Improving wood volume predictions in dry tropical forest in the semi-arid Brazil  

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作  者:Robson B de LIMA Patrícia A B BARRETO-GARCIA Alessandro de PAULA Jhuly E S PEREIRA Flávia F de CARVALHO Silvio H M GOMES 

机构地区:[1]Department of Forest Engineering,State University of Amapá,Macapá68900070,Brazil [2]Department of Forest Science,State University of Southwest Bahia,Vitória da Conquista 45083900,Brazil [3]Universidade Federal de Lavras,Lavras 37200900,Brazil [4]Universidade de São Paulo,Piracicaba 13418900,Brazil

出  处:《Journal of Arid Land》2020年第6期1046-1055,共10页干旱区科学(英文版)

基  金:the National Council for Scientific and Technological Development-CNPq for granting financial support to the project(484260/2013-8).

摘  要:The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates.In this study,we analyzed a database of thinning trees from a forest management plan in the Contendas de SincoráNational Forest,southwestern Bahia State,Brazil.The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm.Adjustments,validation and statistical selection of four volumetric models were performed.Due to the difference in height values for the same diameter and the low correlation between both variables,we do not suggest models which only use the diameter at breast height(DBH)variable as a predictor because they accommodate the largest estimation errors.In comparing the best single entry model(Hohenald-Krenn)with the Spurr model(best fit model),it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion(AIC)and adjusted determination coefficient(R2 adj),which are poorer than the second best model(Schumacher-Hall).Regarding the minimum sample size,errors in estimation(root mean square error(RMSE)and bias)of the best model decrease as the sample size increases,especially when a larger number of trees with DBH≥15.0 cm are randomly sampled.Stratified sampling by diameter class produces smaller volume prediction errors than random sampling,especially when considering all trees.In summary,the Spurr and Schumacher-Hall models perform better.These models suggest that the total variance explained in the estimates is not less than 95%,producing reliable forecasts of the total volume with shell.Our estimates indicate that the bias around the average is not greater than 7%.Our results support the decision to use regression methods to build models and estimate their parameters,seeking stratification strategies in diameter classes for the sample trees.Volume estimates with valid confidence interv

关 键 词:volume modeling minimal sample size CAATINGA Spurr model forest management 

分 类 号:S781[农业科学—木材科学与技术]

 

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