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The height to crown base (HCB) of a tree is a necessary variable that includes many growth and yield models as a predictor, and it is important to develop an HCB model due to its contribution to fo...

Species information of forest plantations is of central interest to forest managers, especially under the current pressing demand for more structurally and functionally healthy and diverse plantations in China. In this study, five evergreen forest types were discriminated using a 4-band very high spatial resolution (VHR) satellite image with an operational image analysis framework which particularly involves a two-step feature selection (TSFS) method and image textural orientations. Two hundred and twenty-five object-based spectral and textural features were retrieved as candidates for the TSFS method to select the most influential feature subsets. Five schemes using random forest classification with optimal user-defined parameters were tested and compared. Both qualitative and quantitative assessment results indicated that the best scheme is the integration of spectral and textural features with individual orientations. The feature selection rate as low as 4% for the optimal scheme reflected the high effectiveness of the TSFS. The geometric intersections showed that many archive even-aged pure coniferous plantations were in fact mixed with nonconiferous species with different proportions (8.05–16.02%) and spatial aggregation patterns over very rugged terrain. Special management strategies are needed to perform the transition of pure coniferous plantations into more structurally balanced coniferous-broadleaved mixed woodland in subtropical China.

Scenic beauty estimation (SBE) models that quantify landscape aesthetics through picture-based questionnaires are complicated to develop. Therefore, precise, simple, and widely applicable models of SBE prediction based on tree and stand measurements must be developed. To investigate the application of tree- and stand-based SBE models to Chinese fir forest management, the relationships between diameter at breast height (DBH) and tree height (H) measurements and tree- (TSBE) and stand (SSBE)-based SBE predictions were assessed. TSBE and SSBE were strongly influenced by DBH and H measurements according to either a power function or linear function, depending on the variables. When the mean stand DBH was less than 8.4 cm or H was less than 9.8 m, dense canopies resulted in negative SSBE residuals. In stands with large trees and non-dense canopies, the residuals of SSBE were positive. We conclude that average H and DBH measurements within a stand can be used to predict SSBE by extrapolating the tree-ba...