Investigation of Sentinel-1-derived land subsidence using wavelet tools and triple exponential smoothing algorithm in Lagos, Nigeria

Lagos has a history of long-term groundwater abstraction that is often compounded by the rising indiscriminate siting of private borehole and water well. This has resulted in various forms of environmental degradation, including land subsidence. Prediction of the temporal evolution of land subsidence is central to successful land subsidence management. In this study, a triple exponential smoothing algorithm was applied to predict the future trend of land subsidence in Lagos. Land subsidence time series was computed with SBAS-InSAR technique with Sentinel-1 acquisitions from 2015 to 2019. Besides, Matlab wavelet tool was implemented to investigate the periodicity within land displacement signal components and to understand the relationship between the observed land subsidence, and groundwater level change and that of soil moisture. Results show that land subsidence in the LOS direction varied approximately between - 94 and 15 mm/year. According to the wavelet-based analysis result, land subsidence in Lagos is partly influenced by both groundwater-level fluctuations and soil moisture variability. Evaluation of the proposed model indicates good accuracy, with the highest residual of approximately 8%. We then used the model to predict land subsidence between the years 2020 and 2023. The result showed that by the end of 2023 the maximum subsidence would reach 958 mm, which is approximately a 23% increase.

Automated summary

Long-term groundwater abstraction and indiscriminate siting of private borehole in Lagos have led to land subsidence, with the study showing that land subsidence is influenced by groundwater-level fluctuations and soil moisture variability. The predicted model has high accuracy in forecasting land subsidence.