Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification

ABSTRACT

Urban landscape pattern analysis is conducive to understanding the land cover distribution and changes and promoting urban spatial layout rationality and sustainable development. Previous research has often used low and medium spatial resolution data to classify urban land cover at the pixel level and then analyse landscape pattern indices. However, the underlying semantic information provided by pixel classification needs to consider the spatial correlation of surface features, and it is challenging to obtain high-level semantic information on urban functional areas and analyse the relationship between human activities and land use. Leveraging the abundant spatial characteristics in high-resolution remote sensing images, high spatial urban scene classification can infer the feature categories and spatial relationships between features to realize higher-level image interpretation and obtain regional urban scene details. And it contributes to urban planning and decision-making, better meeting the different demands. Therefore, this study aims at a novel methodological framework for landscape pattern analysis of cross-temporal multi-scale urban scene classification. The results imply that: (1) The high-resolution images can make up for the lack of information on urban functional zones in low and medium resolution images; (2) Landscape pattern analysis based on scene classification can analyse urban spatial relationships more rationally than traditional research; and (3) With the continuous advancement of urbanization, the urban functional zoning becomes more apparent, and the dominant industries have changed significantly from industry to commerce and services. This study expands the analytical application range of landscape ecology and remote sensing technology and provides new inspirations for urban planning, environmental monitoring, ecological protection.

KEYWORDS:

• Urban scene understanding;
• multi-scale;
• multi-temporal
• landscape pattern analysis

Acknowledgement

The authors would like to thank the editor, associate editor, and anonymous reviewers for their helpful comments and advice. This work was supported by National Key Research and Development Program of China under Grant No. 2022YFB3903404, National Natural Science Foundation of China under Grant Nos. 42325105, 42071350, and 42211530032, and Special Research Funding of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Key Research and Development Program of China under Grant No. 2022YFB3903404, National Natural Science Foundation of China under Grant Nos. 42325105, 42071350, and 42211530032, and Special Research Funding of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University.