@article{hagen-zankerComputationalFrameworkGeneralized2016,
  title = {A Computational Framework for Generalized Moving Windows and Its Application to Landscape Pattern Analysis},
  author = {Hagen-Zanker, Alex},
  date = {2016-02},
  journaltitle = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {44},
  pages = {205--216},
  issn = {0303-2434},
  doi = {10.1016/j.jag.2015.09.010},
  url = {https://doi.org/10.1016/j.jag.2015.09.010},
  abstract = {[Highlights]

[::] Moving window analysis is a prominent means of analyzing the spatial variability of landscape patterns at multiple scales. [::] A new computational framework is presented that overcomes technical and computational barriers to the use and implementation of moving windows based landscape pattern analysis of raster maps. [::] For a small window of 41 × 41 pixels, computation time was reduced by a factor 600 compared to the most commonly used software. These gains will be greater for larger windows. [::] The framework facilitates distance-weighted moving window analysis, which currently is not commonly used. Distance-weighting prevents discontinuities due to the discrete delineation of windows and is the natural choice for geographical analysis.

[Abstract]

Land cover products based on remotely sensed data are commonly investigated in terms of landscape composition and configuration; i.e. landscape pattern. Traditional landscape pattern indicators summarize an aspect of landscape pattern over the full study area. Increasingly, the advantages of representing the scale-specific spatial variation of landscape patterns as continuous surfaces are being recognized. However, technical and computational barriers hinder the uptake of this approach. This article reduces such barriers by introducing a computational framework for moving window analysis that separates the tasks of tallying pixels, patches and edges as a window moves over the map from the internal logic of landscape indicators. The framework is applied on data covering the UK and Ireland at 250 m resolution, evaluating a variety of indicators including mean patch size, edge density and Shannon diversity at window sizes ranging from 2.5 km to 80 km. The required computation time is in the order of seconds to minutes on a regular personal computer. The framework supports rapid development of indicators requiring little coding. The computational efficiency means that methods can be integrated in iterative computational tasks such as multi-scale analysis, optimization, sensitivity analysis and simulation modelling.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13834476,computational-science,landscape-modelling,mathematics,spatial-pattern}
}

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[::] A new computational framework is presented that overcomes technical and computational barriers to the use and implementation of moving windows based landscape pattern analysis of raster maps. [::] For a small window of 41 × 41 pixels, computation time was reduced by a factor 600 compared to the most commonly used software. These gains will be greater for larger windows. [::] The framework facilitates distance-weighted moving window analysis, which currently is not commonly used. Distance-weighting prevents discontinuities due to the discrete delineation of windows and is the natural choice for geographical analysis. [Abstract] Land cover products based on remotely sensed data are commonly investigated in terms of landscape composition and configuration; i.e. landscape pattern. Traditional landscape pattern indicators summarize an aspect of landscape pattern over the full study area. Increasingly, the advantages of representing the scale-specific spatial variation of landscape patterns as continuous surfaces are being recognized. However, technical and computational barriers hinder the uptake of this approach. This article reduces such barriers by introducing a computational framework for moving window analysis that separates the tasks of tallying pixels, patches and edges as a window moves over the map from the internal logic of landscape indicators. The framework is applied on data covering the UK and Ireland at 250 m resolution, evaluating a variety of indicators including mean patch size, edge density and Shannon diversity at window sizes ranging from 2.5 km to 80 km. The required computation time is in the order of seconds to minutes on a regular personal computer. The framework supports rapid development of indicators requiring little coding. The computational efficiency means that methods can be integrated in iterative computational tasks such as multi-scale analysis, optimization, sensitivity analysis and simulation modelling.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-13834476,computational-science,landscape-modelling,mathematics,spatial-pattern","bibtex":"@article{hagen-zankerComputationalFrameworkGeneralized2016,\n title = {A Computational Framework for Generalized Moving Windows and Its Application to Landscape Pattern Analysis},\n author = {Hagen-Zanker, Alex},\n date = {2016-02},\n journaltitle = {International Journal of Applied Earth Observation and Geoinformation},\n volume = {44},\n pages = {205--216},\n issn = {0303-2434},\n doi = {10.1016/j.jag.2015.09.010},\n url = {https://doi.org/10.1016/j.jag.2015.09.010},\n abstract = {[Highlights]\n\n[::] Moving window analysis is a prominent means of analyzing the spatial variability of landscape patterns at multiple scales. [::] A new computational framework is presented that overcomes technical and computational barriers to the use and implementation of moving windows based landscape pattern analysis of raster maps. [::] For a small window of 41 × 41 pixels, computation time was reduced by a factor 600 compared to the most commonly used software. These gains will be greater for larger windows. [::] The framework facilitates distance-weighted moving window analysis, which currently is not commonly used. Distance-weighting prevents discontinuities due to the discrete delineation of windows and is the natural choice for geographical analysis.\n\n[Abstract]\n\nLand cover products based on remotely sensed data are commonly investigated in terms of landscape composition and configuration; i.e. landscape pattern. Traditional landscape pattern indicators summarize an aspect of landscape pattern over the full study area. 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