“Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China
Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogen...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/6/1213 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849705786507264000 |
|---|---|
| author | Quanchuan Fu Jingyuan Zhu Xiaodi Zheng Zhengxiang Li Maini Chen Yuyuwei He |
| author_facet | Quanchuan Fu Jingyuan Zhu Xiaodi Zheng Zhengxiang Li Maini Chen Yuyuwei He |
| author_sort | Quanchuan Fu |
| collection | DOAJ |
| description | Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice. |
| format | Article |
| id | doaj-art-e3bd466f83954fd8a669d48a93f028f4 |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-e3bd466f83954fd8a669d48a93f028f42025-08-20T03:16:22ZengMDPI AGLand2073-445X2025-06-01146121310.3390/land14061213“Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, ChinaQuanchuan Fu0Jingyuan Zhu1Xiaodi Zheng2Zhengxiang Li3Maini Chen4Yuyuwei He5School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture and Urban Planning, Chongqing University, Chongqing 400045, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture and Design, Beijing Jiaotong University, Beijing 100044, ChinaBrownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice.https://www.mdpi.com/2073-445X/14/6/1213brownfieldabandoned sitesspatial identificationspatial characteristicsgeographic dataurban regeneration |
| spellingShingle | Quanchuan Fu Jingyuan Zhu Xiaodi Zheng Zhengxiang Li Maini Chen Yuyuwei He “Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China Land brownfield abandoned sites spatial identification spatial characteristics geographic data urban regeneration |
| title | “Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China |
| title_full | “Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China |
| title_fullStr | “Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China |
| title_full_unstemmed | “Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China |
| title_short | “Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China |
| title_sort | target classification modification method for spatial identification of brownfields a case study of tangshan city china |
| topic | brownfield abandoned sites spatial identification spatial characteristics geographic data urban regeneration |
| url | https://www.mdpi.com/2073-445X/14/6/1213 |
| work_keys_str_mv | AT quanchuanfu targetclassificationmodificationmethodforspatialidentificationofbrownfieldsacasestudyoftangshancitychina AT jingyuanzhu targetclassificationmodificationmethodforspatialidentificationofbrownfieldsacasestudyoftangshancitychina AT xiaodizheng targetclassificationmodificationmethodforspatialidentificationofbrownfieldsacasestudyoftangshancitychina AT zhengxiangli targetclassificationmodificationmethodforspatialidentificationofbrownfieldsacasestudyoftangshancitychina AT mainichen targetclassificationmodificationmethodforspatialidentificationofbrownfieldsacasestudyoftangshancitychina AT yuyuweihe targetclassificationmodificationmethodforspatialidentificationofbrownfieldsacasestudyoftangshancitychina |