Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data
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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 and 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. The performances of the harvester models were evaluated using Norwegian national forest inventory plots in an 8.7Mha study area. We estimated the bias of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of the MA estimators resulted in efficiency gains that were the largest for HL (relative efficiency, RE=6.0) and the smallest for QMD (RE=1.5). The bias of the synthetic estimator was largest for N (39 was due to an overestimation of deciduous and an underestimation of spruce forest 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.
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