The relationship between stem-form, stand-closure and site -conditions : the influence of environmental conditions on tree allometry and forest structure in west-central Alberta

Changes in the configuration of a tree stern result insignificant differences in its total volume and in the proportion of that volume that is merchantable timber. Tree allometry, as represented by stem-fo~, is the result of the vertical force of gravity and the horizontal force of wind. The effect of wind force is demonstrated in the relationship between stem-form, standclosure and site-conditions. An increase in wind force on the individual tree due to a decrease in stand density should produce a more tapered tree. The density of the stand is determined by the conditions that the trees are growing under. The ability of the tree to respond to increased wind force may also be a function of these conditions . This stem-form/stand-closure/site-conditions relationship was examined using a pre-existing database from westcentral Alberta. This database consisted of environmental, vegetation, soils and timber data covering a wide range of sites. There were 653 sample trees with 82 variables that formed the basis of the analysis. There were eight tree species consisting of Pinus contorta, Picea mariana, Picea engelmannii x glauca, Abies lasiocarpa, Larix laricina, Populus tremuloides, Betula papyrifera and Populus balsamifera plus a comprehensive all-species data set. As the actual conformation of the stern is very individual, stem-fo~was represented by the diameter at breast height to total height r~tio. The four stand-closure variables, crown closure, total basal area, total volume and total number of stems were reduced to total basal area and total number of stems utilizing a bivariate correlation matrix by species. Site-conditions were subdivided into macro, meso and micro variables and reduced in number 3 using cross-tabulations, bivariate correlation and principal components analysis as screening tools. The stem-fo~/stand-closure relationship was examined using bivariate correlation coefficients for stem-fo~ with total number of stems and stem-fo~ with total basal area. The stem-fo~/site-conditions and the stand-closure/site- conditions relationships were examined using multiple correlation coefficients. The stem-form/stand-closure/site-conditions relationship was examined using multiple correlation coefficients in separate analyses for both total number of stems and total basal area. An increase in stand-closure produced a decrease in stem-form for both total number of stems and total basal area for most species. There was a significant relationship between stem-form and site-conditions and between stand-closure and site-conditions for both total number of stems and total basal area for most species. There was a significant relationship between the stemform and site-conditions, including the stand-closure, for most species; total number of stems was involved independently of the site-conditions in the prediction of stem-form and total basal area was not. Larix laricina and Betula papyrifera were the exceptions to the trends observed with most species. The influence of both stand-closure (total number of stems in particular) and site-conditions (elevation in particular) suggest that forest management practices should include these- ecological parameters in determining appropriate restocking levels.

Joyce Chapman posing by an orange tree, 1998

Joyce Chapman posing by an orange tree, West Covina, California, 1998.

Excavating a fossilized tree that is now in front of Hashinger Hall

Excavating a fossilized tree, which was later placed in front of Hashinger Hall, Chapman University, 346 N. Center Street, Orange, California. The late Dr. Edward H. Hashinger, former trustee and past chairman of the board is the man whose name has graced the walls of this building since 1969. The Hashinger Science Center (3 floors, 65,364 sq.ft.) houses all science departments including biology, natural and applied sciences, environmental and chemical sciences, food science and nutrition, kinesiology and physical therapy.

Placing a fossilized tree in front of Hashinger Hall, Chapman College, Orange, California

Placing a fossilized tree in front of Hashinger Hall, Chapman College, 346 N. Center Street, Orange, California. The late Dr. Edward H. Hashinger, former trustee and past chairman of the board is the man whose name has graced the walls of this building since 1969. The Hashinger Science Center (3 floors, 65,364 sq.ft.) houses all science departments including biology, natural and applied sciences, environmental and chemical sciences, food science and nutrition, kinesiology and physical therapy.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Tree Fruit (peach) Nursery - Report done for the University of Guelph

A report done by Donald Ziraldo on the topic of Tree Fruit, specifically peach, in nurseries. The report was likely done while he was a student of the University of Guelph in the 1970s.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Photograph - Percy Band standing by a tree

A photograph of Percy Band wearing a suit as he stands in front of a large tree. A house can be seen just behind the tree.

Photograph - Woman Standing by a Large Tree Trunk

A woman wearing a hat and holding a small purse. She is standing next to a large tree trunk and there are benches behind her.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

The tree of knowledge ; a document by a woman

UANL

AutoFACT: An Automatic Functional Annotation and Classification Tool

Affiliation: Centre Robert-Cedergren de l'Université de Montréal en bio-informatique et génomique & Département de biochimie, Université de Montréal