State-and-Evolution Detection Model for Characterizing Farmland Spatial Pattern Variation in Hengyang Using Long Time Series Remote Sensing Product

Yunong Ma, Shi Cao (), Xia Lu, Jiqing Peng, Lina Ping, Xiang Fan, Xiongwei Guan, Xiangnan Liu and Meiling Liu
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Yunong Ma: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Shi Cao: The Second Surveying and Mapping Institute of Hunan Province, Changsha 410004, China
Xia Lu: The Second Surveying and Mapping Institute of Hunan Province, Changsha 410004, China
Jiqing Peng: The Second Surveying and Mapping Institute of Hunan Province, Changsha 410004, China
Lina Ping: The Second Surveying and Mapping Institute of Hunan Province, Changsha 410004, China
Xiang Fan: The Second Surveying and Mapping Institute of Hunan Province, Changsha 410004, China
Xiongwei Guan: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Xiangnan Liu: School of Information Engineering, China University of Geosciences, Beijing 100083, China
Meiling Liu: School of Information Engineering, China University of Geosciences, Beijing 100083, China

Land, 2024, vol. 13, issue 12, 1-16

Abstract: Analyzing farmland landscape pattern variations induced by human activities can support effective decision making by governments to improve land use efficiency. However, research on long-term and continuous spatial process changes in farmland is scarce, and spatial pattern changes in farmlands remain insufficiently understood. Moreover, studies in which researchers have utilized dynamic process analysis to describe farmlands are relatively limited. This study aimed to apply the state-and-evolution detection model (SEDM), generated from long-term remote sensing data, to characterize farmland spatial pattern variations in Hengyang City, Hunan Province. Annual farmland data from 1990 to 2022, change type samples, and auxiliary data were collected, and six types of spatial pattern variations (perforation, dissection, shrinkage, creation, enlargement, and aggregation) were defined for the study area. Subsequently, the SEDM was applied based on four landscape indices. Finally, spatiotemporal evolution features, namely evolution times, evolution duration, and dominant evolution pattern, were quantified. The farmland in the study area exhibited a generally upward trend with fluctuations. The maximum area was followed by shrinkage (S), perforation (P), and enlargement (E). In over 70% of the study area, fewer than three evolution times occurred over three decades. The dominant evolution patterns were P–S, S–P, and E–P for single evolution events, and P–S–P, S–P–S, and P–S–S for double events. The model achieved an overall accuracy of 85%, thus demonstrating its effectiveness in characterizing landscape pattern variations and providing valuable insights for researchers and policy makers to develop strategies for farmland protection.

Keywords: landscape pattern evolution; state-and-evolution detection models; spatial pattern of farmland (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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