Using Airborne Light Detection and Ranging as a Sampling Tool for Estimating Forest Biomass Resources in the Upper Tanana Valley of Interior Alaska

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.Keywords: biomass, sampling, LiDAR, forest inventoryKeywords: biomass, sampling, LiDAR, forest inventor

Prediction of Forest Attributes with Field Plots, Landsat, and a Sample of Lidar Strips: A Case Study on the Kenai Peninsula, Alaska

In this study we demonstrate that sample strips of lidar in combination with Landsat can be used to predict forest attributes more precisely than from Landsat alone. While lidar and Landsat can each be used alone in vegetation mapping, the cost of wall to wall lidar may exceed users' financial resources, and Landsat may not support the desired level of prediction precision. We compare fitted linear models and k nearest neighbors (kNN) methods to link field measurements, lidar, and Landsat. We also compare 900 m² and 8,100 m² resolutions to link lidar to Landsat. An approach with lidar and Landsat together reduced estimates of residual variability for biomass by up to 36 percent relative to using Landsat alone. Linear models generally performed better than kNN approaches, and when linking lidar to Landsat, using 8,100 m² resolution performed better than 900 m²

Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska

The emergence of a new generation of remote sensing and geopositioning technologies, as well as increased capabilities in image processing, computing, and inferential techniques, have enabled the development and implementation of increasingly efficient and cost-effective multilevel sampling designs for forest inventory. In this paper, we (i) describe the conceptual basis of multilevel sampling, (ii) provide a detailed review of several previously implemented multilevel inventory designs, (iii) describe several important technical considerations that can influence the efficiency of a multilevel sampling design, and (iv) demonstrate the application of a modern multilevel sampling approach for estimating the forest biomass resources in a remote area of interior Alaska. This approach utilized a combination of ground plots, lidar strip sampling, satellite imagery (multispectral and radar), and classified land cover information. The variability in the total biomass estimate was assessed using a bootstrapping approach. The results indicated only marginal improvement in the precision of the total biomass estimate when the lidar sample was post-stratified using the classified land cover layer (reduction in relative standard error from 7.3% to 7.0%), whereas there was a substantial improvement in the precision when the estimate was based on the biomass map derived via nearest-neighbor imputation (reduction in relative standard error from 7.3% to 5.1%).This is the publisher’s final pdf. The published article is copyrighted by the Canadian Aeronautics and Space Institute and can be found at: http://www.casi.ca/cdn-journal-of-remote-sensing