Enhanced understanding of forest stocks and dynamics can be gained through improved forest measurement, which is required to assist with sustainable forest management decisions, meet Australian and international reporting needs, and improve research efforts to better respond to a changing climate. Integrated sampling schemes that utilise a multi-scale approach, with a range of data sourced from both field and remote sensing, have been identified as a way to generate the required forest information. Given the multi-scale approach proposed by these schemes, it is important to understand how scale potentially affects the interpretation and reporting of forest from a range of data. ¶ To provide improved forest assessment at a range of scales, this research has developed a strategy for facilitating tree and stand level retrieval of structural attributes within an integrated multi-scale analysis framework. The research investigated the use of fine-scale (~1m) airborne Light Detection and Ranging (LiDAR) data (1,125 ha in central Queensland, and 60,000 ha in NE Victoria) to calibrate other remotely sensed data at the two study sites. The strategy refines forest structure mapping through three-dimensional (3D) modelling combined with empirical relationships, allowing improved estimation of maximum and predominant height, as well as foliage and crown cover at multiple scales. Tree stems (including those in the sub-canopy) were located using a height scaled crown openness index (HSCOI), which integrated the 3D density of canopy elements within the vertical profile into a two-dimensional spatial layer. The HSCOI modelling also facilitated the reconstruction of the 3D distribution of foliage and branches (of varying size and orientation) within the forest volume. ¶ Comparisons between forests at the Queensland and NE Victorian study sites indicated that accurate and consistent retrieval of cover and height metrics could be achieved at multiple scales, with the algorithms applicable for semi-automated use in other forests with similar structure. This information has facilitated interpretation and evaluation of Landsat imagery and ICESat satellite laser data for forest height and canopy cover retrieval. The development of a forest cover translation matrix allows a range of data and metrics to be compared at the plot scale, and has initiated the development of continuous transfer functions between the metrics and datasets. These data have been used subsequently to support interpretation of SAR data, by providing valuable input to 2D and 3D radar simulation models. Scale effects have been identified as being significant enough to influence national forest class reporting in more heterogeneous forests, thus allowing the most appropriate use and integration of remote sensed data at a range of scales. An empirically based forest minimum mapping area of 1 ha for reporting is suggested. The research has concluded that LiDAR can provide calibration information just as detailed and possibly more accurately than field measurements for many required forest attributes. Therefore the use of LiDAR data offers a unique opportunity to bridge the gap between accurate field plot structural information and stand to landscape scale sampling, to provide enhanced forest assessment in Australia.

Enhanced understanding of forest stocks and dynamics can be gained through improved forest measurement, which is required to assist with sustainable forest management decisions, meet Australian and international reporting needs, and improve research efforts to better respond to a changing climate. Integrated sampling schemes that utilise a multi-scale approach, with a range of data sourced from both field and remote sensing, have been identified as a way to generate the required forest information. Given the multi-scale approach proposed by these schemes, it is important to understand how scale potentially affects the interpretation and reporting of forest from a range of data. ¶ To provide improved forest assessment at a range of scales, this research has developed a strategy for facilitating tree and stand level retrieval of structural attributes within an integrated multi-scale analysis framework. The research investigated the use of fine-scale (~1m) airborne Light Detection and Ranging (LiDAR) data (1,125 ha in central Queensland, and 60,000 ha in NE Victoria) to calibrate other remotely sensed data at the two study sites. The strategy refines forest structure mapping through three-dimensional (3D) modelling combined with empirical relationships, allowing improved estimation of maximum and predominant height, as well as foliage and crown cover at multiple scales. Tree stems (including those in the sub-canopy) were located using a height scaled crown openness index (HSCOI), which integrated the 3D density of canopy elements within the vertical profile into a two-dimensional spatial layer. The HSCOI modelling also facilitated the reconstruction of the 3D distribution of foliage and branches (of varying size and orientation) within the forest volume. ¶ Comparisons between forests at the Queensland and NE Victorian study sites indicated that accurate and consistent retrieval of cover and height metrics could be achieved at multiple scales, with the algorithms applicable for semi-automated use in other forests with similar structure. This information has facilitated interpretation and evaluation of Landsat imagery and ICESat satellite laser data for forest height and canopy cover retrieval. The development of a forest cover translation matrix allows a range of data and metrics to be compared at the plot scale, and has initiated the development of continuous transfer functions between the metrics and datasets. These data have been used subsequently to support interpretation of SAR data, by providing valuable input to 2D and 3D radar simulation models. Scale effects have been identified as being significant enough to influence national forest class reporting in more heterogeneous forests, thus allowing the most appropriate use and integration of remote sensed data at a range of scales. An empirically based forest minimum mapping area of 1 ha for reporting is suggested. The research has concluded that LiDAR can provide calibration information just as detailed and possibly more accurately than field measurements for many required forest attributes. Therefore the use of LiDAR data offers a unique opportunity to bridge the gap between accurate field plot structural information and stand to landscape scale sampling, to provide enhanced forest assessment in Australia.

Analysis of forest canopy density based on textural features of high resolution imagery and airborne laser scanning data Abstract The objective of this thesis is to assess the forest canopy density in the Šumava Mountains, Czech Republic. The spruce forests in this area have been suffering from the bark beetle outbreak for almost 20 years resulting in a mixture of dead and young trees, mature forest stands and peat bogs. The canopy density was evaluated using a very high spatial resolution panchromatic imagery and low point density LiDAR, combined with an object oriented approach. The classification based on three GLCM texture measures (contrast, entropy and correlation), which were derived from the image objects, resulted in a kappa index of accuracy of 0.45. Adding the information from the LiDAR data, the accuracy of the classification improved up to 0.95.

The influence of rootstock and canopy density on grape berry composition was investigated over the summer of 2003-2004 on a commercial vineyard at Waipara, North Canterbury. This experiment was designed to investigate the influence of rootstock and canopy density on the acid composition, potassium (K) content and final pH of harvested fruit (Pinot Noir AM 10/5 Lincoln Selection). The trial block consisted of eight rootstocks laid out to an 8 x 8 latin square, each plot consisting of five vines of the same rootstock. Two canopy treatments were overlaid the block (down whole rows, assigned randomly, four rows to each treatment); one treatment allowed to grow naturally, in the other treatment the canopy was thinned removing double burst shoots and laterals. The bunch numbers were adjusted in the Unthinned canopy treatment (UCT) to match the Thinned canopy treatment (TCT). Information was gathered to assess: the canopy size and density (Pinot Quadrat Leaf Layer and Percent Gaps and canopy porosity), the plant yield (and berry size, berries per cluster, cluster weight, clusters per plant), plant K levels at flowering and veraison (from petioles and leaf blades) and berry composition at harvest (soluble solids (as brix), K, titratable acidity (TA), tartaric acid concentration, malic acid concentration and pH). The trial area was non-irrigated on clay loam soils and viticultural management was to best commercial practice. It was found that although rootstock influenced the levels of K in the plant and in the juice at harvest, the level of K in the juice did not influence pH in this experiment (range of rootstock juice K: 808 ppm to 928 ppm, l.s.d. = 75 ppm). The level of tartaric acid concentration in the juice was found to be the dominant influence on the level of pH in this experiment (rootstock pH range: 3.21 to 3.39, l.s.d. = 0.05). The juice concentration of tartaric acid was influenced by both rootstock (rootstock range 4.0 to 4.7 g/L, l.s.d = 0.4) and canopy density (UCT = 4.1, TCT = 4.7, l.s.d. = 0.4), decreased shading positively increasing the level of tartaric acid. The malic acid concentration in the juice was positively influenced by increasing canopy density (UCT = 4.7 g/L, TCT = 4.1 g/L, l.s.d = 0.4) and this played a minor role in the determination of pH in this experiment; an influence of rootstock on the level of malic acid concentration was found. The malic acid concentration strongly influenced the determination of TA (UCT = 11.0 g/L, TCT = 10.2 g/L, l.s.d = 0.5); tartaric acid concentration had a minor influence on the recorded TA. Attempts to characterise the influence of rootstock on malic acid, tartaric acid and pH were inconclusive. Rootstock was found to influence the canopy variables measured in this experiment and the recorded average plant yield. Crosses of Vitis rupestris were found to exhibit the most canopy vigour and those derived from Vitis berlandieri and Vitis riparia the least. The Canopy treatment did not show an influence over yield but the rootstock was found to influence plant yield, through the numbers of berries set in a cluster and the final harvest cluster weight. The influence of rootstock on pH may be described by the influence it exerts on canopy growth and yield but this was thought unlikely. Further research is required to describe the nature of the rootstock influence on K, malic acid, tartaric acid and pH.