Airborne laser scanning to optimize the sampling efficiency of a forest management inventory in South-Eastern Germany

Effective forest stewardship relies on comprehensive field-inventories describing forest resources. Increasing demands for data and indicators that improve understanding of climate change impacts, timber production, and ecosystem processes make access to robust field inventories crucial. A trade-off between cost and statistical efficacy exists however, necessitating that practitioners be familiar with the spatial and structural composition and variability of their management areas. Remotely sensed data, like airborne laser scanning (ALS), can improve data availability and sampling efficiency. In this study, we simulate sampling approaches and provide an indication of the benefits of incorporating ALS-derived auxiliary data. We evaluate the ability of sub-samples from an existing field-inventory to accurately estimate ALS structural metrics. Additionally, we explore data-driven approaches to allocate new field plots, reducing bias and improving accuracy. The Monte Carlo simulation compared the local pivotal method (LPM), Latin hypercube sampling (LHS), and simple random sampling (SRS) at a variety of sample sizes. Precision and variability measures were used to comparatively assess the efficacy of sampling method and sample size. Results demonstrate the value of ALS as an auxiliary dataset, with LPM and LHS achieving sampling efficiencies over SRS of up to 88.6% and 94.3%, respectively. By applying the adapted Latin hypercube evaluation of a legacy sample (AHELS) algorithm, we reduced the mean average percent deviation (MAPD) by over 20% between sample measures and wall-to-wall ALS metrics. These methods can aid practitioners in planning cost-effective and statistically rigorous forest inventory campaigns, particularly in determining where to re-sample within an existing plot network.