Kriging prediction of stand-level forest information using mobile laser scanning data adjusted for nondetection

Svetlana Saarela, Johannes Breidenbach, Pasi Raumonen, Anton Grafström, Göran Ståhl, Mark J. Ducey, Rasmus Astrup

    Research output: Contribution to journalArticleScientificpeer-review

    6 Citations (Scopus)

    Abstract

    This study presents an approach for predicting stand-level forest attributes utilizing mobile laser scanning data collected as a nonprobability sample. Firstly, recordings of stem density were made at point locations every 10th metre along a subjectively chosen mobile laser scanning track in a forest stand. Secondly, kriging was applied to predict stem density values for the centre point of all grid cells in a 5 m x 5 m lattice across the stand. Thirdly, due to nondetectability issues, a correction term was computed based on distance sampling theory. Lastly, the mean stem density at stand level was predicted as the mean of the point-level predictions multiplied with the correction factor, and the corresponding variance was estimated. Many factors contribute to the uncertainty of the stand-level prediction; in the variance estimator, we accounted for the uncertainties due to kriging prediction and due to estimating a detectability model from the laser scanning data. The results from our new approach were found to correspond fairly well to estimates obtained using field measurements from an independent set of 54 circular sample plots. The predicted number of stems in the stand based on the proposed methodology was 1366 with a 12.9% relative standard error. The corresponding estimate based on the field plots was 1677 with a 7.5% relative standard error.

    Original languageEnglish
    Pages (from-to)1257-1265
    Number of pages9
    JournalCANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE
    Volume47
    Issue number9
    DOIs
    Publication statusPublished - 2017
    Publication typeA1 Journal article-refereed

    Keywords

    • covariogram
    • detectability function
    • forest management
    • model-based inference
    • TREE STEM
    • ABOVEGROUND BIOMASS
    • ASSISTED ESTIMATION
    • SPACEBORNE LIDAR
    • BOUNDARY OVERLAP
    • AIRBORNE
    • INVENTORY
    • MODELS
    • SYSTEM
    • VOLUME

    Publication forum classification

    • Publication forum level 3

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