the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Apr 2025
Land subsidence dynamics and their interplay with spatial and temporal land-use transitions in the Douala coastland, Cameroon
Abstract. The Douala coastland (DCL), situated within the Douala sedimentary basin along the Gulf of Guinea, is characterised by its low elevation and alluvial geology, making it particularly susceptible to coastal erosion, land subsidence, and relative sea-level rise. The DCL is home to numerous rapidly growing cities, such as Douala, Tiko, and Limbe, which are currently experiencing alarming rates of coastal erosion, frequent flooding, and significant loss of land. Regional and continental investigations have provided evidence of coastal subsidence in this region; however, knowledge of its drivers and impact on the DCL remains limited. To address this knowledge gap, interferometric synthetic aperture radar (InSAR) datasets from the Sentinel-1 C-band satellite were used to quantify vertical land motion (VLM) between 2018 and 2023 with respect to the IGS14 global reference frame and assumed to represents absolute VLM. Digital Elevation Model datasets were used to analyse the elevation of the study area. The results revealed that the rate of VLM ranges from -17.6 mm/year to 3.8 mm/year (standard deviation of 0.2 mm/year), with a mean and median land subsidence rate of 2.7 mm/year and 2.5 mm/year. The analysis of land cover datasets from 1992 to 2022 suggests that urbanisation increased fivefold from 1992 to 2022 and that all contemporary urban areas experienced land subsidence, with the highest rates observed in non-residential zones with building heights ranging between 3 and 6 m. Subsidence rates of the DCL are inversely proportional to the time at which a particular land use and land cover (LULC) class changed into an urban area, highlighting the impact of the timing of LULC changes and urban expansion on present-day subsidence. The land subsidence rates decreased with an increase in building height, suggesting the potential influence of foundation type on land subsidence.
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Status: open (until 05 Oct 2025)
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RC1: 'Comment on egusphere-2025-336', Anonymous Referee #1, 27 Aug 2025
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Review of “Land subsidence dynamics and their interplay with spatial and temporal land-use transitions in the Douala coastland, Cameroon.”
General comments:
The paper presents original research on land subsidence in a rapidly growing coastal region. This work is important as the paper notes, because “there have been no specific studies on land subsidence mechanisms for the coast of Cameroon, leaving it uncharted”. The paper is generally well written. I am particularly interested by the analysis of how subsidence rates change following urbanization. However, I have concerns about how the InSAR analysis was performed, especially considering that it is the primary source of data supporting the authors' analysis and conclusions. The section describing the InSAR analysis is severely lacking detail and contains a number of erroneous statements. This casts doubt over the entire subsequent analysis and conclusion. In addition, I have some suggestions for how the data are presented, because they are not easily interpreted in their current form.
Specific comments (major):
1. No time series analysis or accompanying coherence matrices were provided in the either the article or the supplementary material. I find this rather problematic, as the entire premise of the paper revolves around analyzing that data correctly. Particularly with regard to the non-urban to urban transition zones, it is very likely that no coherent interferometric combinations exist for these pixels which cross the transition to urban. The authors should show the coherence matrix for such a region. It is probable that two separate subsidence rates would need to be estimated, one preceding urbanization and one after.
2. Lines 138--139: “To improve the signal-to-noise ratio of the interferometric phase, a multi-looking factor was used to create a pixel dimension of approximately 75 m in the range and azimuth directions”. A 75x75 m2 multilooking is approximately 55 pixels, which for S1 IW mode means approx. 35 looks/independent samples. Why was this number chosen? For DS regions 35 looks is likely insufficient to suppress noise, and for PS regions a 75x75m2 pixel is far too large for such a mixed environment.
3. Lines 143--145: “To this end, the so-called noisy pixels with coherence less than 0.65 (for distributed scatterers) and amplitude dispersion more than 0.3 (for permanent scatterers) were discarded following Lee and Shirzaei (2023).”
a) Coherence is a time-varying quantity and depends on the combination of images used to form an interferogram. The text seems to imply that each pixel can be assigned a single static value of coherence. How was this implemented? And if it is per interferogram, how do you ensure a continuous time series of coherent phases?
b) 0.65 is a very high threshold for DS pixels in agricultural or urbanizing regions. I find it hard to believe that more than only a few pixels would pass this threshold spanning the entire archive of imagery used in this study.
c) The article makes no mention of a pixel-based PS algorithm being used, however the use of amplitude dispersion as a quality metric seems to imply a PS methodology. What is actually being done with these pixels? If a PS algorithm is used in combination with the DS, how are the two analyses integrated?
4. Lines 147--148: “A 2D phase-unwrapping algorithm was used to examine the complex interferometric phase noises and identify elite pixels (i.e., pixels with an average coherence greater than 0.7) (Shirzaei, 2013).” This is a very problematic statement. PU algorithms attempt to estimate relative ambiguity levels in adjacent pixels by applying spatial continuity constraints. They do not “examine noise”. The description provided in the reference is about low-pass filtering interferograms based on the values of some identified high-quality pixels (called “elite pixels”). This is not a standard approach, and the authors risk removing high frequency parts of the signal of interest by doing so. This is likely only going to be effective at finding slowly varying, deep-seated subsidence drivers, which is not the focus of this paper. I recommend that the authors discard this method in favour of approaches accepted by the community in general such as SqueeSAR (Ferretti 2011) or similar. If the authors insist on this method, they must at least provide strong justification for doing so.
5. Line 151--152: “….using a local reference point (longitude: 9.61,latitude: 4.22) located 20 km outside the study area” Why was this point chosen? How was the atmospheric phase correctly removed if the reference point was not part of the analysis?
6. Line 166: “Given {𝐿𝑂𝑆𝐴, 𝐿𝑂𝑆𝐷} and {𝜎𝐴^2, 𝜎𝐷^2} are the LOS displacement and variances for a given pixel” How are these variances obtained?
7. Table 6: The extreme values reported in this table need to be checked. In particular, the cropland and wetland areas are prone to temporal decorrelation and phase unwrapping errors, which often results in overestimated subsidence rates (see Morishita and Hanssen 2015, Tampuu 2022). InSAR analysis often fails in regions like these and the authors need to take the dynamics of the region they are monitoring into account. Again, an analysis of the coherence matrices in these areas would be very beneficial.
8. Line 370: The discussion on groundwater extraction is lacking. Are we talking about deep or shallow aquifers, and how does the urban data support the conclusion? “The land subsidence rates decreased with an increase in building height, suggesting the potential influence of foundation type on land subsidence” -- that is highly doubtful. More likely, deeply founded buildings are less prone to subsidence caused by draining the phreatic groundwater. i.e. we can’t see the subsidence happening on the surface at those locations. This would suggest that the city is not obtaining water from a deep aquifer. Is that the case? Again, the lack of a real PS processing methodology casts doubt over the entire conclusion.
9. Regarding the comparisons between LULC classes. The authors should include time series data, in order to demonstrate the differing dynamics of these types of regions, ideally at least one from each of the identified classes.
Specific comments (minor):
1. Why are orbital and topographic phases removed after unwrapping? This should be done beforehand to reduce variability in the phases.
2. I find Table 6 very hard to interpret, and it is arguably the main result of the paper. It should be presented in a graphical way, preferably geographically. This would allow the reader to comprehend the scale of change over the region and study period.
3. I think the LULC figures and tables waste a lot of real estate in the paper. I suggest to keep one of them, and put the rest into supplementary. Put the InSAR results into the paper, as they are what the authors derive their conclusions from.
4. Figure 8: please provide a date corresponding to these maps. It would perhaps be more interesting to show how the urbanization has changed over the course of the study period.
Citation: https://doi.org/10.5194/egusphere-2025-336-RC1
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