Data collection
Stand information
The data for this study were obtained during late summer 2011 from a 25-year-old forest located in Eastern Bay of Plenty, New Zealand. This forest is located on steep dissected country with an elevation range of approximately 150 to 300 metres above sea level. A standard pre-harvest inventory comprising 163 plots was carried out within the forested area of 217.8 hectares. The circular plots were laid out on a systematic grid. Plot size was varied from 0.06 to 0.09 ha to ensure that approximately 20 trees were included in each plot. The location of the plots was measured using a Trimble Pathfinder Pro XT high grade GPS with data corrected using post processing (accuracy = 0.5m). Within each plot all trees were measured for stem diameter.
Total stem volume, TSV (m3 ha-1), was determined from stand basal area, BA (m2 ha-1), and mean top height, Ht, (m; mean height of the 100 largest diameter trees per ha), using the following nationally applicable equation (Kimberley & Beets, 2007),
(1)
For Equation 1, Ht was determined directly from LiDAR metrics using the following unbiased and accurate (R2=0.95; root mean square error = 1.91 m) nationally applicable equation, derived from an extensive set of LiDAR metrics and field measurements (Watt & Watt, in press),
(2)
where H95 (m) is the 95th percentile of the LiDAR height distribution. Stem slenderness was defined from the plot data as Ht/mean stem diameter. For each plot stand density was determined as the actual number of trees ha-1.
Outerwood velocity
Outerwood stress-wave velocity (V) was measured in 32 of the inventory plots. Plots were selected to cover the range in stem slenderness and stand density present throughout the forest as both factors are significant determinants of V (Lasserre et al., 2008; Waghorn et al., 2007; Watt & Zoric, 2010). Where possible, measurements were taken from at least 20 trees within the plot.
Velocity measurements, centred about breast height (1.4 m), were taken using the ST300 tool (Fibre-gen, New Zealand). Using paths that avoided any large branch stubs or obvious malformations, two measurements were taken either side of the stem (ca. 180° apart) and averaged. The path length between transmitter probe and receiver probe sensors was approximately 1 m.
LiDAR dataset
Light detection and ranging data and aerial imagery were collected by New Zealand Aerial Mapping, who flew over the site between 24 May and 1 June 2011. Data were captured using New Zealand Aerial Mapping’s Optech ALTM 3100EA LiDAR system (05SEN178) and Trimble AIC medium-format digital camera. The LiDAR data were collected at a minimum of 2 points m-2 on open ground. The raw LiDAR data was processed by the supplier into LAS format and georeferenced into the New Zealand Transverse Mercator (NZTM) coordinate system. Classified ground returns were used to construct a Digital Terrain Model (DTM) by connecting them into a Triangulated Irregular Network (TIN) followed by linear interpolation onto a regular grid. All returns within 0.5 m of the ground were eliminated to remove the effects of understorey vegetation. LiDAR metrics used in the modelling were extracted above the circular plots using corrected GPS locations with the ClipData and CloudMetrics tools from the Fusion software (McGaughey & Carson, 2003).
Predictive variables used for the modelling
The LiDAR metrics used in the modelling consisted of height percentiles (H5 – H95), the mean (Hmean) and maximum height (Hmax), several metrics describing the LiDAR height distribution through the canopy (skewness, coefficient of variation, standard deviation (Hsd), kurtosis) and measures of canopy density such as the percentage of returns reaching within 0.5 m of the ground (PCzero) and the percentage of first returns above 0.5 m (PCveg).
Variables describing site topography and stand structure were also used in the modelling. These variables included aspect, slope, stand density, stem slenderness, basal area, mean diameter and Ht. Aspect was determined using a digital terrain model while slope was measured in the field.
Analysis
Linear and non-linear models to predict TSV and V were developed using PROC GLM and PROC NLIN in SAS (SAS-Institute-Inc, 2000). Variables were introduced sequentially into each model starting with the variable that exhibited the strongest correlation, until further additions were either not significant or did not markedly improve model precision (R2 gains of < 5%). Variable selection was undertaken manually, one variable at a time, and plots of residuals were examined prior to variable addition to ensure that the variable was included in the model using the least biased functional form.
Model precision was determined using the coefficient of determination (R2) and the root mean square error (RMSE). Model bias was determined through plotting predicted against measured values. Residual values (measured – predicted values) were plotted against predicted values, all independent variables in the model and key variables not included in the models. The contribution and functional form of each variable in both of the final models were examined through partial response functions. These partial response functions were generated across the range of each variable whilst holding other variables at mean values in the dataset.