Correlating the subsidence pattern and land use in Bandung, Indonesia with both Sentinel-1/2 and ALOS-2 satellite images

Du, Zheyuan; Liu, Ge; Ng, Alex Hay‐Man; Zhu, Qinggaozi; Yang, Xihua; Li, Liyuan

doi:10.1016/j.jag.2018.01.001

Cited by 58 publications

(40 citation statements)

References 29 publications

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“…Land subsidence due to groundwater extraction exceeding recharge can be seen in many cities and is a world-wide problem. However, the magnitude of subsidence in Mexico City is more serious compared to other cities, for example, Beijing (maximum subsidence~10 cm yr −1 ; Du, Ge, Ng, Xiaojing, & Li al., 2018) and Bandung (maximum subsi-dence~20 cm yr −1 ; Du, Ge, Ng, Zhu, et al, 2018). This is mainly due to three reasons: First, earlier between the 1500s and 1700s, to control and divert the flooding, the government of Mexico City artificially opened the basin and constructed other hydraulic projects (Ortega & Farvolden, 1989).…”

Section: Causes Of Land Subsidence and The Associate Impactmentioning

confidence: 99%

“…Yan et al () integrated both SBAS and PSInSAR to quantitatively characterise the motion of selected targets. Furthermore, the regional extent of land subsidence has been studied by many researchers (Castellazzi et al, ; Chaussard, Wdowinski, Cabral‐Cano, & Amelung, ; Du, Ge, Ng, Zhu, et al, ). Chaussard et al () compared a large‐scale InSAR result (17 cities, including Mexico City) with a land use map and concluded that subsidence is mostly located within urban and agricultural land use.…”

Section: Introductionmentioning

confidence: 99%

“…Yan et al (2012) integrated both SBAS and PSInSAR to quantitatively characterise the motion of selected targets. Furthermore, the regional extent of land subsidence has been studied by many researchers (Castellazzi et al, 2018;Chaussard, Wdowinski, Cabral-Cano, & Amelung, 2014;Du, Ge, Ng, Zhu, et al, 2018 Mexico City) with a land use map and concluded that subsidence is mostly located within urban and agricultural land use. As for the country scale, TSInSAR has been used by Castellazzi et al (2018) to constrain the GRACE (Gravity Recovery and Climate Experiments)-derived groundwater mass loss in depletion areas.…”

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confidence: 99%

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Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors

Liu

et al. 2019

Land Degrad Dev

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Anthropogenic land subsidence is an example of changes to the natural environment due to human activities and is one of the key factors in causing land degradation at a range of scales. Previous studies assessing land subsidence in the Valley of Mexico either focused on regional scale or short (noncontinuous) temporal scale. In this study, long‐term land subsidence (~15 years) is mapped in Mexico City (Mexico) using two interferometric synthetic aperture radar (InSAR) methods, namely, GEOS (Geoscience and Earth Observing Systems Group)‐Advance Time‐series Analysis and GEOS‐Small Baseline Subset. An inverse distance weighted‐based integration module and maximum likelihood regression‐based M estimator are introduced to further enhance these two methods. The land subsidence was continuously mapped using ENVISAT (2004–2007), ALOS‐1 (2007–2011), COSMO‐SkyMed (2011–2014), ALOS‐2 (2014–2018), and SENTINEL‐1 (2015–2017) data sets. A comparison between InSAR time‐series and GPS measurement shows that the subsidence rates are consistent over 2004–2018. The subsidence map over 15 years was generated finding a maximum subsidence over 4.5 m. By comparing our InSAR results with a land use map, we find that the subsidence centre in Mexico City is mostly located in the residential regions with the consumption of groundwater contributing considerably to the local subsidence rate. A total volume of 1.20 × 108 m3 of the land in Ciudad Nezahualcoyotl subsided/degraded. A continuing subsidence process limits the potential land use causing serious land degradation. Our results may be used to assist disaster reduction plans.

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Section: Causes Of Land Subsidence and The Associate Impactmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors

Liu

et al. 2019

Land Degrad Dev

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“…In most cities, land subsidence is due to human activities [6]. Urban land subsidence has caused the deformation of road surfaces and subgrades, even damaging buildings and urban infrastructure, resulting in significant casualties and economic losses [7]. Therefore, to maintain the sustainability of urban areas, it is very important to understand the deformation pattern and evolution of municipal ground [8].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Surface Deformation in Lanzhou with Sentinel-1A TOPS

Wang

Yan

et al. 2020

Geosciences

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While surface deformations and their impact on buildings have been observed in the city of Lanzhou, it is difficult to find studies of surface deformation and the influential factors in the recent decades. This study was designed to detect the spatial position of these surface deformations and to understand the mechanism behind them. Sentinel-1A TOPS model image data acquired in descending orbits between March 2015 and May 2019 were processed by using Small Baseline Subsets Interferometry (SBAS)-Interferometric Synthetic Aperture Radar (InSAR) technology, and then compared with geology, tectonical aspects of the study area and land cover types in Lanzhou. The results reveal that the land surface deformation is uneven in this city, and seven surface deformation regions were detected in the following areas: the north freight yard, Jiuzhou, Country garden, Donggang, Yanjiaping, Zhongxinping and Liuquan town. The land surface deformation rate in Lanzhou ranges from −82.13 mm/year to 19.31 mm/year. Time-series land surface deformation analysis showed that deformation increased over time in major deformation regions. Surface deformation expansion was significant after June 2017and it continued to expand. The surface deformation of Lanzhou is affected by natural factors (geology and geological faults) and human activities (land cover types/land cover changes). Local geological conditions control the location of the surface deformation process. These findings provide compelling data and theoretical support for disaster prevention and reduction in Lanzhou.

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“…More specifically, in case of roads, the deformation causes failure in serviceability and performance of the infrastructure and induces high maintenance and repair costs (Peduto et al, 2016;Du et al, 2018). Unevenly deformed roads are dangerous, damaging and inconvenient for both the vehicles and passengers (Wijeyesekera et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Predicting land deformation by integrating InSAR data and cone penetration testing through machine learning techniques

et al. 2020

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Built environments developed on compressible soils are susceptible to land deformation. The spatiotemporal monitoring and analysis of these deformations are necessary for sustainable development of cities. Techniques such as Interferometric Synthetic Aperture Radar (InSAR) or predictions based on soil mechanics using in situ characterization, such as Cone Penetration Testing (CPT) can be used for assessing such land deformations. Despite the combined advantages of these two methods, the relationship between them has not yet been investigated. Therefore, the major objective of this study is to reconcile InSAR measurements and CPT measurements using machine learning techniques in an attempt to better predict land deformation.

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Correlating the subsidence pattern and land use in Bandung, Indonesia with both Sentinel-1/2 and ALOS-2 satellite images

Cited by 58 publications

References 29 publications

Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors

Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors

Characteristics of Surface Deformation in Lanzhou with Sentinel-1A TOPS

Predicting land deformation by integrating InSAR data and cone penetration testing through machine learning techniques

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