Crown development is closely related to the biomass and growth rate of the tree and its width(CW)is an important covariable in growth and yield models and in forest management.To date,various CW models have been propo...Crown development is closely related to the biomass and growth rate of the tree and its width(CW)is an important covariable in growth and yield models and in forest management.To date,various CW models have been proposed.However,limited studies have explicitly focused on additive and inherent correlation of crown components and total CW as well as the influence of competition on crown radius from the corresponding direction.In this study,two model systems were used,i.e.,aggregation method system(AMS)and disaggregation method system(DMS),to develop crown width additive model systems.For calculating spatially explicit competition index(CI),four neighbor tree selection methods were evaluated.CI was decomposed into four cardinal directions and added into the model systems.Results show that the power model form was more proper for our data to fit CW growth.For each crown radius and total CW,height to the diameter at breast height(HDR)and basal area of trees larger than the subject tree(BAL)significantly contributed to the increase of prediction accuracy.The 3-m fixed radius was optimal among the four neighborhoods selection ways.After adding decomposed competition Hegyi index into model systems AMS and DMS,the prediction accuracy improved.Of the model systems evaluated,AMS based on decomposed CI provided the best performance as well as the inherent correlation and additivity properties.Our study highlighted the importance of decomposed CI in tree CW modelling for additive model systems.This study focused on methodology and could be applied to other species or stands.展开更多
Crown width(CW)is one of the most important tree metrics,but obtaining CW data is laborious and timeconsuming,particularly in natural forests.The Deep Learning(DL)algorithm has been proposed as an alternative to tradi...Crown width(CW)is one of the most important tree metrics,but obtaining CW data is laborious and timeconsuming,particularly in natural forests.The Deep Learning(DL)algorithm has been proposed as an alternative to traditional regression,but its performance in predicting CW in natural mixed forests is unclear.The aims of this study were to develop DL models for predicting tree CW of natural spruce-fir-broadleaf mixed forests in northeastern China,to analyse the contribution of tree size,tree species,site quality,stand structure,and competition to tree CW prediction,and to compare DL models with nonlinear mixed effects(NLME)models for their reliability.An amount of total 10,086 individual trees in 192 subplots were employed in this study.The results indicated that all deep neural network(DNN)models were free of overfitting and statistically stable within 10-fold cross-validation,and the best DNN model could explain 69%of the CW variation with no significant heteroskedasticity.In addition to diameter at breast height,stand structure,tree species,and competition showed significant effects on CW.The NLME model(R^(2)=0.63)outperformed the DNN model(R^(2)=0.54)in predicting CW when the six input variables were consistent,but the results were the opposite when the DNN model(R^(2)=0.69)included all 22 input variables.These results demonstrated the great potential of DL in tree CW prediction.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
This study was undertaken to examine which factors contributed to the correction of crowding in two patients who underwent nonextraction orthodontic treatment. A study model analysis was conducted to determine the eff...This study was undertaken to examine which factors contributed to the correction of crowding in two patients who underwent nonextraction orthodontic treatment. A study model analysis was conducted to determine the effects of the orthodontic treatment for crowding with high canines on crown angulation and dental arch width in two patients. The results showed that the crown angulation was significantly increased, indicating distal tipping in the maxillary dental arch. This tendency was most commonly observed in the premolars among the lateral teeth. With respect to the dental arch width, the largest change was evident in the first molar and first premolar regions in cases 1 and 2, respectively. On the basis of these results, up-righting of mesially tipped lateral teeth and expansion of narrow dental arches could prove to be the keys to the success of space regaining or correction of high canines and mild crowding.展开更多
In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in pa...In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in particular for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle(UAV)to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas,USA.An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation(a young stand before canopy closure,a mature stand with a closed canopy).For each site,the images were split into two subsets:one for training and one for validation purposes.Three widely used object detection methods in deep learning,the Faster region-based convolutional neural network(Faster R-CNN),You Only Look Once version 3(YOLOv3),and single shot detection(SSD),were applied to the training data,respectively.Each was used to train the model for performing crown recognition and crown extraction.Each model output was evaluated using an independent test data set.All three models were successful in detecting tree crowns with an accuracy greater than 93%,except the Faster R-CNN model that failed on the mature site.On the young site,the SSD model performed the best for crown extraction with a coefficient of determination(R^(2))of 0.92,followed by Faster R-CNN(0.88)and YOLOv3(0.62).As to the mature site,the SSD model achieved a R^(2)as high as 0.94,follow by YOLOv3(0.69).These deep leaning algorithms,in particular the SSD model,proved to be successfully in identifying tree crowns and estimating crown widths with satisfactory accuracy.For the purpose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object detention application is a cost-effective alternative to traditional ground measurement.展开更多
基金supported by the National Natural Science Foundation of China,“Study on crown models for L arix olgensis based on tree growth” (No.31870620)。
文摘Crown development is closely related to the biomass and growth rate of the tree and its width(CW)is an important covariable in growth and yield models and in forest management.To date,various CW models have been proposed.However,limited studies have explicitly focused on additive and inherent correlation of crown components and total CW as well as the influence of competition on crown radius from the corresponding direction.In this study,two model systems were used,i.e.,aggregation method system(AMS)and disaggregation method system(DMS),to develop crown width additive model systems.For calculating spatially explicit competition index(CI),four neighbor tree selection methods were evaluated.CI was decomposed into four cardinal directions and added into the model systems.Results show that the power model form was more proper for our data to fit CW growth.For each crown radius and total CW,height to the diameter at breast height(HDR)and basal area of trees larger than the subject tree(BAL)significantly contributed to the increase of prediction accuracy.The 3-m fixed radius was optimal among the four neighborhoods selection ways.After adding decomposed competition Hegyi index into model systems AMS and DMS,the prediction accuracy improved.Of the model systems evaluated,AMS based on decomposed CI provided the best performance as well as the inherent correlation and additivity properties.Our study highlighted the importance of decomposed CI in tree CW modelling for additive model systems.This study focused on methodology and could be applied to other species or stands.
基金funded by National Natural Science Foundation of China(Grant No.31870623)National Key R&D Program of China(Grant No.2022YFD2200501).
文摘Crown width(CW)is one of the most important tree metrics,but obtaining CW data is laborious and timeconsuming,particularly in natural forests.The Deep Learning(DL)algorithm has been proposed as an alternative to traditional regression,but its performance in predicting CW in natural mixed forests is unclear.The aims of this study were to develop DL models for predicting tree CW of natural spruce-fir-broadleaf mixed forests in northeastern China,to analyse the contribution of tree size,tree species,site quality,stand structure,and competition to tree CW prediction,and to compare DL models with nonlinear mixed effects(NLME)models for their reliability.An amount of total 10,086 individual trees in 192 subplots were employed in this study.The results indicated that all deep neural network(DNN)models were free of overfitting and statistically stable within 10-fold cross-validation,and the best DNN model could explain 69%of the CW variation with no significant heteroskedasticity.In addition to diameter at breast height,stand structure,tree species,and competition showed significant effects on CW.The NLME model(R^(2)=0.63)outperformed the DNN model(R^(2)=0.54)in predicting CW when the six input variables were consistent,but the results were the opposite when the DNN model(R^(2)=0.69)included all 22 input variables.These results demonstrated the great potential of DL in tree CW prediction.
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.
文摘This study was undertaken to examine which factors contributed to the correction of crowding in two patients who underwent nonextraction orthodontic treatment. A study model analysis was conducted to determine the effects of the orthodontic treatment for crowding with high canines on crown angulation and dental arch width in two patients. The results showed that the crown angulation was significantly increased, indicating distal tipping in the maxillary dental arch. This tendency was most commonly observed in the premolars among the lateral teeth. With respect to the dental arch width, the largest change was evident in the first molar and first premolar regions in cases 1 and 2, respectively. On the basis of these results, up-righting of mesially tipped lateral teeth and expansion of narrow dental arches could prove to be the keys to the success of space regaining or correction of high canines and mild crowding.
基金supported by the Mc IntireStennis program and East Texas Pine Plantation Research Project at Stephen F.Austin State UniversityPart of the research was also supported by Zhejiang Provincial Key Science and Technology Project(2018C02013)。
文摘In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in particular for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle(UAV)to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas,USA.An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation(a young stand before canopy closure,a mature stand with a closed canopy).For each site,the images were split into two subsets:one for training and one for validation purposes.Three widely used object detection methods in deep learning,the Faster region-based convolutional neural network(Faster R-CNN),You Only Look Once version 3(YOLOv3),and single shot detection(SSD),were applied to the training data,respectively.Each was used to train the model for performing crown recognition and crown extraction.Each model output was evaluated using an independent test data set.All three models were successful in detecting tree crowns with an accuracy greater than 93%,except the Faster R-CNN model that failed on the mature site.On the young site,the SSD model performed the best for crown extraction with a coefficient of determination(R^(2))of 0.92,followed by Faster R-CNN(0.88)and YOLOv3(0.62).As to the mature site,the SSD model achieved a R^(2)as high as 0.94,follow by YOLOv3(0.69).These deep leaning algorithms,in particular the SSD model,proved to be successfully in identifying tree crowns and estimating crown widths with satisfactory accuracy.For the purpose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object detention application is a cost-effective alternative to traditional ground measurement.
