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Research And Construction Of Growth Model Of Typical Arbor Species In China And Analysis Of Resource Changes

Posted on:2021-04-01Degree:MasterType:Thesis
Country:ChinaCandidate:J Q GuoFull Text:PDF
GTID:2370330611469602Subject:Agriculture
Abstract/Summary:PDF Full Text Request
Forest resources are important natural resources on land,which can not only provide necessary economic support for human production and life,but also play a role in regulating climate,conserving water sources,purifying air and protecting biodiversity.Due to the excessive cutting and utilization of forest resources,the supply balance of wood and forest products has also been greatly damaged.On the other hand,forest management depends on the current and predicted forest status information.Therefore,it is necessary to explore the growth change law of the main forest trees and the prediction and analysis of the future status of forest resources.In this study,according to the specific situation of forest resources in China,based on the continuous inventory data of forest resources in 2003,2008,2013 and 2018,topography data,stand density,canopy density and other related data,using multivariate statistical analysis,Kriging interpolation,data standardization and other methods,The DBH growth models of 34 typical arbor species in China were established.At the same time,the wood acquisition time of large diameter group and extra large diameter group of Arbor forest was predicted based on the growth model.In addition,the rainfall-density model of tree growth was fitted according to the classification of main tree species,and the ideal density of realistic fixed sample plots was predicted.The volume and growth of Chinese land surface tree forest in 2003,2008,2013 and 2018 were calculated by using the volume M and growth ?M of land surface tree forest.The volume and growth of different age groups of Chinese land surface tree forest in 2020,2030,2040 and 2050 were predicted.The main innovation is to study the relationship between the tree growth and multi-factor environmental factors,to establish a growth model of typical tree species on the national scale and to predict the distribution of large diameter group and extra large diameter group of forest in the future.In addition,the volume and growth of Arbor forest in China in the next 30 years were quantitatively evaluated.The results show that the adjustment determination coefficient R2 of 34 typical arbor species(groups)in China is between 0.517 and 0.875,which can accurately predict the DBH growth of 34 arbor species in China.The DBH growth of 34 main tree species in China can be accurately predicted according to longitude and latitude,altitude,air temperature,rainfall data,slope gradient,slope aspect,slope position,soil thickness,stand density index,canopy density and so on.The time required for all Arbor forests in China to reach the large diameter group is between 0-52 years,and the time for the emergence of extra-large diameter group is between 0-75 years.For the four fixed plots selected from Beijing No.30,Sichuan No.1195,Liaoning No.3217 and Zhejiang No.11353,it is predicted that the ideal density is 3449 / ha,3812 / ha,3069 / ha and 6016 / ha,respectively.The volume of Arbor forests in China's land surface forests increased by 26.116 billion cubic meters in 2050 from 17.058 billioncubic meters in 2018,with a net increase of 9.058 billion cubic meters,and the area increased from179.8885 million hectares to 213.9043 million hectares,with a net increase of 34.0158 million hectares.The results provide reasonable suggestions and references for specific forest management measures.At the same time,the temporal and spatial pattern changes of volume and growth of land surface forest in China are analyzed,and the future timber yield of forest in China is estimated,which not only realizes the sustainable development of forest resources,but also obtains higher economic value benefit.
Keywords/Search Tags:Arbor growth model, Large diameter wood, Ideal density prediction, Arbor forest volume, Growth amount decision
PDF Full Text Request
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