Tree-Lists Estimation for Chinese Boreal Forests by Integrating Weibull Diameter Distributions with MODIS-Based Forest Attributes from kNN Imputation

Wall-to-wall tree-lists information (lists of species and diameter for every tree) at a regional scale is required for managers to assess forest sustainability and design effective forest management strategies. Currently, the k-nearest neighbors (kNN) method and the Weibull diameter distribution function have been widely used for estimating tree lists. However, the kNN method usually relies on a large number of field inventory plots to impute tree lists, whereas the Weibull function relies on strong correlations between stand attributes and diameter distribution across large regions. In this study, we developed a framework to estimate wall-to-wall tree lists over large areas based on a limited number of forest inventory plots. This framework integrates the ability of extrapolating diameter distribution from Weibull and kNN imputation of wall-to-wall forest stand attributes from Moderate Resolution Imaging Spectroradiometer (MODIS). We estimated tree lists using this framework in Chinese boreal forests (Great Xing’an Mountains) and evaluated the accuracy of this framework. The results showed that the passing rate of the Kolmogorov–Smirnov (KS) test for Weibull diameter distribution by species was from 52% to 88.16%, which means that Weibull distribution could describe the diameter distribution by species well. The imputed stand attributes (diameter at breast height (DBH), height, and age) from the kNN method showed comparable accuracy with the previous studies for all species. There was no significant difference in the tree density between the estimated and observed tree-lists. Results suggest that this framework is well-suited to estimating the tree-lists in a large area. Our results were also ecologically realistic, capturing dominant ecological patterns and processes.