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A new approach for rescaling land cover data

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Abstract

The resolution of satellite imagery must often be increased or decreased to fill data gaps or match preexisting project requirements. It is well known that a change in resolution introduces systematic errors of size, shape, location and amount of contiguous land cover types. Nevertheless, robust methods for rescaling landscape data are frequently required to assess patterns of landscape change through time and over large areas. We developed a new method for rescaling spatial data that allows map resolution (grain size) to be either increased or decreased while holding the total proportion of land cover types constant. The method uses a weighted sampling net of variable resolution to sample an existing map and then randomly selects from the frequency of cover types derived from this sample to assign the cover type for the corresponding location in the rescaled map. The properties of the sampling net had a variable effect on measures of landscape pattern with the characteristic patch size (S) the most robust metric and the number of clusters (A) the most variable. A comparison of up-scaled and down-scaled maps showed that this process is not symmetrical, producing different errors for increases versus decreases in grain size. Rescaling Landsat (30 m) imagery to the 10 m resolution of SPOT imagery for four National Park units within Maryland and Virginia resulted in errors due to rescaling that were small (1–2%) relative to the total error (∼11%) associated with these images. The new rescaling method is general because it provides a single method for increasing or decreasing resolution, can be applied to maps with multiple land cover types, allows grid geometry to be transformed (i.e., square to hexagonal grids), and provide a more consistent basis for landscape comparisons when maps must be derived from multiple sources of classified imagery.

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Acknowledgements

Special thanks to Clayton Kingdon and J. B. Churchill whose assistance with Landsat and SPOT imagery provided the data for testing the PDW algorithm. Access to the data for ground verification was provided by John Paul Schmit and is greatly appreciated. Funding was provided by the National Park Service, NCR I&M Network through the Chesapeake Watershed Cooperative Ecosystem Study Unit, Task Agreement J3992050104.

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Authors and Affiliations

  1. Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, 21532, USA

    Robert H. Gardner, Todd R. Lookingbill & Joseph Ferrari

  2. University of Wisconsin-Madison, 1630 Linden Drive, Russell Labs, Madison, WI, 53706, USA

    Philip A. Townsend

Authors
  1. Robert H. Gardner
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  2. Todd R. Lookingbill
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  3. Philip A. Townsend
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  4. Joseph Ferrari
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Correspondence to Robert H. Gardner.

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Gardner, R.H., Lookingbill, T.R., Townsend, P.A. et al. A new approach for rescaling land cover data. Landscape Ecol 23, 513–526 (2008). https://doi.org/10.1007/s10980-008-9213-z

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  • DOI: https://doi.org/10.1007/s10980-008-9213-z

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