文献类型：期刊文献

英文题名：Improving tree survival prediction with forecast combination and disaggregation

作者：Zhang, Xiongqing[1] Lei, Yuancai[1] Cao, Quang V.[2] Chen, Xinmei[1] Liu, Xianzhao[1]

通信作者：Lei, YC[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Louisiana State Univ, Sch Renewable Nat Resources, Baton Rouge, LA 70803 USA

年份：2011

卷号：41

期号：10

起止页码：1928-1935

外文期刊名：CANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE

收录：;EI(收录号：20114414479248);Scopus(收录号：2-s2.0-80055060869);WOS:【SCI-EXPANDED(收录号:WOS:000297275500003)】；

基金：The authors express their appreciation to the Ministry of Science and Technology (MOST) and State Forestry Administration (SFA) for fiscal support (Project Research Grants 2005DIB5JI42, CAFYBB2008008 and IFRIT200901) and the Inventory Institute of Beijing Forestry for its data. The authors also thank the Associate Editor and two anonymous reviewers for improving the scientific quality of this manuscript and the editors for their careful work.

语种：英文

外文关键词：Forestry - Plants (botany)

摘要：The tree mortality model plays an important role in simulating stand dynamic processes. Past work has shown that the disaggregation method was successful in improving tree survival prediction. This method was used in this study to forecast tree survival probability of Chinese pine (Pinus tabulaeformis Carriere) in Beijing. Outputs from the tree survival model were adjusted from either the stand-level model prediction or the combined estimator from the forecast combination method. Our results show that the disaggregation approach improved the performance of tree survival models. We also showed that stand-level prediction played a crucial role in refining outputs from a tree survival model, especially when it is a very simple model. Because the forecast combination method produced better stand-level prediction, we prefer the use of this method in conjunction. with the disaggregation approach, even though the performance gain in using the forecast combination method shown for this data set was modest.