# Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations

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## Abstract

**:**

## 1. Introduction

^{−1}[15,18,19,20,21], being about ten times larger than global sea-level rise which occurs at rates of approximately 3.3 mm yr

^{−1}[22]. As a result, the present-day rates of relative sea-level rise in the MKD are dominated by land subsidence [2].

#### Site Description and Study Areas

## 2. Materials and Methods

#### 2.1. InSAR Data Collection

#### 2.2. Field Data

## 3. Results

## 4. Discussion

#### 4.1. Piled Foundation Depths and Building Sizes

#### 4.2. Lithology

#### 4.3. Land-Use Change and Drainage

#### 4.4. Recommendations for Future Studies to Unravel Urban Differential Subsidence

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Selection of Individual Building Data

**Figure A1.**(

**A**) Selection of InSAR-based velocity data from GIS, for a building in Ca Mau. (

**B**) Offset shown by PS points compared to a power pylon in Long Xuyen. Source base map: Open Street Map.

## Appendix B. Analysis of the Vertical Velocity Data

#### Appendix B.1. Introduction

#### Appendix B.2. Methods

#### Appendix B.2.1. Pearson Correlation Test

#### Appendix B.2.2. Spearman’s Rank Order Test

#### Appendix B.2.3. Linear Fit (From MATLAB 2018b)

^{2}, which indicates the significance of the linear trend. The R

^{2}varies between 0 and 1 with a higher R

^{2}indicating a stronger significant correlation. The R

^{2}indicates which portion of data is explained by the linear model that was fitted. In this case the adjusted R

^{2}was used, which considers the total size of the data and corrects the R

^{2}for this. Because not all datasets had the same size it was better to use this adjusted R

^{2}. The adjusted R

^{2}is calculated using Equation (A3):

#### Appendix B.2.4. Calculating Mean Offset

#### Appendix B.3. Results

#### Appendix B.3.1. Comparing the EMSN57 and KIT Vertical Velocity Datasets

^{2}of 0.77 for the linear fit (Table A1 and Figure A2). The correlations are weaker for the data from Ca Mau than that from Long Xuyen. The absolute velocity data from Ca Mau shows the EMSN57 velocity data to be slightly higher than the KIT velocity data, whilst in Long Xuyen it is the other way around. Combining the data from both cities shows that the linear fit is close to a perfect fit but shows a slight offset with the velocities from the KIT data being 0.49 mm/year lower than the velocities from the EMSN57 dataset.

^{2}of 0.65. The linear fit shows that there is slight offset with the KIT velocity data being 1.09 mm/year higher than the EMSN57 velocity data, but otherwise the two datasets align very well. The relative velocities are higher in Ca Mau than in Long Xuyen according to Figure A3. The average offsets are small for the dataset with Long Xuyen and Ca Mau combined, −0.88 mm/year and σ is 4.91 mm/year for the absolute velocities and 1.36 mm/year and σ is 6.87 mm/year for the relative velocities (Table A2). The standard deviations are of a similar magnitude as the uncertainty of an individual InSAR velocity dataset which is approximately 5 mm/year.

**Figure A2.**Comparison between the average absolute vertical velocities extracted from the KIT InSAR-based velocity dataset (x-axis) and EMSN57 InSAR-based velocity dataset (y-axis). Both the velocity from the buildings (*) and the surroundings (x) are plotted. The data from Ca Mau (blue) and Long Xuyen (yellow) are included in the linear fit (dashed black line). The equation belonging to this fit is y = 0.97x + 0.49 with R

^{2}= 0.77. The red dotted line shows a one-to-one perfect fit between the datasets.

**Table A1.**Results of statistical analysis of Ca Mau and Long Xuyen together between the velocity data of the KIT and EMSN57 InSAR-based datasets. N

_{tot}shows the total number of buildings included in the dataset, and n shows the number that was used for the correlations. N

_{tot}for all absolute data is twice the normal N

_{tot}because this is the data from the buildings and surroundings combined. For both Spearman’s and Pearson correlation the Rho shows the strength of the correlation (between −1 and 1, with zero showing no correlation) and the p-value shows the statistical significance of the correlation (p-values < 0.05 shows is statistically valid). Both the mean offset between the datasets (Δ), and the standard deviation (σ) are given. The parameters from the linear fit depict the values from y = ax + b. The R

^{2}gives the significance of the linear fit adjusted to n.

Dataset | n | Spearman’s | Pearson | Mean Data Offset | Linear Fit | |||||
---|---|---|---|---|---|---|---|---|---|---|

Rho | p-Value | Rho | p-Value | Δ [mm/yr] | σ [mm/yr] | Interception (b) | Direction Coefficient (a) | R^{2} | ||

Buildings | 51 | 0.59 | 5.5 × 10^{−6} | 0.86 | 3.6 × 10^{−16} | −1.59 | 4.23 | 0.65 | 0.87 | 0.74 |

Surroundings | 40 | 0.58 | 7.6 × 10^{−5} | 0.52 | 5.2 × 10^{−4} | 0.03 | 5.59 | −6.08 | 0.70 | 0.26 |

All absolute | 91 | 0.85 | 1.0 × 10^{−26} | 0.88 | 3.7 × 10^{−30} | −0.88 | 4.91 | 0.49 | 0.97 | 0.77 |

Relative | 40 | 0.76 | 1.8 × 10^{−8} | 0.81 | 2.0 × 10^{−10} | 1.36 | 6.87 | −1.09 | 1.02 | 0.65 |

**Figure A3.**Comparison between the average relative vertical velocities of the surroundings compared to the buildings calculated from the KIT InSAR-based velocity dataset (x-axis) and EMSN57 InSAR-based velocity dataset (y-axis). The data from Ca Mau (blue) and Long Xuyen (yellow) are included in the linear fit (dashed black line). The equation belonging to this fit is y = 1.02x − 1.09 with R

^{2}= 0.65. The red dotted line shows a one-to-one perfect fit between the datasets.

#### Appendix B.3.2. Comparing the EMSN62 and KIT Vertical Velocity Datasets

^{2}of 0.65. The relative velocities of the surroundings compared to the buildings align quite well comparing the fitted trend between the KIT and EMSN62 dataset with the perfect fit, but with the EMSN62 velocity data being 6.8 mm/year higher than the KIT velocity data (Figure A5 and Table A2). The Pearson rho is 0.62 for the relative data, and the linear fit has a R

^{2}of 0.34. The relative velocities are highest in Ca Mau and of similar rates in Long Xuyen and Can Tho according to Figure A5.

**Figure A4.**Comparison between the average absolute vertical velocities extracted from the KIT InSAR-based velocity dataset (x-axis) and EMSN62 InSAR-based velocity dataset (y-axis). Both the velocity from the buildings (*) and the surroundings (x) are plotted. The data from Ca Mau (blue), Can Tho (red) and Long Xuyen (yellow) are included in the linear fit (dashed black line). The equation belonging to this fit is y = 1.12x − 4.31 with R

^{2}= 0.65. The red dotted line shows a 1-to-1 perfect fit between the datasets.

**Table A2.**Results of statistical analysis of Ca Mau, Can Tho and Long Xuyen together between the velocity data of the KIT and EMSN62 InSAR-based datasets. Ntot shows the total number of buildings included in the dataset, and n shows the number that was used for the correlations. Ntot for all absolute data is twice the normal Ntot because this is the data from the buildings and surroundings combined. For both Spearman’s and Pearson correlation the Rho shows the strength of the correlation (between −1 and 1, with zero showing no correlation) and the p-value shows the statistical significance of the correlation (p-values < 0.05 shows is statistically valid). Both the mean offset between the datasets (Δ), and the standard deviation (σ) are given. The parameters from the linear fit depict the values from y = ax + b. The R

^{2}gives the significance of the linear fit adjusted to n.

Dataset | n | Spearman’s | Pearson | Mean Data Offset | Linear Fit | |||||
---|---|---|---|---|---|---|---|---|---|---|

Rho | p-Value | Rho | p-Value | Δ [mm/yr] | σ [mm/yr] | Interception (b) | Direction Coefficient (a) | R^{2} | ||

Buildings | 78 | 0.42 | 1.4 × 10^{−4} | 0.75 | 1.5 × 10^{−15} | 2.92 | 5.36 | −3.36 | 0.89 | 0.56 |

Surroundings | 63 | 0.47 | 1.1 × 10^{−4} | 0.45 | 2.3 × 10^{−4} | 8.74 | 8.19 | −14.3 | 0.67 | 0.19 |

All absolute | 141 | 0.80 | 1.1 × 10^{−32} | 0.81 | 5.0 × 10^{−34} | 5.52 | 7.34 | −4.31 | 1.12 | 0.65 |

Relative | 56 | 0.55 | 9.4 × 10^{−6} | 0.62 | 3.3 × 10^{−7} | 6.24 | 9.75 | −6.81 | 0.95 | 0.37 |

**Figure A5.**Comparison between the average relative vertical velocities of the surroundings compared to the buildings calculated from the KIT InSAR-based velocity dataset (x-axis) and EMSN62 InSAR-based velocity dataset (y-axis). The data from Ca Mau (blue), Can Tho (red) and Long Xuyen (yellow) are included in the linear fit (dashed black line). The equation belonging to this fit is y = 0.95x − 6.81 with R

^{2}= 0.37. The red dotted line shows a 1-to-1 perfect fit between the datasets.

#### Appendix B.3.3. Comparing the EMSN57 and EMSN62 Vertical Velocity Datasets

^{2}is 0.31, Table A3).

**Figure A6.**Comparison between the average absolute vertical velocities extracted from the EMSN57 InSAR-based velocity dataset (x-axis) and EMSN62 InSAR-based velocity dataset (y-axis). Both the velocity from the buildings (*) and the surroundings (x) are plotted. The data from Ca Mau (blue) and Long Xuyen (yellow) are included in the linear fit (dashed black line). The equation belonging to this fit is y = 1.16x − 2.90 with R

^{2}= 0.77. The red dotted line shows a one-to-one perfect fit between the datasets.

**Table A3.**Results of statistical analysis of Ca Mau and Long Xuyen together between the velocity data of the EMSN57 and EMSN62 InSAR-based datasets. Ntot shows the total number of buildings included in the dataset, and n shows the number that was used for the correlations. Ntot for all absolute data is twice the normal Ntot because this is the data from the buildings and surroundings combined. For both Spearman’s and Pearson correlation the Rho shows the strength of the correlation (between −1 and 1, with zero showing no correlation) and the p-value shows the statistical significance of the correlation (p-values < 0.05 shows is statistically valid). Both the mean offset between the datasets (Δ), and the standard deviation (σ) are given. The parameters from the linear fit depict the values from y = ax + b. R

^{2}gives the significance of the linear fit adjusted to n.

Dataset | n | Spearman’s | Pearson | Mean Data Offset | Linear Fit | |||||
---|---|---|---|---|---|---|---|---|---|---|

Rho | p-Value | Rho | p-Value | Δ [mm/yr] | σ [mm/yr] | Interception (b) | Direction Coefficient (a) | R^{2} | ||

Buildings | 46 | 0.58 | 3 × 10^{−5} | 0.87 | 2 × 10^{−15} | 2.83 | 4.94 | −1.81 | 1.21 | 0.76 |

Surroundings | 37 | 0.56 | 4 × 10^{−4} | 0.57 | 2 × 10^{−4} | 7.04 | 7.46 | −12.1 | 0.75 | 0.31 |

All absolute | 83 | 0.87 | 5 × 10^{−26} | 0.88 | 4 × 10^{−28} | 4.71 | 6.50 | −2.90 | 1.16 | 0.77 |

Relative | 34 | 0.60 | 2 × 10^{−4} | 0.75 | 3 × 10^{−7} | 4.14 | 9.79 | −2.53 | 1.11 | 0.55 |

^{2}of the linear fit is 0.55, showing a larger uncertainty than the absolute velocities (Table A3). The relative velocities are again higher in Ca Mau than in Long Xuyen (Figure A7).

**Figure A7.**Comparison between the average relative vertical velocities of the surroundings compared to the buildings calculated from the EMSN57 InSAR-based velocity dataset (x-axis) and EMSN62 InSAR-based velocity dataset (y-axis). The data from Ca Mau (blue) and Long Xuyen (yellow) are included in the linear fit (dashed black line). The equation belonging to this fit is y = 1.11x − 2.53 with R

^{2}= 0.55. The red dotted line shows a 1-to-1 perfect fit between the datasets.

#### Appendix B.3.4. Comparing Field-, and InSAR-Based Velocity Datasets

**Figure A8.**Comparison between the average relative vertical velocities of the surroundings compared to the buildings in Ca Mau calculated from the offsets measured in the field (x-axis) and calculated from the InSAR-based velocity dataset (y-axis). The velocity datasets from KIT (blue), EMSN57 (yellow) and EMSN62 (red) are included and the colored dashed lines and equations show the corresponding linear trends between the separate InSAR velocity datasets and the field velocity datasets. The dotted black line shows the one-to-one perfect fit between field-, and InSAR-based relative velocities. All InSAR datasets show a weak trend with the velocity dataset, but most data points are close to the perfect fit line, indicating a similarity in the values.

**Figure A9.**Comparison between the average relative vertical velocities of the surroundings compared to the buildings in Can Tho calculated from the offsets measured in the field (x-axis) and calculated from the InSAR-based velocity dataset (y-axis). The velocity datasets from KIT (blue) and EMSN62 (red) are included and the colored dashed lines and equations show the corresponding linear trends between the separate InSAR velocity datasets and the field velocity datasets. The dotted black line shows the one-to-one perfect fit between field-, and InSAR-based relative velocities. All InSAR datasets show a weak trend with the velocity dataset, but most data points are somewhat close to the perfect fit line, indicating a similarity in the values.

**Figure A10.**Comparison between the average relative vertical velocities of the surroundings compared to the buildings in Long Xuyen calculated from the offsets measured in the field (x-axis) and calculated from the InSAR-based velocity dataset (y-axis), using the year construction was started as a correction for the large offsets in relative velocity. The velocity datasets from KIT (blue), EMSN57 (yellow) and EMSN62 (red) are included and the colored dashed lines and equations show the corresponding linear trends between the separate InSAR velocity datasets and the field velocity datasets. The dotted black line shows the one-to-one perfect fit between field-, and InSAR-based relative velocities. All InSAR datasets show a moderate trend with the velocity dataset, but the field-based velocities are three times higher than those from the InSAR-based datasets.

**Table A4.**Results of statistical analysis between the velocity data of the relative velocities calculated from the offsets measured in the field and from KIT InSAR-based velocity dataset. Ntot shows the total number of buildings included in the dataset, and n shows the number that was used for the correlations. Ntot for all absolute data is twice the normal Ntot because this is the data from the buildings and surroundings combined. For both Spearman’s and Pearson correlation, Rho shows the strength of the correlation (between −1 and 1, with zero showing no correlation) and the p-value shows the statistical significance of the correlation (p-values < 0.05 shows is statistically valid). Both the mean offset between the datasets (Δ), and the standard deviation (σ) are given. The parameters from the linear fit depict the values from y = ax + b. R

^{2}gives the significance of the linear fit adjusted to n.

Dataset | n | Spearman’s | Pearson | Mean Data Offset | Linear Fit | |||||
---|---|---|---|---|---|---|---|---|---|---|

Rho | p-Value | Rho | p-Value | Δ [mm/yr] | σ [mm/yr] | Interception (b) | Direction Coefficient (a) | R^{2} | ||

All cities | 74 | 0.29 | 0.01 | 0.22 | 0.06 | −7.79 | 15.0 | −9.16 | 0.12 | 0.04 |

Ca Mau | 21 | −0.02 | 0.95 | −0.05 | 0.84 | −5.0 | 17.1 | −17.57 | −0.02 | −0.05 |

Can Tho | 31 | 0.29 | 0.11 | 0.37 | 0.04 | −4.7 | 11.2 | −7.48 | 0.19 | 0.11 |

Long Xuyen | 22 | 0.31 | 0.16 | 0.33 | 0.13 | −14.8 | 15.7 | −3.97 | 0.18 | 0.06 |

**Table A5.**Results of statistical analysis between the velocity data of the relative velocities calculated from the offsets measured in the field and from EMSN62 InSAR-based velocity dataset. Ntot shows the total number of buildings included in the dataset, and n shows the number that was used for the correlations. Ntot for all absolute data is twice the normal Ntot because this is the data from the buildings and surroundings combined. For both Spearman’s and Pearson correlation, Rho shows the strength of the correlation (between −1 and 1, with zero showing no correlation) and the p-value shows the statistical significance of the correlation (p-values < 0.05 shows is statistically valid). Both the mean offset between the datasets (Δ), and the standard deviation (σ) are given. The parameters from the linear fit depict the values from y = ax + b. R

^{2}gives the significance of the linear fit adjusted to n.

Dataset | n | Spearman’s | Pearson | Mean Data Offset | Linear Fit | |||||
---|---|---|---|---|---|---|---|---|---|---|

Rho | p-Value | Rho | p-Value | Δ [mm/yr] | σ [mm/yr] | Interception (b) | Direction Coefficient (a) | R^{2} | ||

All cities | 57 | 0.45 | 4.1 × 10^{−4} | 0.34 | 0.01 | −3.18 | 16.1 | −12.0 | 0.27 | 0.10 |

Ca Mau | 14 | 0.41 | 0.14 | 0.24 | 0.40 | 0.92 | 17.1 | −23.7 | 0.10 | −0.02 |

Can Tho | 24 | 0.30 | 0.15 | 0.18 | 0.41 | 1.19 | 13.0 | −14.3 | 0.10 | −0.01 |

Long Xuyen | 19 | 0.64 | 2.9 × 10^{−3} | 0.50 | 0.03 | −11.7 | 16.3 | −0.04 | 0.53 | 0.20 |

**Table A6.**Results of statistical analysis between the velocity data of the relative velocities calculated from the offsets measured in the field and from EMSN57 InSAR-based velocity dataset. Ntot shows the total number of buildings included in the dataset, and n shows the number that was used for the correlations. Ntot for all absolute data is twice the normal Ntot because this is the data from the buildings and surroundings combined. For both Spearman’s and Pearson correlation, Rho shows the strength of the correlation (between −1 and 1, with zero showing no correlation) and the p-value shows the statistical significance of the correlation (p-values < 0.05 shows is statistically valid). Both the mean offset between the datasets (Δ), and the standard deviation (σ) are given. The parameters from the linear fit depict the values from y = ax + b. R

^{2}gives the significance of the linear fit adjusted to n.

Dataset | n | Spearman’s | Pearson | Mean Data Offset | Linear Fit | |||||
---|---|---|---|---|---|---|---|---|---|---|

Rho | p-Value | Rho | p-Value | Δ [mm/yr] | σ [mm/yr] | Interception (b) | Direction Coefficient (a) | R^{2} | ||

All cities | 46 | 0.29 | 0.05 | 0.26 | 0.08 | −9.28 | 20.5 | −11.2 | 0.15 | 0.05 |

Ca Mau | 24 | 0.23 | 0.27 | 0.14 | 0.51 | −4.91 | 23.30 | −20.1 | 0.04 | −0.02 |

Long Xuyen | 22 | 0.45 | 0.03 | 0.39 | 0.07 | −14.0 | 16.2 | −1.53 | 0.30 | 0.11 |

**Figure A11.**The average offset in relative velocity of the surroundings compared to the buildings, between each InSAR-based velocity dataset and the velocity data calculated from the offsets measured in the field, for every city. The velocity data of each dataset were sorted by age and the offset from the field-based relative velocities was calculated by subtracting the moving mean of the relative velocities (using a window of three data points) of the InSAR-based datasets from the moving mean of the field-based dataset (offset = field-InSAR). Furthermore, a fourth-order polynomial was fitted through the data to show the trend in the anomaly with age. Negative offsets show that the InSAR-based velocities are lower than the field-based velocities, and thus that the InSAR underestimates the field-based velocities.

## Appendix C. Building Information

Building | Year of Construction | Age (Years) | Piling Depth (m) | Building Height (m) | Area (m^{2}) | Estimate Volume (m^{3}) | Previous Land Use |
---|---|---|---|---|---|---|---|

A | 2015 | 4 | 45.6 | 19 | 1000 | 18,996 | Urban |

B | 2005 | 14 | 22 | n/a | 676 | n/a | n/a |

C | 2007 | 12 | n/a | 6.2 | 531 | 3294 | Vacant land |

D | 2011 | 8 | 40 | 13 | 874 | 11,361 | Vacant land |

E | 2001 | 18 | 15 | n/a | 1448 | n/a | n/a |

F | 2008 | 11 | 32 | 9 | 3343 | 30,086 | Orchard |

G | 2014 | 5 | n/a | n/a | 5080 | n/a | n/a |

H | 2006 | 13 | 17.2 | n/a | 601 | n/a | n/a |

I | 2010 | 9 | 38 | 24 | 720 | 17,280 | Grassland |

J | 2007 | 12 | 22 | n/a | 746 | n/a | n/a |

K | 2006 | 13 | 18 | n/a | 1157 | n/a | n/a |

L | 2011 | 8 | 22.3 | n/a | 519 | n/a | n/a |

M | 2010 | 9 | 34 | 9 | 1516 | 13,643 | Vacant land from government |

N | 2008 | 11 | 32 | 9 | 1345 | 12,108 | Orchard |

O | 2014 | 5 | 13.5 | n/a | 2044 | n/a | n/a |

P | 2011 | 8 | 24 | n/a | 912 | n/a | n/a |

Q | 2009 | 10 | 45 | 14.8 | 1588 | 23,497 | n/a |

R | 2015 | 4 | 24.5 | n/a | 2789 | n/a | n/a |

S | 2015 | 4 | 35 | 9 | 2632 | 23,684 | Agricultural land |

T | 2005 | 14 | 22.5 | n/a | 820 | n/a | n/a |

U | 2013 | 6 | 24.5 | n/a | 1112 | n/a | n/a |

V | 2013 | 6 | 35.1 | 26 | 865 | 22,494 | Pond/vacant grassland |

W | 2011 | 8 | 24 | n/a | 957 | n/a | n/a |

X | 2013 | 6 | 36 | 18 | 1116 | 20,092 | Orchard |

Y | 2014 | 5 | 35 | n/a | 3887 | n/a | n/a |

Z | 2008 | 11 | 22 | n/a | 1774 | n/a | n/a |

AA | 2011 | 8 | 23 | n/a | 1678 | n/a | n/a |

BB | 2011 | 8 | 36 | 25 | 478 | 11,961 | n/a |

CC | 2012 | 7 | 30 | 13 | 892 | 11,591 | Orchard |

DD | 2014 | 5 | 49.05 | 27 | 1270 | 34,296 | Other building |

EE | 2015 | 4 | 40 | 42 | 9684 | 406,730 | Agricultural land |

FF | 2008 | 11 | 30 | 15 | 1859 | 27,890 | Agricultural land |

GG | 2012 | 7 | 39 | 31.8 | 339 | 10,766 | n/a |

HH | 2012 | 7 | 18.6 | n/a | 1425 | n/a | n/a |

II | 2008 | 11 | 21.5 | n/a | 7690 | n/a | n/a |

JJ | 2004 | 15 | 24 | n/a | 2109 | n/a | n/a |

KK | 2013 | 6 | 30 | 8 | 719 | 5756 | Vacant land |

LL | 2010 | 9 | 40 | n/a | 330 | n/a | n/a |

MM | n/a | n/a | n/a | n/a | 4238 | n/a | n/a |

NN | 2011 | 8 | 40 | 17 | 2157 | 36,664 | Urban |

OO | 2005 | 14 | 40 | 13 | 2605 | 33,865 | Vacant grassland/fen |

PP | 2014 | 5 | 40 | 20 | 434 | 8678 | Orchard |

2009 | 10 | 45.6 | 19 | 1000 | 18,996 | Urban | |

RR | 2005 | 14 | 22 | n/a | 324 | n/a | n/a |

Building | Year of Construction | Age in 2019 (Years) | Piling Depth (m) | Building Height (m) | Area (m^{2}) | Estimated size (m^{3}) | Previous Land Use |
---|---|---|---|---|---|---|---|

A | 2008 | 11 | 30 | 9 | 1570 | 14,131 | Pond |

B | 2016 | 3 | 36 | 11 | 684 | 7525 | Fen |

C | n/a | n/a | 35 | n/a | 4541 | n/a | n/a |

D | 2008 | 11 | 24 | 7 | 2199 | 15,396 | Orchards |

E | 2007 | 12 | 28 | 26 | 565 | 14,677 | Vacant land |

F | 2012 | 7 | 26 | 24 | 1452 | 34,852 | Other building |

G | 2016 | 3 | 20 | 19 | 2168 | 41,196 | Building/vacant land |

H | 2011 | 8 | 26 | 15.2 | 927 | 14,088 | Pond |

I | 2012 | 7 | 27 | 14 | 284 | 3978 | Agricultural Land |

J | 2012 | 7 | 27 | 14 | 540 | 7555 | Agricultural Land |

K | 2011 | 8 | 28 | 17 | 822 | 13,968 | Vacant land/fen |

L | 2009 | 10 | 30 | 9 | 521 | 4689 | Grassland/ fen |

M | 2014 | 5 | 28 | 19 | 8426 | 160,102 | Fen |

N | 2011 | 8 | 26 | 13.5 | 478 | 6450 | Reed land |

O | 2014 | 5 | 30 | 11 | 4855 | 53,409 | Aquaculture |

P | 2011 | 8 | 38 | 31.5 | 1221 | 38,451 | Vacant land |

Q | 2004 | 15 | 26 | 12 | 645 | 7743 | Grassland |

R | 2014 | 5 | 26 | 16.3 | 1164 | 18,971 | Vacant land |

S | 2013 | 6 | 22 | 8.5 | 2399 | 20,391 | Vacant grassland |

T | 2013 | 6 | 25 | 17 | 743 | 12,627 | Pond/reeds |

U | 2011 | 8 | 24 | 11.8 | 489 | 5771 | Lake |

V | 2013 | 6 | 28 | 14.5 | 2379 | 34,496 | Agricultural Land |

W | 2011 | 8 | 18 | 11 | 406 | 4466 | Aqua cultural land |

X | 2009 | 10 | 28 | 12 | 453 | 5441 | Vacant land/fen |

Y | 2010 | 9 | 30 | 15 | 550 | 8243 | Fen/grassland |

Z | 2005 | 14 | 23.6 | 13.4 | 1024 | 13,726 | Vacant land |

AA | 2012 | 7 | 28 | 9 | 601 | 5409 | Lake |

BB | 2008 | 11 | 26 | 13.5 | 400 | 5400 | Aqua cultural land |

CC | 2009 | 10 | 25 | 9 | 542 | 4881 | Pond/grassland |

DD | 2015 | 4 | 24 | 6.5 | 3339 | 21,704 | Vacant land |

EE | 2009 | 10 | 24 | 27 | 1579 | 42,636 | Reed/vacant land |

FF | 2009 | 10 | 26 | 16.3 | 1308 | 21,315 | Vacant land |

Building | Year of Construction | Age (Years) | Piling Depth (m) | Building Height (m) | Area (m^{2}) | Estimated Volume (m^{3}) | Previous Land Use |
---|---|---|---|---|---|---|---|

A | 2012 | 7 | 12 | 8 | 842 | 6737 | Food company |

B | 2010 | 9 | n/a | 8 | 4062 | 32,499 | Unknown |

C | 2015 | 4 | n/a | 8 | 3803 | 30,424 | Unknown |

D | 2010 | 9 | n/a | 8 | 6139 | 49,115 | Unknown |

E | 2002 | 17 | 8 | 12 | 1342 | 16,105 | Rice field |

F | 2002 | 17 | n/a | 8 | 5571 | 44,571 | Unknown |

G | 2008 | 11 | 6 | 12 | 436 | 5228 | Rice field |

H | 2008 | 11 | 6 | 20 | 1648 | 32,964 | Rice field |

I | 2010 | 9 | 35 | 12 | 1484 | 17,804 | Food company |

J | 2007 | 12 | 5.7 | 10.75 | 994 | 10,683 | Rice field |

K | 2012 | 7 | 5.2 | 7.5 | 614 | 4602 | Rice field |

L | 2015 | 4 | 24 | 22 | 2058 | 45,266 | n/a |

M | 2015 | 4 | 24 | 22 | 1861 | 40,945 | n/a |

N | 2010 | 9 | 24 | 24 | 4218 | 101,222 | n/a |

O | 2009 | 10 | 22.5 | 5 | 5655 | 28,277 | n/a |

P | 2006 | 13 | n/a | 8 | 5367 | 42,938 | n/a |

Q | 2006 | 13 | 22.2 | 24 | 1392 | 33,411 | Museum Conservation Office. Rice fields before 1976 |

R | 2016 | 3 | 35 | 54 | 11,574 | 625,005 | Rice field |

S | 2014 | 5 | 23 | 17 | 4814 | 81,830 | n/a |

T | 2010 | 9 | n/a | 20 | 3599 | 71,981 | Rice field |

U | 2009 | 10 | 35 | 18 | 1953 | 35,145 | n/a |

V | 2012 | 7 | 25 | 22 | 9947 | 218,837 | Pond |

W | 2009 | 10 | 22 | 16 | 2824 | 45,177 | n/a |

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**Figure 1.**(

**a**) Damage caused by differential subsidence because the surroundings are subsiding faster than the buildings in a hospital (upper) and a college (lower) in Can Tho city, Vietnam. (

**b**) Schematic example of how differences in piling depth (foundation) or loading can cause differential subsidence. The red arrows symbolize the vertical movement of the surface, which is largest under the unfounded building due to loading. The buildings with deeper foundations stand on the coarse-grained sediment layer, which is less compressible. These buildings are subsiding less than their surroundings, and an offset forms between the buildings ground floor and the ground surface. The road lies on the surface and represents the total subsidence of the subsurface.

**Figure 2.**Vietnamese Mekong delta with the locations of the study areas and the reference areas used by the different InSAR studies. Source: ESRI World Ocean base map.

**Figure 3.**Example of the selection of a building that shows less subsidence than its surroundings in Ca Mau city, using PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) vertical velocity data. The individual reflectors are represented by a dot with corresponding vertical velocity according to the legend. Source base map: Open Street Map.

**Figure 4.**Time series of the surface elevation from persistent scatterer (PS) points from the EMSN57 velocity dataset on the Convention Center in Ca Mau (blue) and its surroundings (black). The red lines show the average displacement of the different PS points combined. The yellow arrow shows the offset in elevation formed due to the difference in average velocity between the building and the surroundings. There is a gap in the data for 15 December.

**Figure 5.**Example of determining the original level or starting point (SP) of the surface before measuring the offset (Δh) with the current surface level.

**Figure 6.**Schematic visualization of measuring an offset in surface level using a theodolite. (

**A**) The original elevation of the surface is measured by holding the measuring staff at the elevation of the determined starting point (SP). The theodolite registers the elevation by reading a barcode on the staff. (

**B**) The measuring staff is placed on the current surface level and the theodolite, which is still at the same location, registers the new elevation on the staff, and determines the offset between the level measured in (

**A**,

**B**). This is the offset between the original and the current surface level. These measurements are repeated to create a transect (measurements 1 and 2 etc.).

**Figure 7.**(

**A**) Example of local variation in current surface level causing uncertainty in determining the actual offset caused by subsidence (1 or 2). (

**B**) Sloping surface constructed as a water management measure instead of resulting from subsidence.

**Figure 8.**(

**A**) Locations of measured buildings and InSAR-based vertical velocities in Can Tho, Ca Mau and Long Xuyen. For each location, the average vertical velocity of the building (inner circle) and surroundings (outer circle) is shown when this was available from the Ensemble InSAR velocity dataset. When the vertical velocity of the surroundings was unknown only the velocity of the building is shown. The buildings are named according to their vertical velocity, with building A having the highest velocity. (

**B**) Example of the inner and outer circle representing the building and surroundings vertical velocity rate. Base map: ESRI Imagery base map.

**Figure 9.**Boxplot showing for each building in (

**A**) Can Tho, (

**B**) Ca Mau and (

**C**) Long Xuyen the vertical velocity data, based on the ensemble data of the KIT, EMSN62 and EMSN57 datasets. The buildings are sorted and named by mean vertical velocity from high (left, building A) to low velocities (right). The box extends between the 25th and 75th percentile (Q1 and Q3) of the data, with the central mark being the median. The maximum length of the whiskers is determined by taking 1.5 times the interquartile range (i.e., Q3−Q1). Datapoints outside this range are marked as outliers (+). The blue lines show the average velocities from the separate datasets. The red line shows the average velocity from the combined InSAR dataset of the reference area around the building. Buildings without velocity data from reference surroundings are excluded. Underneath each building the depth of the piled foundation is given, both by the length of the columns and their color.

**Figure 10.**Schematic representation of the shallow subsurface of the cities Ca Mau, Can Tho and Long Xuyen. The layer of soft, fine-grained sediments is thicker in Ca Mau, causing overall more subsidence in this city compared to Can Tho and Long Xuyen,. The shown piling depths indicate the range of piling depths found for the studied buildings in each city. The buildings with a piled foundation in the soft, fine-grained sediments are subsiding faster due to compaction of underlying soft sediments. In case the piled foundation reached into the coarser sediments, difference in depth of the piles within the coarser sediment did not result in much vertical movement variation. N.B., The y-axis is representative for subsurface sediments and piled foundations, the vertical displacement of the surface level and the buildings is not scaled and exaggerated for visualization purpose.

**Figure 11.**Schematic representation showing an example of how land-use change, in this case a lake drained to create space for constructions, can locally result in additional subsidence. (

**a**,

**b**) During natural conditions, the lake is gradually filled with fine-grained, compressible sediments, during which some initial compaction of the lake sediments occurs. (

**c**) Ditches are created that drain the lake and its surroundings, resulting in a lowering of the phreatic water table and increased compaction of the soft sediments. (

**d**) A building is constructed with a piled foundation resting on the deeper coarse-grained sediments. (

**e**) Compaction of the shallow unconsolidated sediments continues, resulting in differential subsidence.

**Table 1.**Specifications of the Copernicus EMS Risk & Recovery Mapping Activations 57 and 62 (EMSN57, EMSN62), and KIT InSAR-based velocity datasets.

Dataset Reference | Coverage/Spatial Resolution | Temporal Coverage | Satellite | Estimated Accuracy |
---|---|---|---|---|

KIT (unpublished) | Cities of Ca Mau, Long Xuyen and Can Tho | 2015–2020 for Ca Mau 2017–2020 for Long Xuyen and Can Tho | Sentinel-1 | 3–5 mm local scale, larger error for delta scale |

EMSN57 (https://emergency.copernicus.eu/mapping/list-of-components/EMSN057) | Cities of Ca Mau, Long Xuyen and Rach Gia | November 2014–September 2018 | Sentinel-1 | Approximately 95.8% in Ca Mau and 98.1% in Long Xuyen |

EMSN62 (https://emergency.copernicus.eu/mapping/list-of-components/EMSN062) | Delta wide | 23 November 2014–31 January 2019 (descending), 13 March 2017–26 January 2019 (ascending) | Sentinel-1 | Delta scale 5–8 mm. Local scale (up to 10 km) 3–4 mm |

**Table 2.**Summary of the number of buildings showing little subsidence and the number of locations at which differential subsidence is occurring between the building and the surroundings, in each city and for all cities combined (Total).

Can Tho | Ca Mau | Long Xuyen | Total | |
---|---|---|---|---|

Total number of buildings | 34 | 28 | 23 | 85 |

Buildings with average subsidence rate below 5 mm/year | 27 | 22 | 12 | 61 (71.8%) |

Locations with differential subsidence (>5 mm/year) | 30 | 27 | 15 | 72 (84.7%) |

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**MDPI and ACS Style**

de Wit, K.; Lexmond, B.R.; Stouthamer, E.; Neussner, O.; Dörr, N.; Schenk, A.; Minderhoud, P.S.J. Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations. *Remote Sens.* **2021**, *13*, 189.
https://doi.org/10.3390/rs13020189

**AMA Style**

de Wit K, Lexmond BR, Stouthamer E, Neussner O, Dörr N, Schenk A, Minderhoud PSJ. Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations. *Remote Sensing*. 2021; 13(2):189.
https://doi.org/10.3390/rs13020189

**Chicago/Turabian Style**

de Wit, Kim, Bente R. Lexmond, Esther Stouthamer, Olaf Neussner, Nils Dörr, Andreas Schenk, and Philip S. J. Minderhoud. 2021. "Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations" *Remote Sensing* 13, no. 2: 189.
https://doi.org/10.3390/rs13020189