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A new model for predicting the total tree height for stems cut-to-length by harvesters in Pinus radiata plantations

Journal of Forestry Research 2021年第1期32卷 21-41页

作者： Chenxi Shan Huiquan Bi Duncan Watt Yun Li Martin Strandgard Mohammad Reza Ghaff ariyan School of Information Science and Technology Beijing Forestry UniversityBeijing 100083People’s Republic of China Forest Science NSW Department of Primary Industries–ForestryLevel 1210 Valentine AveParramattaNSW 2150Australia School of Ecosystem and Forest Sciences The University of MelbourneParkvilleVIC 3010Australia Snowy Region Forestry Corporation of NSW76 Capper StreetTumutNSW 2720Australia Australian Forest Operations Research Alliance(AFORA) University of the Sunshine CoastSippy DownsQLD 4556Australia

A new model for predicting the total tree height for harvested stems from cut-to-length(CTL)harvester data was constructed for Pinus radiata(***)following a conceptual analysis of relative stem profi les,comparisons of candidate models forms and extensive selections of predictor *** profi les of more than 3000 trees in a taper data set were each processed 6 times through simulated log cutting to generate the data required for this *** CTL simulations not only mimicked but also covered the full range of cutting patterns of nearly 0.45×106 stems harvested during both thinning and harvesting *** single-equation model was estimated through the multipleequation generalized method of moments estimator to obtain effi cient and consistent parameter estimates in the presence of error correlation and heteroscedasticity that were inherent to the systematic structure of the *** predictive performances of our new model in its linear and nonlinear form were evaluated through a leave-one-tree-out cross validation process and compared against that of the only such existing *** evaluations and comparisons were made through benchmarking statistics both globally over the entire data space and locally within specifi c subdivisions of the data *** statistics indicated that the nonlinear form of our model was the best and its linear form ranked *** prediction accuracy of our nonlinear model improved when the total log length represented more than 20%of the total tree *** poorer performance of the existing model was partly attributed to the high degree of multicollinearity among its predictor variables,which led to highly variable and unstable parameter *** new model will facilitate and widen the utilization of harvester data far beyond the current limited use for monitoring and reporting log productions in *** *** will also facilitate the estimation of bark thickness and help make harvester data a

关键词： stem profi les Cut-to-length simulations Harvester data Model construction Nonlinear multipleequation GMM estimation Benchmarking prediction accuracy

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