates poplar(Populus euphratica) has a high proportion of irregularly shaped tree trunks along the Tarim River, NW China, where the habitat is very fragile owing to long-term water stress. This causes uncertainty in estimation accuracy as well as technical challenges for forest surveys. Our study aimed to acquire P. euphratica Vtusing terrestrial laser scanning(TLS) and to establish a species-specific Vtprediction model.Methods: A total of 240 individual trees were measured by TLS multiple-station in 12 sampling plots in three sections along the lower reaches of the Tarim River. Vtwas calculated by a definite integration method using trunk diameters(Di) at every 0.1-m tree height obtained from TLS, and all data were split randomly into two sets:70% of data were used to estimate the model parameter calibration, and the remaining 30% were used for model validation. Sixteen widely used candidate tree Vtestimation models were fitted to the TLS-measured Vtand tree structural parameter data, including tree height(H), diameter at breast height(DBH), and basal diameter(BD). All model performances were evaluated and compared by the statistical parameters of determination coefficient(R^(2)),root mean square error(RMSE), Bayesian information criterion(BIC), mean prediction error(ME), mean absolute error(MAE), and modeling efficiency(EF), and accordingly the best model was selected.Results: TLS point cloud reflection intensity(RI) has advantageous in the extraction of data from irregular tree trunk structures. The P. euphratica tree Vtvalues showed obvious differences at the same tree height(H). There was no significant correlation between Vtand H(R^(2)=0.11, P < 0.01), which reflected the irregularity of P. euphratica trunk shape in the study area. Among all the models, model(14): Vt=0.909DBH1.184H0.487BD0.836(R^(2)=0.97, RMSE=0.14) had the best prediction capability for irregularly shaped Vtwith the highest R^(2), BIC(-37.96), and EF(0.96), and produced a smaller ME(0.006) and MAE(1.177) compared to other models. The prediction accuracy was 93.18%.Conclusions: TLS point cloud RI has a potential for nondestructively measuring irregularly shaped trunk structures of P. euphratica and developed Vtprediction models. The multivariate models more effectively predicted Vtfor irregularly shaped trees compared to one-way and general volume models.
基金supported by the National Natural Science Foundation of China,Grant Number 41961060by the Program for Innovative Research Team (in Science and Technology) in the University of Yunnan Province,Grant Number IRTSTYN+1 种基金by the Scientific Research Fund Project of the Education Department of Yunnan Province,Grant Numbers 2020J0256 and 2021J0438by the Postgraduate Scientific Research and Innovation Fund Project of Yunnan Normal University,Grant Number YJSJJ21-A08
文摘Airborne laser scanning(ALS)and terrestrial laser scanning(TLS)has attracted attention due to their forest parameter investigation and research applications.ALS is limited to obtaining fi ne structure information below the forest canopy due to the occlusion of trees in natural forests.In contrast,TLS is unable to gather fi ne structure information about the upper canopy.To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform,this study proposes data registration without control points.The ALS and TLS original data were cropped according to sample plot size,and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin.The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data.The initial registered point cloud data was fi nely and optimally registered via the iterative closest point(ICP)algorithm.The results show that the proposed method achieved highprecision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch.and Picea asperata Mast.which included diff erent species and environments.An average registration accuracy of 0.06 m and 0.09 m were obtained for P.yunnanensis and P.asperata,respectively.
