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Quantifying dwarf shrub biomass in an arid environment : comparing empirical methods in a high dimensional setting

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Zandler, Harald ; Samimi, Cyrus ; Brenning, Alexander:
Quantifying dwarf shrub biomass in an arid environment : comparing empirical methods in a high dimensional setting.
In: Remote Sensing of Environment. Bd. 158 (1 März 2015) . - S. 140-155.
ISSN 0034-4257
DOI: 10.1016/j.rse.2014.11.007

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Offizieller ProjekttitelProjekt-ID
Transformation Processes in the Eastern Pamirs of Tajikistan. The presence and future of energy resources in the framework of sustainable development.Ohne Angabe

Projektfinanzierung: VolkswagenStiftung

Abstract

Remote sensing based biomass estimation in arid environments is essential for monitoring degradation and carbon dynamics. However, due to the low vegetation cover in these regions, satellite-based research is challenging. Numerous potentially useful remotely-sensed predictor variables have been proposed, and several statistical and machine-learning techniques are available for empirical spatial modeling, but their predictive performance is yet unknown in this context. We therefore modeled total biomass in the Eastern Pamirs of Tajikistan, a region with extremely low vegetation cover, with a large set of satellite based predictors derived from two commonly used sensors (Landsat OLI, RapidEye), and assessed their utility in this environment using several suitable modeling approaches (stepwise, lasso, partial least squares and ridge regression, random forest). The best performing model (lasso regression) resulted in a RMSE of 992 kg ha− 1 in spatial cross-validation, indicating that biomass quantification in this arid setting is feasible but subject to large uncertainties. Furthermore, pronounced over-fitting in some commonly used models (e.g. stepwise regression, random forest) underlined the importance of adequate variable selection and shrinkage techniques in spatial modeling of high dimensional data. The applied sensors showed very similar performance and a combination of both only slightly improved results of better performing models. A permutation-based assessment of variable importance showed that some of the most frequently used vegetation indices are not suitable for dwarf shrub biomass prediction in this environment. We suggest that predictor variables based on several bands accounting for vegetation as well as background information are required in this arid setting. © 2014 Elsevier Inc.

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Publikationsform: Artikel in einer Zeitschrift
Begutachteter Beitrag: Ja
Keywords: Biomass; Arid Environment; Multispectral remote sensing; Empirical modeling; Landsat OLI; RapidEye




Landsat OLI
RapidEye
Institutionen der Universität: Fakultäten
Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften
Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Geowissenschaften
Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Geowissenschaften > Professur Klimatologie > Professur Klimatologie - Univ.-Prof. Dr. Cyrus Samimi
Profilfelder > Advanced Fields > Ökologie und Umweltwissenschaften
Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Geowissenschaften > Professur Klimatologie
Profilfelder
Profilfelder > Advanced Fields
Titel an der UBT entstanden: Ja
Themengebiete aus DDC: 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
500 Naturwissenschaften und Mathematik > 550 Geowissenschaften, Geologie
900 Geschichte und Geografie > 910 Geografie, Reisen
Eingestellt am: 11 Dec 2014 12:27
Letzte Änderung: 30 Mai 2016 07:25
URI: https://eref.uni-bayreuth.de/id/eprint/4872