The impact of landscape characteristics on the performance of upscaled maps

Abstract

Upscaled maps, as necessary data sources, have drawn much attention to fill data gaps or match the spatial resolution of pre-existing projects. Nevertheless, it remains a challenging task to quantitatively assess the impact of landscape characteristics on the upscaled maps. To simplify the investigation, three characteristics: fragmentation, number of classes and major class impact factor (MCIF), were selected. We utilized SIMMAP to produce categorical maps for generating base maps with different landscape characteristics. The Majority Rule Based algorithm was then used to produce upscaled maps at 11 different spatial resolutions. The findings indicate that the combined effect of landscape patterns greatly impacts upscaling accuracy. This important result should be carefully considered when developing the next generation of upscaling techniques. Overall, extending our understanding of the impacts of landscape characteristics is a critical step forward improving upscaling accuracy and therefore, our use of these maps.

Keywords:

• land cover map
• upscaling technique
• upscaling accuracy
• landscape characteristics

Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful suggestions and insightful comments that greatly improved our manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 41901344]. Partial funding was provided by the New Hampshire Agricultural Experiment Station. This is Scientific Contribution Number: 2827. This work was supported by the USDA National Institute of Food and Agriculture McIntire Stennis Project #NH00077-M (Accession #1002519).