Jacob L. Strunk

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%).

Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables

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 Loreys 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 sample size has a dramatic effect on the validity of inferences. Our investigations indicate that for our test dataset that central limit theorem based confidence intervals were too small on average for sample sizes smaller than 55. The results from this study can aid in identifying design components for forest inventory with lidar which satisfy users’ objectives.

Predicting the spatial pattern of trees by airborne laser scanning

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 Ripleys 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.

Edge-Tree Correction for Predicting Forest Inventory Attributes Using Area-Based Approach With Airborne Laser Scanning

We describe a novel method to improve the correspondence between field and airborne laser scanning (ALS) measurements in an area-based approach (ABA) forest inventory framework. An established practice in forest inventory is that trees with boles falling within a fixed border field measurement plot are considered “in” trees; yet their crowns may extend beyond the plot border. Likewise, a tree bole may fall outside of a plot, but its crown may extend into a plot. Typical ABA approaches do not recognize these discrepancies between the ALS data extracted for a given plot and the corresponding field measurements. In the proposed solution, enhanced ABA (EABA), predicted tree positions, and crown shapes are used to adjust plot and grid cell boundaries and how ALS metrics are computed. The idea is to append crowns of “in” trees to a plot and cut down “out” trees, then EABA continues in the traditional fashion as ABA. The EABA method requires higher density ALS data than ABA because improvement is obtained by means of detecting individual trees. When compared to typical ABA, the proposed EABA method decreased the error rate (RMSE) of stem volume prediction from 23.16% to 19.11% with 127 m2 plots and from 19.08% to 16.95% with 254 m2 plots. The greatest improvements were obtained for plots with the largest residuals.