Hazard assessment model of ground subsidence coupling AHP, RS and GIS – A case study of Shanghai

Zhang, Zhihua; Zhang, Shaobin; Hu, Changtao; Zhang, Xinxiu; Yang, Shuwen; Yan, Haowen; Zhang, Zhen

doi:10.1016/j.gr.2023.01.014

Cited by 15 publications

(4 citation statements)

References 67 publications

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“…The rate of land subsidence reflects the development degree of land subsidence under various external influencing factors, which is a description of the current situation of the area where land subsidence has occurred. The time series land subsidence results obtained in this study are used as an important factor index of the land subsidence risk in Shanghai [56]. The land subsidence vulnerability V includes six aspects.…”

Section: Risk Of Land Subsidence (R)mentioning

confidence: 99%

Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City

Wang,

Yang,

Xia

et al. 2023

Applied Sciences

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As one of the most developed coastal cities, Shanghai experiences long-term ground surface settlement disasters during urban expansion periods, which has adverse effects on economic development. To date, many studies regarding Shanghai’s ground surface sedimentation have been conducted with microwave remote sensing technology. However, the systematic and timely analysis of the time series deformation results and risk evaluation is still absent. Therefore, we focused on the following aspects in this study: Firstly, revealing in detail the time series deformation characteristics during 2016–2022 with Sentinel-1A images and verifying the deformation results with different InSAR technologies and SAR data. Secondly, fully discussing the reasons for ground sedimentation from the aspects of subway construction, land use type, monthly rainfall, and human activities, and studying the correlation between surface deformation and rainfall with the singular spectrum analysis (SSA) method. Finally, conducting a risk evaluation and risk level division using the entropy method, combining the long time series deformation results and geoinformation data. Meanwhile, the following conclusions were reached: 1. There are six typical deformation areas, distributed in the Baoshan District, Minhang District, and Jinshan District of Pudong New District from 2016 to 2022. The maximum annual rate is −32.3 mm/a, and the maximum cumulative sedimentation reaches −188.6 mm. 2. Ground sedimentation is mainly due to engineering construction during city development and verifies the weak correlation between surface deformation and rainfall. 3. We obtained different levels of geological hazard risk areas, and Huangpu, Yangpu, Hongkou District, the northwest area of Pudong New Area, and the vicinity of Dishui Lake belong to higher-risk areas. The above time series deformation research results and systematic analysis of induced factors, and the higher-risk-area division, will provide valuable insights for urban risk management.

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Section: Risk Of Land Subsidence (R)mentioning

confidence: 99%

Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City

Wang,

Yang,

Xia

et al. 2023

Applied Sciences

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“…It is a fundamental and crucial task in optical remote sensing image processing. This task has various applications in military navigation, 5 – 7 aerial reconnaissance, 8 environmental monitoring, 9 – 13 and other domains. Therefore, studying object detection methods for remote sensing images and their applications holds significant research value.…”

Section: Introductionmentioning

confidence: 99%

GODANet: an object detection model for remote sensing images fusing contextual information and dynamic convolution

Rong,

Zhang,

Yuan

et al. 2024

J. Appl. Rem. Sens.

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Remote sensing images are characterized by complex feature backgrounds and large target scale differences, so object detection for remote sensing images is a challenging problem. This work proposes a one-stage structure remote sensing image object detection model called GODANet. First, the GODANet incorporates a Global Context Network (GCNet) in the feature extraction structure. The GCNet focuses the model on the image region of interest from a global perspective. Second, the output layer utilizes an omni-dimensional dynamic convolution technique, allowing for more flexible adaptation to targets or edges in specific regions. Finally, an adaptive spatial feature fusion structure, IR-ASFF, which fuses improved-RFB (IRFB) modules is proposed to fuse the critical information of multiple levels of features to realize the adaptability to object detection at different scales. The GODANet efficiently aggregates network performance and possesses two main advantages: adaptability to multi-scale targets and focus on features of interest. The mean average precision (mAP) on the DIOR dataset and the NWPU VHR-10 dataset reached 93.7% and 92.9%, respectively, and compared with YOLOv7, the mAP was improved by 3.1% and 0.3%, respectively. Therefore, we believe the GODANet suits remote sensing image detection tasks.

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“…Inspired by the above analysis, we adopt PS-InSAR and SBAS-InSAR for settlement monitoring in Shanghai, obtaining data for a subsidence rate map and DEM, which will provide a basis for the future study’s assessment of subsidence disaster risk 49 .…”

Section: Introductionmentioning

confidence: 99%

Monitoring and analysis of ground subsidence in Shanghai based on PS-InSAR and SBAS-InSAR technologies

Zhang

et al. 2023

Sci Rep

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Shanghai is susceptible to land subsidence due to its unique geological environment and frequent human activities. Traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. Interferometric Synthetic Aperture Radar (InSAR) technology is a widely used method for monitoring ground subsidence due to its low cost, high efficiency, and ability to cover large areas. To monitor the surface sink condition of Shanghai over the past 2 years, monitoring data were obtained through the technical processing of 24 images from Sentinel-1A data covering Shanghai from 2019 to 2020 using the Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS-InSAR) technique. The ground subsidence (GS) results were extracted via PS and SBAS interferometry processing, while Shuttle Radar Topography Mission data were used to correct the residual phase. According to PS and SBAS methods, the maximum ground subsidence in the study area reached 99.8 mm and 47.2 mm, respectively. The subsidence rate and the accumulated amount of subsidence derived from the monitoring results revealed the urban area in Shanghai to be principally characterized by uneven GS, with multiple settlement funnels being found to be distributed across the main urban area. Moreover, when compared with the historical subsidence data, geological data, and urban construction distribution data, the individual settlement funnels were observed to correspond to those data concerning the historical surface settlement funnel in Shanghai. By randomly selecting GS time-series data regarding three feature points, it was determined that the morphological variables of the GS remained largely consistent at all time points and that their change trends exhibited a high degree of consistency, which verified the reliability of the PS-InSAR and SBAS-InSAR monitoring method. The results can provide data support for decision making in terms of geological disaster prevention and control in Shanghai.

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Hazard assessment model of ground subsidence coupling AHP, RS and GIS – A case study of Shanghai

Cited by 15 publications

References 67 publications

Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City

Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City

GODANet: an object detection model for remote sensing images fusing contextual information and dynamic convolution

Monitoring and analysis of ground subsidence in Shanghai based on PS-InSAR and SBAS-InSAR technologies

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