| Site quality evaluation is an important research content in forest management.Due to the existence of multiple tree species and different ages,site quality evaluation of mixed forests has always been a research difficulty.So far,there is still no unified site quality assessment method.In this study,uneven-aged coniferous and broad-leaved mixed forests in Guangdong province were used as study object.Using measured age and tree heights data of 59fixed-sample,neural network algorithms in machine learning were introduced,and site quality evaluation methods based on site index and site form were developed.A comparative study was conducted to analyze the impact of stand density on the site form,and a non-forest land site quality assessment technique was proposed.The main contents and results are as follows:(1)Study on calculation method of dominant height of uneven-aged coniferous and broad-leaved mixed forest.Based on whether to classify the tree species and weight the basal area or not,7 dominant height calculation methods were constructed.Specifically:1)For each tree species,take advantage of the dominant height for basal area weight.2)For each tree species,weighted basal area with the maximum value of the dominant height.3)Mean Dominant height for tree species,not weighted.4)The maximum dominant height for tree species,not weighted.5)The mean stand dominant height of the tree species that was with top basal area composition.6)An average value of dominant height that is not classified as tree species,that is,a mean value of dominant height of all the species.7)The maximum value of dominant height,ie,the average height of value of the 3 highest trees.Based on the analysis of the correlations of the 7 classes of dominant height,it was found that the correlation coefficients of the seven stand dominant height all reached a value of 0.9,which was highly correlated.The paired t-test was used to analyze the differences among seven dominant height.The results showed that there was a significant difference in the stand dominant height obtained by the maximum or the average height of different tree species.In the end,the maximum dominant height of all tree species despite tree species method was screened out as to determine the dominant height.(2)Research on site quality evaluation of uneven-aged coniferous and broad-leaved mixed forests based on site index.The neural network method was used to analyze age,altitude,slope direction,slope position,slope,soil thickness and the effect of humus layer thickness on the stand dominant height.When the input factor was age,the coefficient of determination(R~2)between the predicted value and the observed value with stand dominant height was 0.4783,the root mean square error(RMSE)was 1.8171m;the average absolute error(MAE)was1.4482m;and the relative average absolute error was 0.1024.When the input factors were age,altitude,slope direction,slope position,slope,soil thickness and the humus layer thickness,the coefficient of determination(R~2)between the predicted value and the actual value was 0.5327,the root mean square error(RMSE)was 1.7197m,the average absolute error(MAE)was1.1220m,and the relative mean absolute error(RMAE)was 0.0756.The results showed that age is the main factor affecting the stand dominant height.Altitude,slope direction,slope position,slope,soil thickness and humus layer thickness have certain impact on stand dominant height.Since it is a uneven-aged coniferous and broad-leaved mixed forest,which involves different tree species,the reference age of the different forest stands needs to be determined.The reference age of the stand was weighted and determined based on the reference age of the first tree species and the second tree species in the stand.The neural network built a site index model containing site factors.The site index had a maximum value of 21.4 m and a minimum value of 6.1m.The average value was 13.7 m and the standard deviation was 3.2 m.(3)Study on site quality evaluation of uneven-aged coniferous and broad-leaved mixed forest based on site form.The method of determining the reference DBH was studied.According to the calculation results and the actual conditions of the stand,the reference DBH of the uneven-aged coniferous and broad-leaved mixed forest was determined to be 13 cm.Based on the Schumacher growth model,the formula of the site form was deduced,and the site form of each plot was obtained.The correlation coefficient between the site form and the site index was found to be 0.8036.The site index and the site form were divided into 8 intervals.The site index showed a normal distribution trend,while the site form showed a negative skewness.The consistency of the classification of site index and site form was tested.The overall classification accuracy of the two was 11.86%,the kappa coefficient was-0.0507,and the degree of consistency was"poor".It could be known that the replacement of the site index by the site form was not reliable.In order to further verify the conclusion,an optimized multi-hidden layer neural network model was established for the stand dominant height and dominant DBH.It was found that the fitting accuracy was much better than the Schumacher growth equation.The determination coefficient(R~2)was 0.4880,and the root mean square error(RMSE)was 1.7765m,the average absolute error(MAE)was 1.4022 m,and the relative average absolute error(RMAE)was 0.1005.The neural network based on three hidden layers was used as the basic model to establish a formula for solving the site form.The relationship between the site form and the site index was analyzed.The results show that the correlation coefficient between the site form and the site index was 0.5557,which was moderately correlated.The site index and the site form were divided into 8 levels.The site index and the site form both showed the phenomenon of more distribution in the center and less in two ends,and showed that most of the forest stands in the middle and low grade of site index.According to the establishment of the confusion matrix between the site index and the level of the site index,the overall classification accuracy was 40.68%,and the kappa coefficient was 0.2680.The consistency of the two were acceptable.Therefore,for the site form,it is feasible to use the method of evaluating the site quality instead of the site index.(4)In this study,the impact of stand density of site form was studied.Based on the Schumacher growth model and neural network model,the stand density was added to study the impact.Compared these types of models with the site index,the results show that the site form based on neural network inclusion SDI had the highest correlation coefficient with the site index,reaching 0.8359,which was highly correlated with the site index.The site form and the site index were divided into 8 levels and 4 levels,respectively,showing a tendency of normal distribution with more in the middle and few on two ends.When divided into 8 levels,the overall classification accuracy was 42.37%,and the kappa coefficient was 0.3020.According to the kappa coefficient division ranks,the consistency of the two was acceptable;when divided into 4 levels,the overall accuracy was 61.02%and the kappa coefficient was 0.4246.According to the kappa coefficient classification,the consistency of the two was moderate consistency.Therefore,for the uneven-aged coniferous and broad-leaved mixed forest in Guangdong Province,the site form based on neural network containing density index(SDI)had a moderately substitution for the site index.(5)For the evaluation of non-forest land based on neural network,the site index and the site form were used as the output factors,respectively,altitude,slope direction,slope position,slope,soil thickness,humus layer thickness,soil gravel content,soil character,and landform were input factors,and a neural network model of non-forest land was constructed.3/4 of the data was used for modeling,and 1/4 of the data was used to predict.When the input factors were altitude,slope direction,slope position,slope,soil thickness and humus layer thickness,the coefficient of determination(R~2)between the predicted value and the observed value of the site index was 0.3136,the root mean square error(RMSE)was 1.7619 m,and the average absolute error(MAE)was 1.4130 m.The coefficient of determination(R~2)between the predicted value and the observed value of the site form was 0.3657,the root mean square error(RMSE)was 1.4969 m,and the average absolute error MAE was 1.2615 m.When the input factors were altitude,slope direction,slope position,slope,soil thickness,humus layer thickness,soil gravel content,soil character,and landform,the coefficient of determination(R~2)of the predicted value and the observed value of the site index was 0.3662,the root mean square error(RMSE)was 1.6931 m,the average absolute error(MAE)was 1.4018 m,the coefficient of determination(R~2)of the predicted value and the observed value of the site form was 0.4157,the root mean square error(RMSE)was 1.4374 m,the average absolute error(MAE)was 1.0672 m,Using site factors can explain about 40%of the site index and site form.The research results above provided a new method for site quality evaluation of uneven-aged coniferous and broad-leaved mixed forests,which can provide a basis for management decisions of uneven-aged coniferous and broad-leaved mixed forests. |
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