https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:24569 1.3). Tree taper data were coupled with stump dimensions data in a mixed-effects model to predict each stump's BA at 1.3 m, and sapwood taper data from buttress logs were used to improve each stems ̂R_1.3. Using this procedure, we found our study site to have an ̂R_1.3 of 0.21 and total stand SA at 1.3 m height of 9.3 m² ha⁻¹. We quantified the bias in traditional ̂R_1.3 estimates that use cores to measure sapwood thickness and diameter tape to calculate SA. We show traditional methods underestimate inline image that increases with tree diameter and decreases with stem circularity, whereas our methodology gave more accurate measures of SA in large buttressing trees. Our methodology also provides a more efficient way of generating maps of SA variation across large forested catchments.]]> Sat 24 Mar 2018 07:11:30 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:24993 1.3) in a large number of trees to understand the spatial heterogeneity of tree and stand sapwood area within and between forest communities, and develop allometric relationships that predict SA_1.3 with forest inventory data. We also apply tree competition models to determine the degree to which the relationship between SA_1.3 and tree basal area at 1.3 m height (BA_1.3) is influenced by competition. Methods: We visited 25 recently harvested southeastern Australian forest sites consisting of 1379 trees and 5 Eucalyptus species to evaluate a new efficient data collection method for estimating SA_1.3 with tree taper and stump dimensions data using mixed effects models. The locations of 784 stumps within one 5-ha site were accurately mapped using an unmanned aerial vehicle (UAV), and four distance-dependent tree competition models were applied across the site to explain within-stand variation in the ratio of SA_1.3 to BA_1.3. Data from 24 additional sites, consisting of ten 15 m radial plots per site, were used to analyse within-site variation in R_Ha (the ratio of stand sapwood area SA_Ha to stand basal area BA_Ha). The radial plots were merged within each site to evaluate between-site variations in R_Ha across the landscape. For predicting SA_Ha with forest inventory data, we computed the relationship between SA_Ha and a new index of total stem perimeter per hectare, defined as √BA_HaN_T, where N_T is tree stocking density. Important Findings: Our 1379 measured stems represent the most comprehensive measure of sapwood area, surpassing the 757 measured stems in native eucalypt forests published in literature. The species-specific R_Ha varied considerably across sites and therefore extrapolating SA_Ha with spatially distributed BA_Ha maps and a generalized R_Ha would introduce local uncertainty. We found that the species-specific stem perimeter index was more effective at capturing variability in SA_Ha across the landscape using forest composition, structure and density data (R²: 0.72–0.77). The strong correlation between tree SA_1.3 and BA_1.3 improved slightly using tree competition models (R² increased from 0.86 to 0.88). Relating SA_Ha to routinely measured forest inventory attributes within permanent plots and Light Detection and Ranging (LiDAR) data may provide opportunities to map forest water use in time and space across large areas disturbed by wildfire and logging.]]> Sat 24 Mar 2018 07:09:55 AEDT ]]>