| In this thesis we provide a summary of the methods by which remote sensing may be applied in forestry, while also acknowledging the various limitations which are faced. The application of spatial statistics to high spatial resolution imagery is explored as a means of increasing the information which may be extracted from digital images. A number of high spatial resolution optical remote sensing satellites that are soon to be launched will increase the availability of imagery for the monitoring of forest structure. This technological advancement is timely as current forest management practices have been altered to reflect the need for sustainable ecosystem level management.; The low accuracy level at which forest structural parameters have been estimated in the past is partly due to low image spatial resolution. A large pixel is often composed of a number of surface features, resulting in a spectral value which is due to the reflectance characteristics of all surface features within that pixel. In the case of small pixels, a portion of a surface feature may be represented by a single pixel. When a single pixel represents a portion of a surface object, the potential to isolate distinct surface features exists. Spatial statistics, such as the Gets statistic, provide for an image processing method to isolate distinct surface features. In this thesis, high spatial resolution imagery sensed over a forested landscape is processed with spatial statistics to combine distinct image objects into clusters, representing individual or groups of trees.; Tree clusters are a means to deal with the inevitable foliage overlap which occurs within complex mixed and deciduous forest stands. The generation of image objects, that is, clusters, is necessary to deal with the presence of spectrally mixed pixels. The ability to estimate forest inventory and biophysical parameters from image clusters generated from spatially dependent image features is tested in this thesis. The inventory parameter of crown closure is successfully estimated from image clusters, yet the grouping of trees into clusters causes mixed results when estimating stem counts. The assignment of a cover class of each cluster is also undertaken. The knowledge of cluster cover class has also enabled the estimation of leaf area index. Further, spatial information alone may be used to estimate LAI under described conditions. |
