Skip to main content
SpringerLink
Log in

Linking Biological Survey Information to Remote Sensing Datasets: A Case Study

  • Chapter
  • First Online:
Innovations in Remote Sensing and Photogrammetry

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

  • 1981 Accesses

Abstract

Remote sensing is widely used as a tool to map and monitor environmental attributes, such as vegetation. This paper describes a native vegetation ground data collection protocol that attempts to integrate the spatial resolution of several remotely sensed datasets and the spatial variation of vegetation into a framework. A particular challenge of this study was to use pre-existing vegetation survey methodology and adapt this for use with a number of remote sensing satellite systems. The spatial properties of remotely sensed data were explored by calculating textural measures for images at progressively coarser spatial resolutions, allowing sources of remotely sensed data for this project to be evaluated, with respect to spatial scale. This study forms part of a larger project which investigates the potential use of remotely sensed data in the development of a vegetation assessment framework, providing linkages between variables at site, landscape, and regional scales.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Armitage, R. P., R. E. Weaver, and M. Kent. 2000. Remote sensing of semi-natural upland vegetation: The relationship between species composition and spectral response.Pages 83–102. In R. Alexander and A. C. Millington, editors. Vegetation Mapping: From Patch to Planet. John Wiley & Sons, LTD, England.

    Google Scholar 

  • Armston, J. D., T. J. Danaher, and L. J. Collett. 2004. A regression approach to mapping woody foliage projective cover in Queensland with Landsat data. In Proceedings of the 12th Australasian Remote Sensing and Photogrammetry Conference, Fremantle, Australia.

    Google Scholar 

  • Aspinall, J., and D. Pearson. 1996. Data quality and spatial analysis: analytical use of GIS for ecological modelling. In M. F. Goodchild, L. Steyaert, and B. O. Parks, editors. GIS and Environmental Modelling: Progress and Research Issues. GIS World Books.

    Google Scholar 

  • Bannari, A., D. Morin, F. Bonn, and A. R. Huete. 1995. A review of vegetation indices. Remote Sensing Reviews 13:95–120.

    Google Scholar 

  • Brassel, K., F. Bucher, E. Stephan, and A. Vckovski. 1995. Completeness. In S. C. Guptill and J. L. Morrison, editors. Elements of Spatial Data Quality. Elsevier Science, published on behalf of the International Cartographic Association, Oxford, UK.

    Google Scholar 

  • Brogaard, S., and R. Ólafsdóttir. 1997. Ground-truths or Ground-lies? Environmental sampling for remote sensing application exemplified by vegetation cover data. Lund Electronic Reports in Physical Geography. Department of Physical Geography, Lund University, Sweden.

    Google Scholar 

  • Campbell, J. L., S. Burrows, S. T. Gower, and W. B. Cohen. 1999. BigFoot. Characterizing Land Cover, LAI, and NPP at the Landscape Scale for EOS/MODIS Validation. Field Manual Version 2.1. Oak Ridge National Laboratory, Tennessee.

    Google Scholar 

  • Carreiras, J. M. B., J. M. C. Pereira, and J. S. Pereira. 2006. Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. Forest Ecology and Management 223:45–53.

    Article  Google Scholar 

  • Comber, A. J., P. F. Fisher, F. Harvey, M. Gahegan, and R. Wadsworth. 2006. Using metadata to link uncertainty and data quality assessments. In A. Riedl, W. Kainz, and G. A. Elmes, editors. Progress in Spatial Data Handling. 12th International Symposium of Spatial Data Handling. Springer, Berlin.

    Google Scholar 

  • Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37:35–46.

    Article  Google Scholar 

  • Coops, N. C., and D. S. Culvenor. 2000. Utilizing local variance of simulated high spatial resolution imagery to predict spatial pattern of forest stands. Remote Sensing of Environment 71:248–260.

    Article  Google Scholar 

  • Curran, P. J. 1988. The semivariogram in remote sensing: An introduction. Remote Sensing of Environment 24:493–507.

    Article  Google Scholar 

  • Defries, R. S., M. Hansen, J. R. G. Townshend, A. C. Janetos, and T. R. Loveland. 2000. A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology 6:247–254.

    Article  Google Scholar 

  • Emerson, C. W., S. Lam, and D. A. Quattrochi. 2005. A comparison of local variance, fractal dimension, and Moran’s I as aids to multispectral image classification. International Journal of Remote Sensing 26:1575–1588.

    Article  Google Scholar 

  • Fassnacht, K. S., W. B. Cohen, and T. A. Spies. 2006. Key issues in making and using satellite-based maps in ecology: A primer. Forest Ecology and Management 222:167–181.

    Article  Google Scholar 

  • Fischer, J., D. B. Lindenmayer, and A. Cowling. 2004. The challenge of managing multiple species at multiple scales: reptiles in an Australian grazing landscape. Journal of Applied Ecology 41:32–44.

    Article  Google Scholar 

  • Gallo, K., L. Ji, B. Reed, J. Eidenshink, and J. Dwyer. 2005. Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data. Remote Sensing of Environment 99:221–231.

    Article  Google Scholar 

  • Gibbons, P., D. Ayers, J. Seddon, S. Doyle, and S. Briggs. 2005. BioMetric Version 1.8. A Terrestrial Biodiversity Assessment Tool for the NSW Property Vegetation Plan Developer. Operational Manual. NSW Department of Environment and Conservation, Canberra.

    Google Scholar 

  • Gibbons, P., and M. Boak. 2002. The value of paddock trees for regional conservation in an agricultural landscape. Ecological Management and Restoration 3:205–210.

    Article  Google Scholar 

  • Golevitch, B. M., T. J. Danaher, A. J. Stewart, D. P. Harris, and L. J. Lawrence. 2002. Mapping woody vegetation cover over the state of Queensland using Landsat TM and ETM+ Imagery.In Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference, Brisbane, Australia.

    Google Scholar 

  • Guptill, S. C., and J. L. Morrison, editors. 1995. Elements of Spatial Data Quality. 1st edition. Elsevier Science, published on behalf of the International Cartographic Association, Oxford, UK.

    Google Scholar 

  • Hay, G. J., P. Dubé, A. Bouchard, and D. J. Marceau. 2002. A scale-space primer for exploring and quantifying complex landscape. Ecological Modelling 153:27–49.

    Article  Google Scholar 

  • Huang, C., L. Yang, and C. Homer. 2001. A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas. In Proceedings of the 3rd International Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado.

    Google Scholar 

  • Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25:295–309.

    Article  Google Scholar 

  • Huete, A. R., K. Didan, R. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195–213.

    Article  Google Scholar 

  • Hunter, G. J., S. D. Jones, A. K. Bregt, and E. G. Masters. 2003a. Spatial data quality. In C. M. B. Medeiros, editor. Advanced Geographic Information Systems. Encyclopaedia of Life Support Systems (EOLSS). Developed under the auspices of the UNESCO. EOLSS Publishers, Oxford, UK.

    Google Scholar 

  • Hunter, G. J., M. Wachowicz, and A. K. Bregt. 2003b. Understanding spatial data usability. Data Science Journal (Spatial Data Usability Special Section) 2:79–89.

    Google Scholar 

  • Jensen, J. R. 2000. Remote sensing of the environment: an earth resource perspective. Prentice Hall, New Jersey.

    Google Scholar 

  • Jiang, J., S. Chen, S. Cao, H. Wu, L. Zhang, and H. Zhang. 2005. Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China. Journal of Geographical Sciences 15:247–254.

    Google Scholar 

  • Johansen, K., N. C. Coops, S. E. Gergel, and Y. Stange. 2007. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing of Environment 110:29–44.

    Article  Google Scholar 

  • Landres, P. B., P. Morgan, and F. J. Swanson. 1999. Overview of the use of natural variability concepts in managing ecological systems. Ecological Applications 9:1179–1188.

    Google Scholar 

  • Lee, A., P. Norman, M. Wood, and S. Davey. 2003. Integrated sampling strategies – application at the regional and continental levels for monitoring sustainable forest management. In Proceedings Joint Australia and New Zealand Institute of Forestry Conference, Queenstown, New Zealand.

    Google Scholar 

  • Lefsky, M. A., W. B. Cohen, S. A. Acker, G. G. Parker, T. A. Spies, and D. Harding. 1999. Lidar remote sensing of the canopy structure and biophysical properties of douglas-fir western hemlock forests. Remote Sensing of Environment 70:339–361.

    Article  Google Scholar 

  • Liang, S. 2004. Quantitative Remote Sensing of Land Surfaces. John Wiley & Sons Inc., New Jersey, USA.

    Google Scholar 

  • Liu, Z., J. Huang, X. Wu, and Y. Dong. 2007. Comparison of vegetation indices and red-edge parameters for estimating grassland cover from canopy reflectance data. Journal of Integrative Plant Biology 49:299–306.

    Article  Google Scholar 

  • Marceau, D. J., P. J. Howarth, and D. J. Gratton. 1994. Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem. Remote Sensing of Environment 49:93–104.

    Article  Google Scholar 

  • McCoy, R. M. 2005. Field Methods in Remote Sensing. The Guilford Press, New York.

    Google Scholar 

  • Means, J. E., S. A. Acker, D. J. Harding, J. B. Blair, M. A. Lefsky, W. B. Cohen, M. E. Harmon, and W. A. McKee. 1999. Use of Large-Footprint scanning airborne Lidar to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sensing of Environment 67:298–308.

    Article  Google Scholar 

  • Merino de Miguel, S. 2000. Variograms of Real and Simulated High Resolution Images for Analysis of Forest Canopy Structure. Masters thesis. University College of London, London, UK.

    Google Scholar 

  • Milne, B. T., and W. B. Cohen. 1999. Multiscale assessment of binary and continuous landcover variables for MODIS validation, mapping, and modeling applications. Remote Sensing of Environment 70:82–98.

    Article  Google Scholar 

  • Morrison, J. L. 2005. Spatial data quality. In S. C. Guptill and J. L. Morrison, editors. Elements of Spatial Data Quality. Elsevier Science, published on behalf of the International Cartographic Association, Oxford, UK.

    Google Scholar 

  • Murray CMA. 2006. Murray Catchment Action Plan. Murray Catchment Management Authority, Deniliquin, New South Wales.

    Google Scholar 

  • Nagendra, H. 2001. Using remote sensing to assess biodiversity. International Journal of Remote Sensing 22:2377–2400.

    Article  Google Scholar 

  • Nagler, P. L., E. P. Glenn, and A. R. Huete. 2001. Assessment of spectral vegetation indices for riparian vegetation in the Colorado River delta, Mexico. Journal of Arid Environments 49:91–110.

    Article  Google Scholar 

  • North, P. R. J. 2002. Estimation of f APAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment 80:114–121.

    Article  Google Scholar 

  • Nusser, S. M., and E. E. Klaas. 2003. Survey methods for assessing land cover map accuracy. Environmental and Ecological Statistics 10:309–331.

    Article  Google Scholar 

  • Reinke, K., and S. Jones. 2006. Integrating vegetation field surveys with remotely sensed data. Ecological Management and Restoration 7:S18–S23.

    Article  Google Scholar 

  • Song, C., and C. E. Woodcock. 2002. The spatial manifestation of forest succession in optical imagery. The potential of multiresolution imagery. Remote Sensing of Environment 82:271–284.

    Article  Google Scholar 

  • Stehman, S. V., and R. L. Czaplewski. 1998. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment 64:331–344.

    Article  Google Scholar 

  • Thomlinson, J. R., P. V. Bolstad, and W. B. Cohen. 1999. Coordinating methodologies for scaling landcover classification from site-specific to global: Steps toward validating global map products. Remote Sensing of Environment 70:16–28.

    Article  Google Scholar 

  • Treitz, P. M., P. J. Howerth, R. C. Shuffling, and P. Smith. 1992. Application of detailed ground information to vegetation mapping with high spatial resolution digital imagery. Remote Sensing of Environment 42:65–82.

    Article  Google Scholar 

  • Tso, B., and P. M. Mather. 2001. Classification Methods for Remotely Sensed Data. Taylor and Francis, New York.

    Book  Google Scholar 

  • Turner, M. G. 2005. Landscape ecology: What is the state of the science? Annual Review of Ecology, Evolution and Systematics 36:319–344.

    Article  Google Scholar 

  • White, P. S., and J. L. Walker. 1997. Approximating nature’s variation: selecting and using reference information in restoration ecology. Restoration Ecology 5:338–349.

    Article  Google Scholar 

  • Woodcock, C. E., and A. H. Strahler. 1987. The factor of scale in remote sensing. Remote Sensing of Environment 21:311–332.

    Article  Google Scholar 

  • Woodcock, C. E., A. H. Strahler, and D. Jupp, L. B. 1988a. The use of variograms in remote sensing: I scene models and simulated images. Remote Sensing of Environment 25:323–348.

    Article  Google Scholar 

  • Woodcock, C. E., A. H. Strahler, and D. Jupp, L. B. 1988b. The use of variograms in remote sensing: II real digital images. Remote Sensing of Environment 25:349–379.

    Article  Google Scholar 

  • Zawadzki, J., C. J. Cieszewski, M. Zasada, and R. C. Lowe. 2005. Applying geostatistics for investigations of forest ecosystems using remote sensing imagery. Silva Fennica 39:599–618.

    Google Scholar 

Download references

Acknowledgements

This work was supported by an ARC Linkage Grant (LP0455316) and by the NSW Department of Environment and Conservation.

Author information

Authors and Affiliations

  1. School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, VIC, Australia

    K.J. Sheffield, S.D. Jones, J.G. Ferwerda & P. Gibbons

  2. CSIRO Sustainable Ecosystems, Canberra, ACT, Australia

    A. Zerger

Authors
  1. K.J. Sheffield
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S.D. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J.G. Ferwerda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Gibbons
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Zerger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K.J. Sheffield .

Editor information

Editors and Affiliations

  1. Dept. Geospatial Science, RMIT University, Melbourne, 3001, Australia

    Simon Jones

  2. Dept. Geospatial Science, RMIT University, Melbourne, 3001, Australia

    Karin Reinke

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sheffield, K., Jones, S., Ferwerda, J., Gibbons, P., Zerger, A. (2009). Linking Biological Survey Information to Remote Sensing Datasets: A Case Study. In: Jones, S., Reinke, K. (eds) Innovations in Remote Sensing and Photogrammetry. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93962-7_5

Download citation

Publish with us

Policies and ethics

Search

Navigation