Spatiotemporal Evolution of Land Subsidence in the Beijing Plain 2003–2015 Using Persistent Scatterer Interferometry (PSI) with Multi-Source SAR Data

Land subsidence is one of the most important geological hazards in Beijing, China, and its scope and magnitude have been growing rapidly over the past few decades, mainly due to long-term groundwater withdrawal. Interferometric Synthetic Aperture Radar (InSAR) has been used to monitor the deformation in Beijing, but there is a lack of analysis of the long-term spatiotemporal evolution of land subsidence. This study focused on detecting and characterizing spatiotemporal changes in subsidence in the Beijing Plain by using Persistent Scatterer Interferometry (PSI) and geographic spatial analysis. Land subsidence during 2003–2015 was monitored by using ENVISAT ASAR (2003–2010), RADARSAT-2 (2011–2015) and TerraSAR-X (2010–2015) images, with results that are consistent with independent leveling measurements. The radar-based deformation velocity ranged from −136.9 to +15.2 mm/year during 2003–2010, and −149.4 to +8.9 mm/year during 2011–2015 relative to the reference point. The main subsidence areas include Chaoyang, Tongzhou, Shunyi and Changping districts, where seven subsidence bowls were observed between 2003 and 2015. Equal Fan Analysis Method (EFAM) shows that the maximum extensive direction was eastward, with a growing speed of 11.30 km2/year. Areas of differential subsidence were mostly located at the boundaries of the seven subsidence bowls, as indicated by the subsidence rate slope. Notably, the area of greatest subsidence was generally consistent with the patterns of groundwater decline in the Beijing Plain

Imaging Land Subsidence Induced by Groundwater Extraction in Beijing (China) Using Satellite Radar Interferometry

Beijing is one of the most water-stressed cities in the world. Due to over-exploitation of groundwater, the Beijing region has been suffering from land subsidence since 1935. In this study, the Small Baseline InSAR technique has been employed to process Envisat ASAR images acquired between 2003 and 2010 and TerraSAR-X stripmap images collected from 2010 to 2011 to investigate land subsidence in the Beijing region. The maximum subsidence is seen in the eastern part of Beijing with a rate greater than 100 mm/year. Comparisons between InSAR and GPS derived subsidence rates show an RMS difference of 2.94 mm/year with a mean of 2.41 ± 1.84 mm/year. In addition, a high correlation was observed between InSAR subsidence rate maps derived from two different datasets (i.e., Envisat and TerraSAR-X). These demonstrate once again that InSAR is a powerful tool for monitoring land subsidence. InSAR derived subsidence rate maps have allowed for a comprehensive spatio-temporal analysis to identify the main triggering factors of land subsidence. Some interesting relationships in terms of land subsidence were found with groundwater level, active faults, accumulated soft soil thickness and different aquifer types. Furthermore, a relationship with the distances to pumping wells was also recognized in this work.This work was supported by the National Natural Science Foundation of China under Grant 41201419 and a China Scholarship Council (CSC) scholarship to Mi Chen. Roberto Tomás was supported by the Ministry of Education, Culture and Sport through the project PRX14/00100. Part of this work is also supported by the Spanish Ministry of Economy and Competitiveness and EU FEDER funds under projects TIN2014-55413-C2-2-P, by the UK Natural Environmental Research Council (NERC) through the LICS and IRNHiC projects (ref. NE/K010794/1 and NE/N012151/1, respectively), the ESA-MOST DRAGON-3 projects (ref. 10607 and 10665) and the Open Fund from the Key Laboratory of Earth Fissures Geological Disaster, Ministry of Land and Resources (Geological Survey of Jiangsu Province)

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Tracking hidden crisis in India's capital from space: implications of unsustainable groundwater use.

Funder: Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZNational Capital Region (NCR, Delhi) in India is one of the fastest-growing metropolitan cities which is facing a severe water crisis due to increasing water demand. The over-extraction of groundwater, particularly from its unconsolidated alluvial deposits makes the region prone to subsidence. In this study, we investigated the effects of plummeting groundwater levels on land surface elevations in Delhi NCR using Sentinel-1 datasets acquired during the years 2014-2020. Our analysis reveals two distinct subsidence features in the study area with rates exceeding 11 cm/year in Kapashera-an urban village near IGI airport Delhi, and 3 cm/year in Faridabad throughout the study period. The subsidence in these two areas are accelerating and follows the depleting groundwater trend. The third region, Dwarka shows a shift from subsidence to uplift during the years which can be attributed to the strict government policies to regulate groundwater use and incentivizing rainwater harvesting. Further analysis using a classified risk map based on hazard risk and vulnerability approach highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of ground movement, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR

An improved Stanford Method for persistent scatterers applied to 3D building reconstruction and monitoring

Persistent scatterers interferometric Synthetic Aperture Radar (PS-InSAR) is capable of precise topography measurement up to sub-meter scale and monitoring subtle deformation up to mm/year scale for all the radar image pixels with stable radiometric characteristics. As a representative PS-InSAR method, the Stanford Method for Persistent Scatterers (StaMPS) is widely used due to its high density of PS points for both rural and urban areas. However, when it comes to layover regions, which usually happen in urban areas, the StaMPS is limited locally. Moreover, the measurement points are greatly reduced due to the removal of adjacent PS pixels. In this paper, an improved StaMPS method, called IStaMPS, is proposed. The PS pixels are selected with high density by the improved PS selection strategy. Moreover, the topography information not provided in StaMPS can be accurately measured in IStaMPS. Based on the data acquired by TerraSAR-X/TanDEM-X over the Terminal 3 E (T3 E) site of Beijing Capital International Airport and the Chaobai River of Beijing Shunyi District, a comparison between StaMPS-retrieved results and IStaMPS-retrieved ones was performed, which demonstrated that the density of PS points detected by IStaMPS is increased by about 1.8 and 1.6 times for these two areas respectively. Through comparisons of local statistical results of topography estimation and mean deformation rate, the improvement granted by the proposed IStaMPS was demonstrated for both urban areas with complex buildings or man-made targets and non-urban areas with natural targets. In terms of the spatiotemporal deformation variation, the northwest region of T3 E experienced an exceptional uplift during the period from June 2012 to August 2015, and the maximum uplift rate is approximately 4.2 mm per year

Inferring subsidence characteristics in Wuhan (China) through multitemporal InSAR and hydrogeological analysis

Wuhan (China) is facing severe consolidation subsidence of soft soil and karst collapse hazards. To quantitatively explore the extent and causes of land subsidence in Wuhan, we performed multitemporal interferometry (MTI) analysis using synthetic aperture radar (SAR) data from the TerraSAR-X satellite from 2013 to 2017 and the Sentinel-1A satellite from 2015 to 2017. MTI results reveal four major subsidence zones in Wuhan, namely, Hankou (exceeding −6 cm/yr), Xudong-Qingshan (−3 cm/yr), Baishazhou-Jiangdi (−3 cm/yr), and Jianshe-Yangluo (−2 cm/yr). Accuracy assessment using 106 levelling benchmarks and cross-validation between the two InSAR-based results indicate an overall root-mean-square error (RMSE) of 2.5 and 3.1 mm/yr, respectively. Geophysical and geological analyses suggest that among the four major subsiding zones, Hankou, Xudong-Qingshan, and Jianshe-Yangluo are located in non-karstic soft soil areas, where shallow groundwater (< 30 m) declines driven by engineering dewatering and industrial water depletion contribute directly to soft soil compaction. Subsidence in the Baishazhou-Jiangdi zone develops in the karst terrain with abundant underground caves and fissures, which are major natural factors for gradual subsidence and karst collapse. Spatial variation analysis of the geological conditions indicates that the stage of karst development plays the most important role in influencing kart subsidence, followed by municipal construction, proximity to major rivers, and overlying soil structure. Moreover, land subsidence in this zone is affected more via coupling effects from multiple factors. Risk zoning analysis integrating subsidence horizontal gradient, InSAR deformation rates, and municipal construction density show that the high-risk areas in Wuhan are mainly distributed in the Tianxingzhou and Baishazhou-Jiangdi zone, and generally spread along the metro lines. © 202

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches