Recent developments in the application of line-transect models to aerial surveys have used double-observer sampling to account for undercounting on the transect line, a crucial step in obtaining correct population estimates. This method is commonly called the mark–recapture line-transect sampling method and estimates the detection probability at zero distance to correct line-transect estimates of abundance. An alternative approach, which uses the same methodology during data collection, is to use a range of covariates, including distance from the transect, in a mark–recapture model. This approach overcomes the implicit assumption of uniform distribution of distances in line-transect estimators. In this paper, we use three alternative approaches (a multiple-covariates distance method, a distance method incorporating adjustment for incomplete detection on the transect line using mark–recapture sampling, and a mark–recapture method with distance as a covariate) to estimate the abundance of several medium-sized mammals in semiarid ecosystems. Densities determined with the three estimators varied considerably within species and sites. In some cases distance estimates were larger than mark–recapture estimates and vice versa. Despite large numbers of observations, distance uniformity was not observed for any species at any site, nor for any species where sites were combined. Possible reasons, which include sampling variability, movement in response to the aircraft and failure of the mark–recapture independence assumption, are discussed in detail.

Precision in describing plantation attributes is a key requirement for forestry managers and inventory surveys aim to extract the most precise information possible using the smallest number of plots. This paper quantifies the potential efficiencies to be gained by using Light Detection and Ranging (LiDAR) data as an aid to estimation of standing timber volume in softwood plantations. A range of inventory design and estimation methods were investigated in terms of their overall predictive efficiency. Field measurements representing four different populations from two Pinus radiata D. Don plantations in New South Wales, Australia, were used to inform statistical models which were then employed to simulate populations of inventory plots. These plots were then “surveyed” using a variety of simulated sampling strategies to quantify the benefits from using LiDAR data as auxiliary information. Model-based and design-based methods were both investigated. Survey design options included stratification and plot selection strategies; estimation options included ratio estimation and regression modelling. Results were compared in terms of the relative bias and root mean squared error of the estimates. The study suggests that relative efficiencies of two-fold or better, are possible with either model-based or model-assisted estimators compared to traditional inventory surveys which use grid samples and simple design-based estimators. This would enable a halving in the required sample size for the same precision for field inventories in these plantations. The use of LiDAR data as an aid to survey design produces marked efficiency gains compared to traditional inventory methods.

Better information regarding the spatial variability of height, Diameter at Breast Height (DBH) and stocking could improve inventory estimates at the operational Planning Unit since these parameters are used extensively in allometric equations, including stem volume, biomass and carbon calculations. In this study, the influence of stand stocking on height and DBH of two even aged radiata pine (Pinus radiata D. Don) stands were investigated using airborne Light Detection and Ranging (LiDAR) data at a study site in New South Wales, Australia. Both stands were characterized by irregular stocking due to patchy establishment and self-thinning in the absence of any silvicultural thinning events. For the purpose of this study, a total of 34 plots from a 34 year old site and 43 plots from a nine year old site were established, from which a total of 447 trees were sampled. Within these plots, DBH and height measurements were measured and their relationships with stocking were evaluated. LiDAR was used for height estimation as well as stem counts in fixed plots (stocking). The results showed a significant relationship between stem DBH and stocking. At both locations, trees with larger diameters were found on lower stocking sites. Height values were also significantly correlated with stocking, with taller trees associated with high stocking. These results were further verified of additional tree samples, with independent field surveys for DBH and LiDAR-derived metrics for height analysis. This study confirmed the relationship between P. radiata tree heights and stem diameter with stocking and demonstrated the capacity of LiDAR to capture sub-compartment variation in these tree-level attributes.

The rapid adoption of LiDAR-assisted forest inventory, together with nearest neighbour imputation, has created new challenges for those involved in sample selection. It is not obvious a priori what type of sample will be the most efficient when used with imputation. In this paper we explore a number of sampling approaches, including conventional methods such as stratification, spatial methods based on tessellations, and balanced sampling. We introduce a new approach called nearest centroid (NC) that optimises the survey design by using the distance properties of the sample in the space defined by the auxiliary variables, and examine this and other methods across a range of survey situations and objectives. The NC method is shown to be highly efficient when compared with other methods and is very flexible in the way it can be implemented.