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

Remote Sensing

Harald Zandler²  Alexander Brenning³  Cyrus Samimi²  George Petropoulos¹ 

[1] Department of Geography, University of Bayreuth, Bayreuth 95440, Germany; E-Mail;Department of Geography, University of Bayreuth, Bayreuth 95440, Germany; E-Mail:;Department of Geography, Friedrich Schiller University, Löbdergraben 32, Jena 07743, Germany; E-Mail:

关键词: arid environment;    hyperspectral vegetation indices;    hyperspectral bands;    Hyperion;    Landsat OLI;    biomass;    drylands;    spatial transferability;   

DOI  :  10.3390/rs70404565

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.