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Does a post-stratification of ground units improve the forest biomass estimation by remote sensing data?

Latifi, Hooman and Fassnacht, Fabian and Hartig, Florian and Berger, Christian and Hernandez, J and Koch, Barbara (2015) Does a post-stratification of ground units improve the forest biomass estimation by remote sensing data? The 36th International Symposium on Remote Sensing of Environment (ISRSE), 11. - 15. Mai 2015, Berlin.

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Abstract

Remote sensing-assisted estimates of aboveground forest biomass are essential for modeling carbon budget on various scales. For these estimates, multiple factors such as sensor type, statistical prediction method, sampling design for the reference inventory data or the splitting of prediction models into species- strata-specific submodels affect the quality and robustness of the resulting predictions. Yet, few studies have attempted a systematic analysis of how these factors (and their interactions) contribute to the yielded predictive quality. We addressed this topic by conducting two tests based on performance of small scale, remote sensing-assisted biomass models under post-stratification of sampling units. We used arbitrarily-selected predictors from airborne LiDAR and hyperspectral data obtained in a managed mixed forest site in southwestern Germany. They were evaluated in terms of their predictive power by means of 5 commonly in-use spatial models. The bootstrap cross validated RMSE and r2 diagnostics were additionally analyzed in a factorial design by an Analysis of Variance (ANOVA) to rank the factor effects. 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 accentuated on area-based prediction maps. Yet, these findings are concluded to be partially site-specific. Input data type and statistical prediction method are concluded to remain the two most crucial factors for the quality of remote sensing-assisted biomass models.

Item URL in elib:https://elib.dlr.de/96830/
Document Type:Conference or Workshop Item (Poster)
Title:Does a post-stratification of ground units improve the forest biomass estimation by remote sensing data?
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Latifi, Hoomanhooman.latifi (at) uni-wuerzburg.deUNSPECIFIED
Fassnacht, Fabianfabian.fassnacht (at) kit.eduUNSPECIFIED
Hartig, FlorianFlorian.Hartig (at) biom.uni-freiburg.deUNSPECIFIED
Berger, Christianchristian.berger (at) uni-jena.deUNSPECIFIED
Hernandez, Jjhernand (at) uchile.clUNSPECIFIED
Koch, Barbarabarbara.koch (at) felis.uni-freiburg.deUNSPECIFIED
Date:2015
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:LiDAR and hyperspectral remote sensing, aboveground biomass, statistical prediction, post-stratification, model performance, factorial design
Event Title:The 36th International Symposium on Remote Sensing of Environment (ISRSE)
Event Location:Berlin
Event Type:international Conference
Event Dates:11. - 15. Mai 2015
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:18 Aug 2015 10:27
Last Modified:18 Aug 2015 10:27

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