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.

Coastal lowland areas support much of the world population on only a small part of its terrestrial surface. Yet these areas face rapidly increasing land surface subsidence and flooding, and are most vulnerable to future sea level rise. The accurate and up to date digital terrain models (DTMs) that are required to predict and manage such risks are absent in many of the areas affected, especially in regions where populations are least developed economically and may be least resilient to such changes. Airborne LiDAR is widely seen as the most accurate data type for elevation mapping but can be prohibitively expensive, as are detailed field surveys across a broad geographic scale. We present an economical method that utilizes airborne LiDAR data along parallel flight lines ('strips') covering between 10% and 35% of the land depending on terrain characteristics, and manual interpolation. We present results for lowland areas in Central Kalimantan and East Sumatra (Indonesia), for which no accurate DTM currently exists. The study areas are covered with forest, plantations and agricultural land, on mineral soils and peatlands. The method is shown to yield DTM differences within 0.5 m, relative to full coverage LiDAR data, for 87.7-96.4% of the land surface in a range of conditions in 15 validation areas, and within 1.0 m for 99.3% of the area overall. After testing, the method was then applied to the entire eastern coastal zone of Sumatra, yielding a DTM at 100 m spatial resolution covering 7.1 Mha of lowland area from 1.45 Mha of effective LiDAR coverage. The DTM shows that 36.3%, or 2.6 Mha, of this area is below 2 m +MSL and, therefore, at risk of flooding in the near future as sea level rise continues. This DTM product is available for use in flood risk mapping, peatland mapping and other applications.