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.

Using lidar in an area-based model-assisted approach to forest inventory has the potential to increase estimation precision for some forest inventory variables. This study documents the bias and precision of a model-assisted (regression estimation) approach to forest inventory with lidar-derived auxiliary variables relative to lidar pulse density and the number of sample plots. For managed forests on the Lewis portion of the Lewis-McChord Joint Base (35025 ha, 23290 forested) in western Washington state, we evaluated a regression estimator for combinations of pulse density (.05–3 pulses/m2) and sample size (15–105 plots) to estimate five forest yield variables: basal area, volume, biomass, number of stems, and Lorey's height. The results indicate that there is almost no loss in precision in using as few as .05 pulses/m2 relative to 3 pulses/m2. We demonstrate that estimation precision declined quickly for reduced sample sizes (as expected from sampling theory); but of more importance we demonstrate that s... L'utilisation du lidar pour la réalisation des inventaires forestiers en utilisant une approche basée sur la modélisation de la surface permet d'améliorer la précision des estimations pour certaines variables des inventaires forestiers. Dans cette étude, on documente le biais et la précision d'une approche basée sur la modélisation (estimation par régression) pour les inventaires forestiers utilisant des variables auxiliaires dérivées par lidar relativement à la densité de l'impulsion lidar et au nombre de placettes-échantillons. Dans le cas des forêts aménagées situées dans la portion Lewis du territoire de la Lewis-McChord Joint Base (35025 ha, 23290 en forêt), dans l'ouest de l’état de Washington, on a évalué un estimateur par régression pour des combinaisons de densités d'impulsions (,05–3 impulsions/m2) et de tailles d’échantillons de (15–105 parcelles) pour estimer cinq variables du rendement forestier, i.e. la surface terrière, le volume, la biomasse, le nombre de tiges et la hauteur de Lorey. Les ...

The spatial pattern of trees can be defined as a property of their location in relation to each other. In this study, the spatial pattern was summarized into three categories, regular, random, and clustered, using Ripley's L-function. The study was carried out at 79 sample plots located in a managed forest in Finland. The goal was to study how well the spatial pattern of trees can be predicted by airborne laser scanning (ALS) data. ALS-derived predictions were based upon individual tree detection (ITD), semi-individual tree detection (semi-ITD), and plot-level metrics calculated from the canopy height model, AREA. The kappa value for ITD was almost zero, which indicates no agreement. The semi-ITD and AREA methods performed better, although kappa values were only 0.34 and 0.24, respectively. It appears difficult to detect a particularly clustered spatial pattern.