# On the Characterization and Forecasting of Ground Displacements of Ocean-Reclaimed Lands

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## Abstract

**:**

## 1. Introduction

^{2}from 1985 to 2010 [9]. Shenzhen, as an special economic zone, located on the south coast of China, has also reclaimed about 100 km

^{2}of lands from the ocean since 1979 [3]. Based on the engineering and geological properties of the materials used for the reclamation projects, the ground subsidence can be distinguished into three stages. Among them, the secondary compression stage of the alluvial deposits is the indispensable observation stage, which can last for more than ten years after the end of reclamation projects. Interestingly, earth observation (EO) systems have monitored the ongoing surface displacement phenomena for dozens of years [10]. Therefore, synthetic aperture radar (SAR) datasets acquired by different satellites provide us with an excellent opportunity to recover the spatial-temporal evolution of ground deformation of the reclaimed lands.

## 2. Study Areas and Datasets

#### 2.1. Lingang New City of Shanghai

^{2}of new land for Lingang, which accounts for about 42% of the total area in Lingang New City. In addition to the previous area formed by sediment deposition before 1994, Lingang New City covers a total area of 315 km

^{2}in the southeast corner of Pudong New District of Shanghai.

#### 2.2. Shenzhen

^{2}of lands have been reclaimed in Shenzhen since 1979 [3,36]. The consolidation settlement of the reclamation area also brings potential public safety risks to the Shenzhen coastal area.

## 3. InSAR Algorithms for the Estimation of the Ground Displacement over Ocean-Reclaimed Lands

## 4. Ground Deformation Results of Shanghai and Analyses

#### 4.1. RST-2 Ground Deformation Time-Series: As the Third Party Inspection Data

**P**into the overlapped spatial region between the two processed CSK and RST-2 slices (see Figure 1a), ${\mathit{d}}_{\mathit{R}\mathit{S}\mathit{T}\mathbf{-}\mathbf{2}}^{\mathbf{\sim}}\mathbf{\left(}\mathit{P}\mathbf{,}\mathit{t}\mathbf{\right)}$ is the adjusted RST-2 ground-deformation time-series in the same point

**P**, and ${\mathit{d}}_{\mathit{R}\mathit{S}\mathit{T}\mathbf{-}\mathbf{2}}\mathbf{\left(}\mathit{P}\mathbf{,}\mathit{t}\mathbf{\right)}$ is the corresponding, original RST-2 ground deformation time-series. Moreover, we calculated, for every point

**P**, the unique value of the ground-deformation bias

**Δ**that minimizes $\Vert {d}_{CSK}-{d}_{RST-2}^{~}{\Vert}_{2}$. The mean value of root mean square error (RMSE) between the adjusted CSK and RST-2 ground deformation time-series, calculated at common coherent points between ${d}_{CSK}$ and ${d}_{RST2}^{~}$, was estimated. Its average value over the scene is about 3.5 mm, which is in agreement with the expected accuracy of SBAS measurements, which is in the order of 5 mm for the ground-deformation time-series [43]. We also cross-compared the (vertical) mean ground deformation velocity values over the common coherent points of the RST-2 and CSK datasets, considering the overlapped period from December 2013 to March 2016. A consistency analysis between the two velocities maps was performed by least-square estimation using the total shared high-coherent pixels. The scatter plot is shown in Figure 6, which also portrays the corresponding mathematical expressions of the regression line between the RST-2 and the CSK ground-deformation velocities. Considering also the values of the ground deformation mean velocity, the agreement of the adjusted independent CSK and RST-2 SAR datasets is consistent.

#### 4.2. Analysis of Time-Gapped ENV+CSK and RST-2 Ground Displacement Time-Series

**t**is the vector of the ordered times of the available SAR acquisitions,

**S**is the asymptotic value of vertical deformation (theoretically assumed at infinite time),

_{0}**k**and $\mathit{\lambda}$ are two parameters that control the shape of a given curve among the family of all possible curves, and $\mathit{\delta}$ is a time-delay, which takes into account the uncertainty of the knowledge of the exact time when reclamation processes ended. Figure 7 reproduces the ground mean deformation velocity map of the Lingang New City from February 2007 to March 2016, as obtained by using the model (5) and the method to link time-gapped data presented in [23]. Note also that the model (5) represents a generalization of the model adopted in [46]. The RST-2 SAR data, however, partially time-overlap the CSK data sets (see the red region highlighted in Figure 4). This circumstance has led us to the idea of testing the validity of the retrieved best-fit models using this new independent set of RST-2 data. To this aim, we estimated and compensated, for every coherent point common to the ENV+CSK and RST-2 datasets, the ground deformation bias, namely $\mathsf{\Omega}$, existing between the best-fit model ${d}_{model}$ [23] and the new RST-2 ground deformation time-series in the period from January 2012 and December 2013. It is evident (see also Figure 4) that:

#### 4.3. Engineering Geology Analyses

## 5. Ground Deformation Results of Shenzhen and Analyses

^{2}(as shown in Figure 2a), has been modified in its geomorphology [3,36] due to land reclamation activities. Thus, they are anthropogenic at most [49,50]. In this section, we test the validity of such different reclamation models in the Shenzhen area. To this aim, three SAR datasets (the ascending ENV, the descending ENV, and the S1A) were used.

#### 5.1. The Ground Deformation Results of Shenzhen City

#### 5.2. Comparison and Verification of the Geotechnical Model and Other Deformation Models in Shenzhen Reclamation Area

**H**is the initial thickness of the soil element or layer which has an initial void ratio

_{0}**e**,

_{0}**t**is the time corresponding to the completion of primary consolidation, and

_{0}**t**is any time beyond primary consolidation. From Equation (8), we can derive the following simplified logarithmic model:

**S**(

**t**) is the dynamic ground deformation at time

**t**calculated with respect to the reference at the starting time

**t**= 0 (i.e.,

_{0}**S**(

**t**= 0) ≡0), and

_{0}**a**represents the shape parameters of the logarithmic curve.

**S**(

**t**) is the dynamic ground deformation at time

**t**with respect to the reference at the starting time

**t**= 0 (i.e.,

_{0}**S**(

**t**= 0) ≡0),

_{0}**S**denotes the maximum possible deformation, and

_{max}**S**and

_{max}**b**are the parameters of the used model.

## 6. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**(

**a**) Location of the Lingang New City of Shanghai, the coverages of the ENVISAT ASAR (ENV), Radarsat-2 (RST-2), and COSMO-SkyMed (CSK) synthetic aperture radar (SAR) datasets are depicted by the green, blue, black, and red rectangles, respectively. (

**b**) Temporal distribution of the ENV, RST-2, and CSK datasets.

**Figure 2.**(

**a**) Location of Shenzhen, the coverages of the ascending ENV, descending ENV, and Sentinel-1A (S1A) SAR datasets used for Shenzhen are depicted by the green, blue, black and red rectangles, respectively. (

**b**) Temporal distribution of those three SAR datasets used for Shenzhen.

**Figure 4.**Pictorial representation of the procedure adopted to align the vertical RST-2 ground deformation time-series with that of the ENV+CSK SAR data set.

**Figure 5.**Lingang New City ground deformation. (

**a**) Map of mean velocity calculated with the CSK vertical ground deformation time-series, from December 2013 to March 2016. (

**b**) Map of mean velocity calculated with the RST-2 vertical ground deformation time-series, from January 2012 to October 2016.

**Figure 6.**The comparison of CSK and RST-2 vertical deformation velocities at common coherent pixels. The blue line represents the linear fitting model by considering the 85% shared high-coherent pixels limited to the blue ellipse. The X axis stands for the CSK vertical deformation velocities, and the Y axis stands for the RST-2 vertical deformation velocities.

**Figure 7.**Map of the mean (vertical) deformation velocity from 2007 to 2016 Lingang New City, as retrieved by combining the time-gapped ENV and CSK LOS displacement time-series.

**Figure 8.**Map of RMSE between the adjusted RST-2 ground-deformation time-series and the combined ENV+CSK ground-deformation time-series at the common coherent points in the area of Lingang New City. The plots of the modeled vertical ENV+CSK deformation time-series (blue triangles), the vertical RST-2 deformation time-series (green triangles) from January 2012 to December 2013, and adjusted vertical RST-2 deformation time-series (red triangles), calculated in correspondence with the three pixels, labeled as P1, P2, and P3, respectively. The σ represents the RMSE between the modeled and adjusted time-series. The cumulative deformation at pixels P1, P2, and P3 is −118 mm, −134 mm, and −267 mm, respectively, from February 2007 to October 2016.

**Figure 9.**Lingang New City. Map of three ocean-reclaimed zones and the location of eight boreholes (blue circles) along the profile (red line) from I to I’ considered for the presented analyses.

**Figure 10.**Sub-soil characteristics of the Lingang New City area. (

**a**) Scatter diagram of the absolute value of the difference between the ENV+CSK velocities and RST-2 velocities over the I-I’ profile. The average absolute value of the difference between the modeled and the measured RST-2 mean ground- deformation values were also calculated, and it equals 0.89 mm/year. (

**b**) Plots of the ENV+CSK ground deformation velocities and the measured RST-2 ground deformation velocities over the I-I’ profile. (

**c**) The profile of engineering geologic layers from I to I’ (refer to [47]).

**Figure 11.**Shenzhen area results. Maps of ascending (

**left**) and descending (

**right**) mean deformation velocities retrieved by the ascending and descending ENV datasets. Only well-processed coherent SAR pixels exhibiting a temporal coherence larger than 0.6 were considered.

**Figure 12.**Shenzhen area results. Maps of Up–Down (

**left**) and East–West (

**right**) mean deformation velocities retrieved by the ascending and descending ENV deformation time-series obtained using the MinA method.

**Figure 13.**Shenzhen area results. (

**a**) Map of masked Up–Down ENV deformation velocity from June 2007 to April 2010. (

**b**) Map of Up–Down S1A deformation velocity from June 2019 to June 2020. The area within the blue curve is the reclamation area of Shenzhen.

**Figure 14.**The plots of the ENV vertical deformation time-series (magenta circle) from June 2007 to April 2010 and the S1A vertical deformation time-series (green circle) from June 2019 to June 2020, at four points labeled as P1, P2, P3, and P4.

**Figure 15.**The plots of the ENV (vertical) deformation time-series relative to the four pixels, labeled as P1, P2, P3, and P4, are shown. The ENV deformation time-series are plotted in pink circles, whereas the corresponding best-fit geotechnical models, logarithmic models, and exponential decay models are plotted by continuous red, green, and blue lines, respectively. The black vertical dotted lines correspond to the present stage (July 2020).

**Figure 16.**Shenzhen area results. The difference between the estimated vertical ground deformation obtained by using the geotechnical model and the S1A vertical deformation products, from June 2019 to June 2020.

Zone | Mean RMSE (mm) |
---|---|

A | 1.2 |

B | 3.8 |

C | 5.6 |

**Table 2.**The vertical deformation velocities obtained by the ENV SAR data from 2007 to 2010, the vertical deformation velocities obtained by the S1A SAR data from 2019 to 2020, and the vertical deformation velocities estimated by three models from 2019 to 2020 at four pixels labeled as P1, P2, P3, and P4.

Velocity(mm/Year)\Point | P1 | P2 | P3 | P4 |
---|---|---|---|---|

ENV (2007~2010) | −12.89 | −7.81 | −11.07 | −8.77 |

S1A (2019~2020) | −0.13 | −1.62 | −2.97 | −2.11 |

Geotechnical Model | −0.13 | −0.54 | −1.11 | −0.89 |

Logarithmic Model | −1.54 | −1.44 | −1.57 | −1.46 |

Exponential Model | −0.25 | −0.01 | −0.07 | −0.02 |

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## Share and Cite

**MDPI and ACS Style**

Ding, J.; Zhao, Q.; Tang, M.; Calò, F.; Zamparelli, V.; Falabella, F.; Liu, M.; Pepe, A. On the Characterization and Forecasting of Ground Displacements of Ocean-Reclaimed Lands. *Remote Sens.* **2020**, *12*, 2971.
https://doi.org/10.3390/rs12182971

**AMA Style**

Ding J, Zhao Q, Tang M, Calò F, Zamparelli V, Falabella F, Liu M, Pepe A. On the Characterization and Forecasting of Ground Displacements of Ocean-Reclaimed Lands. *Remote Sensing*. 2020; 12(18):2971.
https://doi.org/10.3390/rs12182971

**Chicago/Turabian Style**

Ding, Jingzhao, Qing Zhao, Maochuan Tang, Fabiana Calò, Virginia Zamparelli, Francesco Falabella, Min Liu, and Antonio Pepe. 2020. "On the Characterization and Forecasting of Ground Displacements of Ocean-Reclaimed Lands" *Remote Sensing* 12, no. 18: 2971.
https://doi.org/10.3390/rs12182971