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Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment : Advantages and Limitations

Title data

Zandler, Harald ; Samimi, Cyrus ; Brenning, Alexander:
Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment : Advantages and Limitations.
In: Remote Sensing. Vol. 7 (15 April 2015) Issue 4 . - pp. 4565-4580.
ISSN 2072-4292
DOI: https://doi.org/10.3390/rs70404565

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
Pamir2. Transformation Processes in the Eastern Pamirs of Tajikistan. The presence and future of energy resources in the framework of sustainable development.No information
Open Access PublizierenNo information

Project financing: VolkswagenStiftung

Abstract in another language

In spite of considerable efforts to monitor global vegetation, biomass quantification in drylands is still a major challenge due to low spectral resolution and considerable background effects. Hence, this study examines the potential of the space-borne hyperspectral Hyperion sensor compared to the multispectral Landsat OLI sensor in predicting dwarf shrub biomass in an arid region characterized by challenging conditions for satellite-based analysis: The Eastern Pamirs of Tajikistan. We calculated vegetation indices for all available wavelengths of both sensors, correlated these indices with field-mapped biomass while considering the multiple comparison problem, and assessed the predictive performance of single-variable linear models constructed with data from each of the sensors. Results showed an increased performance of the hyperspectral sensor and the particular suitability of indices capturing the short-wave infrared spectral region in dwarf shrub biomass prediction. Performance was considerably poorer in the area with less vegetation cover. Furthermore, spatial transferability of vegetation indices was not feasible in this region, underlining the importance of repeated model building. This study indicates that upcoming space-borne hyperspectral sensors increase the performance of biomass prediction in the world’s arid environments. © 2015 by the authors; licensee MDPI, Basel, Switzerland.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: arid environment; hyperspectral vegetation indices; hyperspectral bands; Hyperion; Landsat OLI; biomass; drylands; spatial transferability
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: 06 Jul 2015 06:34
Last Modified: 13 Jan 2017 12:59
URI: https://eref.uni-bayreuth.de/id/eprint/15768