Dynamic prediction of uneven-aged natural forest for yield of Pinus taiwanensis using joint modelling

• Published in: Environmental monitoring and assessment 2020-04, Vol.192 (4), p.239-239, Article 239
• Main Authors: Hua, Weiping, Ye, Hongmeng, Rau, Jui-Yeh, Qiu, Tian
• Format: Article
• Language: English
• Subjects: Age; Atmospheric Protection/Air Quality Control/Air Pollution; Biomass; Carbon capture and storage; Carbon Sequestration; Climate change; Decision making; Earth and Environmental Science; Ecological effects; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring; Environmental science; Evaluation; Evolutionary algorithms; Forest biomass; Forest management; Forest resources; Forests; Independent variables; Laboratories; Logging; Model accuracy; Monitoring/Environmental Analysis; Parameter estimation; Pinus - growth & development; Prediction models; Quality; Resource management; Species diversity; Technical services; Trees; Variables
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-020-8204-7

In order to determine the stand age in the uneven-aged natural forest, a dynamic prediction model of stand volume and biomass was established in this study. In the model, the site quality grade was used as the dumb variable and the interval was used as the independent variable. In addition, the parameters of the model were estimated using immune evolutionary algorithm. The model was verified with the field data and the result revealed that the model had high accuracy. On this basis, the dynamic prediction model for forest stock was applied to evaluate the asset evaluation of uneven-aged natural forest and estimate carbon storage/sink potential of forest biomass. The selective logging period of the forest in the four plots was determined at the selective logging intensity of 40%. However, at the selective logging intensity of 40%, the forest ecological environment was suffered from the adverse effect to a certain extent from the perspective of scientific management, diversity of species, etc. Based on the comprehensive consideration of all the factors, it is recommended to set the selective cutting intensity in the range of 30 to 35%. The results can provide technical support for the application of selective logging income method in asset evaluation. Therefore, the results of this study have theoretical significance and practical application value in dynamic prediction of forest resources, asset evaluation and management, decision-making, etc.