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Using airborne light detection and ranging as a sampling tool for estimating forest biomass resources in the upper Tanana Valley of interior AlaskaAuthor(s): Hans-Erik Andersen; Jacob Strunk; Hailemariam Temesgen
Source: Western Journal of Applied Forestry. 26(4): 157-164
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
PDF: Download Publication (1.03 MB)
DescriptionAirborne 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 on 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.
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CitationAndersen, Hans-Erik; Strunk, Jacob; Temesgen, Hailemariam. 2011. Using airborne light detection and ranging as a sampling tool for estimating forest biomass resources in the upper Tanana Valley of interior Alaska. Western Journal of Applied Forestry. 26(4): 157-164.
KeywordsLiDAR, biomass, forest inventory, sampling
- Using airborne lidar as a sampling tool for estimating forest biomass resources in the upper Tanana Valley of interior Alaska
- Using multi-level remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska
- Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference
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