Dispersal of soredia from individual soralia of the lichen Hypogymnia physodes (L.) Nyl. in a simple wind tunnel

Dispersal of soredia from individual soralia of the lichen Hypogymnia physodes (L.) Nyl. was studied using a simple wind tunnel constructed in the field. Individual lobes with terminal soralia were placed in the wind tunnel on the adhesive surface of dust particle collectors. Air currents produced by a fan were directed over the surface of the lobes. The majority of soredia were deposited within 5 cm of the source soralium but some soredia were dispersed to at least 80 cm at a wind speed of 6 m s-1. Variation in wind speed had no statistically significant effect on the total number of soredial clusters deposited averaged over soralia but the mean size of cluster and the distance dispersed were greater at higher wind speeds. The number of soredia deposited was dependent on the orientation of the soralium to the air currents. More soredia were deposited with the soralium facing the fan at a wind speed of 9 m s-1. Moisture in the form of a fine mist reduced substantially the number of soredia deposited at a wind speed of 6 m s-1 but had no effect on the mean number of soredia per cluster or on the mean distance dispersed. The data suggest: (1) that wind dispersal from an individual soralium is influenced by wind speed, the location of the soralium on the thallus and the level of moisture and (2) that air currents directed over the surfaces of thalli located on the upper branches of trees would effectively disperse soredia of H. physodes vertically and horizontally within a tree canopy. © 1994.

The use of spot simulation imagery for forestry mapping and management

SPOT simulation imagery was acquired for a test site in the Forest of Dean in Gloucestershire, U.K. This data was qualitatively and quantitatively evaluated for its potential application in forest resource mapping and management. A variety of techniques are described for enhancing the image with the aim of providing species level discrimination within the forest. Visual interpretation of the imagery was more successful than automated classification. The heterogeneity within the forest classes, and in particular between the forest and urban class, resulted in poor discrimination using traditional `per-pixel' automated methods of classification. Different means of assessing classification accuracy are proposed. Two techniques for measuring textural variation were investigated in an attempt to improve classification accuracy. The first of these, a sequential segmentation method, was found to be beneficial. The second, a parallel segmentation method, resulted in little improvement though this may be related to a combination of resolution in size of the texture extraction area. The effect on classification accuracy of combining the SPOT simulation imagery with other data types is investigated. A grid cell encoding technique was selected as most appropriate for storing digitised topographic (elevation, slope) and ground truth data. Topographic data were shown to improve species-level classification, though with sixteen classes overall accuracies were consistently below 50%. Neither sub-division into age groups or the incorporation of principal components and a band ratio significantly improved classification accuracy. It is concluded that SPOT imagery will not permit species level classification within forested areas as diverse as the Forest of Dean. The imagery will be most useful as part of a multi-stage sampling scheme. The use of texture analysis is highly recommended for extracting maximum information content from the data. Incorporation of the imagery into a GIS will both aid discrimination and provide a useful management tool.

A computational model of intonation for yorùbá text-to-speech synthesis:design and analysis

In this paper we present the design and analysis of an intonation model for text-to-speech (TTS) synthesis applications using a combination of Relational Tree (RT) and Fuzzy Logic (FL) technologies. The model is demonstrated using the Standard Yorùbá (SY) language. In the proposed intonation model, phonological information extracted from text is converted into an RT. RT is a sophisticated data structure that represents the peaks and valleys as well as the spatial structure of a waveform symbolically in the form of trees. An initial approximation to the RT, called Skeletal Tree (ST), is first generated algorithmically. The exact numerical values of the peaks and valleys on the ST is then computed using FL. Quantitative analysis of the result gives RMSE of 0.56 and 0.71 for peak and valley respectively. Mean Opinion Scores (MOS) of 9.5 and 6.8, on a scale of 1 - -10, was obtained for intelligibility and naturalness respectively.