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


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.


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



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.


  1. Bannari, A., Khurshid, K. S., Staenz, K., & Schwarz, J. W. (2007). A comparison of hyperspectral chlorophyll indices for wheat crop chlorophyll content estimation using laboratory reflectance measurements. IEEE Transactions on Geoscience and Remote Sensing, 45, 3063–3074.CrossRefGoogle Scholar
  2. Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., & Blonda, P. (2010). Automatic spectral-rule-based preliminary classification of radiometrically calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery—part I: system design and implementation. IEEE Transactions on Geoscience and Remote Sensing, 48, 1326–1354.CrossRefGoogle Scholar
  3. Baret, F., Clevers, J. G. P. W., & Steven, M. D. (1995). The robustness of canopy gap fraction estimates from red and near-infrared reflectances: a comparison of approaches. Remote Sensing of Environment, 54, 141–151.CrossRefGoogle Scholar
  4. Carter, G. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15, 697–703.CrossRefGoogle Scholar
  5. Chen, C.-M., Hepner, G. F., & Forster, R. R. (2003). Fusion of hyperspectral and radar data using the IHS transformation to enhance urban surfaces. ISPRS ISPRS—International Society for Photogrammetry and Remote Sensing, 58, 19–30.CrossRefGoogle Scholar
  6. Dennison, P. E., & Roberts, D. A. (2003). Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sensing of Environment, 87, 123–135.CrossRefGoogle Scholar
  7. Dungan, J. L. (2001). Scaling up and scaling down: the relevance of the support effect on remote sensing of vegetation. In N. J. Tate & M. P. Atkinson (Eds.), Modelling Scale in Geographic Information Science. Chichester: Wiley.Google Scholar
  8. Ettema, C. H., & Wardle, D. A. (2002). Spatial soil ecology. Trends in Ecology & Evolution, 17, 177–183.CrossRefGoogle Scholar
  9. Gascon, F., Gastellu-Etchegorry, J. P., Lefevre-Fonollosa, M. J., & Dufrene, E. (2004). Retrieval of forest biophysical variables by inverting a 3-D radiative transfer model and using high and very high resolution imagery. International Journal of Remote Sensing, 25, 5601–5616.CrossRefGoogle Scholar
  10. Gastellu-Etchegorry, J. P., Martin, E., & Gascon, F. (2004). DART: a 3D model for simulating satellite images and studying surface radiation budget. International Journal of Remote Sensing, 25, 73–96.CrossRefGoogle Scholar
  11. Gibson, C. C., Ostrom, E., & Ahn, T. K. (2000). The concept of scale and the human dimensions of global change: a survey. Ecological Economics, 32, 217–239.CrossRefGoogle Scholar
  12. Gitelson, A. A., Kaufmann, Y. J., & Merzylak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.CrossRefGoogle Scholar
  13. Gobron, N., Belward, A. S., Pinty, B., & Knorr, W. (2010). Monitoring biosphere vegetation 1998–2009. Geophysical Research Letters, 37, 0148–0227.CrossRefGoogle Scholar
  14. Guanter, L., Segl, K., & Kaufmann, H. (2009). Simulation of optical remote-sensing scenes with application to the EnMAP Hyperspectral Mission. IEEE Transactions On Geoscience And Remote Sensing, 47, 2340–2351.CrossRefGoogle Scholar
  15. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environmental, 81, 416–426.CrossRefGoogle Scholar
  16. Haboudane, D., Tremblay, N., Miller, J. R., & Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 46, 423–437.CrossRefGoogle Scholar
  17. Hornschuch, F., & Riek, W. (2009). Bodenheterogenität als Indikator von Naturnähe? 2. Biotische und abiotische Diversität in Natur- und Wirtschaftswäldern Brandenburgs und Nordwest-Polens. Waldökologie Landschaftsforschung und Naturschutz, 7, 55–82.Google Scholar
  18. Isaak, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. New York, USA: Oxford University Press.Google Scholar
  19. Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., et al. (2009). PROSPECT+SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment, 113, 56–S66.CrossRefGoogle Scholar
  20. Li, S., Kwok, J. T., & Wang, Y. (2002). Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Information Fusion, 3, 17–23.CrossRefGoogle Scholar
  21. Li, Q., Hu, B., & Pattey, E. (2008). A scale-wise model inversion method to retrieve canopy biophysical parameters from hyperspectral remote sensing data. Canadian Journal of Remote Sensing, 34, 311–319.Google Scholar
  22. Lillesand, T. M., & Kiefer, R. W. (1994). Remote sensing and image interpretation. New York: Wiley.Google Scholar
  23. Mäkisara, K. (1998). AISA data user’s guide, Technical Research Centre of Finland. Research Note, 1894, 1–54.Google Scholar
  24. Mäkisara, K., Meinander, M., Rantasuo, M., Okkonen, J., Aikio, M., Sipola, K., et al. (1993). Airborne Imaging Spectrometer for Applications (AISA). Digest of IGARSS’93, 2, 479–481. Tokyo, Japan.Google Scholar
  25. Malenovský, Z., Bartholomeus, H. M., Acerbi-Junior, F., Schopfer, J. T., Painter, T. H., Epema, G. F., et al. (2007). Scaling dimensions in spectroscopy of soil and vegetation. International Journal of Applied Earth Observation and Geoinformation, 9, 137–164.CrossRefGoogle Scholar
  26. Meentemeyer, V. (1989). Geographical perspectives of space, time, and scale. Landscape Ecology, 3, 163–175.CrossRefGoogle Scholar
  27. Metternicht, G. I., & Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and onstraints. Remote Sensing of Environment, 85, 1–20.CrossRefGoogle Scholar
  28. Myneni, R. B. (1991). Modeling radiative-transfer and photosynthesis in 3-dimensional vegetation canopies. Agricultural and Forest Meteorology, 55, 323–344.CrossRefGoogle Scholar
  29. Myneni, R. B., & Ganapol, B. D. (1991). A simplified formulation of photon transport in leaf canopies with scatterers of finite dimensions. Journal of Quantative Spectroscopy & Radiative Transfer, 46, 135–140.CrossRefGoogle Scholar
  30. Painter, T. H., Dozier, J., Roberts, D. A., Davis, R. E., & Green, R. O. (2003). Retrieval of subpixel snowcovered area and grain size from imaging spectrometer data. Remote Sensing of Environment, 85, 64–77.CrossRefGoogle Scholar
  31. Quattrochi, D. A. (1993). The need for a lexicon of scale terms in integrating remote-sensing data with geographic information-systems. Journal of Geography, 92, 206–212.CrossRefGoogle Scholar
  32. Ranchin, T., Aiazzi, B., Alparone, L., Baronti, S., & Wald, L. (2003). Image fusion-the ARSIS concept and some successful implementation schemes. ISPRS ISPRS—International Society for Photogrammetry and Remote Sensing, 58, 4–18.CrossRefGoogle Scholar
  33. Richter, R., & Schlapfer, D. (2002). Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. International Journal of Remote Sensing, 23, 2631–2649.CrossRefGoogle Scholar
  34. Rogaß, C., Spengler, D., Bochow, M., Segl, K., Lausch, A., Doktor, D., et al. (2011). Reduction of radiometric miscalibration—applications to pushbroom sensors. Sensors, 11, 6370–6395. doi: 10.3390/s110606370.CrossRefGoogle Scholar
  35. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of Third Earth Resources Technology Satellite Symposium (Vol. 1, pp. 309–317). Washington, DC: NASA. Goddart Space Flight Center.Google Scholar
  36. Saunders, S. C., Chen, J., Drummer, T. D., Gustafson, E. J., & Brosofske, K. D. (2005). Identitying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analysis. Ecological Complexity, 2, 87–105.CrossRefGoogle Scholar
  37. Schönermark, M. V., Geiger, B., & Roser, H. P. (2004). Reflection properties of vegetation and soil. Berlin: Wissenschaft und Technik.Google Scholar
  38. Segl, K., Guanter, L., Kaufmann, H., Schubert, J., Kaiser, S., Sang, B., et al. (2010a). Simulation of spatial sensor characteristics in the context of the EnMAP Hyperspectral Mission. IEEE Transactions on Geoscience and Remote Sensing, 48(7), 3046–3054.CrossRefGoogle Scholar
  39. Segl, K., Guanter, L., Kaufmann, H., Schubert, J., Kaiser, S., Sang, B., et al. (2010b). Simulation of spatial sensor characteristics in the context of the EnMAP Hyperspectral Mission. IEEE Transactions on Geoscience and Remote Sensing, 48, 3046–3054.CrossRefGoogle Scholar
  40. Sellers, P. J. (1985). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6, 1335–1372.CrossRefGoogle Scholar
  41. Silvan-Carrdenas, J. L., & Wang, L. (2010). Retrieval of subpixel Tamarix canopy cover from Landsat data along the Forgotten River using linear and nonlinear spectral mixture models. Remote Sensing of Environment, 114, 1777–1790.CrossRefGoogle Scholar
  42. Smith, M. O., Johnson, P. E., & Adams, J. B. (1985). Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis. Journal of Geophysical Research, 90, 797–804.CrossRefGoogle Scholar
  43. Tarnavsky, E., Garrigues, S., & Brown, M. E. (2008). Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sensing of Environment, 112, 535–549.CrossRefGoogle Scholar
  44. Tuia, D., Pacivici, F., Kanevski, M., & Emery, W. J. (2009). Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 47, 3866–3879.CrossRefGoogle Scholar
  45. Verhoef, W. (1984). Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16, 125–141.CrossRefGoogle Scholar
  46. Volk, M., & Ewert, F. (2011). Scaling methods in inegrated assessment of agriculatural system—state-of-the-art and future directions. Agriculture, Ecosystems and Environment, 142, 1–5.CrossRefGoogle Scholar
  47. Webster, R., & Oliver, M. A. (2001). Geostatistics for environmental scientists. Statistics in practice. Chichester: Wiley.Google Scholar
  48. Wiegand, T., Moloney, K. A., Naves, J., & Knauer, F. (1999). Finding the missing link between landscape structure and population dynamics: a spatially explicit perspective. The American Naturalist, 154, 605–627.CrossRefGoogle Scholar
  49. Wu, J. (2009). Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors, 9, 1768–1793. doi: 10.3390/s90301768.CrossRefGoogle Scholar
  50. Wu, J., Jelinski, D. E., Luck, M., & Tueller, P. T. (2000). Multiscale analysis of landscape heterogeneity: scale variance and pattern metrics. Geographic Information Sciences, 6, 6–19.Google Scholar
  51. Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz, T., et al. (2011). A network of terrestrial environmental observatories in Germany. Vadose Zone Journal, 10, 955–973.CrossRefGoogle Scholar
  52. Zeng, Y., Schaepman, M. E., Wu, B., Clevers, J. G. P. W., & Bregt, A. K. (2009). Quantitative forest canopy structure assessment using an inverted geometric-optical model and up-scaling. International Journal of Remote Sensing, 30, 1385–1406.CrossRefGoogle Scholar
  53. Zhou, J., Civco, D. L., & Silander, J. A. (1998). A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Archives of Photogrammetry and Remote Sensing, 19, 743–757.Google Scholar

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

Personalised recommendations