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Emerging Spatio-temporal Hot Spot Analysis of Beijing Subsidence Trend Detection Based on PS-InSAR.
- Source :
- International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; 2024, Vol. 48 Issue 1, p861-866, 6p
- Publication Year :
- 2024
-
Abstract
- Scholars have done a lot of research on urban settlement, but it is difficult to give consideration to the temporal and spatial attributes of settlement at the same time in its display and analysis. Most of them focused on the analysis of regional settlement, single point settlement curve and settlement rate map at a certain time, but few combined time and space for collaborative analysis. Therefore, in this paper, 32 scenes Sentinel-1B SAR data are used to obtain settlement data of Beijing via PS-InSAR method. Secondly, combined with the temporal and spatial attributes of settlement results, the subsidence law revealed by using spatio-temporal cube slicing and attribute filtering. Finally, subsidence development trend and the detection of abnormal subsidence are explored by emerging hot spots (ESH) analysis. The experimental results show that the settlement funnel center in Beijing is mainly concentrated near the junction of Chaoyang district and Tongzhou district. The settlement range tends to expand. There are several local continuous subsidence areas in the settlement oscillating area. Spatio-temporal analysis makes the development trend of urban settlement more intuitive. Emerging hotspot analysis combined with Getis-Ord Gi* statistics and Mann-Kendall trend test could more effectively analyze the settlement trend of the study area and detect new potential settlement centers, so that to provide auxiliary decision-making for urban safety early warning and city development. [ABSTRACT FROM AUTHOR]
- Subjects :
- GEOLOGIC hot spots
LAND subsidence
URBAN growth
URBAN research
Subjects
Details
- Language :
- English
- ISSN :
- 16821750
- Volume :
- 48
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 177679113
- Full Text :
- https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-861-2024