2023
DOI: 10.1139/cjfr-2022-0053
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Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data

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. Performa… Show more

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Cited by 2 publications
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“…However, harvester data primarily comprise mature forest stands ready for harvest. This can introduce sampling biases, and, consequently, lead to systematic errors in forest inventory based on harvester data, as demonstrated by Räty et al (2022). Their study revealed that harvester data proved more useful in productive forests than unproductive forests, although they reported systematic errors in both cases.…”
Section: Error Analysismentioning
confidence: 99%
“…However, harvester data primarily comprise mature forest stands ready for harvest. This can introduce sampling biases, and, consequently, lead to systematic errors in forest inventory based on harvester data, as demonstrated by Räty et al (2022). Their study revealed that harvester data proved more useful in productive forests than unproductive forests, although they reported systematic errors in both cases.…”
Section: Error Analysismentioning
confidence: 99%