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

Ikuemonisan, Femi Emmanuel; Ozebo, V. C.; Olatinsu, O. B.

doi:10.1007/s12665-021-10020-1

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…WTC involves using wavelet transforms, mathematical tools that can decompose signals into their frequency components and provide information on the power and phase of each frequency component. It transforms the two signals and then calculates their coherence at each frequency and time point [70]. The coherence value ranges from 0 to 1, where zero indicates no relationship between the two signals, and one indicates a perfect linear relationship between the two signals.…”

Section: Wtc Analysismentioning

confidence: 99%

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Rebi

Hussain

et al. 2023

Atmosphere

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The effects of climate change are unparalleled in magnitude, ranging from changing weather patterns that endanger food production to increasing sea levels that increase the likelihood of catastrophic flooding. Therefore, determining the extent of such variations on regional and local scales is imperative. We used monthly precipitation data from 25 meteorological stations in northern Pakistan (NP) to document the observed changes in seasonal and annual precipitation. The station density in the NP is small and unevenly distributed; therefore, ERA-5 reanalysis data were used to supplement the observed dataset to assess the spatial trends in NP. The non-parametric Mann–Kendall (MK), Sen’s Slope estimator (SSE), and Sequential Mann–Kendall (SQMK) tests were performed to assess the trends. In addition, the wavelet analysis technique was used to determine the association of precipitation with various oceanic indices from 1960 to 2016. Results indicate that maximum precipitation was shown in the annual and summer seasons. In NP, annual, winter, spring, and summer precipitation declined, while an increase in autumn was observed at a rate of 0.43 mm/decade between 1989 and 2016. The spatial trends for observed and ERA-5 reanalysis datasets were almost similar in winter, spring, and autumn; however, some disagreement was observed in both datasets in the summer and annual precipitation trends in NP during 1960–2016. Between 1989 and 2016, summer and annual precipitation increased significantly in Region III. However, seasonal and annual precipitation decreased in NP between 1960 and 2016. Moreover, there were no prominent trends in annual precipitation until the mid-1980s, but an apparent increase from 1985 onwards. Annual precipitation increased in all elevations except at the 500–1000 m zone. The ENSO (El Niño–Southern Oscillation) shared notable interannual coherences among all indices above 16–64 months. Inter-decadal coherence with the ENSO, AO (Arctic Oscillation), and PDO (Pacific Decadal Oscillation) in NP for 128 months and above. Generally, AO, AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation) exhibited less coherence with precipitation in NP. The regression of seasonal and annual precipitation revealed that winter and spring precipitation levels had higher linear regression with the AO and ENSO, respectively, while both the AO and ENSO also dominated at the annual scale. Similarly, the IOD and PDO indices had a higher influence in summer precipitation. The findings may help water resource managers and climate researchers develop a contingency plan for better water resource management policies in the face of changing climate change in Pakistan, particularly in NP.

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Section: Wtc Analysismentioning

confidence: 99%

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Rebi

Hussain

et al. 2023

Atmosphere

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“…Miller et al employed multitemporal synthetic aperture radar data sets acquired by European Remote-Sensing Satellites (ERSs) and Envisat satellites to investigate ground deformation and used continuous wavelet transform to decompose time-series land subsidence, proving the capability of wavelet transform in comparing the short-term signal components of hydraulic head and vertical land subsidence [36]. Ikuemonisan et al studied Sentinel-1-derived land subsidence using wavelet tools and a triple exponential smoothing algorithm in Lagos, Nigeria [37].…”

Section: Studies On Multi-scale Geoprocessing Analyses Using Wavelet ...mentioning

confidence: 99%

Multi-Scale Analysis of Surface Building Density and Land Subsidence Using a Combination of Wavelet Transform and Spatial Autocorrelation in the Plains of Beijing

Jiao,

Li,

et al. 2024

Sustainability

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Land subsidence is a major issue in the Beijing Plain in China, caused by the construction of new buildings and infrastructure combined with groundwater extraction. This study employs a multi-level two-dimensional wavelet decomposition to decompose land subsidence into high- and low-frequency components, and Moran’s I index to analyze the spatial distribution of land subsidence and its main influencing factors. By comparing the spatial distributions of the high- and low-frequency components, we estimate the correlation between land subsidence and influencing factors at different scales. Utilizing a combination of wavelet decomposition and Moran’s I analysis, our study establishes a clear spatial correlation between continuously varying factors such as groundwater and clay layer thickness, and the low-frequency components of land subsidence, allowing for a focused analysis of the relationship between surface building density and the high-frequency components of land subsidence. Quantitatively, the study identifies a significant correlation at specific granularities, particularly at 480 m and 960 m, underscoring the nuanced interaction between urban development and land subsidence patterns. These insights into the spatial distribution of land subsidence and its contributing factors can inform the development of effective strategies to address this issue.

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A TDFC-RNNs framework integrated temporal convolutional attention mechanism for InSAR surface deformation prediction: A case study in Beijing Plain

Yao,

Jing,

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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

Cited by 8 publications

References 25 publications

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

Multi-Scale Analysis of Surface Building Density and Land Subsidence Using a Combination of Wavelet Transform and Spatial Autocorrelation in the Plains of Beijing

A TDFC-RNNs framework integrated temporal convolutional attention mechanism for InSAR surface deformation prediction: A case study in Beijing Plain

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