Species-specific and mixed-species volume and above ground biomass allometric equations were developed for 15 indigenous tree species and four tree fern species in New Zealand. A mixed-species tree equation based on breast height diameter (DBH) and tree height (H) provided acceptable estimates of stem plus branch (>10 cm in diameter over bark) volume, which was multiplied by live tree density to estimate dry matter. For dead standing spars, DBH, estimated original height, actual spar height and compatible volume/taper functions provided estimates of dead stem volume, which was multiplied by live tree density and a density modifier based on log decay class from field assessments to estimate dry matter. Live tree density was estimated using ratio estimators. Ratio estimators were based on biomass sample trees, and utilized density data from outerwood basic density surveys which were available for 35 tree species sampled throughout New Zealand. Foliage and branch ( < 10 cm in diameter over bark) dry matter were estimated directly from tree DBH. Tree fern above ground dry matter was estimated using allometric equations based on DBH and H. Due to insufficient data, below ground carbon for trees was estimated using the default IPCC root/shoot ratio of 25%, but for tree ferns it was estimated using measured root/shoot ratios which averaged 20%.

Relationships between discrete-return light detection and ranging (LiDAR) data and radiata pine leaf area index (LAI), stem volume, above ground carbon, and carbon sequestration were developed using 10 plots with directly measured biomass and leaf area data, and 36 plots with modelled carbon data. The plots included a range of genetic types established on north- and south-facing aspects. Modelled carbon was highly correlated with directly measured crown, stem, and above ground biomass data, with r = 0.92, 0.97 and 0.98, respectively. LiDAR canopy percentile height (P30) and cover, based on all returns above 0.5 m, explained 81, 88, and 93% of the variation in directly measured crown, stem, and above ground live carbon and 75, 89 and 88% of the modelled carbon, respectively. LAI (all surfaces) ranged between 8.8–19.1 in the 10 plots measured at age 9 years. The difference in canopy percentile heights (P95–P30) and cover based on first returns explained 80% of the variation in total LAI. Periodic mean annual increments in stem volume, above ground live carbon, and total carbon between ages 9 and 13 years were significantly related to (P95–P30), with regression models explaining 56, 58, and 55%, respectively, of the variation in growth rate per plot. When plot aspect and genetic type were included with (P95–P30), the R² of the regression models for stem volume, above ground live carbon, and total carbon increment increased to 90, 88, and 88%, respectively, which indicates that LiDAR regression equations for estimating stock changes can be substantially improved by incorporating supplementary site and crop data.

Many studies have quantified uncertainty in forest carbon (C) storage estimation, but there is little work examining the degree of uncertainty in shrubland C storage estimates. We used field data to simulate uncertainty in carbon storage estimates from three error sources: (1) allometric biomass equations; (2) measurement errors of shrubs harvested for the allometry; and (3) measurement errors of shrubs in survey plots. We also assessed uncertainty for all possible combinations of these error sources. Allometric uncertainty had the greatest independent effect on C storage estimates for individual plots. The largest error arose when all three error sources were included in simulations (where the 95% confidence interval spanned a range equivalent to 40% of mean C storage). Mean C sequestration (1.73 Mg C ha^–1 year^–1) exceeded the margin of error produced by the simulated sources of uncertainty. This demonstrates that, even when the major sources of uncertainty were accounted for, we were able to detect relatively modest gains in shrubland C storage.

Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates. In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included.

Carbon fractions are applied to dry matter estimates to calculate carbon stocks in forest stands. A default carbon fraction has been applied to planted forest species in New Zealand; however, various studies have shown that the carbon fraction can differ among species and between tree components. New Zealand-specific carbon fractions were, therefore, developed to improve the accuracy of carbon stock estimates for international reporting purposes. Carbon fractions were analysed using subsamples of tree components from 684 stems, 1125 crowns and 70 root systems from 14 sites distributed throughout New Zealand. The carbon fractions for needles, branches, cones, stem wood, stem bark and roots reported by the laboratory at a drying temperature of 104 °C were corrected using published procedures to the moisture content attained after drying subsamples to constant weight at 70 °C, the drying temperature used in New Zealand biomass studies. Carbon fraction averaged 0.514 g C g⁻¹dm in needles, 0.507 g C g⁻¹dm in branches, 0.519 g C g⁻¹dm in cones, 0.498 g C g⁻¹dm in stem wood, 0.501 g C g⁻¹dm in roots, and 0.539 g C g⁻¹dm in stem bark of radiata pine. The stem bark carbon fraction increased asymptotically with stand age. The default carbon fraction (0.50 g C g⁻¹dm) used previously in the FCP model underestimates carbon stocks in New Zealand’s planted forest estate. Applying carbon fractions derived from New Zealand biomass studies will increase carbon stock estimates for the planted forest land by approximately 1% and also increase estimates of removals during harvesting operations. Information on in-forest debarking activities will further improve estimates of removals associated with harvesting.

Repeated measurements of plots are usually made to directly determine carbon stock changes over time. However, it is sometimes only practical or feasible to inventory plots at the end of a period of interest, and stock changes need to be predicted retrospectively from supplementary information on growth rate. This situation applied to the natural stratum of post-1989 forest in New Zealand, for which carbon sequestration over Commitment Period 1 (2008–2012) of the Kyoto Protocol needed to be estimated from inventory data acquired in 2012. A pilot study was undertaken to test and refine methods that could be applied in the national inventory, utilizing plots that had been installed in eligible post-1989 natural forest in 2008. The plots had actual measurements and shrub biomass sampling to directly estimate carbon stocks in 2008. These plots were re-measured and sampled in 2012, and basal disc samples from plants growing adjacent to each plot collected to provide data to model stem annual increment in diameter and height of shrubs growing on the plot. We present the results of this test of methods, and discuss refinements to field procedures and calculation methods to be applied in the national inventory of this stratum of post-1989 forest in 2012.