Stratified aboveground forest biomass estimation by remote sensing data
Author
dc.contributor.author
Latifi, Hooman
Author
dc.contributor.author
Fassnachtb, Fabian
Author
dc.contributor.author
Hartig, Florian
Author
dc.contributor.author
Berger, Christian
Author
dc.contributor.author
Hernández, Jaime
Author
dc.contributor.author
Corvalán Vera, Carlos
Author
dc.contributor.author
Koch, Barbara
Admission date
dc.date.accessioned
2015-08-03T19:53:59Z
Available date
dc.date.available
2015-08-03T19:53:59Z
Publication date
dc.date.issued
2015
Cita de ítem
dc.identifier.citation
International Journal of Applied Earth Observation and Geoinformation 38 (2015) 229–241
en_US
Identifier
dc.identifier.issn
0303-2434
Identifier
dc.identifier.other
10.1016/j.jag.2015.01.016
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/132308
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
dc.description.abstract
Remote sensing-assisted estimates of aboveground forest biomass are essential for modeling carbon
budgets. It has been suggested that estimates can be improved by building species- or strata-specific
biomass models. However, few studies have attempted a systematic analysis of the benefits of such
stratification, especially in combination with other factors such as sensor type, statistical prediction
method and sampling design of the reference inventory data. We addressed this topic by analyzing
the impact of stratifying forest data into three classes (broadleaved, coniferous and mixed forest). We
compare predictive accuracy (a) between the strata (b) to a case without stratification for a set of preselected
predictors from airborne LiDAR and hyperspectral data obtained in a managed mixed forest
site in southwestern Germany. We used 5 commonly applied algorithms for biomass predictions on
bootstrapped subsamples of the data to obtain cross validatedRMSEand r2 diagnostics. Those values were
analyzed in a factorial design by an analysis of variance (ANOVA) to rank the relative importance of each
factor. Selected models were used for wall-to-wall mapping of biomass estimates and their associated
uncertainty. The results revealed marginal advantages for the strata-specific prediction models over
the unstratified ones, which were more obvious on the wall-to-wall mapped area-based predictions.
Yet further tests are necessary to establish the generality of these results. Input data type and statistical
prediction method are concluded to remain the two most crucial factors for the quality of remote sensingassisted
biomass models
en_US
Patrocinador
dc.description.sponsorship
German Aerospace Center
(DLR) and the German Federal Ministry of Economy and Technology
based on the Bundestag resolution50EE1025 and 50EE1265-66