Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data

TL;DR

This study evaluates the accuracy of forest attribute maps derived from harvester and airborne laser scanning data, highlighting systematic errors and proposing model-assisted estimators to improve unbiasedness and efficiency.

Contribution

It introduces a method to assess and mitigate systematic errors in forest attribute maps using harvester and ALS data, emphasizing the importance of probability sampling for unbiased estimators.

Findings

Model-assisted estimators significantly improved efficiency, especially for height (RE=6.0).

Systematic errors were present in harvester models across forest productivity levels.

Bias varied among attributes, with the largest for stem frequency (39%) and smallest for volume (1).

Abstract

Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE=6.0) and smallest for QMD (RE=1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data.