Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City

Mingyan Ni, Yindi Zhao (), Caihong Ma, Xiaolin Hou and Yanmei Xie
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Mingyan Ni: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Yindi Zhao: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Caihong Ma: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Xiaolin Hou: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Yanmei Xie: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Sustainability, 2023, vol. 15, issue 12, 1-24

Abstract: It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area.

Keywords: remote sensing; point of interest; steel plant segmentation; industrial transformation; resource-based cities; land use/cover change (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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