An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in Lanzhou
Spaceborne interferometric synthetic aperture radar (InSAR) has been extensively employed to detect surface displacements. However, the automatic extraction of locations and boundaries of active geohazards from surface displacement data remains a significant research challenge. In this study, we pro...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11091363/ |
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| author | Yixin Ma Bo Chen Zhenhong Li Guanjun Wei Chuang Song Roberto Tomas Fan Wen Yi Chen Jianbing Peng |
| author_facet | Yixin Ma Bo Chen Zhenhong Li Guanjun Wei Chuang Song Roberto Tomas Fan Wen Yi Chen Jianbing Peng |
| author_sort | Yixin Ma |
| collection | DOAJ |
| description | Spaceborne interferometric synthetic aperture radar (InSAR) has been extensively employed to detect surface displacements. However, the automatic extraction of locations and boundaries of active geohazards from surface displacement data remains a significant research challenge. In this study, we propose an improved spatial clustering method to automatically detect active geohazards in Lanzhou City, Gansu Province, China. First, we applied the general atmospheric correction online service for InSAR-assisted InSAR stacking technique to derive the annual surface deformation rate. Then, the C-index was employed to eliminate false deformation signals, and a spatial clustering method was used to delineate the boundaries of active geohazards efficiently. Subsequently, the geohazards were classified, and their spatial distribution characteristics were analyzed. Our results revealed that the annual surface deformation rates in Lanzhou city ranged from −176 to 74 mm/yr. The combination of ascending- and descending-track SAR images increased the observable area from 86.3% (ascending only) and 93.4% (descending only) to 96.8% . In addition, applying the C-index reduced misdetection probabilities by 14.4% and 10.9% for the ascending and descending tracks, respectively. Using the improved spatial clustering method, 775 active geohazards, including 331 active landslides and 444 land subsidence areas, were identified and mapped in Lanzhou City for the first time. Active landslides are predominantly located in the northern and southern hills of the urban area, while land subsidence mainly occurs in areas where hills have been excavated or flattened through land grading and leveling for urban development. The improved spatial clustering approach effectively and automatically extracts, classifies, and characterizes active geohazards, enabling rapid cataloging and providing essential data for geohazard management and risk assessment. |
| format | Article |
| id | doaj-art-01c8c38b4c0441e3a11dd5ccdd1129f1 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-01c8c38b4c0441e3a11dd5ccdd1129f12025-08-20T03:36:58ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118199161993510.1109/JSTARS.2025.359151911091363An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in LanzhouYixin Ma0https://orcid.org/0009-0004-1288-2298Bo Chen1https://orcid.org/0009-0002-2461-5256Zhenhong Li2https://orcid.org/0000-0002-8054-7449Guanjun Wei3Chuang Song4https://orcid.org/0000-0001-9181-9529Roberto Tomas5https://orcid.org/0000-0003-2947-9441Fan Wen6Yi Chen7Jianbing Peng8https://orcid.org/0000-0002-3813-3322State Key Laboratory of Loess Science, Chang’an University, Xi’an, ChinaBig Data Center for Geosciences and Satellites, Chang’an University, Xi’an, ChinaState Key Laboratory of Loess Science, Chang’an University, Xi’an, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, ChinaState Key Laboratory of Loess Science, Chang’an University, Xi’an, ChinaDepartment of Civil Engineering, University of Alicante, Alicante, SpainState Key Laboratory of Loess Science, Chang’an University, Xi’an, ChinaState Key Laboratory of Loess Science, Chang’an University, Xi’an, ChinaState Key Laboratory of Loess Science, Chang’an University, Xi’an, ChinaSpaceborne interferometric synthetic aperture radar (InSAR) has been extensively employed to detect surface displacements. However, the automatic extraction of locations and boundaries of active geohazards from surface displacement data remains a significant research challenge. In this study, we propose an improved spatial clustering method to automatically detect active geohazards in Lanzhou City, Gansu Province, China. First, we applied the general atmospheric correction online service for InSAR-assisted InSAR stacking technique to derive the annual surface deformation rate. Then, the C-index was employed to eliminate false deformation signals, and a spatial clustering method was used to delineate the boundaries of active geohazards efficiently. Subsequently, the geohazards were classified, and their spatial distribution characteristics were analyzed. Our results revealed that the annual surface deformation rates in Lanzhou city ranged from −176 to 74 mm/yr. The combination of ascending- and descending-track SAR images increased the observable area from 86.3% (ascending only) and 93.4% (descending only) to 96.8% . In addition, applying the C-index reduced misdetection probabilities by 14.4% and 10.9% for the ascending and descending tracks, respectively. Using the improved spatial clustering method, 775 active geohazards, including 331 active landslides and 444 land subsidence areas, were identified and mapped in Lanzhou City for the first time. Active landslides are predominantly located in the northern and southern hills of the urban area, while land subsidence mainly occurs in areas where hills have been excavated or flattened through land grading and leveling for urban development. The improved spatial clustering approach effectively and automatically extracts, classifies, and characterizes active geohazards, enabling rapid cataloging and providing essential data for geohazard management and risk assessment.https://ieeexplore.ieee.org/document/11091363/Active geohazard detectiongeohazard mappingimproved spatial clusteringinterferometric synthetic aperture radar (InSAR)Lanzhou city |
| spellingShingle | Yixin Ma Bo Chen Zhenhong Li Guanjun Wei Chuang Song Roberto Tomas Fan Wen Yi Chen Jianbing Peng An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in Lanzhou IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Active geohazard detection geohazard mapping improved spatial clustering interferometric synthetic aperture radar (InSAR) Lanzhou city |
| title | An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in Lanzhou |
| title_full | An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in Lanzhou |
| title_fullStr | An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in Lanzhou |
| title_full_unstemmed | An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in Lanzhou |
| title_short | An Improved Spatial Clustering Method for Automatic Detection of Active Geohazards in Lanzhou |
| title_sort | improved spatial clustering method for automatic detection of active geohazards in lanzhou |
| topic | Active geohazard detection geohazard mapping improved spatial clustering interferometric synthetic aperture radar (InSAR) Lanzhou city |
| url | https://ieeexplore.ieee.org/document/11091363/ |
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