Studies on European beech (Fagus sylvatica L.). Part 1: Variations of wood colour parameters

Colour parameters of European beech were measured using CIELab system. 103 logs from 87 trees in 9 sites were cut into boards to study the variations of wood colour parameters. Both site and tree effect on colour were observed. Patterns of red heartwood occurrence were defined. When excepting red heartwood there was still a highly significant effect of site and tree; differences remained after veneer processing. Axial variations were small, except very near the pith or in red heartwood, suggesting possible early selection at periphery under colour criteria. Red heartwood is darker, redder and more yellow than normal peripheral wood.Comment: to be published in Annals of Forest Science reception 12.8.04; acceptation 15.2.0

A tree approach to pp-variation and to integration

We consider a real-valued path; it is possible to associate a tree to this path, and we explore the relations between the tree, the properties of $p$-variation of the path, and integration with respect to the path. In particular, the fractal dimension of the tree is estimated from the variations of the path, and Young integrals with respect to the path, as well as integrals from the rough paths theory, are written as integrals on the tree. Examples include some stochastic paths such as martingales, L\'evy processes and fractional Brownian motions (for which an estimator of the Hurst parameter is given)

An evaluation of possible relationships between solar activity and tree-ring growth in western North America

Relationship between tree ring growth variations and solar activity since 1700 in western North Americ

Abiotic controls on macroscale variations of humid tropical forest height

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.The research was funded by Gabon National Park (ANPN) under the contract of 011-ANPN/2012/SE-LJTW at UCLA. We thank IIASA, FAO, USGS, NASA, Worldclim science teams for making their data available. (011-ANPN/2012/SE-LJTW - Gabon National Park (ANPN) at UCLA

Image-based tree variations

The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette-defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.Peer ReviewedPostprint (author's final draft

Introduction to IND and recursive partitioning, version 1.0

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, lists the manual pages for the routines, and instructions on installation

Introduction in IND and recursive partitioning

This manual describes the IND package for learning tree classifiers from data. The package is an integrated C and C shell re-implementation of tree learning routines such as CART, C4, and various MDL and Bayesian variations. The package includes routines for experiment control, interactive operation, and analysis of tree building. The manual introduces the system and its many options, gives a basic review of tree learning, contains a guide to the literature and a glossary, and lists the manual pages for the routines and instructions on installation