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

Titelangaben

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. Bd. 7 (15 April 2015) Heft 4 . - S. 4565-4580.
ISSN 2072-4292
DOI: 10.3390/rs70404565

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Angaben zu Projekten

Projekttitel:
Offizieller ProjekttitelProjekt-ID
Pamir2. Transformation Processes in the Eastern Pamirs of Tajikistan. The presence and future of energy resources in the framework of sustainable development.Ohne Angabe
Open Access PublizierenOhne Angabe

Projektfinanzierung: VolkswagenStiftung

Abstract

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.

Weitere Angaben

Publikationsform: Artikel in einer Zeitschrift
Keywords: arid environment; hyperspectral vegetation indices; hyperspectral bands; Hyperion; Landsat OLI; biomass; drylands; spatial transferability
DOI der Veröffentlichung: 10.3390/rs70404565
Themengebiete aus DDC: 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
500 Naturwissenschaften und Mathematik > 550 Geowissenschaften, Geologie
900 Geschichte und Geografie > 910 Geografie, Reisen
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
Sprache: Englisch
Titel an der UBT entstanden: Ja
Eingestellt am: 06 Jul 2015 06:34
Letzte Änderung: 08 Feb 2017 10:44
URI: https://epub.uni-bayreuth.de/id/eprint/3132