Although Quercus mongolica is a widely distributed,economically and ecologically important deciduous tree in northern China,models to accurately predict stand growth at a regional scale are limited.The physiological p...Although Quercus mongolica is a widely distributed,economically and ecologically important deciduous tree in northern China,models to accurately predict stand growth at a regional scale are limited.The physiological process model(3-PG)has the potential to predict stand growth dynamics under varying site conditions and climate change scenarios.Here,we used field inventory,tree ring sampling,and Bayesian calibration to parameterize a model for Q.mongolica.Stand volume and productivity were then predicted under present conditions and three future climate scenarios(RCP26,RCP45 and RCP85).Our results demonstrated that after Bayesian calibration,the posterior ranges of the sensitivity parameters apha Cx,wSx1000 and pRn accounted for 34%,45%and 65%,respectively,of their prior range.Calibration and validation results revealed a strong correlation between predicted and measured values(R^(2)>0.87,P<0.01),with<20%bias for all growth indicators.Stand volume was projected to increase by 145%and productivity by 80%by the year 2100 under the RCP85 scenario,although these projections may vary across regions.The present study developed a tailored set of 3-PG model parameters for Q.mongolica,based on a comprehensive range of climate conditions,stand structure,and age classes.These parameters offer a scientific basis to accurately predict growth of other monospecific oak or mixed-species stands.展开更多
The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)...The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)point cloud data.We proposed the Nyström-based spectral clustering(NSC)algorithm to decrease the computational burden.This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data.The K-nearest neighbour-based sampling(KNNS)was proposed for the Nyström approximation of voxels to improve the efficiency.The NSC algorithm showed good performance for 32 plots in China and Europe.The overall matching rate and extraction rate of proposed algorithm reached 69%and 103%.For all trees located by Global Navigation Satellite System(GNSS)calibrated tape-measures,the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error(RMSE)of 5.97%.For all trees located by GNSS calibrated total-station measures,the values were 0.89 and 4.49%.The method also showed good performance in a benchmark dataset with an improvement of 7%for the average matching rate.The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.展开更多
基金supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF(CAFYBB2022ZA001)the National Natural Science Foundation of China(42071065)。
文摘Although Quercus mongolica is a widely distributed,economically and ecologically important deciduous tree in northern China,models to accurately predict stand growth at a regional scale are limited.The physiological process model(3-PG)has the potential to predict stand growth dynamics under varying site conditions and climate change scenarios.Here,we used field inventory,tree ring sampling,and Bayesian calibration to parameterize a model for Q.mongolica.Stand volume and productivity were then predicted under present conditions and three future climate scenarios(RCP26,RCP45 and RCP85).Our results demonstrated that after Bayesian calibration,the posterior ranges of the sensitivity parameters apha Cx,wSx1000 and pRn accounted for 34%,45%and 65%,respectively,of their prior range.Calibration and validation results revealed a strong correlation between predicted and measured values(R^(2)>0.87,P<0.01),with<20%bias for all growth indicators.Stand volume was projected to increase by 145%and productivity by 80%by the year 2100 under the RCP85 scenario,although these projections may vary across regions.The present study developed a tailored set of 3-PG model parameters for Q.mongolica,based on a comprehensive range of climate conditions,stand structure,and age classes.These parameters offer a scientific basis to accurately predict growth of other monospecific oak or mixed-species stands.
文摘The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)point cloud data.We proposed the Nyström-based spectral clustering(NSC)algorithm to decrease the computational burden.This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data.The K-nearest neighbour-based sampling(KNNS)was proposed for the Nyström approximation of voxels to improve the efficiency.The NSC algorithm showed good performance for 32 plots in China and Europe.The overall matching rate and extraction rate of proposed algorithm reached 69%and 103%.For all trees located by Global Navigation Satellite System(GNSS)calibrated tape-measures,the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error(RMSE)of 5.97%.For all trees located by GNSS calibrated total-station measures,the values were 0.89 and 4.49%.The method also showed good performance in a benchmark dataset with an improvement of 7%for the average matching rate.The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.
