Spatial statistical analysis in forestry: tree deaths in Gnangara Mound, Western Australia

Abstract

Spatial statistical analysis in forestry: tree deaths in Gnangara Mound, Western Australia Ya-mei Chang Department of Statistics, Tamkang University, Taiwan ?: High resolution digital airborne imagery offers unprecedented opportunities for observation and monitoring of vegetation, providing the potential to identify, locate and track individual vegetation objects over time. Analytical tools are required to quantify relevant information. In this paper, locations of trees over a large area of native woodland vegetation were identified morphological image analysis techniques. Methods of spatial statistics were then applied to estimate spatially-varying tree death risk, and show that it is significantly non-uniform. [Tree deaths over the area were detected in our previous work (Wallace et al., 2008)]. The area is a major ground water supply for the city of Perth, and the work was motivated by the need to understand and quantify vegetation changes in the context of water extraction and drying climate. The influence of hydrological variables on tree death risk was investigated using spatial statistics (graphical exploratory methods, spatial point pattern modelling and diagnostics). Keyword: airborne multi-spectral imagery, covariate effect, kernel estimation, monitoring vegetation, morphological image analysis, partial residual, spatial point pattern, spatial point process model, spatial statistics, tree location detection.