Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Orient...Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.展开更多
Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree he...Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra J.F.Arnold ssp.pallasiana(Lamb.)Holmboe)in Konya Forest Enterprise.The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures.The 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network structure.Results:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect models.The alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(max.AE,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset.Conclusion:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.展开更多
Background:Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research.The geometric-opti...Background:Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research.The geometric-optical mutual shadowing(GOMS)model can be used to invert the forest canopy structural parameters at the regional scale.However,this method can obtain only the ratios among the horizontal canopy diameter(CD),tree height,clear height,and vertical CD.In this paper,we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale.We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.Results:The semi-variance model can be used to calculate the CD at the regional scale that closely matches(mainly with in a range from-1 to 1 m)the CD derived from the canopy height model(CHM)data.The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small,with a root mean square error(RMSE)of 1.96 for a 500-m area with high fractional vegetation cover(FVC)(i.e.,forest area coverage index values greater than 0.8).Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height.And the error caused by the unmatched spatial resolution is small in dense forest.Conclusions:The semi-variance model can be used to calculate the CD at the regional scale,together with the canopy structure parameters inverted by the GOMS model,the mean tree height at the regional scale can be obtained.Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.展开更多
We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Se...We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.展开更多
This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane fore...This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane forest blocks, 2) among Agro ecological zones (AEZ) within each forest block and 3) between similar AEZ in different forest blocks. Forest height data from the Geoscience Laser Altimeter System (GLAS) on the Ice Cloud and Land Elevation Satellite (ICE-SAT) for the period 2003-2009 was used for 2146 circular plots, of 0.2 - 0.25 ha in size. Results indicate that, tree height is largely influenced by Agro ecological conditions and the wetter zones have taller trees in the upper, middle and lower highlands. In the upper highland zones of limited human activity, tree heights did not vary among forest blocks. Variations in height among forest blocks and within forest blocks were exaggerated in regions of active human intervention.展开更多
In this paper, the new formulae of tree height curve and volume cdrie were derived from the theory of column buckling. They were applied to artificial Pine (Pinus sylvestris var. mongolica) and Larch (Larix principis ...In this paper, the new formulae of tree height curve and volume cdrie were derived from the theory of column buckling. They were applied to artificial Pine (Pinus sylvestris var. mongolica) and Larch (Larix principis rupprechtii). The results demonsed that the new formulae wee more effeCtive and precise than conventional formulae of height curve and volume curve.展开更多
Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The objective of this study was to develop mode...Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The objective of this study was to develop models to predict longleaf pine tree diameter at breast height （dbh） and merchantable stem volume （V） using data obtained from field measurements. We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the U.S. Gulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height （H） and crown area （CA）. Prediction of dbh as a function of H improved when CA was added as an additional independent variable. Similarly, predic- tions of V based on H improved when CA was included. Incorporation of additional stand variables such as age, site index, dominant height, and stand density were also evaluated but resulted in only small improvements in model performance. For model testing we used data from planted and naturally-regenerated trees located inside and outside the geographic area used for model fitting. Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands, across a wide range of ages. We discuss the importance of these models for use with metrics derived from remote sensing data.展开更多
Residential greening constitutes a significant portion of the urban environment. Trees, as the largest entities in the tree-shrub-herb greening system, are the best choice for residential afforestation. Hence, tree ar...Residential greening constitutes a significant portion of the urban environment. Trees, as the largest entities in the tree-shrub-herb greening system, are the best choice for residential afforestation. Hence, tree arrangement in green space between buildings is significant, for which may exert negative impact on building sunshine. This study takes He Qingyuan residential area in Beijing as a case study to predict the growth in tree height between buildings to meet good sunshine requirements. The procedures were draw as follows: 1) models including building layout and trees were built using computer-aided design (Auto CAD). Afterwards, according to tree crown shape, tree height limits were determined for the same building layout;2) and after that, the growth in tree height was predicted using the nonlinear height-diameter functions to meet the good sunshine requirements. The results allow us to determine which trees to plant between buildings in that the designers can predict the effects of future tree growth on building sunshine.展开更多
This study investigated biomass allocation in young stands of European beech(Fagus sylvatica L.)and Norway spruce(Picea abies(L.)Karst.)across 31 forest sites in the Western Carpathians,Slovakia.A total of 541 trees a...This study investigated biomass allocation in young stands of European beech(Fagus sylvatica L.)and Norway spruce(Picea abies(L.)Karst.)across 31 forest sites in the Western Carpathians,Slovakia.A total of 541 trees aged 2–10 years,originating from natural regeneration and planting,were destructively sampled to quantify biomass in four components:foliage,branches,stems,and roots.Generalized non-linear least squares(GNLS)models with a weighing variance function outperformed log-transformed seemingly unrelated regression(SUR)models in terms of accuracy and robustness,especially for foliage and branch biomass.When using height as the predictor,SUR models tended to underestimate biomass in planted beech,leading to notable underprediction of aboveground and total biomass.Biomass allocation patterns varied significantly by species and regeneration origin.Using a non-linear system of equations and component ratio modelling,we found out that planted spruce displayed low variability and a consistent dominance of needle biomass,while naturally regenerated beech showed greater variability and a higher proportion of stem biomass,reflecting stronger competition-driven vertical growth.Interspecific differences in total biomass were more pronounced when using tree height,with spruce generally exhibiting greater biomass than beech at equivalent heights.Overall,stem base diameter marginally outperformed tree height as a predictor of biomass.However,tree height-based models showed strong performance and are particularly suitable for integration with remote sensing applications.These findings can directly support forest managers and modellers in comparing regeneration methods and biomass estimation approaches for early-stage stand development,carbon accounting,and remote sensing calibration.展开更多
Tree height is a key indicator in forest ecology,reflecting tree growth status and ecosystem structure.Traditional methods of tree height measurement rely on ground-based measurements,which are limited by cost and tim...Tree height is a key indicator in forest ecology,reflecting tree growth status and ecosystem structure.Traditional methods of tree height measurement rely on ground-based measurements,which are limited by cost and time.In recent years,the development of machine learning and multi-source remotely sensed technologies has provided new ways to measure tree height.In this study,we utilized light detection and ranging and satellite data to extract spectral,vegetation,texture,polarization,terrain,and season features.By integrating these features with machine learning,deep learning,and optimization methods,we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability.It was found that(a)the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation(R^(2)=0.72,mean absolute error=1.89 m),and the optimization process of Shapley additive explanations reduced 23 features,which improved the prediction accuracy(R^(2)=0.80,mean absolute error=1.58 m)and saved computational resources;(b)the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions;and(c)during 2018 to 2023,tree heights in Shenzhen were mainly concentrated in 6 to 14 m,and the spatial distribution has a strong autocorrelation.Tree canopy heights in winter are generally lower than those in summer,and the tree growth rate shows spatial heterogeneity.Overall,this study uncovers the intricate interplay between tree growth and seasonal variations in its traits throughout the urbanization process in Shenzhen.It offers valuable data support and a theoretical foundation for urban greening management and ecological protection.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
Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest ecosystem.There i...Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest ecosystem.There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution.In this study,we presented a method to map wall-to-wall forest tree height(defined as Lorey’s height)across the SN at 70-m resolution by fusing multi-source datasets,including over 1600 in situ tree height measurements and over 1600 km^(2) airborne light detection and ranging(LiDAR)data.Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements,and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System(GLAS)footprints.Finally,the random forest algorithm was used to model the SN tree height from these GLAS tree heights,optical imagery,topographic data,and climate data.The results show that our fine-resolution SN tree height product has a good correspondence with field measurements.The coefficient of determination between them is 0.60,and the root-mean-squared error is 5.45 m.展开更多
Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines.To solve these problems,in this study,we proposed a rapid canopy height information extraction me...Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines.To solve these problems,in this study,we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR,and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region.The results showed that the relative error of tree height extraction using UAV optical images was less than 5%,and the lowest relative error was 0.11%.The determination coefficient R^(2) between the optical image tree height extraction results and the measured tree height was 0.97,thus indicating a high correlation for both.In the field of tree barrier monitoring,the determination coefficient R^(2) of tree height extracted using airborne LiDAR point cloud,and canopy height model(CHM)and of the measured tree height were 0.947 and 0.931,respectively.The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91%and 0.2%,respectively,with an extraction accuracy of over 95%.The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately,which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.展开更多
Tree height composition describes the relative abundance of trees in different height levels and performs as a critical characteristic for community ecology.The recent launched full-waveform spaceborne LiDAR(Light Det...Tree height composition describes the relative abundance of trees in different height levels and performs as a critical characteristic for community ecology.The recent launched full-waveform spaceborne LiDAR(Light Detection and Ranging),i.e.,Global Ecosystem Dynamics Investigation(GEDI),can map canopy height,but whether this observation reflects tree height composition remains untested.In this study,we firstly conduct numerical simulations to explore to what extent tree height composition can be obtained from GEDI waveform signals.We simulate waveforms for diverse forest scenarios using GEDI simulator coupled with LESS(LargE-Scale remote sensing data and image Simulation),a state-of-the-art radiative transfer model.We devise a minimalistic model,Tree generation based on Asymmetric generalized Gaussian(TAG),for customizing tree objects to accelerate forest scene creation.The results demonstrate that tree objects generated by TAG perform similarly in LiDAR simulation with objects from commercial 3-dimensional software.Results of simulated GEDI waveforms reasonably respond to the variation of crown architectures in even-aged forests.GEDI waveforms have an acceptable ability to identify different height layers within multi-layer forests,except for fir forests with a cone-shaped crown.The shape metric of waveforms reflects the height of each layer,while retrieval accuracy decreases with the increases in height variations within each layer.A 5-m interval between layers is the minimum requirement so that the different height layers can be separated.A mixture of different tree species reduces the retrieval accuracy of tree height layers.We also utilize real GEDI observations to retrieve tree heights in multi-height-layer forests.The findings indicate that GEDI waveforms are also efficient in identifying tree height composition in practical forest scenarios.Overall,results from this study demonstrate that GEDI waveforms can reflect the height composition within typical forest stands.展开更多
Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costl...Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.展开更多
Tree rings provide long-term records of tree growth and climate changes,which makes them ideal benchmarks for forest modeling.Tree-ring information has greatly improved the reliability of 3-PG,which is one of the most...Tree rings provide long-term records of tree growth and climate changes,which makes them ideal benchmarks for forest modeling.Tree-ring information has greatly improved the reliability of 3-PG,which is one of the most commonly used process-based forest growth models.Here,we strengthen 3-PG's ability to simulate tree-ring width and stable carbon isotopes(δ^(13)C)by enhancing its descriptions of tree physiology.The major upgrade was adding a carbon storage pool for tree-ring formation using stored carbohydrates.We also incorporated previous modifications(replacing the age modifier with a height modifier)of 3-PG and tested their efficacy in improving tree-ring simulations.We ran the model based on two grand fir(Abies grandis)stands.The updated model greatly improved the simulations for both tree-ring widths andδ^(13)C.The results represent one of the best tree-ringδ^(13)C simulations,which accurately captured the amplitude in annual variations ofδ^(13)C.The correlations(R^(2))between simulations and observations reached 0.50 and 0.73 at two stands,respectively.The new model also greatly improved the simulations of raw tree-ring widths and detrended ring-width index(RWI).Because of better descriptions of tree physiology and more accurate simulations of tree rings than the previous model version,the updated 3-PG should provide more reliable simulations than previous 3-PG versions when tree-ring information is used as a benchmark in future studies.展开更多
Based on the data of stand investigation and stem analysis, the effects of climate factors on the poplar protection forest increment in the riverbank field of the Dalinghe and Xiaolinghe rivers of Liaoning Province, C...Based on the data of stand investigation and stem analysis, the effects of climate factors on the poplar protection forest increment in the riverbank field of the Dalinghe and Xiaolinghe rivers of Liaoning Province, China were studied by step-wise regression procedure and grey system theories and methods. A regression model reflecting the correlation between the height increment of poplar protection forest and climatic factors was developed. The order of grey relevance for the effect of climatic factors on the height increment of poplar protection forest is: light>water>heat, and it could be interpreted that the poplar increment was mainly influenced by light factor, water factor, and heat factor. This result will provide scientific basis for the in-tensive cultivation and regeneration of the poplar protection forest in riverbank field in similar regions in China.展开更多
基金This research received no specific grant from any funding agency in the public,commercial,or not-for-profit sectors
文摘Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.
文摘Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra J.F.Arnold ssp.pallasiana(Lamb.)Holmboe)in Konya Forest Enterprise.The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures.The 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network structure.Results:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect models.The alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(max.AE,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset.Conclusion:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.
基金partially supported by the National Natural Science Foundation of China(No.41871231)partially supported by the National Key Research and Development Program of China(No.2016YFB0501502)the Special Funds for Major State Basic Research Project(No.2013CB733403)。
文摘Background:Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research.The geometric-optical mutual shadowing(GOMS)model can be used to invert the forest canopy structural parameters at the regional scale.However,this method can obtain only the ratios among the horizontal canopy diameter(CD),tree height,clear height,and vertical CD.In this paper,we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale.We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.Results:The semi-variance model can be used to calculate the CD at the regional scale that closely matches(mainly with in a range from-1 to 1 m)the CD derived from the canopy height model(CHM)data.The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small,with a root mean square error(RMSE)of 1.96 for a 500-m area with high fractional vegetation cover(FVC)(i.e.,forest area coverage index values greater than 0.8).Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height.And the error caused by the unmatched spatial resolution is small in dense forest.Conclusions:The semi-variance model can be used to calculate the CD at the regional scale,together with the canopy structure parameters inverted by the GOMS model,the mean tree height at the regional scale can be obtained.Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.
文摘We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.
文摘This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane forest blocks, 2) among Agro ecological zones (AEZ) within each forest block and 3) between similar AEZ in different forest blocks. Forest height data from the Geoscience Laser Altimeter System (GLAS) on the Ice Cloud and Land Elevation Satellite (ICE-SAT) for the period 2003-2009 was used for 2146 circular plots, of 0.2 - 0.25 ha in size. Results indicate that, tree height is largely influenced by Agro ecological conditions and the wetter zones have taller trees in the upper, middle and lower highlands. In the upper highland zones of limited human activity, tree heights did not vary among forest blocks. Variations in height among forest blocks and within forest blocks were exaggerated in regions of active human intervention.
文摘In this paper, the new formulae of tree height curve and volume cdrie were derived from the theory of column buckling. They were applied to artificial Pine (Pinus sylvestris var. mongolica) and Larch (Larix principis rupprechtii). The results demonsed that the new formulae wee more effeCtive and precise than conventional formulae of height curve and volume curve.
基金supported by the U.S.Department of Defense,through the Strategic Environmental Research and Development Program(SERDP)
文摘Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The objective of this study was to develop models to predict longleaf pine tree diameter at breast height （dbh） and merchantable stem volume （V） using data obtained from field measurements. We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the U.S. Gulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height （H） and crown area （CA）. Prediction of dbh as a function of H improved when CA was added as an additional independent variable. Similarly, predic- tions of V based on H improved when CA was included. Incorporation of additional stand variables such as age, site index, dominant height, and stand density were also evaluated but resulted in only small improvements in model performance. For model testing we used data from planted and naturally-regenerated trees located inside and outside the geographic area used for model fitting. Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands, across a wide range of ages. We discuss the importance of these models for use with metrics derived from remote sensing data.
文摘Residential greening constitutes a significant portion of the urban environment. Trees, as the largest entities in the tree-shrub-herb greening system, are the best choice for residential afforestation. Hence, tree arrangement in green space between buildings is significant, for which may exert negative impact on building sunshine. This study takes He Qingyuan residential area in Beijing as a case study to predict the growth in tree height between buildings to meet good sunshine requirements. The procedures were draw as follows: 1) models including building layout and trees were built using computer-aided design (Auto CAD). Afterwards, according to tree crown shape, tree height limits were determined for the same building layout;2) and after that, the growth in tree height was predicted using the nonlinear height-diameter functions to meet the good sunshine requirements. The results allow us to determine which trees to plant between buildings in that the designers can predict the effects of future tree growth on building sunshine.
基金funded by the grant“EVA4.0”,No.Z.02.1.01/0.0/0.0/16_019/0000803 supported by OP RDE as well as by the projects APVV-19-0387,APVV-22-0056,and APVV-23-0293 from the Slovak Research and Development Agencyco-funded by the European Commission under the Horizon Europe Teaming for Excellence action+1 种基金project Ligno Silvagrant agreement No.101059552。
文摘This study investigated biomass allocation in young stands of European beech(Fagus sylvatica L.)and Norway spruce(Picea abies(L.)Karst.)across 31 forest sites in the Western Carpathians,Slovakia.A total of 541 trees aged 2–10 years,originating from natural regeneration and planting,were destructively sampled to quantify biomass in four components:foliage,branches,stems,and roots.Generalized non-linear least squares(GNLS)models with a weighing variance function outperformed log-transformed seemingly unrelated regression(SUR)models in terms of accuracy and robustness,especially for foliage and branch biomass.When using height as the predictor,SUR models tended to underestimate biomass in planted beech,leading to notable underprediction of aboveground and total biomass.Biomass allocation patterns varied significantly by species and regeneration origin.Using a non-linear system of equations and component ratio modelling,we found out that planted spruce displayed low variability and a consistent dominance of needle biomass,while naturally regenerated beech showed greater variability and a higher proportion of stem biomass,reflecting stronger competition-driven vertical growth.Interspecific differences in total biomass were more pronounced when using tree height,with spruce generally exhibiting greater biomass than beech at equivalent heights.Overall,stem base diameter marginally outperformed tree height as a predictor of biomass.However,tree height-based models showed strong performance and are particularly suitable for integration with remote sensing applications.These findings can directly support forest managers and modellers in comparing regeneration methods and biomass estimation approaches for early-stage stand development,carbon accounting,and remote sensing calibration.
基金supported by the National Key Research and Development Program of China(2022YFB3903703 and 2022YFE0209300).
文摘Tree height is a key indicator in forest ecology,reflecting tree growth status and ecosystem structure.Traditional methods of tree height measurement rely on ground-based measurements,which are limited by cost and time.In recent years,the development of machine learning and multi-source remotely sensed technologies has provided new ways to measure tree height.In this study,we utilized light detection and ranging and satellite data to extract spectral,vegetation,texture,polarization,terrain,and season features.By integrating these features with machine learning,deep learning,and optimization methods,we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability.It was found that(a)the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation(R^(2)=0.72,mean absolute error=1.89 m),and the optimization process of Shapley additive explanations reduced 23 features,which improved the prediction accuracy(R^(2)=0.80,mean absolute error=1.58 m)and saved computational resources;(b)the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions;and(c)during 2018 to 2023,tree heights in Shenzhen were mainly concentrated in 6 to 14 m,and the spatial distribution has a strong autocorrelation.Tree canopy heights in winter are generally lower than those in summer,and the tree growth rate shows spatial heterogeneity.Overall,this study uncovers the intricate interplay between tree growth and seasonal variations in its traits throughout the urbanization process in Shenzhen.It offers valuable data support and a theoretical foundation for urban greening management and ecological protection.
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This study is supported by the National Science Foundation of China[project numbers 41471363 and 31270563]National Science Foundation[DBI 1356077]the USDA Forest Service Pacific Southwest Research Station.
文摘Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest ecosystem.There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution.In this study,we presented a method to map wall-to-wall forest tree height(defined as Lorey’s height)across the SN at 70-m resolution by fusing multi-source datasets,including over 1600 in situ tree height measurements and over 1600 km^(2) airborne light detection and ranging(LiDAR)data.Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements,and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System(GLAS)footprints.Finally,the random forest algorithm was used to model the SN tree height from these GLAS tree heights,optical imagery,topographic data,and climate data.The results show that our fine-resolution SN tree height product has a good correspondence with field measurements.The coefficient of determination between them is 0.60,and the root-mean-squared error is 5.45 m.
基金funded by Key R&D project of Ningxia Hui Autonomous Region(2021BDE931027)Science and technology project of State Grid Ningxia Electric Power Co.Ltd.(229DK2004P).
文摘Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines.To solve these problems,in this study,we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR,and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region.The results showed that the relative error of tree height extraction using UAV optical images was less than 5%,and the lowest relative error was 0.11%.The determination coefficient R^(2) between the optical image tree height extraction results and the measured tree height was 0.97,thus indicating a high correlation for both.In the field of tree barrier monitoring,the determination coefficient R^(2) of tree height extracted using airborne LiDAR point cloud,and canopy height model(CHM)and of the measured tree height were 0.947 and 0.931,respectively.The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91%and 0.2%,respectively,with an extraction accuracy of over 95%.The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately,which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.
基金supported by the National Science Foun-dation of China(42141005)supported by the High-performance Computing Platform of Peking University.
文摘Tree height composition describes the relative abundance of trees in different height levels and performs as a critical characteristic for community ecology.The recent launched full-waveform spaceborne LiDAR(Light Detection and Ranging),i.e.,Global Ecosystem Dynamics Investigation(GEDI),can map canopy height,but whether this observation reflects tree height composition remains untested.In this study,we firstly conduct numerical simulations to explore to what extent tree height composition can be obtained from GEDI waveform signals.We simulate waveforms for diverse forest scenarios using GEDI simulator coupled with LESS(LargE-Scale remote sensing data and image Simulation),a state-of-the-art radiative transfer model.We devise a minimalistic model,Tree generation based on Asymmetric generalized Gaussian(TAG),for customizing tree objects to accelerate forest scene creation.The results demonstrate that tree objects generated by TAG perform similarly in LiDAR simulation with objects from commercial 3-dimensional software.Results of simulated GEDI waveforms reasonably respond to the variation of crown architectures in even-aged forests.GEDI waveforms have an acceptable ability to identify different height layers within multi-layer forests,except for fir forests with a cone-shaped crown.The shape metric of waveforms reflects the height of each layer,while retrieval accuracy decreases with the increases in height variations within each layer.A 5-m interval between layers is the minimum requirement so that the different height layers can be separated.A mixture of different tree species reduces the retrieval accuracy of tree height layers.We also utilize real GEDI observations to retrieve tree heights in multi-height-layer forests.The findings indicate that GEDI waveforms are also efficient in identifying tree height composition in practical forest scenarios.Overall,results from this study demonstrate that GEDI waveforms can reflect the height composition within typical forest stands.
基金supported by grants provided within the research project»EO4Forest:Use of multi-temporal Sentinel-2 and VHR Pleiades stereo data for sustainable forest monitoring and management«funded by the Austrian Federal Ministry for Climate Action,Environ-ment,Energy,Mobility,Innovation and Technology(BMK)within the FFG Austrian Space Applications Program ASAP 12(grant agreement number 854027).
文摘Accurate,reliable,and regularly updated information is necessary for targeted management of forest stands.This information is usually obtained from sample-based field inventory data.Due to the time-consuming and costly procedure of forest inventory,it is imperative to generate and use the resulting data optimally.Integrating field inventory information with remote sensing data increases the value of field approaches,such as national forest inventories.This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models(CHM)for forest growing stock estimation.For this purpose,fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis.Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots,the paired t-test results revealed no statistical difference between the two methods.The R2 increased by 0.08 points and the RMSE decreased by 7.7 percentage points(24.8m^(3)·ha^(−1))using fixed-area plots.Since tree height is the most critical variable essential for modeling forest growing stock using aerial images,we also compared the tree heights obtained from CHM to those from the typical field inventory approach.The result shows a high correlation(R^(2)=0.781)between the tree heights extracted from the CHM and those measured in the field.However,the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points(1.04m)when the allometrically derived tree heights were analyzed.Moreover,the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field,but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared.This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations.Overall,the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock.The produced maps reflect the variability of growth conditions and developmental stages of different forest stands.This information is required to characterize the status and changes,e.g.,in forest structure diversity,parameters for volume,and can be used for forest aboveground biomass estimation,which plays an important role in managing and controlling forest resources in mid-term forest management.This is of particular interest to forest managers and forest ecologists.
基金supported by the National Natural Science Foundation of China(Nos.42271048,42430503,and 31971492).
文摘Tree rings provide long-term records of tree growth and climate changes,which makes them ideal benchmarks for forest modeling.Tree-ring information has greatly improved the reliability of 3-PG,which is one of the most commonly used process-based forest growth models.Here,we strengthen 3-PG's ability to simulate tree-ring width and stable carbon isotopes(δ^(13)C)by enhancing its descriptions of tree physiology.The major upgrade was adding a carbon storage pool for tree-ring formation using stored carbohydrates.We also incorporated previous modifications(replacing the age modifier with a height modifier)of 3-PG and tested their efficacy in improving tree-ring simulations.We ran the model based on two grand fir(Abies grandis)stands.The updated model greatly improved the simulations for both tree-ring widths andδ^(13)C.The results represent one of the best tree-ringδ^(13)C simulations,which accurately captured the amplitude in annual variations ofδ^(13)C.The correlations(R^(2))between simulations and observations reached 0.50 and 0.73 at two stands,respectively.The new model also greatly improved the simulations of raw tree-ring widths and detrended ring-width index(RWI).Because of better descriptions of tree physiology and more accurate simulations of tree rings than the previous model version,the updated 3-PG should provide more reliable simulations than previous 3-PG versions when tree-ring information is used as a benchmark in future studies.
文摘Based on the data of stand investigation and stem analysis, the effects of climate factors on the poplar protection forest increment in the riverbank field of the Dalinghe and Xiaolinghe rivers of Liaoning Province, China were studied by step-wise regression procedure and grey system theories and methods. A regression model reflecting the correlation between the height increment of poplar protection forest and climatic factors was developed. The order of grey relevance for the effect of climatic factors on the height increment of poplar protection forest is: light>water>heat, and it could be interpreted that the poplar increment was mainly influenced by light factor, water factor, and heat factor. This result will provide scientific basis for the in-tensive cultivation and regeneration of the poplar protection forest in riverbank field in similar regions in China.
