Landscape heterogeneity and scale considerations for super-resolution mapping

Abstract

Super-resolution mapping (SRM) is a rapidly emerging field of remote sensing that seeks to map the spatial distribution of land cover proportions resulting from soft classification techniques. Many methods have been proposed, but there has been little impetus to converge these efforts or compare the results. One reason for this lack of comparison is the issue of landscape heterogeneity and the range of scaling issues it embodies. Landscape heterogeneity refers to the complex distribution of land cover types across the landscape and the spatial patterns or spatial frequency of those land cover types. Landscape heterogeneity and the spatial patterns it produces are inherently scale-dependent while SRM is fundamentally an inverse scaling challenge being applied to real landscapes. The result is that SRM techniques developed for a particular landscape or scaling factor may not translate well when applied to other landscapes with different heterogeneity or computed for a different scaling factor. This lack of transferability due to landscape heterogeneity has largely been ignored in the literature. Heterogeneity is rarely reported in SRM studies and its behaviour across resolutions rarely integrated into SRM techniques. This article discusses the importance of heterogeneity in SRM, demonstrates how heterogeneity impacts SRM results, proposes several solutions for reporting landscape heterogeneity, and discusses the difficulties associated with incorporating heterogeneity into SRM. By highlighting the interrelatedness between landscape heterogeneity and SRM, the aim is for studies to begin reporting heterogeneity to facilitate inter-comparisons and ultimately incorporate the scale-dependency of landscape heterogeneity into SRM techniques to improve accuracy and applicability.

ORCID

Amy E. Frazier http://orcid.org/0000-0003-4552-4935

Additional information

Funding

This work was supported by the National Science Foundation (NSF) under Doctoral Dissertation Research Improvement (DDRI) [grant number 1303086].