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

Title data

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. Vol. 158 (1 March 2015) . - pp. 140-155.
ISSN 0034-4257
DOI: https://doi.org/10.1016/j.rse.2014.11.007

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Project information

Project title:
Project's official titleProject's id
Transformation Processes in the Eastern Pamirs of Tajikistan. The presence and future of energy resources in the framework of sustainable development.No information

Project financing: VolkswagenStiftung

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Biomass; Arid Environment; Multispectral remote sensing; Empirical modeling; Landsat OLI; RapidEye




Landsat OLI
RapidEye
Institutions of the University: Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professorship Climatology > Professorship Climatology - Univ.-Prof. Dr. Cyrus Samimi
Profile Fields > Advanced Fields > Ecology and the Environmental Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Professorship Climatology
Profile Fields
Profile Fields > Advanced Fields
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 500 Natural sciences
500 Science > 550 Earth sciences, geology
900 History and geography > 910 Geography, travel
Date Deposited: 11 Dec 2014 12:27
Last Modified: 30 May 2016 07:25
URI: https://eref.uni-bayreuth.de/id/eprint/4872