Introduction

Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan; Marx, Brian D.

doi:10.1007/978-3-642-34333-9_1

Cited by 3 publications

(1 citation statement)

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“…All the models were constructed in the R statistical computing environment (R Core Team 2016) using the nlme package (Pinheiro et al 2018). The models were fitted with Restricted Maximum Likelihood (Fahrmeir et al 2013) and had the form as follows (Eq. 3).…”

Section: General Approachesmentioning

confidence: 99%

Predicting factual sawlog volumes in Scots pine dominated forests using airborne laser scanning data

Karjalainen¹,

Packalén²,

Räty³

et al. 2019

Silva Fenn.

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The aim in the study was to compare alternatives for the prediction of factual sawlog volumes using airborne laser scanning (ALS) data in Scots pine ( L.) dominated forests in eastern Finland. Accurate estimates of factual sawlog volume are desirable to ease the planning of harvesting operations. The factual sawlog volume of pines was derived from visual bucking, i.e. a procedure where the defects were located on each stem during sample plot measurements. For other species, the theoretical sawlog volume was considered also as the factual sawlog volume due to data restrictions. We predicted factual sawlog volume with eight alternatives that were based on either linear mixed-effects models or k-nearest neighbour imputations. An existing sawlog reduction model, commonly used in Finland, was also tested individually and combined with a number of the alternatives, and site type information was also utilised. Model fitting and prediction was implemented at the 15 Ã 15 m level, but accuracy was assessed at the 30 Ã 30 m level. The relative root mean squared error (RMSE%) values for the factual sawlog volume predictions varied between 20.9% and 33.5%, and the best accuracy was obtained with a linear mixed-effects model. These results indicate that factual sawlog volumes in Scots pine dominated forests can be predicted with reasonable accuracy with ALS data.Pinus sylvestris

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Section: General Approachesmentioning

confidence: 99%

Predicting factual sawlog volumes in Scots pine dominated forests using airborne laser scanning data

Karjalainen¹,

Packalén²,

Räty³

et al. 2019

Silva Fenn.

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Prediction and model-assisted estimation of diameter distributions using Norwegian national forest inventory and airborne laser scanning data

2021

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Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed- effects (PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95–1.47 and 0.96–1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.

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