Raster Data Transformation for Land change Analyses

Zachary Christman1 and John Rogan2

1. Rutgers University Department of Geography, Piscataway, NJ, USA
2. Clark University Graduate School of Geography Worcester, MA, USA
1. zachxman@rci.rutgers.edu 2. jrogan@clarku.edu

Abstract: The use of raster-based categorical maps from multiple sources necessitates the transformation of the geometric characteristics to directly compare maps, such as in land change analyses. Through the operations of reprojecting maps to a new geographic reference framework and rescaling pixel values to a new size, distortions of map information are introduced that can affect both the proportion and arrangement of thematic classes across the landscape. Using a sample land cover dataset, images were reprojected and rescaled using three common raster-based transformation methods and one new vector-based method. Through an evaluation of changing class areas and landscape ecology metrics, results demonstrate that the values of more than a third of pixels in a categorical map may be affected by common reprojecting and rescaling methods. While relative class area was best preserved by a nearest-neighbour resampling method, the contiguity of thematic classes and the overall fragmentation of the landscape was lowest when using a vector-based reprojecting and resampling method. Results reinforce the need for careful attention to categorical data transformations in land change analyses.

Keywords: land change; data integration; projection; scale; reference system; transformation

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