Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Abstract

Earth observation technology has improved the detection of land cover changes. However, current pixel-based change detection methods cannot adequately describe the evolutionary process and spatiotemporal association of geographic entities. Therefore, we developed a method for analyzing the processes and patterns of land cover evolution based on spatiotemporal graphs. First, a spatiotemporal graph was generated from a time series of land cover maps according to the spatial and temporal relationships between land cover objects, as defined by spatial adjacency and temporal transition, respectively. Subsequently, structural characteristics, such as the spatial roles, adjacency type, temporal transitions and evolution trajectories, were derived from the spatiotemporal graph to describe and analyze the evolution of land cover. Finally, this method was applied to analyze land cover evolution in Fujian Province, China, from 2001 to 2019. The proposed method not only completely preserves the spatial adjacency and temporal transition details among land cover objects in a spatiotemporally unified graph framework but also extracts evolution-related spatiotemporal structural characteristics. This study provides a reliable scientific basis for analyzing the consistency of long-term land cover dynamics and has practical value for other geographic applications.

Keywords:

• Remote sensing time series
• land cover
• graph analysis
• spatiotemporal structure
• evolution pattern

Acknowledgements

The authors thank the associate editor Professor Shawn Laffan and the anonymous reviewers for their constructive comments and suggestions.

Disclosure statement

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

Data and codes availability statement

The data and codes that support the findings of this study are available with a DOI at https://doi.org/10.5281/zenodo.7512033.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant 41871223.

Notes on contributors

Xinyu Zou

Xinyu Zou is currently pursuing the PhD degree in surveying science and technology in China University of Geosciences, Beijing, China. He contributed to the study design, methodology, implementation, and writing.

Xiangnan Liu

Xiangnan Liu is a Professor with the School of Information Engineering, China University of Geosciences, Beijing, China. He contributed to the idea, conceptualization, supervision, and project administration.

Meiling Liu

Meiling Liu is a Professor with the School of Information Engineering, China University of Geoscience. She contributed to the conceptualization, methodology, and formal analysis.

Lingwen Tian

Lingwen Tian is currently pursuing the PhD degree in surveying science and technology in China University of Geosciences, Beijing, China. She contributed to the study design, visualization, and review and editing of this article.

Lihong Zhu

Lihong Zhu is currently pursuing the PhD degree in surveying science and technology in China University of Geosciences, Beijing, China. She contributed to the formal analysis, visualization, and review and editing of this article.

Qian Zhang

Qian Zhang is currently pursuing the PhD degree in surveying science and technology in China University of Geosciences, Beijing, China. She contributed to the study design, and review and editing of this article.