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Remote Sensing of Biological Soil Crusts at Different Scales

  • Bettina WeberEmail author
  • Joachim Hill
Chapter
Part of the Ecological Studies book series (ECOLSTUD, volume 226)

Abstract

Biological soil crusts (biocrusts) are widely but patchily distributed in dry regions throughout the world. As they fulfill important ecosystem services, a universal method to map and monitor their distribution patterns would be extremely helpful to analyze their relevance and the impact of global change across spatial scales.

Moving toward this goal, spectral analyses of biocrusts revealed few characteristics, which are universally present, as the chlorophyll a absorption around 680 nm, whereas many other features have only been found in some, but not in other crust types and organisms. Upon watering, the spectral characteristics of biocrusts intensified immediately, and dry again after 24 h in a wet stage, chlorophyll a absorption had intensified, suggesting the formation of new pigments. Disturbance of biocrusts was observed to cause overall higher albedo values and shallower absorption features.

Mapping biocrusts with airborne or spaceborne remote sensing systems was so far largely confined to the reflective optical range of the electromagnetic spectrum (0.4–2.5 μm); only few studies covered also the emissive thermal range. Consequently, a number of spectral indices involving the visible and near-infrared spectral range have proven their usefulness with respect to specific crust types and regional settings. But they fail to provide a generic solution for mapping different biocrust types across the full range of environmental conditions under which they occur. Spectral unmixing inherently holds a greater potential for dealing with this complexity. However, available studies suggest that advanced multiple endmember approaches are required to cope with this diversity. Irrespectively of the chosen approach, the application of efficient atmospheric correction methods prior to data analysis will be mandatory.

Keywords

Normalize Difference Vegetation Index Spectral Index Bare Soil Spectral Reflectance Biological Soil Crust 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

BW gratefully acknowledges support by the Max Planck Society (Nobel Laureate Fellowship) and the German Research Foundation (projects WE2393/2-1 and WE2393/2-2).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Multiphase Chemistry DepartmentMax Planck Institute for ChemistryMainzGermany
  2. 2.Faculty of Regional and Environmental Sciences, Environmental Remote Sensing and GeoinformaticsTrier UniversityTrierGermany

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