Spatiotemporal Dynamics of Cropland Non-agriculturalization in Zhengzhou City Using Deep Learning-Based Interpretation

Yaqing Zheng; Xiaoping Lu; Guosheng Cai

doi:10.54097/pzhj5007

Authors

Yaqing Zheng
Xiaoping Lu
Guosheng Cai

DOI:

https://doi.org/10.54097/pzhj5007

Keywords:

Land use; Non-agriculturalization of Cropland; Spatiotemporal Evolution.

Abstract

Traditional monitoring of cropland non-agriculturalization heavily relies on land use/cover change data, which is constrained by long update cycles and the difficulty of accurately isolating the unidirectional conversion from cropland to non-agricultural uses. To address these limitations, this study utilizes a semantic change detection network with GaoFen-2 (GF-2) high-resolution time-series remote sensing imagery of Zhengzhou City from 2021 to 2025 as the primary data source. Integrating a land-use transfer matrix, grid cell analysis, spatial autocorrelation analysis, and the Geographic Detector model, we systematically analyze the spatiotemporal evolution dynamics and driving mechanisms of cropland non-agriculturalization in the region. The results demonstrate that: (1) Temporally, the intensity of cropland conversion in Zhengzhou City exhibits an unbalanced fluctuating pattern characterized by "initial intensity, subsequent stabilization, and localized rebound," with construction land being the absolute dominant destination of cropland outflow, resulting in a cumulative converted area exceeding 80 km² over the five-year period. (2) Spatially, cropland non-agriculturalization exhibits a distinct gradient pattern described as "commencing in the central urban core, highly aggregating in near suburbs, and dispersedly distributing with low intensity in far suburbs." Hot spot zones are heavily clustered in the near-suburb plains (e.g., Zhongmu County and Xinzheng City), whereas cold spot zones remain long-term stable in the western and southern hilly and mountainous regions (e.g., Dengfeng and Xinmi). The Global Moran's I indices are consistently positive and statistically significant, revealing pronounced spatial polarization and clustering features. (3) Regarding the driving mechanisms, cropland non-agriculturalization in Zhengzhou City is generally characterized by "transportation location dominance, industrial economic support, and topographical constraints."

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