Surface deformation evolution in the Pearl River Delta between 2006 and 2011 derived from the ALOS1/PALSAR images

Li, Genger; Feng, Guangcai; Xiong, Zhiqiang; Liu, Qi; Xie, Rongan; Zhu, Xiaoling; Luo, Shuran; Du, Yanan

doi:10.1186/s40623-020-01310-2

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…However, the areas with more pronounced deformation in the results align with the spatial distribution of factors such as the thickness of the soft soil layer, the intensity of groundwater extraction, and the density of buildings. Overall, the magnitude and spatial distribution of ground deformation in Zhuhai obtained in this study for the years 2020-2021 are consistent with the historical deformation patterns in Zhuhai observed by previous researchers for the years 2006-2011 [11,63] and 2015-2020 [5,10,49]. The maximum sedimentation rate, as reported in previous studies [10,11,49], is consistently observed in the reclaimed area of Zhuhai, specifically in the Gaolan Port area (Area C), which aligns with the findings of this study.…”

Section: Results Of Ground Deformation In Zhuhaisupporting

confidence: 91%

Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City

Tian,

Wang,

Xiao

2023

Remote Sensing

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Ground deformation poses an imminent threat to urban development. This study uses the multiscale geographically weighted regression (MGWR) model to investigate the spatial heterogeneity in factors influencing ground deformation, thereby elucidating the drivers behind regional variations in ground deformation patterns. To gain insights into the characteristics of ground deformation in Zhuhai, China, and its spatial relationship with natural and anthropogenic features, we initially utilized the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method to collect data on ground deformation and its distribution across the entire area. Concurrently, remote sensing imagery was used to identify the various mechanisms affecting ground deformation during the same period, including geotectonic conditions, geographic environment, and human activities. Subsequently, we used the MGWR model to quantitatively estimate the effects of these driving force factors on ground deformation in Zhuhai. Our findings reveal significant ground deformation in specific areas, including Baijiao Town (Doumen District), Hongqi Town (Jinwan District), the Gaolan Port Economic Zone, and the northern part of Hengqin Town, with peak deformation rates reaching 117 mm/y. Key drivers of ground deformation in Zhuhai include NDVI, groundwater extraction intensity, and soft soil thickness. The application of the MGWR model, with an R-sq value of 0.910, outperformed both the global regression model ordinary least squares (OLS), with an R-sq value of 0.722, and the local regression model geographically weighted regression (GWR), with an R-sq value of 0.770, in identifying driving forces. This study can provide valuable insights for government policies aimed at mitigating the disaster risks associated with urban ground deformation.

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Section: Results Of Ground Deformation In Zhuhaisupporting

confidence: 91%

Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City

Tian,

Wang,

Xiao

2023

Remote Sensing

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“…Additionally, Dongguan City is an important economic city in China, so human activities are frequent, such as engineering construction, factory operations, and groundwater extraction. These factors, together, contribute to the continuous subsidence [12,48], which deserve timely monitoring.…”

Section: Deformation Extraction Results Of Dongguan Citymentioning

confidence: 99%

An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements

et al. 2021

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Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been widely used for ground motion identification and monitoring over large-scale areas, due to its large spatial coverage and high accuracy. However, automatically locating and assessing the state of the ground motion from the massive Interferometric Synthetic Aperture Radar (InSAR) measurements is not easy. Utilizing the spatial-temporal characteristics of surface deformation on the basis of the Small Baseline Subsets InSAR (SBAS-InSAR) measurements, this study develops an improved method to locate potential unstable or dangerous regions, using the spatial velocity gradation and the temporal evolution trend of surface displacements in large-scale areas. This method is applied to identify the potential geohazard areas in a mountainous region in northwest China (Lajia Town in Qinghai province) using 73 and 71 Sentinel-1 images from the ascending and descending orbits, respectively, and an urban area (Dongguan City in Guangdong province) in south China using 32 Sentinel-1 images from the ascending orbit. In the mountainous area, 23 regions with potential landslide hazards have been identified, most of which have high to very high instability levels. In addition, the instability is the highest at the center and decreases gradually outward. In the urban area, 221 potential hazards have been identified. The moderate to high instability level areas account for the largest proportion, and they are concentrated in the farmland irrigation areas, and construction areas. The experiment results show that the improved method can quickly identify and evaluate geohazards on a large scale. It can be used for disaster prevention and mitigation.

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“…In this paper, surface activity is defined as the cumulative duration of ground motion and can provide the basis for a quantitative and qualitative analysis of land subsidence and its influencing factors. It is believed that surface activity can also provide preparatory information for the subsequent prediction and early warning of geological hazards such as land subsidence [50].…”

Section: Surface Activitymentioning

confidence: 99%

A Mask R-CNN Network for Wide-Area Mining Subsidence Automatic Detection With InSAR Observations

He,

Zhang,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Land subsidence caused by mining activity is one of the most serious anthropogenic geohazards. The rapid detection and continuous monitoring of mining subsidence facilitate the swift detection of geohazards. Traditional methods of monitoring mining subsidence have shortcomings, such as offering only a limited coverage and being time consuming. Interferometric Synthetic Aperture Radar (InSAR) has been proven to be a powerful tool to identify mining subsidence hazards from unwrapped interferograms but this method can be complex and inefficient, particularly for wide areas. In this paper, a Mask R-CNN model is presented to automatically detect mining subsidence and monitor the surface activity over wide areas using original SAR interferograms to avoid the time-consuming and error-prone phase unwrapping procedure. Using Sentinel-1 wrapped interferograms as the real dataset and simulated wrapped interferograms generated with the Gaussian surface function combined with Generic Atmospheric Correction Online Service for InSAR (GACOS) as the simulated dataset, the Mask R-CNN deep neural network was used to train the mining subsidence detection model. It turned out that the accuracy of the detection model was 91.48%, while the precision was 96.44%, the recall rate was 93.88% and the F1 index was 0.949. The detection model was then utilized to detect mining subsidence in south-western Shanxi Province, China between 2016 and 2022. A total of 152 land subsidence points were detected and long-term monitoring results were also obtained. An analysis of the state of land subsidence in the mining areas was carried out to obtain land subsidence activity during the monitoring period.

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Surface deformation evolution in the Pearl River Delta between 2006 and 2011 derived from the ALOS1/PALSAR images

Cited by 10 publications

References 35 publications

Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City

Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City

An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements

A Mask R-CNN Network for Wide-Area Mining Subsidence Automatic Detection With InSAR Observations

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