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Airborne laser scanning, collected in a sampling mode, has the potential to be a valuable tool for estimating the biomass resources available to support bioenergy production in rural communities of interior Alaska. In this study, we present a methodology for estimating forest biomass over a 201,226-ha area (of which 163,913 ha are forested) in the upper Tanana valley of interior Alaska using a combination of 79 field plots and high-density airborne light detection and ranging (LiDAR) collected in a sampling mode along 27 single strips (swaths) spaced approximately 2.5 km apart. A model-based approach to estimating total aboveground biomass for the area is presented. Although a design-based sampling approach (based on a probability sample of field plots) would allow for stronger inference, a model-based approach is justified when the cost of obtaining a probability sample is prohibitive. Using a simulation-based approach, the proportion of the variability associated with sampling error and modeling error was assessed. Results indicate that LiDAR sampling can be used to obtain estimates of total biomass with an acceptable level of precision (8.1 ± 0.7 [8%] teragrams [total ± SD]), with sampling error accounting for 58% of the SD of the bootstrap distribution. In addition, we investigated the influence of plot location (i.e., GPS) error, plot size, and field-measured diameter threshold on the variability of the total biomass estimate. We found that using a larger plot (1/30 ha versus 1/59 ha) and a lower diameter threshold (7.6 versus 12.5 cm) significantly reduced the SD of the bootstrap distribution (by approximately 20%), whereas larger plot location error (over a range from 0 to 20 m root mean square error) steadily increased variability at both plot sizes.

The accuracy of recreational- and survey-grade global positioning system (GPS) receivers was evaluated across a range of forest conditions in the Tanana Valley of interior Alaska. High-accuracy check points, established using high-order instruments and closed-traverse surveying methods, were then used to evaluate the accuracy of positions acquired in different forest types using a recreational-grade GPS unit and a Global Navigation Satellite System (GLONASS)-enabled survey-grade unit, over a range of acquisition and postprocessing alternatives, including distance to base station, or baseline length (0ߝ10, 10ߝ50, 50ߝ100, and >100 km), use of Russian GLONASS satellites, and occupation times (5, 10, and 20 minutes). The accuracy of recreational-grade GPS was 3ߝ7 m across all sites. For survey-grade units, accuracies were influenced by forest type and baseline length, with lower errors observed with more open stands and shorter baseline lengths. The use of GLONASS satellites improved positions by a small but appreciable amount, and longer observation times (20 minutes) resulted in more reliably accurate positions across all sites. In general, these results indicate that if forest inventory plots in interior Alaska and other high-latitude regions of the world are occupied for 20 minutes with survey-grade instruments, positions with submeter error can be consistently obtained across a wide range of conditions.

While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R 2 ), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km 2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

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