Skip to main content
SpringerLink
Log in

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

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Carter, G. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15, 697–703.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Ettema, C. H., & Wardle, D. A. (2002). Spatial soil ecology. Trends in Ecology & Evolution, 17, 177–183.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gobron, N., Belward, A. S., Pinty, B., & Knorr, W. (2010). Monitoring biosphere vegetation 1998–2009. Geophysical Research Letters, 37, 0148–0227.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Isaak, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. New York, USA: Oxford University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Lillesand, T. M., & Kiefer, R. W. (1994). Remote sensing and image interpretation. New York: Wiley.

    Google Scholar 

  • Mäkisara, K. (1998). AISA data user’s guide, Technical Research Centre of Finland. Research Note, 1894, 1–54.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Meentemeyer, V. (1989). Geographical perspectives of space, time, and scale. Landscape Ecology, 3, 163–175.

    Article  Google Scholar 

  • Metternicht, G. I., & Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and onstraints. Remote Sensing of Environment, 85, 1–20.

    Article  Google Scholar 

  • Myneni, R. B. (1991). Modeling radiative-transfer and photosynthesis in 3-dimensional vegetation canopies. Agricultural and Forest Meteorology, 55, 323–344.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Schönermark, M. V., Geiger, B., & Roser, H. P. (2004). Reflection properties of vegetation and soil. Berlin: Wissenschaft und Technik.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sellers, P. J. (1985). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6, 1335–1372.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Verhoef, W. (1984). Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16, 125–141.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Webster, R., & Oliver, M. A. (2001). Geostatistics for environmental scientists. Statistics in practice. Chichester: Wiley.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wu, J. (2009). Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors, 9, 1768–1793. doi:10.3390/s90301768.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Computational Landscape Ecology, UFZ—Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany

    Angela Lausch, Daniel Doktor, Martin Volk & Ralf Seppelt

  2. Water & Earth System Science Competence Centre, University of Tuebingen, Keplerstrasse 17, 72074, Tuebingen, Germany

    Marion Pause

  3. Department of Community Ecology, Helmholtz Centre for Environmental Research—UFZ, Theodor-Lieser-Str. 4, 06120, Halle, Germany

    Ines Merbach

  4. Department Monitoring & Exploration Technologies, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, 04318, Leipzig, Germany

    Steffen Zacharias

Authors
  1. Angela Lausch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marion Pause
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ines Merbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steffen Zacharias
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel Doktor
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Martin Volk
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ralf Seppelt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Angela Lausch.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lausch, A., Pause, M., Merbach, I. et al. A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape. Environ Monit Assess 185, 1215–1235 (2013). https://doi.org/10.1007/s10661-012-2627-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-012-2627-8

Keywords

Search

Navigation