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

Authors :
Raty, Janne
Hauglin, Marius
Astrup, Rasmus
Breidenbach, Johannes
Source :
Canadian Journal of Forest Research. April, 2023, Vol. 53 Issue 4, p284, 18 p.
Publication Year :
2023

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. Key words: cut-to-length harvester data, model-assisted estimation, national forest inventory, airborne LiDAR, large- area estimation<br />1. Introduction The increased availability of wall-to-wall airborne laser scanning (ALS) data during the past 20 years has revolutionized the mapping of forest resources (Maltamo et al. 2021). Many countries [...]

Details

Language :
English
ISSN :
00455067
Volume :
53
Issue :
4
Database :
Gale General OneFile
Journal :
Canadian Journal of Forest Research
Publication Type :
Academic Journal
Accession number :
edsgcl.745046166
Full Text :
https://doi.org/10.1139/cjfr-2022-0053