Sense of place: A tool to assist the meaningful engagement of communities in protected area management

This study used ‘sense of place’ as a research tool to help understand the relationship between a community and their local protected area, Brisbane Forest Park. To establish an indication of the community’s relative degree of sense of place, we considered and measured both the strength (intensity) and orientation (focus) of sense of place. We developed a new method to measure sense of place that considers and measures the elements constituting sense of place, independent of one another, utilising qualitative data collected in in-depth semi-structured interviews. Exploring both the strength and orientation of an individual's sense of place provides a way of exploring the desired nature of community involvement in the management of the Park. It was found that the stronger an individuals’ sense of place, the greater their place dependence and commitment, and the greater their desire to be involved in management. Analysing the strength and orientation of sense of place illustrated that there is a high degree of diversity in how individuals perceive and feel about area, and their desire to be involved in management. The type of information obtained in this study is important and useful to the management agencies if they are to successfully engage the community in meaningful ways.

Geometric-optical modelling of a tropical forest canopy

Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference

Estimation and mapping of foliar chemical levels in a coastal eucalypt forest using CASI data

Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference

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.