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Land subsidence in Jakarta in Three Dimensions (2014-2025) using InSAR-GNSS Datum Connection and the Strapdown Decomposition

Land subsidence in Jakarta in Three Dimensions (2014-2025) using InSAR-GNSS Datum Connection and the Strapdown Decomposition

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Authors

Alexandru Mihai Lapadat, Heri Andreas, Wietske S Brouwer, Simon van Diepen, Dhota Pradipta, Ramon Hanssen 

Abstract

Coastal megacities face compounding hazards from rising sea levels and land subsidence. Jakarta, one of the fastest-sinking megacities, already experiences recurrent flooding amplified by rapid land subsidence. Assessing and mitigating this hazard requires reliable estimates of three-dimensional ground motion over wide spatial and temporal scales in a well-defined geodetic reference frame and datum. Here we combine spaceborne InSAR and GNSS measurements to characterize Jakarta’s land deformation. We develop a datum-connection procedure that aligns multi-track InSAR line-of-sight datasets acquired between 2014 and 2025 to a common datum, enabling unbiased three-dimensional velocity decomposition. The resulting displacement field is then connected to the Sunda Plate Fixed Frame using GNSS observations, yielding a three-dimensional characterization of land deformation in a globally consistent reference frame. Our results show that Jakarta’s land motion is dominated by six main subsidence bowls, with vertical subsidence rates of up to −7.7 cm/yr and horizontal rates of up to 1.7 cm/yr, superimposed on slow regional subsidence of approximately −1.1 cm/yr across the metropolitan area. As these results rely on the availability of a single continuous GNSS station, we recommend the installation of dedicated geodetic ground-based infrastructure to ensure sustainable and rigorous long-term monitoring capabilities.

DOI

https://doi.org/10.31223/X5R45H

Subjects

Earth Sciences

Keywords

geodesy, InSAR, Land subsidence, decomposition, Earth Observation, GNSS, Jakarta, 3D, Velocity

Dates

Published: 2025-12-30 18:00

Last Updated: 2025-12-30 18:00

License

CC BY Attribution 4.0 International