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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PDF
- Accepted Version
1MB |
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 | ||||||
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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: |
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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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