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dc.contributor.author
Hill, Andreas
dc.contributor.author
Buddenbaum, Henning
dc.contributor.author
Mandallaz, Daniel
dc.date.accessioned
2018-11-27T11:49:21Z
dc.date.available
2018-11-09T04:55:27Z
dc.date.available
2018-11-27T11:49:21Z
dc.date.issued
2018-08
dc.identifier.issn
1612-4677
dc.identifier.issn
1612-4669
dc.identifier.other
10.1007/s10342-018-1118-z
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/302177
dc.identifier.doi
10.3929/ethz-b-000302177
dc.description.abstract
A timber volume regression model applicable to the state and communal forest area of the federal German state of Rhineland-Palatinate is identified using a combination of airborne laser scanning (ALS)-derived metrics and information from a satellite-based tree species classification map available on the federal state level. As is common in many forest inventory datasets, strong heterogeneity in the ALS data due to different acquisition dates and misclassifications in the tree species classification map had noticeable effects on the regression model’s performance. This article specifically addresses techniques that improve the performance of ordinary least square regression models under such restricting conditions. We introduce a calibration technique to neutralize the effect of misclassifications in the tree species variable that originally caused a residual inflation of 0.05 in adjusted R2. Incorporating the calibrated tree species information improved the model accuracy by up to 0.07 in adjusted R2 and suggests the use of such information in forthcoming inventories. We also found that including ALS quality information as categorical variables within the regression model considerably mitigates issues with time lags between the ALS and terrestrial data acquisition and ALS quality variations (increase of 0.09 in adjusted R2). The model achieved an adjusted R2 of 0.48 and a cross-validated root-mean-square error (RMSEcv) of 46.7% under incorporation of the tree species and ALS quality information and was thus improved by 0.12 in adjusted R2 (5% in RMSEcv) compared to the simple model only containing ALS height metrics (adjusted R2=0.36, RMSEcv=51.7%).
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
OLS regression
en_US
dc.subject
Standing timber volume
en_US
dc.subject
ALS canopy height model
en_US
dc.subject
Satellite-based tree species classification
en_US
dc.subject
Calibration
en_US
dc.subject
Forest inventory
en_US
dc.subject
Angle count sampling
en_US
dc.title
Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2018-05-12
ethz.journal.title
European Journal of Forest Research
ethz.journal.volume
137
en_US
ethz.journal.issue
4
en_US
ethz.journal.abbreviated
Eur. j. for. res. (Print)
ethz.pages.start
489
en_US
ethz.pages.end
505
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Heidelberg
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2018-11-09T04:55:30Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-11-27T11:49:26Z
ethz.rosetta.lastUpdated
2018-11-27T11:49:26Z
ethz.rosetta.versionExported
true
ethz.COinS
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