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

Saarela, Svetlana and Breidenbach, Johannes and Raumonen, Pasi and Grafström, Anton and Ståhl, Göran and Ducey, Mark J. and Astrup, Rasmus (2017). Kriging prediction of stand-level forest information using mobile laser scanning data adjusted for nondetection. Canadian journal of forest research. 47 :9 , 1257-1265
[Journal article]

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Official URL: https://dx.doi.org/10.1139/cjfr-2017-0019

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.

Authors/Creators:Saarela, Svetlana and Breidenbach, Johannes and Raumonen, Pasi and Grafström, Anton and Ståhl, Göran and Ducey, Mark J. and Astrup, Rasmus
Title:Kriging prediction of stand-level forest information using mobile laser scanning data adjusted for nondetection
Series/Journal:Canadian journal of forest research (1208-6037)
Year of publishing :2017
Volume:47
Number:9
Page range:1257-1265
Number of Pages:9
Publisher:Canadian Science Publishing
ISSN:1208-6037
Language:English
Publication Type:Journal article
Refereed:Yes
Article category:Scientific peer reviewed
Version:Accepted version
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Agrovoc terms:forestry, forest inventories, lasers
Keywords:covariogram, detectability function, forest management, model-based inference
URN:NBN:urn:nbn:se:slu:epsilon-e-4739
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-e-4739
Additional ID:
Type of IDID
DOI10.1139/cjfr-2017-0019
Web of Science (WoS)000408223000012
ID Code:15124
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:12 Feb 2018 09:50
Metadata Last Modified:12 Feb 2018 09:50

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