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• W2945180669 startingPage "1785" @default.
• W2945180669 abstract "Abstract 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 × 10 8 m 3 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." @default.
• W2945180669 created "2019-05-29" @default.
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• W2945180669 date "2019-07-28" @default.
• W2945180669 modified "2023-10-16" @default.
• W2945180669 title "Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors" @default.
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• W2945180669 doi "https://doi.org/10.1002/ldr.3347" @default.
• W2945180669 hasPublicationYear "2019" @default.
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