A computational framework for generalized moving windows and itsapplication to landscape pattern analysis

Abstract

Land cover products based on remotely sensed data are commonly investigated in terms of landscapecomposition and configuration; i.e. landscape pattern. Traditional landscape pattern indicators summa-rize an aspect of landscape pattern over the full study area. Increasingly, the advantages of representingthe 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 reducessuch barriers by introducing a computational framework for moving window analysis that separates thetasks of tallying pixels, patches and edges as a window moves over the map from the internal logic oflandscape indicators. The framework is applied on data covering the UK and Ireland at 250 m resolu-tion, evaluating a variety of indicators including mean patch size, edge density and Shannon diversity atwindow sizes ranging from 2.5 km to 80 km. The required computation time is in the order of secondsto minutes on a regular personal computer. The framework supports rapid development of indicatorsrequiring little coding. The computational efficiency means that methods can be integrated in itera-tive computational tasks such as multi-scale analysis, optimization, sensitivity analysis and simulationmodelling.