Geography
Document Type
Article
Abstract
Integrating raster-based categorical maps from multiple sources necessitates the transformation of geometric characteristics to compare maps, as in land change analyses. By projecting maps to a new geographic reference framework and scaling pixel values to a new size, distortions of map information are introduced that can affect the proportion and arrangement of thematic classes across the landscape. Using a sample land cover dataset depicting a heterogeneous landscape, this paper examines these impacts using three common raster-based transformation methods and introduces a new vector-based method that minimizes error propagation. While relative class area was best preserved by a nearest-neighbor resampling method, distortions to the contiguity of thematic classes and the overall fragmentation of the landscape were minimized when using the vectorbased projection and resampling method. Results demonstrate that more than a third of pixel values of a categorical map may be affected by common projection and scaling methods and reinforce the need for careful attention to impacts of error propagation in categorical data transformations. © 2012 American Society for Photogrammetry and Remote Sensing.
Publication Title
Photogrammetric Engineering and Remote Sensing
Publication Date
2012
Volume
78
Issue
6
First Page
617
Last Page
624
ISSN
0099-1112
DOI
10.14358/PERS.78.6.617
Keywords
data set, error analysis, geometry, landscape change, nearest neighbor analysis, pixel, raster
Repository Citation
Christman, Zachary J. and Rogan, John, "Error propagation in raster data integration: Impacts on landscape composition and configuration" (2012). Geography. 664.
https://commons.clarku.edu/faculty_geography/664
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
