Objective: This study was carried out to develop a potential productivity-based approach of site quality evaluation for larch pure forests and birch-aspen mixed forests with the aims to provide scientific evidences for improving forest management level, promoting modern forestry development and ecological civilization construction. Method: Based on the latest compartment data of forest resources of Chongli area, Hebei Province in 2019, the growth models of stand mean height, basal area and volume were constructed for estimating the potential productivity of stand basal area and volume in larch pure forests and birch-aspen mixed forests. Hereafter, we also compared stand potential productivity against stand realized productivity in order to evaluate the computing method and practicability of stand potential productivity. Result: The fitting precision of the growth models of stand mean height, basal area and volume were good, and the stand potential productivity all decreased with the decrease of site quality. Specifically, the stand basal area potential productivity of sample plots of the larch pure forests ranged from 2.06 to 2.11 m²·hm^-2a^-1, and the stand volume potential productivity was 6.27-12.11 m³·hm^-2a^-1. Similarly, the stand basal area and volume potential productivity of sample plots of the birch-aspen mixed forests were 1.72-2.28 m²·hm^-2a^-1 and 4.43-8.72 m³·hm^-2a^-1, respectively. The stand realized productivity was lower than the stand potential productivity, and the difference between them decreased with the increase of stand average age. However, because of higher values of the stand realized productivity in higher site quality, the difference against the stand potential productivity decreased with the increase of value of site class for young and middle aged forest. Conclusion: For young and middle aged larch pure forests and birch-aspen mixed forests, the stand potential productivity was larger than that of mature and overmature forests, and the differences of stand potential productivity from stand realized productivity were large. However, such differences increased with the decrease of site quality. Therefore, young and middle aged forests tending should be strengthened, and the forests with the medium site quality should be taken into more consideration. Furthermore, these results suggested that potential productivity of stand basal area and volume under basal age could be used to assess site productivity effectively.

Objective: The aim of this study was to compare the effects of land cover classification and dominant tree species identification in the core area of Chongli Winter Olympic Games under different method combinations of domestic Gaofen-2(GF-2) satellite images, and to analyze the influences of spatial resolution, processing unit, feature set and classification algorithm on the overall accuracy and tree species classification accuracy, so as to provide empirical reference for the research on land cover and tree species classification in Chongli district and promote the industrial application of domestic Gaofen series data. Method: Taking the Chongli Winter Olympic core area as the research object and GF-2 image as the data source, the land cover classification results of 50 different method combinations were compared and analyzed from four dimensions: different units(pixel and object), different image spatial resolutions (1 m and 4 m), different feature sets(spectral features, texture features and shape features) and different classification algorithms. At the same time, two dominant species of Betula platyphylla and Larix principis-rupprechtii were identified. The UAV(unmanned aerial vehicle) aerial image and sub-compartment data were used to obtain training and test samples. The overall classification effects under different methods were evaluated through the overall accuracy(OA) and kappa coefficient. The classification accuracy of dominant tree species was evaluated by the harmonic average(F1) calculated by production accuracy(PA) and user accuracy(UA). Result: 1) The land cover classification accuracy of the MLC(maximum likelihood) method at pixel level, 4 m resolution and spectral feature set was the highest, with an OA of 79.65% and Kappa coefficient of 0.722. The highest F1-score of Larix principis-rupprechtii was 0.79, and the corresponding classification method combination was Bayes method at object level, 1 m resolution and spectrum + texture + shape feature set. The highest F1-score of Betula platyphylla was 0.77, and the corresponding classification method combination was Bayes method at object level, 1 m resolution and spectrum + texture feature set. 2) There was no definite response between the classification accuracy, spatial resolution and feature set of different classification algorithms. When other conditions were controlled to be the same as possible, the improvement of spatial resolution and the increase of features didn't necessarily improve the classification accuracy. Under different spatial resolutions or feature sets, the response direction and degree of the same classification algorithm to the change of one other factor were also different. 3) Land cover classification performed better at the pixel level. There was no significant difference between the pixel and object level of Larix principis-rupprechtii, and those of Betula platyphylla performed better at the object level. 4) SVM(support vector machine) classification algorithm performed consistently well with high accuracy under different units, different resolutions and different feature sets. Besides, the supervised statistical classification algorithms MLC and Bayes also had good performances. Conclusion: The use of GF-2 data might have promising performances in land cover classification and dominant tree species identification in the core area of Chongli Winter Olympic Games. The classification effect may be influenced by multiple factors such as spatial resolution, processing unit, feature set and classification algorithm.

Objective: The advantages and disadvantages of classification information based on normalized point cloud, first return based on normalized point cloud and canopy height model(CHM) in estimating forest canopy cover in the core area of Winter Olympic Games were studied, and the estimation accuracies of the three algorithms were also analyzed. The purpose of this paper was to explore the optimal estimation method of tree canopy cover, so as to provide technical supports for accurately grasping the information of tree crown canopy in the core area of Winter Olympic Games and to promote the sustainable forest management. Method: Based on the airborne LiDAR data and the ground field data of 67 sample plots in the core area of the Winter Olympics Games, linear regression was used to fit the measured and estimated values of crown canopy, and the determination coefficient(R²) and root mean square error(RMSE) were calculated. The estimation accuracies of crown canopy through classification information based on normalized point cloud, first return based on normalized point cloud and canopy height model were compared. The correlations between sample site canopy cover, sample site laser point cloud density and canopy cover estimation error were analyzed, and the effects of canopy height model resolution on the stability of the canopy cover estimation method were also discussed. Result: 1) The estimation accuracy of canopy cover classification information based on normalized point cloud was the highest(R²=0.790 1, RMSE=0.124 3), and the estimation error was the lowest with an average overestimation of 1.17%. The second was the algorithm based on canopy height model(R²=0.763 8, RMSE=0.134 9), and the accuracy of first return based on normalized point cloud was the lowest(R²=0.758 2, RMSE=0.149 1). 2) There was no significant correlation between the sample site canopy cover and the estimation error. The sample sites with less than 40% canopy cover were generally underestimated by three algorithms, there was similar as for overestimation and underestimation results in plots with crown coverage of 0.4-0.8, wherea the sample sites with more than 80% canopy cover were generally overestimated. There was no correlation between laser point cloud density and estimation error, and the increase in laser point cloud density didn't improve the estimation accuracy of tree crown canopy. 3) The formal stability of the algorithm based on the canopy height model was the highest, and there was no significant difference in the result of the canopy cover estimation for the ten different resolutions of the raster, with R² ranging from 0.755 1 to 0.762 2 and RMSE ranging from 0.150 7 to 0.153 9. The best CHM resolution for canopy cover estimation in the core area of the Winter Olympics Games was 0.8 m×0.8 m. Conclusion: Through the estimation of canopy canopy of 67 plots in the core area of the Winter Olympic Games, it showed that three algorithms, classification information based on normalized point cloud, first return based on normalized point cloud and canopy height model might be suitable, and the estimation accuracy of crown canopy classification information based on normalized point cloud could be the highest. The advantages and disadvantages of the three algorithms were comprehensively analyzed by combining the tree crown canopy of the sample plots, the laser point cloud density of the sample plot and the resolution of the CHM, which could provide technical supports for the investigation of large-scale forest canopy cover.

Object: This study was implemented to develop the stand aboveground biomass model with stable structure based on light detection and ranging(LiDAR) data, considering the ordinary least squares, mixed effects and Bayesian parameter estimation method, and then discussed the selection of the optimal biomass prediction model with the aims to provide a scientific basis for biomass modeling method and biomass estimation and to provide a technical support for achieving the "double carbon" target and biomass model calculation in the core area of the Winter Olympics. Method: Based on the LiDAR data and field measurement of 62 sample plots distributed across the Larix principis-rupprechtii and Betula platyphylla forest stands in the core areas of the Winter Olympics, ordinary least squares (OLS), mixed effects and Bayesian biomass models were established by variable screening.Determination coefficient (R²), root mean square error(RMSE), residual error and total relative error(TRE) were used to evaluate the model, and reserve-one crossover method was used to verify the accuracies of the models. Result: A total of 20 LiDAR variables with high correlations were filtered out, and 3 independent variables were finally entered into the models. The best fitting was the Logistic mixed effect model(RMSE = 22.99 t·hm^-2, R² = 0.768, TRE = 6.08%). After establishing the model by tree species, the accuracy of larch model was improved(RMSE = 22.92 t·hm^-2, R² = 0.795, TRE = 7.45%), and the accuracy of birch model decreased(RMSE = 23.34 t·hm^-2, R² = 0.440, TRE = 4.35%). Using the trained model, the biomass of Chongli Winter Olympic core area was predicted and mapped. Conclusion: As for the estimation of the stand aboveground biomass based on the LiDAR and field measurement data, the nonlinear model was superior to the linear model. The nonlinear mixed effect model with age group as random effects might have the highest prediction accuracy of biomass. Bayesian estimation may be greatly affected by prior conditions and might have further discussion values although with a small sample size in this study.

Objective: This paper was carried out to study the water content measurement based on satellite remote sensing data and to compare the accuracy of deep learning model and traditional machine learning model in order to provide a theoretical basis for the establishment of forest fuel water content database in China. Method: Taking Chongli District, Zhangjiakou City, Hebei Province as the research area, based on the field measurement data, aiming at the problem of a large error of traditional machine learning model, the mutilayer perceptron(MLP) model in deep learning was established. The relationships between remote sensing spectral reflectance and water content of forest canopy vegetation and litter were studied, and the accuracy was compared with the support vector regression(SVR) model in traditional machine learning. In the experiment, sentry remote sensing data of the same season as field investigation were selected, and then the model variables commonly used in remote sensing estimation method such as spectral reflectance and spectral moisture index were used as the influence factors of retrieving the moisture content of canopy vegetation and surface litter. Finally, the model training was carried out based on the data of field investigation. Inversion using the remote sensing estimation method for ever encountered in the process of surface litter moisture content of canopy cover problem, this study used in the processing of remote sensing data bidirectional reflectance distribution function to obtain samples of the remote sensing data of different observation angles, combined with the radiative transfer model, the canopy reflectance model mapped to the surface reflectance after training. Result: In the experimental result, the fitting degree of MLP model with red light, green light, near-infrared and short-wave infrared bands as input variables in the inversion of canopy vegetation water content was 0.843, which was better than the fitting degree of the optimal model in SVR of 0.807, and the accuracy improved by 4.5%. The fitting degree of MLP model in the inversion of surface litter water content was 0.448, which was better than the optimal model in SVR of 0.408, and the accuracy improved by 9.8%.In this study, the optimal fitting model MLP was used to invert the distribution map of fuel water content, and it was concluded that the water content of canopy vegetation was higher in the western Chongli district, while the water content of surface litter was higher in the southeastern Chongli district. Conclusion: The research result of this paper could demonstrate an optimization scheme to solve the problem of poor penetrability of optical remote sensing from canopy to surface, and could also provide a theoretical basis for using remote sensing estimation methods to measure moisture content of regional canopy vegetation and surface litter at a large scale.

Objective: By establishing the regression relationship between stand factors, terrain factors and forest surface combustible loads, the forest surface combustible loads of Larix principis-rupprechtii and Betula platyphylla in the core area of Chongli Winter Olympic Games were estimated, which provides a scientific basis for forest fire prevention and fuel management in this study area. Method: On the basis of 63 sample plots set in the study area in 2021, this study took stand factors(stand age, average DBH, average tree height, stand density, canopy density, vegetation coverage), terrain factors(slope, altitude, soil layer thickness) and forest surface combustible loads (fresh and dry mass of bushes, herbs, litter and humus) as the research object. After processing the data of forest surface combustibles, the correlation analysis was carried out with stand factors and terrain factors as independent variables and forest surface combustible loads as dependent variable. In order to construct the optimal model form, the stepwise screening method was used to screen out the appropriate factors. Finally, stand age, stand density, vegetation coverage and altitude were selected to estimate the total surface combustible loads of L. principis-rupprechtii forest. Besides, stand density, slope and altitude were selected to estimate the total surface combustible loads of B. platyphylla forest. Based on this, the surface combustible loads of forest land sub-compartment in the core area of Chongli Winter Olympic Games were predicted. Result: The total surface combustible loads of L. principis-rupprechtii forests were significantly higher than that of B. platyphylla forests (P < 0.05). According to the correlation analysis among the stand factors, average tree height, average DBH and canopy density were significantly correlated with the total loads of surface combustibles (P < 0.05). Among the terrain factors, slope and soil thickness were significantly correlated with the total loads of surface combustibles on the forest surface (P < 0.05). It can be seen from the construction of forest fuel load model. Stand age, stand density, vegetation coverage and altitude could be used to estimate the total surface combustible loads of L. principis-rupprechtii forest. Stand density, slope and altitude could be used to estimate the total surface combustible loads of B. platyphylla forest. The determination coefficients of two forest type models were greater than 0.6 and the P values were less than 0.01. The model verification indicators also indicated that the models had good estimation accuracy. Through the two models, the total surface combustible loads of L. principis-rupprechtii forests and B. platyphylla forests in the core area of Chongli Winter Olympic Games were estimated and the distribution map was drawn. Conclusion: The total surface combustible loads of L. principis-rupprechtii forests were significantly higher than that of B. platyphylla forests, so L. principis-rupprechtii forest might be more prone to forest fire. Moreover, the influence factors of forest surface combustible loads of different forest types were different. Therefore, when carrying out combustibles management, we should choose different management methods and control measures according to local conditions. Due to the research data source, the forest surface combustible loads models are only applicable to the core area of Chongli Winter Olympic Games, which has a certain reference value for other areas. Most of the variables in the models are easily available survey factors, which makes the models have good applicability and can provide basis for forest fire prevention.

Objective: This study aimed to develope counting model that can accurately predict the number of dead trees in Larix principis-rupprechtii plantation, explore the main reasons affecting the number of dead trees in L. principis-rupprechtii plantation, and provide decision-making basis for the scientific management of L. principis-rupprechtii plantation in the core area of the Winter Olympic Games. Method: 45 sample plots of L. principis-rupprechtii plantation in the core area of Winter Olympic Games in Chongli District of Zhangjiakou City were chosen as the research material. Poisson regression model, negative binomial regression model, zero-inflated Poisson regression model, zero-inflated negative binomial regression model, hurdle passion regression model, and hurdle negative binomial regression model were used to develop the models of L. principis-rupprechtii plantation stand mortality number, and the optimal model would be selected in terms of the AIC value. Based on the optimal model, the random effect of different levels and the combination of various random parameters lying on the intercepts were taken into consideration, and the optimal mixed effect model of stand dead number was constructed. The best random effect level and the most optimal combination of random parameters would be determined according to the model convergence situation and AIC value, and the optimal mixed effect model of stand mortality number would be developed. Result: Stand average diameter, mean height of dominant trees, stand age, stand basal area, stand diameter Gini coefficient were the stand factors affecting stand mortality numbers, and site factors had little effect on stand death. When too many zeros were not considered, the fitting effect of negative binomial regression model was much better than Poisson regression model. After considering the phenomenon of zero expansion, the zero expansion model and hurdle model were used for simulation. It was found that the fitting effect of hurdle negative binomial regression model(HNB) and zero expansion negative binomial regression model(ZINB) were better than other models. Finally, the order of goodness of fit of several counting models considering lots of zero values was HNB regression model ≈ ZINB regression model > HP regression model > ZIP regression model. HNB model and ZINB model were selected for further mixed effect model construction. When the random effect level was specified at plot, the number of random parameters had only one, and the model cannot converge when acting on other covariates except for intercept. Only when the random parameters acted on the intercept at plot level, the model could get to converge and the fitting accuracy was further improved, and the evaluation error ME of HNB model and ZINB model were 14 and 11 trees·hm^-2 respectively. Conclusion: In the core area of the Winter Olympic Games, the main factors affecting the death of L. principis-rupprechtii plantation were stand factors rather than site factors. HNB regression model and ZINB regression model had more advantages than Poisson regression model and negative binomial regression model when fitting too many zeros. The generalized linear mixed effect model considering random intercept effect can improve the fitting accuracy of the model and reduce fitting error.

Objective: Taking larch plantations(Larix principis-rupprechtii) in the core area of Chongli Winter Olympics as objects, analysis of stand structure characteristics and the optimization model of stand spatial structure were carried out, so as to provide scientific basis for making structure regulation measures. Method: Three sample plots with the area of 0.09 hm²(30 × 30 m) were set up and forest stand structure characteristics were analyzed based on plots measurement data. Multi-objective programming of five spatial structure parameters, including uniform angle index(W), mingling(M), crowding(C), intersection competition index(UaCI) and storey index(S), was carried out using multiplication and division method based on objective of best spatial structure. With the objective of forest spatial structure optimization, the spatial structure optimization model for stand cutting was constructed, and the effects of the models before and after cutting were compared. Result: The basal area of Larch was over 70% in the two plots and Betula platyphylla was associated species. The diameter distribution ranged from 6 cm to 26 cm, and both of them were approximately normal distribution. Height distribution was unsymmetrical, and the peak appeared at height of 14 m. The mean values of ${\bar W}$, ${\bar M}$, ${\bar C}$, UaCI and ${\bar S}$ were 0.460, 0.106, 0.918, 0.297 and 0.363 respectively. The stand was in a state of uniform distribution of trees, low species mingling, high crown competition and poor vertical structure differentiation. The thinning cutting intensity was lower than 20% after simulated cutting, and the objective function value increased by 13.62%. Values of ${\bar M}$ and ${\bar S}$ increased by 21.17% and 15.23%, whilst ${\bar C}$ and $\overline{{\rm{UaCI}}}$ decreased by 10.22% and 5.19% respectively. Conclusion: The structure of larch plantation in the area is simple with relative single tree species. The degree of tree differentiation is weak and crown competition is obvious, indicating the whole plantation shows the characteristics of pure forest of the same age. Stand structure can be significantly improved after simulated cutting, showing improved degree of mingling and relieved pressure of light competition. The spatial structure optimization model developed in the paper can provide a feasible method reference for tending and thinning for larch plantation.

Objective: This study was implemented to construct a high-accuracy model of single-wood crowns of Larix principis-rupprechtii and Betula platyphylla in the core area of the Winter Olympics, and to compare the advantages and disadvantages of different models to provide theoretical supports for scientific management decisions. Method: We took 4 537 L. principis-rupprechtii trees and 2 603 B. platyphylla trees in the core area of the Winter Olympics as the research objects. Firstly, we fitted our data with 10 commonly used crown diameter models, and then selected the best performance model as the basic model for L. principis-rupprechtii and B. platyphylla, respectively. Secondly, other variables were further added as covariates to construct an improved model based on the basic model. Finally, on the basis of the improved model, the nonlinear least squares model, single-level mixed effects model, generalized additive model and group-level Bayesian model of L. principis-rupprechtii and B. platyphylla were constructed, respectively. Result: Among the 4 L. principis-rupprechtii crown models, the additive model had the highest prediction accuracy(R²_mean=0.704 3, RMSE_mean=0.512 7), and the mixed effect model among the 4 B. platyphylla crown models had the highest prediction accuracy(R²_mean =0.664 3, RMSE_mean =0.794 4). In terms of variables, the crown width of L. principis-rupprechtii and B. platyphylla both increased with the growth of the diameter at breast height. However, the crown width of L. principis-rupprechtii slowly increased with the height of the tree, and decreased with the height to crown base. The B. platyphylla crown width first decreased and then increased with the crown length ratio increasing, on the other hand, the B. platyphylla crown width fluctuated greatly under the change of stand density. When the stand density ranged from 600 to 800 hm^-2, the B. platyphylla crown width decreased with larger stand density, and appropriate replanting should be carried out at this time. When the stand density was in the range of 800 to 1 000 hm^-2, the B. platyphylla crown width increased with larger stand density, and an inflection point of stand density to crown curve appeared at 1 000 hm^-2. Therefore, if the management purpose was to protect the environment, the stand density could be controlled at 1 000 hm^-2. When the stand density was in the range of 1 000 to 1 200 hm^-2, the B. platyphylla crown width decreased with larger stand density. At this time, the forest should be tended and thinned to adjust its stand density. Conclusion: The crown width of L. principis-rupprechtii in the core area of the Winter Olympics was greatly affected by the diameter at breast height, tree height and height to crown base, while the crown width of B. platyphylla was greatly affected by the diameter at breast height, crown length ratio and stand density. All in all, the performances of the group-level Bayesian model, additive model, and nonlinear mixed-effect model were better than those of the nonlinear least squares model, regardless of whether it was used to predict the crown width of L. principis-rupprechtii or B. platyphylla. When only the random effect of sample plot was added, generalized additive model and the nonlinear mixed effects model should be used first, followed by group-level Bayesian models. However, because of group-level Bayesian model's lengthy training period and sensitivity to expressions, it was recommended that it should not be developed when another model could be used instead.

Objective: This study was implemented to provide a new method for modeling tree height curve. Semiparametric regression model was used to describe the relationships between tree height and diameter at breast height(DBH), and then compared with traditional parametric regression models. Method: The height and DBH of 4 921 larch trees(Larix principis-rupprechtii) and 2 833 birch trees(Betula platyphylla) were collected from 76 plots in the core area of Chongli Winter Olympics in Zhangjiakou city, Hebei Province. The data were randomly selected according to the ratio of 7∶3 for model fitting and validation. The generalized additive model and single index model were chosen as semiparametric models. Dominant tree height and DBH were selected as independent variables, and tree species was separated firstly. In the generalized additive model, the constant term was used as a parametric part, while DBH, dominant tree height and their interaction were set as nonparametric parts respectively. The parametric part in single index models of both species was linear combination of DBH and dominant tree height or their product, and the link function was considered as nonparametric. Four generalized height-diameter equations with dominant tree height were used for comparison. In order to further build a height curve model containing two tree species, the generalized additive model and modified Richard model were selected as basic models. Species composition was added as a parametric part to the generalized additive model. The optimal parametric model was chosen by comparing the fitting statistics of adding dummy variables of tree species to different parameters of modified Richard model. The evaluation indices included the adjusted coefficient of determination($R_{\mathrm{a}}^2$), root mean square error(RMSE) and Akaike information criterion(AIC). Result: Under the condition of modeling species separately, the fitting accuracy of the generalized additive model was the highest for training samples with a $R_{\mathrm{a}}^2$ of 88.98% and 72.35% for larch and birch, increased by 3.13%-4.80% and 7.37%-12.09% compared with parametric models, and with a RMSE of 1.441 3 and 2.033 3, decreased by 0.190 4-0.284 8 and 0.252 9-0.403 4, respectively. The single index model ranked the second for fitting larch with a $R_{\mathrm{a}}^2$ of 85.99% and a RMSE of 1.624 1, while ranked the fourth for fitting birch with a $R_{\mathrm{a}}^2$ of 64.75% and a RMSE of 2.295 6. In predicting validating data, the generalized additive model had the lowest RMSE of 1.580 4 for larch and 2.192 6 for birch. However, the single index model showed relatively poor prediction for the two species. In the case of modeling multi-species height curves, the generalized additive model presented a higher $R_{\mathrm{a}}^2$ of 83.00%, a lower RMSE of 1.722 4 for training data and 1.807 5 for validating data. In both cases, the AIC values of the generalized additive models were always the lowest, indicating their significant simplicity of model structure. Conclusion: In the processes of modeling tree height curve, semiparametric models, combined with the advantages of both parametric and nonparametric models could not only greatly improve the flexibility and applicability, but also increase the fitting accuracy in most cases. The generalized additive model might present a high precision in both data fitting and prediction, and the single index model could be used as a reference to judge whether the selections of link function in other models were appropriate or not. As more variables of stand levels were added into height curve equations, semiparametric models could be used to provide new ways for complex model construction.

Objective: Based on the spectral characteristics and spacial interlacing of healthy tree crowns in new afforestation, the spectral enhancement method and multi-scale segmentation thresholds of healthy tree crowns under complex ground vegetation conditions were discussed to provide technical support for the daily monitoring of afforestation verification. Method: The UAV images of newly planted trees in the core area of the Winter Olympics were chosen as experimental data. Firstly, based on the different color characteristics of healthy tree crowns and other disturbances, the images were enhanced by homomorphic filtering and transformed by ExG spectral index. Then, the Otsu method was used to obtain the binary image, and the multi-scale morphological filtering method was used for segmentation and fusion to segment the interlaced crown areas, correspondingly extract the possible healthy crown areas in the original image. Finally, based on the feature vector constructed by the color vector, the GLCM and the LBP, the random forest was used to classify the extracted area to detect the healthy tree crowns in the image. Result: The method based on spectral index transformation and multi-scale morphological filtering was able to effectively segment the interlaced and continuous crown areas, exclude other interference objects those were similar in color to healthy trees and accurately extract the areas those might be crowns. The 17 UAV orthophoto images with varying stand densities and lighting conditions were tested, and the crown centers were marked by visual interpretation. Furthermore, the three evaluation indexes: precision, recall and F1 score were used to quantitatively compare and analyze the recognition effects of random forest and SVM. The experimental result showed that 96.78% of the crowns were extracted using the multi-scale morphological filtering method, and the F1 score of random forest was higher than 97%, while the recall of support vector machine was significantly lower than that of random forest. Conclusion: Our result showed that the crown extraction method based on spectral index transformation and multi-scale morphological filtering could be able to extract the healthy crown quickly and accurately in the UAV images and effectively complete the afforestation verification.

Objective: The suitability of three tree segmentation algorithms based on airborne LiDAR data in obtaining information of Pinus tabulaeformis and middle-aged Larix principis-rupprechtii in the core area of the Winter Olympic Games was studied, and the accuracy of individual tree segmentation and tree height extraction of the three methods were analyzed, with the aims to explore the optimal single tree segmentation method in the core area of the Winter Olympic Games and also to provide technical supports for mastering the forest structure information and formulating forest management measures in the core area of the Winter Olympic Games. Method: Based on the airborne LiDAR data of the sample plots in the core area of the Winter Olympic Games, the point cloud-based cluster segmentation algorithm, watershed segmentation algorithm and double-tangent crown recognition algorithm were applied, combined with sample plot survey data, orthophoto image and artificial visual interpretation, and tree crown detection rate(r), accuracy(p) and overall accuracy(F) were used to analyze the single tree segmentation accuracy of the algorithms; After individual tree registration, the correlation between field measured tree height and LiDAR estimated tree height was analyzed, and the suitability of three tree segmentation algorithms in the core area of Winter Olympic Games was comprehensively evaluated. Result: 1) The overall segmentation accuracy of the three tree segmentation methods was very high in newly afforestation Pinus tabulaeformis(F=0.90-0.93) and higher in middle-aged forest Larix principis-rupprechtii(F=0.72-0.75). 2) For newly afforestation Pinus tabulaeformis, the segmentation accuracy of point cloud-based cluster segmentation algorithm was the highest(F=0.93), which was better than that of watershed segmentation algorithm(F=0.90) and double-tangent crown recognition algorithm(F=0.90). For middle-aged Larix principis-rupprechtii, the segmentation accuracy of double-tangent crown recognition algorithm was the highest(F=0.75), which was better than that of the point cloud-based cluster segmentation algorithm(F=0.72) and watershed segmentation algorithm(F=0.70). 3) The correlation analysis between field measured tree height and airborne LiDAR estimated tree height showed that the correlation of single tree height extracted from newly afforestation Pinus tabulaeformis by point cloud-based cluster segmentation algorithm was the best, and the correlation of single tree height extracted by double-tangent crown recognition algorithm was the best in middle-aged Larix principis-rupprechtii. Conclusion: Through the single tree segmentation of the airborne LiDAR of different types of forest land in the core area of the Winter Olympic Games, the suitability of the point cloud-based cluster segmentation, watershed segmentation algorithm and double-tangent crown recognition algorithm could be reflected. The best segmentation was achieved using the point cloud-based cluster segmentation algorithm for newly planted Pinus tabulaeformis, and the best suitability segmentation was achieved using the double-tangent crown recognition algorithm for middle-aged Larix principis-rupprechtii.