Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest ...Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest growth and yield, succession and carbon budget models. However, the diameter at breast height （dbh） can be more accurately obtained and at lower cost, than total tree height. Hence, generalized height-diameter （h-d） models that predict tree height from dbh, age and other covariates are needed. For a more flexible but biologically plausible estimation of covariate effects we use shape constrained generalized additive models as an extension of existing h-d model approaches. We use causal site parameters such as index of aridity to enhance the generality and causality of the models and to enable predictions under projected changeable climatic conditions. Methods： We develop unconstrained generalized additive models （GAM） and shape constrained generalized additive models （SCAM） for investigating the possible effects of tree-specific parameters such as tree age, relative diameter at breast height, and site-specific parameters such as index of aridity and sum of daily mean temperature during vegetation period, on the h-d relationship of forests in Lower Saxony, Germany. Results： Some of the derived effects, e.g. effects of age, index of aridity and sum of daily mean temperature have significantly non-linear pattern. The need for using SCAM results from the fact that some of the model effects show partially implausible patterns especially at the boundaries of data ranges. The derived model predicts monotonically increasing levels of tree height with increasing age and temperature sum and decreasing aridity and social rank of a tree within a stand, The definition of constraints leads only to marginal or minor decline in the model statistics like AIC An observed structured spatial trend in tree height is modelled via 2-dimensional surface fitting. Conclusions： We demonstrate that the SCAM approach allows optimal regression modelling flexibility similar to the standard GAM but with the additional possibility of defining specific constraints for the model effects. The longitudinal character of the model allows for tree height imputation for the current status of forests but also for future tree height prediction.展开更多
Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pe...Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa are presented. The Norwegian National Forest Inventory （NFI） is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputatio in forest inventories as well as future tree height predictions in growth and yield scenario simulations. Methods： Generalized additive mixed models （gamm） are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand ag as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models （scare） were fit tc incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity. Results： Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive. Conclusions： In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.展开更多
Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective model...Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.展开更多
Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban citi...Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.展开更多
Volume models for the long-term management of Okomu National Park in Nigeria are not available. The main challenge in assessing forest resources is the lack of accurate, species-specific baseline data and updated info...Volume models for the long-term management of Okomu National Park in Nigeria are not available. The main challenge in assessing forest resources is the lack of accurate, species-specific baseline data and updated information on volume models, growth rates, and disturbances. This complicates the development of effective management plans. This study addresses this by modelling tree volume using temporary sample plots laid out using a systematic line transect method Data was collected from 16 40 m × 50 m plots using a Spiegel relascope. DBH, top, middle, and base diameters, and overall height were measured for trees ≤ 10 cm DBH. Newton’s formula calculated volume of each tree, and per hectare estimates generated. The results showed an average of 132 trees per hectare. Population densities of individual species ranged from 1–11/ha, indicating a low density. Strombosia pustulata was the most abundant species. For coefficients that form the basis for species grouping, species-specific volume equations were developed and grouped into three clusters. Regression equations were fitted and selected based on specific statistical metrics. The volume models showed that generalized (V_(i)=b_(0)+b_(1)(D_(i)^(2)H_(i))+ε_(i)) functions, based on the statistical metrics, performed more effectively. The generalized functions exhibited superior performance, evidenced by the uniform residual plot distribution for DBH^(2)H, implying consistent experimental error and adherence to regression assumptions. A t-test at 95% confidence showed that the discrepancy between predicted and actual values was insignificant. This study indicates that the prediction models provide effective management tools for climate mitigation and determining carbon sequestration by a tropical forest.展开更多
Biological invasions,driven mainly by human activities,pose significant threats to global ecosystems and economies,with fungi and fungal-like oomycetes playing a pivotal role.Ink disease,caused by Phytophthora cinnamo...Biological invasions,driven mainly by human activities,pose significant threats to global ecosystems and economies,with fungi and fungal-like oomycetes playing a pivotal role.Ink disease,caused by Phytophthora cinnamomi and P.×cambivora,is a growing concern for sweet chestnut stands(Castanea sativa)in Europe.Since both pathogens are thermophilic organisms,ongoing climate change will likely exacerbate their impact.In this study,we applied species distribution modeling techniques to identify poten-tial substitutive species for sweet chestnut in the light of future climate scenarios SSP126 and SSP370 in southern Switzerland.Using the presence-only machine learning algorithm MaxEnt and leveraging occurrence data from the global dataset GBIF,we delineated the current and projected(2070-2100)distribution of 28 tree species.Several exotic species emerged as valuable alternatives to sweet chestnut,although careful consideration of all potential ecological consequences is required.We also identified several native tree species as promising substitutes,offering ecological benefits and potential adaptability to climatic conditions.Since species diversification fosters forest resilience,we also determined communities of alternative species that can be grown together.Our findings represent a valuable deci-sion tool for forest managers confronted with the challenges posed by ink disease and climate change.Given that,even in absence of disease,sweet chestnut is not a future-proof tree species in the study region,the identified species could offer a pathway toward resilient and sustainable forests within the entire chestnut belt.展开更多
The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflectio...The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.展开更多
The complex behaviors of expansive soils,particularly their volumetric changes driven by moisture variations,pose significant challenges in urban geotechnical engineering.Although vegetation-induced moisture changes a...The complex behaviors of expansive soils,particularly their volumetric changes driven by moisture variations,pose significant challenges in urban geotechnical engineering.Although vegetation-induced moisture changes are known to affect ground movement,quantitative characterization of tree–soil interactions remains limited due to insufficient field data and unclear relationships between tree water uptake and soil response.This study investigates the mechanical behavior of expansive clay soils influenced by two Lophostemon confertus samples during a 14-month field monitoring program in Melbourne,Australia.The research methodology integrates measurements of soil displacement,total soil suction,moisture content,and tree water consumption through instrumentation and monitoring systems.Field measurements suggest that tree roots reached the limits of their water extraction capacity when total soil suction exceeded 2880 kPa within the active root zone.The spatial extent of tree-induced soil desiccation reached 0.6–0.7 times the tree height laterally and penetrated to depths of 2.5–3.3 m vertically.The mature sample,with an 86%greater crown area and a threefold larger sapwood area,exhibited 142%higher water consumption(35 kL),demonstrating the scalability of tree–soil interaction mechanisms.A multiple linear regression model was developed to quantify the coupled relationships between soil movement and key variables,achieving a high adjusted R2 value of 0.97,which provides engineers and practitioners with a practical tool for estimating ground movement near trees.These findings offer valuable insights for infrastructure design in tree-adjacent environments and can inform computational models and design codes to enable more accurate site assessments and sustainable urban development.展开更多
Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a cor...Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a correction to address this issue and apologize for any confusion this error may have caused.For details,please refer to the modified Supplementary Materials.展开更多
Ecoregion-based height-diameter models were developed in the present study for Scots pine(Pinus sylves-tris L.)stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classifi...Ecoregion-based height-diameter models were developed in the present study for Scots pine(Pinus sylves-tris L.)stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system.The data were obtained from 2831 sample trees in 292 sample plots.Ten generalized height–diameter models were developed,and the best model(HD10)was selected according to statistical criteria.Then,nonlinear mixed-effects modeling was applied to the best model.The R2 for the generalized height‒diameter model(Richards function)modified by Sharma and Parton is 0.951,and the final model included number of trees,dominant height,and diameter at breast height,with a random parameter associated with each ecoregion attached to the inverse of the mean basal area.The full model predictions using the nonlinear mixed-effects model and the reduced model(HD10)predictions were compared using the nonlinear sum of extra squares test,which revealed significant differences between ecore-gions;ecoregion-based height–diameter models were thus found to be suitable to use.In addition,using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.展开更多
Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees rem...Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.展开更多
Allometric equations are fundamental tools in ecological research and forestry management,widely used for estimating above-ground biomass and production,serving as the core foundations of dynamic vegetation models.Usi...Allometric equations are fundamental tools in ecological research and forestry management,widely used for estimating above-ground biomass and production,serving as the core foundations of dynamic vegetation models.Using global datasets from Tallo(a tree allometry and crown architecture database encompassing thousands of species)and TRY(a plant traits database),we fit B ayesian hierarchical models with three alternative functional forms(powerlaw,generalized Michaelis-Menten(gMM),and Weibull)to characterize how diameter at breast height(DBH),tree height(H),and crown radius(CR)scale with and without wood density as a species-level predictor.Our analysis revealed that the saturating Weibull function best captured the relationship between tree height and DBH in both functional groups,whereas the CR-DBH relationship was best predicted by a power-law function in angiosperms and by the gMM function in gymnosperms.Although including wood density did not significantly improve predictive performance,it revealed important ecological trade-offs:lighter-wood angiosperms achieve taller mature heights more rapidly,and denser wood promotes wider crown expansion across clades.We also found that accurately estimating DBH required considering both height and crown size,highlighting how these variables together distinguish trees of similar height but differing trunk diameters.Our results emphasize the importance of applying saturating functions for large trees to improve forest biomass estimates and show that wood density,though not always predictive at broad scales,helps illuminate the biomechanical and ecological constraints underlying diverse tree architectures.These findings offer practical pathways for integrating height-and crown-based metrics into existing carbon monitoring programs worldwide.展开更多
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H...Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.展开更多
The optimum models of harvesting yield and net profits of large diameter trees for broadleaved forest were developed, of which include matrix growth sub-model, harvesting cost and wood price sub-models, based on the d...The optimum models of harvesting yield and net profits of large diameter trees for broadleaved forest were developed, of which include matrix growth sub-model, harvesting cost and wood price sub-models, based on the data from Hongshi Forestry Bureau, in Changbai Mountain region, Jilin Province, China. The data were measured in 232 permanent sample plots. With the data of permanent sample plots, the parameters of transition probability and ingrowth models were estimated, and some models were compared and partly modified. During the simulation of stand structure, four factors such as largest diameter residual tree （LDT）, the ratio of the number of trees in a given diameter class to those in the next larger diameter class （q）, residual basal area （RBA） and selective cutting cycle （C） were considered. The simulation results showed that the optimum stand structure parameters for large diameter trees are as follows： q is 1.2, LDT is 46cm, RBA is larger than 26 m^2 and selective cutting cycle time （C） is between 10 and 20 years.展开更多
The tree shrew （Tupaia belangeri） is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid repro...The tree shrew （Tupaia belangeri） is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid reproduction make the tree shrew an ideal subject for the study of human disease. Numerous tree shrew disease models have been generated in biological and medical studies in recent years. Here we summarize current tree shrew disease models, including models of infectious diseases, cancers, depressive disorders, drug addiction, myopia, metabolic diseases, and immune-related diseases. With the success of tree shrew transgenic technology, this species will be increasingly used in biological and medical studies in the future.展开更多
The Chinese tree shrew （Tupaia belangeri chinensis） a squirrel-like and rat-sized mammal, has a wide distribution in Southeast Asia, South and Southwest China and has many unique characteristics that make it suitabl...The Chinese tree shrew （Tupaia belangeri chinensis） a squirrel-like and rat-sized mammal, has a wide distribution in Southeast Asia, South and Southwest China and has many unique characteristics that make it suitable for use as an experimental animal. There have been many studies using the tree shrew （Tupaia belangeri） aimed at increasing our understanding of fundamental biological mechanisms and for the modeling of human diseases and therapeutic responses. The recent release of a publicly available annotated genome sequence of the Chinese tree shrew and its genome database （www.treeshrewdb.org） has offered a solid base from which it is possible to elucidate the basic biological properties and create animal models using this species. The extensive characterization of key factors and signaling pathways in the immune and nervous systems has shown that tree shrews possess both conserved and unique features relative to primates. Hitherto, the tree shrew has been successfully used to create animal models for myopia, depression, breast cancer, alcohol-induced or non-alcoholic fatty liver diseases, herpes simplex virus type 1 （HSV-1） and hepatitis C virus （HCV） infections, to name a few. The recent successful genetic manipulation of the tree shrew has opened a new avenue for the wider usage of this animal in biomedical research. In this opinion paper, I attempt to summarize the recent research advances that have used the Chinese tree shrew, with a focus on the new knowledge obtained by using the biological properties identified using the tree shrew genome, a proposal for the genome-based approach for creating animal models, and the genetic manipulation of the tree shrew. With more studies using this species and the application of cutting-edge gene editing techniques, the tree shrew will continue to be under the spot light as a viable animal model for investigating the basis of many different human diseases.展开更多
This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting de...This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.展开更多
Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objective...Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.展开更多
文摘Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest growth and yield, succession and carbon budget models. However, the diameter at breast height （dbh） can be more accurately obtained and at lower cost, than total tree height. Hence, generalized height-diameter （h-d） models that predict tree height from dbh, age and other covariates are needed. For a more flexible but biologically plausible estimation of covariate effects we use shape constrained generalized additive models as an extension of existing h-d model approaches. We use causal site parameters such as index of aridity to enhance the generality and causality of the models and to enable predictions under projected changeable climatic conditions. Methods： We develop unconstrained generalized additive models （GAM） and shape constrained generalized additive models （SCAM） for investigating the possible effects of tree-specific parameters such as tree age, relative diameter at breast height, and site-specific parameters such as index of aridity and sum of daily mean temperature during vegetation period, on the h-d relationship of forests in Lower Saxony, Germany. Results： Some of the derived effects, e.g. effects of age, index of aridity and sum of daily mean temperature have significantly non-linear pattern. The need for using SCAM results from the fact that some of the model effects show partially implausible patterns especially at the boundaries of data ranges. The derived model predicts monotonically increasing levels of tree height with increasing age and temperature sum and decreasing aridity and social rank of a tree within a stand, The definition of constraints leads only to marginal or minor decline in the model statistics like AIC An observed structured spatial trend in tree height is modelled via 2-dimensional surface fitting. Conclusions： We demonstrate that the SCAM approach allows optimal regression modelling flexibility similar to the standard GAM but with the additional possibility of defining specific constraints for the model effects. The longitudinal character of the model allows for tree height imputation for the current status of forests but also for future tree height prediction.
基金supported by the Norwegian Institute of Bioeconomy Research(NIBIO)
文摘Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa are presented. The Norwegian National Forest Inventory （NFI） is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputatio in forest inventories as well as future tree height predictions in growth and yield scenario simulations. Methods： Generalized additive mixed models （gamm） are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand ag as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models （scare） were fit tc incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity. Results： Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive. Conclusions： In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.
文摘Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.
基金supports this paper.Project Nos.NSTC-112-2221-E-324-003 MY3,NSTC-111-2622-E-324-002 and NSTC-112-2221-E-324-011-MY2.
文摘Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.
文摘Volume models for the long-term management of Okomu National Park in Nigeria are not available. The main challenge in assessing forest resources is the lack of accurate, species-specific baseline data and updated information on volume models, growth rates, and disturbances. This complicates the development of effective management plans. This study addresses this by modelling tree volume using temporary sample plots laid out using a systematic line transect method Data was collected from 16 40 m × 50 m plots using a Spiegel relascope. DBH, top, middle, and base diameters, and overall height were measured for trees ≤ 10 cm DBH. Newton’s formula calculated volume of each tree, and per hectare estimates generated. The results showed an average of 132 trees per hectare. Population densities of individual species ranged from 1–11/ha, indicating a low density. Strombosia pustulata was the most abundant species. For coefficients that form the basis for species grouping, species-specific volume equations were developed and grouped into three clusters. Regression equations were fitted and selected based on specific statistical metrics. The volume models showed that generalized (V_(i)=b_(0)+b_(1)(D_(i)^(2)H_(i))+ε_(i)) functions, based on the statistical metrics, performed more effectively. The generalized functions exhibited superior performance, evidenced by the uniform residual plot distribution for DBH^(2)H, implying consistent experimental error and adherence to regression assumptions. A t-test at 95% confidence showed that the discrepancy between predicted and actual values was insignificant. This study indicates that the prediction models provide effective management tools for climate mitigation and determining carbon sequestration by a tropical forest.
基金supported by the Pilot program“Adaptation to climate change”of the Swiss Federal Office for the Environment(FOEN,project E03)by the Interreg V A Italy Switzerland Cooperation Program 20142020(project MONGEFITOFOR).
文摘Biological invasions,driven mainly by human activities,pose significant threats to global ecosystems and economies,with fungi and fungal-like oomycetes playing a pivotal role.Ink disease,caused by Phytophthora cinnamomi and P.×cambivora,is a growing concern for sweet chestnut stands(Castanea sativa)in Europe.Since both pathogens are thermophilic organisms,ongoing climate change will likely exacerbate their impact.In this study,we applied species distribution modeling techniques to identify poten-tial substitutive species for sweet chestnut in the light of future climate scenarios SSP126 and SSP370 in southern Switzerland.Using the presence-only machine learning algorithm MaxEnt and leveraging occurrence data from the global dataset GBIF,we delineated the current and projected(2070-2100)distribution of 28 tree species.Several exotic species emerged as valuable alternatives to sweet chestnut,although careful consideration of all potential ecological consequences is required.We also identified several native tree species as promising substitutes,offering ecological benefits and potential adaptability to climatic conditions.Since species diversification fosters forest resilience,we also determined communities of alternative species that can be grown together.Our findings represent a valuable deci-sion tool for forest managers confronted with the challenges posed by ink disease and climate change.Given that,even in absence of disease,sweet chestnut is not a future-proof tree species in the study region,the identified species could offer a pathway toward resilient and sustainable forests within the entire chestnut belt.
文摘The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.
基金funded by the Australian Research Council via the ARC Linkage(Grant No.LP16160100649).
文摘The complex behaviors of expansive soils,particularly their volumetric changes driven by moisture variations,pose significant challenges in urban geotechnical engineering.Although vegetation-induced moisture changes are known to affect ground movement,quantitative characterization of tree–soil interactions remains limited due to insufficient field data and unclear relationships between tree water uptake and soil response.This study investigates the mechanical behavior of expansive clay soils influenced by two Lophostemon confertus samples during a 14-month field monitoring program in Melbourne,Australia.The research methodology integrates measurements of soil displacement,total soil suction,moisture content,and tree water consumption through instrumentation and monitoring systems.Field measurements suggest that tree roots reached the limits of their water extraction capacity when total soil suction exceeded 2880 kPa within the active root zone.The spatial extent of tree-induced soil desiccation reached 0.6–0.7 times the tree height laterally and penetrated to depths of 2.5–3.3 m vertically.The mature sample,with an 86%greater crown area and a threefold larger sapwood area,exhibited 142%higher water consumption(35 kL),demonstrating the scalability of tree–soil interaction mechanisms.A multiple linear regression model was developed to quantify the coupled relationships between soil movement and key variables,achieving a high adjusted R2 value of 0.97,which provides engineers and practitioners with a practical tool for estimating ground movement near trees.These findings offer valuable insights for infrastructure design in tree-adjacent environments and can inform computational models and design codes to enable more accurate site assessments and sustainable urban development.
文摘Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a correction to address this issue and apologize for any confusion this error may have caused.For details,please refer to the modified Supplementary Materials.
基金supported by Scientific Research Projects Management Coordinator of Kastamonu University,under grant number KÜ-BAP01/2019-41.
文摘Ecoregion-based height-diameter models were developed in the present study for Scots pine(Pinus sylves-tris L.)stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system.The data were obtained from 2831 sample trees in 292 sample plots.Ten generalized height–diameter models were developed,and the best model(HD10)was selected according to statistical criteria.Then,nonlinear mixed-effects modeling was applied to the best model.The R2 for the generalized height‒diameter model(Richards function)modified by Sharma and Parton is 0.951,and the final model included number of trees,dominant height,and diameter at breast height,with a random parameter associated with each ecoregion attached to the inverse of the mean basal area.The full model predictions using the nonlinear mixed-effects model and the reduced model(HD10)predictions were compared using the nonlinear sum of extra squares test,which revealed significant differences between ecore-gions;ecoregion-based height–diameter models were thus found to be suitable to use.In addition,using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.
文摘Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.
基金supported by the Xingdian Talent Support Program of Yunnan Province(E5YNR03B01)the Xishuangbanna State Rainforest Talent Support Program(E4BN041B01)the CAS President’s International Fellowship Initiative(2020FYB0003)。
文摘Allometric equations are fundamental tools in ecological research and forestry management,widely used for estimating above-ground biomass and production,serving as the core foundations of dynamic vegetation models.Using global datasets from Tallo(a tree allometry and crown architecture database encompassing thousands of species)and TRY(a plant traits database),we fit B ayesian hierarchical models with three alternative functional forms(powerlaw,generalized Michaelis-Menten(gMM),and Weibull)to characterize how diameter at breast height(DBH),tree height(H),and crown radius(CR)scale with and without wood density as a species-level predictor.Our analysis revealed that the saturating Weibull function best captured the relationship between tree height and DBH in both functional groups,whereas the CR-DBH relationship was best predicted by a power-law function in angiosperms and by the gMM function in gymnosperms.Although including wood density did not significantly improve predictive performance,it revealed important ecological trade-offs:lighter-wood angiosperms achieve taller mature heights more rapidly,and denser wood promotes wider crown expansion across clades.We also found that accurately estimating DBH required considering both height and crown size,highlighting how these variables together distinguish trees of similar height but differing trunk diameters.Our results emphasize the importance of applying saturating functions for large trees to improve forest biomass estimates and show that wood density,though not always predictive at broad scales,helps illuminate the biomechanical and ecological constraints underlying diverse tree architectures.These findings offer practical pathways for integrating height-and crown-based metrics into existing carbon monitoring programs worldwide.
基金funded by National Natural Science Foundation of China Key Program(12431014)Key Project of Hunan Education Department(22A0126)+1 种基金Natural Science Foundation of Hunan Province(2022JJ30555)Postgraduate Scientific Research Innovation Project of Xiangtan University(XDCX2024Y172)。
文摘Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.
基金This paper was supported by National Strategy Key Project, Research and Paradigm on Ecological Harvesting and Regeneration Tech-nique for Northeast Natural Forest (2001BA510B07-02)
文摘The optimum models of harvesting yield and net profits of large diameter trees for broadleaved forest were developed, of which include matrix growth sub-model, harvesting cost and wood price sub-models, based on the data from Hongshi Forestry Bureau, in Changbai Mountain region, Jilin Province, China. The data were measured in 232 permanent sample plots. With the data of permanent sample plots, the parameters of transition probability and ingrowth models were estimated, and some models were compared and partly modified. During the simulation of stand structure, four factors such as largest diameter residual tree （LDT）, the ratio of the number of trees in a given diameter class to those in the next larger diameter class （q）, residual basal area （RBA） and selective cutting cycle （C） were considered. The simulation results showed that the optimum stand structure parameters for large diameter trees are as follows： q is 1.2, LDT is 46cm, RBA is larger than 26 m^2 and selective cutting cycle time （C） is between 10 and 20 years.
基金supported by the National Nature Science Foundation of China(81325016,U1602221,81322038 and U1502222)
文摘The tree shrew （Tupaia belangeri） is a promising laboratory animal that possesses a closer genetic relationship to primates than to rodents. In addition, advantages such as small size, easy breeding, and rapid reproduction make the tree shrew an ideal subject for the study of human disease. Numerous tree shrew disease models have been generated in biological and medical studies in recent years. Here we summarize current tree shrew disease models, including models of infectious diseases, cancers, depressive disorders, drug addiction, myopia, metabolic diseases, and immune-related diseases. With the success of tree shrew transgenic technology, this species will be increasingly used in biological and medical studies in the future.
基金supported by the grant of the National Natural Science Foundation of China(NSFC U1402224)the Chinese Academy of Sciences(CAS zsys-02)
文摘The Chinese tree shrew （Tupaia belangeri chinensis） a squirrel-like and rat-sized mammal, has a wide distribution in Southeast Asia, South and Southwest China and has many unique characteristics that make it suitable for use as an experimental animal. There have been many studies using the tree shrew （Tupaia belangeri） aimed at increasing our understanding of fundamental biological mechanisms and for the modeling of human diseases and therapeutic responses. The recent release of a publicly available annotated genome sequence of the Chinese tree shrew and its genome database （www.treeshrewdb.org） has offered a solid base from which it is possible to elucidate the basic biological properties and create animal models using this species. The extensive characterization of key factors and signaling pathways in the immune and nervous systems has shown that tree shrews possess both conserved and unique features relative to primates. Hitherto, the tree shrew has been successfully used to create animal models for myopia, depression, breast cancer, alcohol-induced or non-alcoholic fatty liver diseases, herpes simplex virus type 1 （HSV-1） and hepatitis C virus （HCV） infections, to name a few. The recent successful genetic manipulation of the tree shrew has opened a new avenue for the wider usage of this animal in biomedical research. In this opinion paper, I attempt to summarize the recent research advances that have used the Chinese tree shrew, with a focus on the new knowledge obtained by using the biological properties identified using the tree shrew genome, a proposal for the genome-based approach for creating animal models, and the genetic manipulation of the tree shrew. With more studies using this species and the application of cutting-edge gene editing techniques, the tree shrew will continue to be under the spot light as a viable animal model for investigating the basis of many different human diseases.
基金This work was supported in part by the National Natural Science Foundation of China(61601418,41602362,61871259)in part by the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring(2020-5)+1 种基金in part by the Qilian Mountain National Park Research Center(Qinghai)(grant number:GKQ2019-01)in part by the Geomatics Technology and Application Key Laboratory of Qinghai Province,Grant No.QHDX-2019-01.
文摘This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.
基金supported by the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
