Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest i...Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.展开更多
To elucidate the development of heartwood,bark,sapwood,pith and specific gravity of wood in fastgrowing teak(Tectona grandis)plantations in Costa Rica,we sampled three trees in each of 55 plantations and modelled each...To elucidate the development of heartwood,bark,sapwood,pith and specific gravity of wood in fastgrowing teak(Tectona grandis)plantations in Costa Rica,we sampled three trees in each of 55 plantations and modelled each variable with age,site and different tree heights.Age and stand density of plantations were significant correlated with stem diameter at breast height and total height of the tree.Formation of heartwood was initiated at the age of 4-year-old and increased in direct proportion with age.The age of plantation had a significant relationship with stem diameter at breast height,heartwood percentage,sapwood thickness,sapwood percentage,percentage of bark,pith diameter and percentage,and specify gravity.The model for these tree parameters was model with these parameters as dependent variable and in relation to age as independent variable.展开更多
Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based ...Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based and ground-based LiDAR struggles to capture complete crown information in dense stands,making parameter extraction challenging such as maximum crown width height(HMCW).This study proposes a canopy spatial relationship-based method for constructing forest spatial structure units and employs five ensemble learning techniques to train 11 machine learning model combinations.By coupling spatial competition with phenotype parameters,the study identifies the optimal fitting model for HMCW of Chinese fir.The results demonstrate that the constructed spatial structure units align closely with existing research while addressing issues of incorrectly selected or omitted neighboring trees.Among the 10,191 trained HMCW models,the Bagging model integrating XGBoost,Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting(GB),and Ridge exhibited the best performance.Compared to the best single model(RF),the Bagging model achieved improved accuracy(R^(2)=0.8346,representing a 1.6%improvement;RMSE=1.4042,reduced by 6.66%;EVS=0.8389;MAE=0.9129;MAPE=0.0508;and MedAE=0.5076,with corresponding improvements of 1.63%,1.49%,0.1%,and 7.06%,respectively).This study provides a viable solution for modeling HMCW in all species with similar structural characteristics and offers a method for extracting other hard-to-measure parameters.The refined spatial structure units better link 3D structural phenotypes with environmental factors.This approach aids in canopy morphology simulation and forest management research.展开更多
基金supported by the National Natural Science Foundation of China(Project No.42171361)the Research Grants Council of the Hong Kong Special Administrative Region,China,under Project PolyU 25211819the Hong Kong Polytechnic University under Projects 1-ZE8E and 1-ZVN6.
文摘Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.
文摘To elucidate the development of heartwood,bark,sapwood,pith and specific gravity of wood in fastgrowing teak(Tectona grandis)plantations in Costa Rica,we sampled three trees in each of 55 plantations and modelled each variable with age,site and different tree heights.Age and stand density of plantations were significant correlated with stem diameter at breast height and total height of the tree.Formation of heartwood was initiated at the age of 4-year-old and increased in direct proportion with age.The age of plantation had a significant relationship with stem diameter at breast height,heartwood percentage,sapwood thickness,sapwood percentage,percentage of bark,pith diameter and percentage,and specify gravity.The model for these tree parameters was model with these parameters as dependent variable and in relation to age as independent variable.
基金funded by Fundamental Research Funds of CAF(CAFYBB2023PA003)Science and Technology Innovation 2030-Major Projects(2023ZD0406103)National Natural Science Foundation of China(32271877).
文摘Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based and ground-based LiDAR struggles to capture complete crown information in dense stands,making parameter extraction challenging such as maximum crown width height(HMCW).This study proposes a canopy spatial relationship-based method for constructing forest spatial structure units and employs five ensemble learning techniques to train 11 machine learning model combinations.By coupling spatial competition with phenotype parameters,the study identifies the optimal fitting model for HMCW of Chinese fir.The results demonstrate that the constructed spatial structure units align closely with existing research while addressing issues of incorrectly selected or omitted neighboring trees.Among the 10,191 trained HMCW models,the Bagging model integrating XGBoost,Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting(GB),and Ridge exhibited the best performance.Compared to the best single model(RF),the Bagging model achieved improved accuracy(R^(2)=0.8346,representing a 1.6%improvement;RMSE=1.4042,reduced by 6.66%;EVS=0.8389;MAE=0.9129;MAPE=0.0508;and MedAE=0.5076,with corresponding improvements of 1.63%,1.49%,0.1%,and 7.06%,respectively).This study provides a viable solution for modeling HMCW in all species with similar structural characteristics and offers a method for extracting other hard-to-measure parameters.The refined spatial structure units better link 3D structural phenotypes with environmental factors.This approach aids in canopy morphology simulation and forest management research.
