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Environmental Monitoring and Assessment

, Volume 185, Issue 2, pp 1215–1235 | Cite as

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

  • Angela LauschEmail author
  • Marion Pause
  • Ines Merbach
  • Steffen Zacharias
  • Daniel Doktor
  • Martin Volk
  • Ralf Seppelt
Article

Abstract

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R 2 of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.

Keywords

Hyperspectral remote sensing Spatiotemporal scale Controlled long-term laboratory experiment Imaging spectroscopy Semivariogram AISA-EAGLE/HAWK (DUAL) 

Notes

Acknowledgments

The research was funded and supported by Terrestrial Environmental Observatories (TERENO), which is a joint collaboration program involving several Helmholtz Research Centers in Germany. The authors wish to thank all technicians for their continuous support at all levels.

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Angela Lausch
    • 1
    Email author
  • Marion Pause
    • 2
  • Ines Merbach
    • 3
  • Steffen Zacharias
    • 4
  • Daniel Doktor
    • 1
  • Martin Volk
    • 1
  • Ralf Seppelt
    • 1
  1. 1.Department of Computational Landscape EcologyUFZ—Helmholtz Centre for Environmental ResearchLeipzigGermany
  2. 2.Water & Earth System Science Competence CentreUniversity of TuebingenTuebingenGermany
  3. 3.Department of Community EcologyHelmholtz Centre for Environmental Research—UFZHalleGermany
  4. 4.Department Monitoring & Exploration TechnologiesHelmholtz Centre for Environmental Research—UFZLeipzigGermany

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