![Progress of the land reclamation project between (a) December 2010 and (b) December 2020 [20].](https://www.researchgate.net/publication/367173006/figure/fig3/AS:11431281211876664@1702504163851/Progress-of-the-land-reclamation-project-between-a-December-2010-and-b-December-2020_Q320.jpg)
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Citation: Tu, M.-c.; Huang, Y.-c.
Impact of Land Reclamation on
Coastal Water in a Semi-Enclosed Bay.
Remote Sens. 2023,15, 510. https://
doi.org/10.3390/rs15020510
Academic Editors: Weiwei Sun,
Xiangchao Meng, Jiangtao Peng,
Xudong Zhu, Xiyong Hou,
Gang Yang, Jorge Vazquez and
Martin Gade
Received: 9 November 2022
Revised: 11 January 2023
Accepted: 12 January 2023
Published: 14 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
remote sensing
Article
Impact of Land Reclamation on Coastal Water in a
Semi-Enclosed Bay
Min-cheng Tu * and Yu-chieh Huang
Department of Civil Engineering, National Taipei University of Technology, Taipei 106, Taiwan
*Correspondence: mtu@ntut.edu.tw
Abstract:
Land reclamation has a profound impact on coastal environments. On the Chinese coast,
the new Xiang’an International Airport has been built on newly reclaimed land. The impact of the
massive land reclamation project (finished in 2018) on water quality and coast conditions in a nearby
semi-enclosed bay is investigated using remotely sensed data. Factors affecting surface water quality
and coast conditions are further analyzed using multiple regression. All water quality and coast
condition indices show no long-term trend from 2005 to 2021. The suspended solid concentration
(with a maximum value of 96.11 mg/L) is much lower than the threshold of 188 mg/L. When
considering variations in sediment concentration, the probability that the concentration reaches the
threshold is less than 1
×
10
−6
%; therefore, suspended solids have little threat to the local oyster-
growing industry. The trend of dissolved inorganic nitrogen concentration is steady, implying
little alteration to nutrient circulation in the semi-enclosed bay. Within the observation timeframe
of
2005–2021
, a recent sedimentation trend (surrogated by the normalized difference water index)
appears after 2018 but it needs to be confirmed by a longer observation. Statistical models based on
multiple regression highlight the following links: (1) the sediment source is outside the bay, (2) the
overland runoff from newly claimed land dilutes nutrient concentrations, and (3) the coast conditions
are mainly affected by tides and rainfall. Neither actively reclaimed or cumulative reclaimed areas
form a direct causal relationship to water quality or coast conditions in the semi-enclosed bay.
Keywords: DIN; Kinmen; land reclamation; oyster; semi-enclosed bay; TSS; Xiang’an
1. Introduction
The quality of water in bays and estuaries is important because a large portion of
human settlements are near the coast. The impact of human activities on bays and estuaries
has been a hot topic in research [
1
]. Focus has been on how inland pollution sources can
affect the water quality of bays and estuaries, and how proper management can mitigate
such an impact [2,3].
On the other hand, land reclamation threatens the water quality of bays and estuaries in
different ways. Dredging of the bottom material directly resuspends sediment particles [
4
].
Drainage of water from the dredged material is required for the material to settle, which
can return a significant amount of sediment to the water [
5
]. Underwater explosions
also cause sediment disturbances, polluting water up to 100 km
2
with high sediment
concentrations [
6
]. Reclaimed land can interfere with the hydrodynamic circulation of bays
and estuaries, thus deteriorating water quality [
7
]. Since heavy metal is often absorbed
by sediments, disturbance by land reclamations was also found to increase heavy metal
concentrations in water [8].
Dredging is a means used by most land reclamation projects to acquire a steady supply
of fill material. Dredging causes water quality issues as mentioned above, and can actively
cause shorelines to retreat. The part of the shore near the dredging location can show
accretion, while the part adjacent to the accretion point is eroded, resulting in net erosion
Remote Sens. 2023,15, 510. https://doi.org/10.3390/rs15020510 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2023,15, 510 2 of 19
of the coast [
9
]. Demir et al. [
9
] found that the shallow coast is the most vulnerable to
deterioration by dredging.
In addition to the above issues, land reclamation deteriorates the marine ecosystem.
The higher concentration of sediment attenuates the penetration of light into the water
column, which interferes with the growth of the benthic flora [
10
]. Underwater explosions
have a serious impact on marine life up to a distance of several hundred meters [
6
]. Wet-
lands and mangrove forests are decimated by land reclamation, leading to a significant loss
of habitat and biodiversity. All of these lead to a reduction in ecosystem services, leading
to a higher tendency of floods and geological disasters (for example, land subsidence and
soil liquefaction) in coastal communities [11,12].
Evaluating the influence of land reclamation projects is a difficult task. Comprehensive
modeling including hydrodynamic and hydro-biogeochemical simulations is required,
but not all land reclamation projects receive equal attention from the scientific society.
Kinmen, an island that borders the Chinese coast, is located a few kilometers away from
a new airport (Xiang’an Airport) built on reclaimed land (approximate location marked
in Figure 1). Despite the sheer scale of the project, only one hydrodynamic simulation
has been performed so far [
5
]. Mao and Hong [
5
] found that most sediment drained from
the reclamation site is unlikely to enter the semi-enclosed bay between Kinmen and the
airport, but the tidal flux is likely to increase in the semi-enclosed bay. The study by Mao
and Hong used readily available bathymetric data and, therefore, did not consider the
bottom topography changed by dredging. In addition to this study, no hydrodynamic or
hydro-biogeochemical simulations have been performed for the semi-enclosed bay between
Kinmen and Xiang’an Airport.
Remote Sens. 2023, 15, x FOR PEER REVIEW 2 of 19
show accretion, while the part adjacent to the accretion point is eroded, resulting in net
erosion of the coast [9]. Demir et al. [9] found that the shallow coast is the most vulnerable
to deterioration by dredging.
In addition to the above issues, land reclamation deteriorates the marine ecosystem.
The higher concentration of sediment attenuates the penetration of light into the water
column, which interferes with the growth of the benthic flora [10]. Underwater explosions
have a serious impact on marine life up to a distance of several hundred meters [6]. Wet-
lands and mangrove forests are decimated by land reclamation, leading to a significant
loss of habitat and biodiversity. All of these lead to a reduction in ecosystem services,
leading to a higher tendency of floods and geological disasters (for example, land subsid-
ence and soil liquefaction) in coastal communities [11,12].
Evaluating the influence of land reclamation projects is a difficult task. Comprehen-
sive modeling including hydrodynamic and hydro-biogeochemical simulations is re-
quired, but not all land reclamation projects receive equal attention from the scientific
society. Kinmen, an island that borders the Chinese coast, is located a few kilometers away
from a new airport (Xiang’an Airport) built on reclaimed land (approximate location
marked in Figure 1). Despite the sheer scale of the project, only one hydrodynamic simu-
lation has been performed so far [5]. Mao and Hong [5] found that most sediment drained
from the reclamation site is unlikely to enter the semi-enclosed bay between Kinmen and
the airport, but the tidal flux is likely to increase in the semi-enclosed bay. The study by
Mao and Hong used readily available bathymetric data and, therefore, did not consider
the bottom topography changed by dredging. In addition to this study, no hydrodynamic
or hydro-biogeochemical simulations have been performed for the semi-enclosed bay be-
tween Kinmen and Xiang’an Airport.
Figure 1. The geographical location of Kinmen with the location of Xiang’an Airport before recla-
mation of the land.
Despite the lack of academic studies, many news articles [13,14] alleged a causal re-
lationship that the land reclamation project had caused the Kinmen coast to change and
the oyster-growing areas of northern Kinmen to shrink. To gain more insight into this
matter, the current study aims to provide an empirical analysis based on remotely sensed
satellite data to investigate the statistical relations linking the progress of the land recla-
mation project with water quality and coast condition in the semi-enclosed bay in north-
ern Kinmen. Remote sensing is an excellent tool with high temporal resolution and wide
spatial coverage. The suspended solid is the water quality constituent most commonly
detected using remote sensing by measuring the light reflected by particles in water. Other
water quality constituents, such as nitrogen and phosphorous concentrations, are also
commonly detected by remote sensing by detecting optically active constituents (such as
phytoplankton) highly correlated with the water quality constituents of interest [15]. The
results of the current study also contribute to our understanding of the integrated coastal
Figure 1.
The geographical location of Kinmen with the location of Xiang’an Airport before reclama-
tion of the land.
Despite the lack of academic studies, many news articles [
13
,
14
] alleged a causal
relationship that the land reclamation project had caused the Kinmen coast to change
and the oyster-growing areas of northern Kinmen to shrink. To gain more insight into
this matter, the current study aims to provide an empirical analysis based on remotely
sensed satellite data to investigate the statistical relations linking the progress of the land
reclamation project with water quality and coast condition in the semi-enclosed bay in
northern Kinmen. Remote sensing is an excellent tool with high temporal resolution and
wide spatial coverage. The suspended solid is the water quality constituent most commonly
detected using remote sensing by measuring the light reflected by particles in water. Other
water quality constituents, such as nitrogen and phosphorous concentrations, are also
commonly detected by remote sensing by detecting optically active constituents (such as
phytoplankton) highly correlated with the water quality constituents of interest [
15
]. The
results of the current study also contribute to our understanding of the integrated coastal
Remote Sens. 2023,15, 510 3 of 19
management of semi-enclosed coastlines where extensive land reclamation projects are
taking place in East Asia and beyond (e.g., in the Gulf States).
2. Regional Description
Kinmen is an island located on the southeast coast of China (Figure 1), only about
10 km from the major Chinese city of Xiamen. The shortest distance from Kinmen to
Chinese-controlled land is only 1.8 km. Due to its proximity to Chinese territory, it expe-
rienced several major battles during the Chinese Civil War. Kinmen has been controlled
by Taiwan as an outpost since then. Kinmen’s land area is 151
km2
with a population of
approximately 141,000 [
16
]. Kinmen’s main economic activities are tourism (42.15% of
all productivity) and iconic local products related to tourism (55.68% of all productivity),
including liquors, candies, ceramics, herbal medicines, cutting utensils, noodles, etc. [17].
Kinmen’s climate is marine subtropical with a high temperature of around 37
◦
C in the
summer and a low temperature of around 4
◦
C in winter. The annual mean temperature is
approximately 21
◦
C. Rainfall is relatively scarce because the island lacks high mountains
(the highest elevation is only 253 m). The annual rainfall is around 1000 mm, which is less
than half of that of Taiwan [18].
Oyster growth is an important fishing activity in Kinmen. Most oyster-growing areas
are located in shallow water in the bay in northern Kinmen, and the five most important
are delineated in Figure 2(coded A-E in the figure). The annual productivity of oysters
is approximately 22 million NTD (about 733 thousand USD), about 30% of all fishery
productivity in Kinmen [19].
Remote Sens. 2023, 15, x FOR PEER REVIEW 3 of 19
management of semi-enclosed coastlines where extensive land reclamation projects are
taking place in East Asia and beyond (e.g., in the Gulf States).
2. Regional Description
Kinmen is an island located on the southeast coast of China (Figure 1), only about 10
km from the major Chinese city of Xiamen. The shortest distance from Kinmen to Chinese-
controlled land is only 1.8 km. Due to its proximity to Chinese territory, it experienced
several major battles during the Chinese Civil War. Kinmen has been controlled by Tai-
wan as an outpost since then. Kinmen’s land area is 151 km with a population of ap-
proximately 141,000 [16]. Kinmen’s main economic activities are tourism (42.15% of all
productivity) and iconic local products related to tourism (55.68% of all productivity), in-
cluding liquors, candies, ceramics, herbal medicines, cutting utensils, noodles, etc. [17].
Kinmen’s climate is marine subtropical with a high temperature of around 37 ℃ in
the summer and a low temperature of around 4 ℃ in winter. The annual mean tempera-
ture is approximately 21 ℃. Rainfall is relatively scarce because the island lacks high
mountains (the highest elevation is only 253 m). The annual rainfall is around 1000 mm,
which is less than half of that of Taiwan [18].
Oyster growth is an important fishing activity in Kinmen. Most oyster-growing areas
are located in shallow water in the bay in northern Kinmen, and the five most important
are delineated in Figure 2 (coded A-E in the figure). The annual productivity of oysters is
approximately 22 million NTD (about 733 thousand USD), about 30% of all fishery
productivity in Kinmen [19].
Figure 2. Important oyster-growing areas (A-E) in northern Kinmen.
Due to the rapid economic growth of China in recent years, the coastal regions of
China have seen widespread urbanization, while the island of Kinmen still retains its nat-
ural appearance. One of such main development activities is the construction of Xiang’an
Airport on newly reclaimed land. In addition to water quality issues, dredging for land
reclamation is also considered detrimental to coast stability [9]. Figure 3 shows the pro-
gress of the land reclamation project from 2010 to 2020. A total of 25.58 km2 of land was
reclaimed in the process.
Figure 2. Important oyster-growing areas (A–E) in northern Kinmen.
Due to the rapid economic growth of China in recent years, the coastal regions of China
have seen widespread urbanization, while the island of Kinmen still retains its natural
appearance. One of such main development activities is the construction of Xiang’an
Airport on newly reclaimed land. In addition to water quality issues, dredging for land
reclamation is also considered detrimental to coast stability [
9
]. Figure 3shows the progress
of the land reclamation project from 2010 to 2020. A total of 25.58 km
2
of land was reclaimed
in the process.
Remote Sens. 2023,15, 510 4 of 19
Remote Sens. 2023, 15, x FOR PEER REVIEW 4 of 19
Figure 3. Progress of the land reclamation project between (a) December 2010 and (b) December
2020 [20].
3. Data Availability
Data used by the current study are obtained from several sources. Water quality data
near the island of Kinmen is downloaded for the time frame 2005–2021 from the iOcean
website [21]. The time frame covers all stages of the land reclamation project (2010–2018)
and provides sufficient baseline information before and after the project. There are three
sampling sites near the coast of Kinmen (Figure 4), and in this study, the one in the semi-
enclosed bay with coordinates of (24.467°N, 118.352°E) is chosen. The current study focuses
only on this area as it is the area most likely to be affected by the land reclamation project.
Figure 4. Location of water quality sampling sites near the main island of Kinmen.
Level-2 Landsat 5/7 reflectance data are downloaded from EarthExplorer [22]. In-
stead of the newer Landsat 8/9 data, Landsat 5/7 data is used because Landsat 5/7 covers
the whole timeframe of 2005–2021, while Landsat 8 did not start working until 2013. Since
Landsat 8 and Landsat 5/7 have different band designations, the current study adopts only
data from Landsat 5/7. Collection 2 Level-2 data are used because they have been cor-
rected for atmospheric gases, aerosols, water vapor, and surface characteristics before be-
ing released [23] and thus are ready to be used.
Regarding the actual schedule of the land reclamation project, there is no official
channel to download or request this information from the Chinese government. The only
Figure 3.
Progress of the land reclamation project between (
a
) December 2010 and (
b
) December
2020 [20].
3. Data Availability
Data used by the current study are obtained from several sources. Water quality data
near the island of Kinmen is downloaded for the time frame 2005–2021 from the iOcean
website [
21
]. The time frame covers all stages of the land reclamation project (2010–2018)
and provides sufficient baseline information before and after the project. There are three
sampling sites near the coast of Kinmen (Figure 4), and in this study, the one in the semi-
enclosed bay with coordinates of (24.467
◦
N, 118.352
◦
E) is chosen. The current study focuses
only on this area as it is the area most likely to be affected by the land reclamation project.
Remote Sens. 2023, 15, x FOR PEER REVIEW 4 of 19
Figure 3. Progress of the land reclamation project between (a) December 2010 and (b) December
2020 [20].
3. Data Availability
Data used by the current study are obtained from several sources. Water quality data
near the island of Kinmen is downloaded for the time frame 2005–2021 from the iOcean
website [21]. The time frame covers all stages of the land reclamation project (2010–2018)
and provides sufficient baseline information before and after the project. There are three
sampling sites near the coast of Kinmen (Figure 4), and in this study, the one in the semi-
enclosed bay with coordinates of (24.467°N, 118.352°E) is chosen. The current study focuses
only on this area as it is the area most likely to be affected by the land reclamation project.
Figure 4. Location of water quality sampling sites near the main island of Kinmen.
Level-2 Landsat 5/7 reflectance data are downloaded from EarthExplorer [22]. In-
stead of the newer Landsat 8/9 data, Landsat 5/7 data is used because Landsat 5/7 covers
the whole timeframe of 2005–2021, while Landsat 8 did not start working until 2013. Since
Landsat 8 and Landsat 5/7 have different band designations, the current study adopts only
data from Landsat 5/7. Collection 2 Level-2 data are used because they have been cor-
rected for atmospheric gases, aerosols, water vapor, and surface characteristics before be-
ing released [23] and thus are ready to be used.
Regarding the actual schedule of the land reclamation project, there is no official
channel to download or request this information from the Chinese government. The only
Figure 4. Location of water quality sampling sites near the main island of Kinmen.
Level-2 Landsat 5/7 reflectance data are downloaded from EarthExplorer [
22
]. Instead
of the newer Landsat 8/9 data, Landsat 5/7 data is used because Landsat 5/7 covers the
whole timeframe of 2005–2021, while Landsat 8 did not start working until 2013. Since
Landsat 8 and Landsat 5/7 have different band designations, the current study adopts
only data from Landsat 5/7. Collection 2 Level-2 data are used because they have been
corrected for atmospheric gases, aerosols, water vapor, and surface characteristics before
being released [23] and thus are ready to be used.
Regarding the actual schedule of the land reclamation project, there is no official
channel to download or request this information from the Chinese government. The only
Remote Sens. 2023,15, 510 5 of 19
piece of information available is the on-site billboard [
24
]. The total reclaimed area of 25.58
km2is divided into the phases described in Table 1.
Table 1. Main phases of land reclamation for Xiang’an Airport.
Description Active Area (km2)Timeframe
1st Phase 3 May 2010–April 2016
2nd Phase 7.58 October 2013–April 2017
3rd Phase 15 July 2016 *–December 2018
* Only specified as “the latter half of 2016”, so July 2016 is assumed.
4. Methodology and Data Analysis
4.1. Derivation of Predictive Statistical Models
Landsat images are used to determine the surface water quality of the entire bay with
high spatial and temporal resolutions. Multiple regression-based statistical models are
derived to calculate the surface concentration of water constituents from combinations of
band reflectance. Following the recommendations of Tu et al. [
25
], environmental factors
in Table 2are included to increase accuracy by accounting for the influence of the gap
between water sampling and image dates, energy transfer between water and air, wind
speed, and solar radiation. Table 2shows all initial predictor (independent) variables in the
forward-selection variable selection process.
Table 2. List of predictor variables initially used in the variable selection process.
Variable Definition Unit Symbol
Image date—sampling date Days Ddi f f
Water surface temperature °C Ts
Air temperature °C Ta
Mean air temperature
between image and
sampling dates
°C Tmean
Difference between the water
surface and air temperature °C Ts−Ta
Difference between the water
surface and mean air
temperature
°C Ts−Tme an
Instantaneous wind speed m/s U
Mean wind speed between
image and sampling dates m/s Umean
Instantaneous solar radiation MJ/m2SR
Band reflectance n/a B1, B2, B3, B4, B5, B7
Band ratio n/a
B3/B1, B2/B1, B4/B1, B5/B1,
B7/B1, B3/B2, B4/B2, B5/B2,
B7/B2, B4/B3, B5/B3, B7/B3,
B5/B4, B7/B4, B7/B5
The relationship between band reflectance and water quality is valid only when the
bottom reflection is negligible [
25
]. Bottom reflection should not be a concern because
the Secchi disk depth (
∼
0.6 m based on the mean Total Suspended Solid (TSS) concentra-
tion) [
26
] is lower than the mean water depth at the sampling site (
∼
1 m) [
27
]. To ensure
sufficient depth of water, only images with a water level higher than the mean value (~3 m)
during satellite flyovers are chosen [
28
]. All Landsat images taken
±
7 days from the water
Remote Sens. 2023,15, 510 6 of 19
sampling dates with water level >3 m are divided into a calibration group and a validation
group provided by Tables 3and 4, respectively.
Table 3.
Summary of Landsat images used in the calibration of the regression model (zero water
level = 3.36 m below the mean sea level).
Satellite Name Image Date Sampling Date Water Level at Image
Acquisition (m)
Landsat 7 22 February 2006 16 February 2006 3.53
Landsat 7 14 August 2008 18 August 2008 5.25
Landsat 5 10 November 2008 12 November 2008 4.95
Landsat 7 16 May 2010 17 May 2010 3.83
Landsat 7 28 February 2011 1 March 2011 4.39
Landsat 7 21 May 2012 28 May 2012 4.61
Landsat 7 31 October 2013 28 October 2013 5.13
Landsat 7 22 January 2015 29 January 2015 3.06
Landsat 7 25 January 2016 18 January 2016 5.10
Landsat 7 20 August 2016 15 August 2016 3.08
Landsat 7 10 August 2018 15 August 2018 5.31
Landsat 7 24 March 2020 17 March 2020 4.44
Landsat 7 14 May 2021 11 May 2021 3.75
Table 4.
Summary of Landsat images used in the validation of the regression model (zero water
level = 3.36 m below the mean sea level).
Satellite Name Image Date Sampling Date Water Level at Image
Acquisition (m)
Landsat 5 22 February 2006 16 February 2006 3.53
Landsat 7 20 February 2008 19 February 2008 5.15
Landsat 5 28 October 2009 2 November 2009 4.42
Landsat 7 8 November 2010 10 November 2010 3.87
Landsat 5 15 August 2011 15 August 2011 4.32
Landsat 7 7 August 2017 4 August 2017 3.98
Landsat 7 13 August 2019 15 August 2019 5.28
Landsat 7 27 March 2021 24 March 2021 5.36
Landsat 7 19 September 2021 13 September 2021 5.58
The forward-selection stepwise regression method is used in building the statistical
model based on multiple regression. Minimal Akaike Information Criterion (AIC) is
considered when choosing variables. At each step of the selection of variables, only the
variable that decreases the most AIC enters the model. A model with minimal AIC explains
the most variation with the least number of predictive variables. AIC is a commonly used
criterion and is often superior to other selection methods [
29
]. The Variation Inflation Factor
(VIF) of each selected variable is monitored to keep multicollinearity in check.
The concentrations of two water constituents (TSS and dissolved inorganic nitrogen
(DIN)) are the focus. The parameters of the predictive statistical models for TSS and
DIN are provided in Tables 5and 6, respectively. Note that the dependent variables are
transformed. The calibration accuracy (represented by the R
2
value) is 0.84 and 0.97 for
TSS and DIN, respectively. The validation accuracy is 0.90 and 0.58 for TSS and DIN,
Remote Sens. 2023,15, 510 7 of 19
respectively. Most accuracy values are considered “Very Good” (defined as R
2
> 0.80 for
sediment or
R2> 0.70
for nitrogen) according to Moriasi et al. [
30
]. The only exception is
the satisfactory performance (defined as 0.3 < R
2≤
0.60) of the DIN validation accuracy.
Scatter plots of measured and predicted TSS and DIN concentrations are provided in
Figures 5and 6, respectively.
Table 5. Statistical model to predict TSS concentration from remotely sensed data.
Dependent
Variable ln(TSS)
Intercept Ddiff Tmean Ts−TaUmean B7/B3
p-value 0.0005 * 0.039 * 0.020 * 0.053 0.15 0.008 *
Coefficient 5.08 −0.057 −0.062 −0.11 −0.26 −3.74
95% CI (3.10,
7.06)
(−0.11,
−0.0040)
(−0.11,
−0.013)
(−0.23,
0.0018)
(−0.64,
0.12)
(−6.16,
−1.33)
VIF - 1.22 1.57 1.13 1.69 1.46
* Statistically significant.
Table 6. Statistical model to predict DIN concentration from remotely sensed data.
Dependent
Variable √DIN
Intercept Ddiff TaSR B2/B1
p-value 0.0005 * 0.018 * 0.12 0.012 * 0.0089 *
Coefficient 1.77 −0.015 −0.010 −0.028 −0.54
95% CI (1.30, 2.24) (-0.026,
−0.0044)
(−0.026,
0.0045)
(−0.046,
−0.010) (-0.86, −0.23)
VIF - 1.32 5.03 4.38 1.19
* Statistically significant.
Remote Sens. 2023, 15, x FOR PEER REVIEW 8 of 19
Figure 5. Scatter plots of observed and predicted TSS concentrations for (a) calibration and (b) vali-
dation (1:1 line provided).
Figure 6. Scatter plots of observed and predicted DIN concentrations for (a) calibration and (b) val-
idation (1:1 line provided).
Figure 5.
Scatter plots of observed and predicted TSS concentrations for (
a
) calibration and (
b
) vali-
dation (1:1 line provided).
Remote Sens. 2023,15, 510 8 of 19
Remote Sens. 2023, 15, x FOR PEER REVIEW 8 of 19
Figure 5. Scatter plots of observed and predicted TSS concentrations for (a) calibration and (b) vali-
dation (1:1 line provided).
Figure 6. Scatter plots of observed and predicted DIN concentrations for (a) calibration and (b) val-
idation (1:1 line provided).
Figure 6.
Scatter plots of observed and predicted DIN concentrations for (
a
) calibration and (
b
) vali-
dation (1:1 line provided).
The variables chosen in the final models show the importance of environmental factors
in the determination of water quality constituents using remotely sensed data. Inclusion
of temporal differences in both models is deemed to increase accuracy. To predict the TSS
concentration, mean air temperature is another important environmental factor, which
might be related to water column convection caused by energy transfer between air and
water. It was found to alter the convective mass flux up to three orders of magnitude [
31
].
Algae and phytoplankton, which are regulated by solar radiation, are closely related to
nitrogen concentration, and this interaction is shown in Table 6.
Utilizing the derived statistical models, Figures 7and 8provide examples of TSS and
DIN concentration distributions, respectively, based on Landsat-7 imagery of 8/10/2018.
In Figures 7and 8, white-out masks indicate the effect of the failed Landsat-7 scan line
corrector (SLC), reclaimed land, cloud cover, or cloud shadow [
32
]. Pollutant plumes can
be distinguished around the island in Figures 7and 8, with one TSS plume particularly
distinguishable in the north of the island (at approximately 24
◦
30
0
N, 118
◦
21
0
E). It was
possibly caused by dredging activities, but the sediment appears to redeposit quickly so
it did not affect water quality at the shore. Because no DIN plume is found in the same
area, it can be concluded that the bottom material does not contain a lot of nutrients.
Figures 7and 8
also show great variation at different parts of the island’s shore, which is
a good demonstration of why remote sensing techniques were chosen by many studies
similar to the present one.
Remote Sens. 2023,15, 510 9 of 19
Remote Sens. 2023, 15, x FOR PEER REVIEW 9 of 19
Figure 7. Distribution of TSS concentration on 10 August 2018 derived by the model of Table 5.
Figure 8. Distribution of DIN concentration on 10 August 2018 derived by the model of Table 6.
4.2. Temporal Variation of Water Quality
To delineate the variation of the concentration of water quality constituents, Landsat
images are selected for each season (that is, January–March, April–June, July–September,
and October–December) from 2005 to 2021. The water level of all selected images at the
flyover is higher than the mean water level of 3 m. A total of 54 images are chosen.
The distribution of the concentration of surface water quality constituents can be cal-
culated for the 54 images utilizing the statistical models in Tables 5 and 6. The mean and
maximum concentrations of TSS and DIN in the five oyster-growing areas from 2005 to
2021 are provided in Figure 9 and Figure 10, respectively. Note that only an area with a
mean depth > 1 m is included in the calculation of the mean and maximum concentration.
Figure 7. Distribution of TSS concentration on 10 August 2018 derived by the model of Table 5.
Remote Sens. 2023, 15, x FOR PEER REVIEW 9 of 19
Figure 7. Distribution of TSS concentration on 10 August 2018 derived by the model of Table 5.
Figure 8. Distribution of DIN concentration on 10 August 2018 derived by the model of Table 6.
4.2. Temporal Variation of Water Quality
To delineate the variation of the concentration of water quality constituents, Landsat
images are selected for each season (that is, January–March, April–June, July–September,
and October–December) from 2005 to 2021. The water level of all selected images at the
flyover is higher than the mean water level of 3 m. A total of 54 images are chosen.
The distribution of the concentration of surface water quality constituents can be cal-
culated for the 54 images utilizing the statistical models in Tables 5 and 6. The mean and
maximum concentrations of TSS and DIN in the five oyster-growing areas from 2005 to
2021 are provided in Figure 9 and Figure 10, respectively. Note that only an area with a
mean depth > 1 m is included in the calculation of the mean and maximum concentration.
Figure 8. Distribution of DIN concentration on 10 August 2018 derived by the model of Table 6.
4.2. Temporal Variation of Water Quality
To delineate the variation of the concentration of water quality constituents, Landsat
images are selected for each season (that is, January–March, April–June, July–September,
and October–December) from 2005 to 2021. The water level of all selected images at the
flyover is higher than the mean water level of 3 m. A total of 54 images are chosen.
The distribution of the concentration of surface water quality constituents can be
calculated for the 54 images utilizing the statistical models in Tables 5and 6. The mean and
maximum concentrations of TSS and DIN in the five oyster-growing areas from 2005 to
2021 are provided in Figures 9and 10, respectively. Note that only an area with a mean
depth > 1 m is included in the calculation of the mean and maximum concentration.
Remote Sens. 2023,15, 510 10 of 19
Remote Sens. 2023, 15, x FOR PEER REVIEW 10 of 19
Figure 9. Seasonal variation of (a) mean and (b) maximum TSS concentration in the five oyster-
growing areas (A–E in Figure 2).
Figure 9.
Seasonal variation of (
a
) mean and (
b
) maximum TSS concentration in the five oyster-
growing areas (A–E in Figure 2).
Remote Sens. 2023, 15, x FOR PEER REVIEW 10 of 19
Figure 9. Seasonal variation of (a) mean and (b) maximum TSS concentration in the five oyster-
growing areas (A–E in Figure 2).
Figure 10. Cont.
Remote Sens. 2023,15, 510 11 of 19
Remote Sens. 2023, 15, x FOR PEER REVIEW 11 of 19
Figure 10. Seasonal variation of (a) mean and (b) maximum DIN concentration in the five oyster-
growing areas (A–E in Figure 2).
4.3. Temporal Variation of the Coastline Recession
The normalized difference water index (NDWI, given by Equation (1)) is used as a
surrogate for the conditions of the coast. In Equation (1), 𝑅 is the reflectance at the
visible green band, and 𝑅 is the reflectance at the near-infrared band.
NDWI = 𝑅 −𝑅
𝑅 +𝑅
(1)
NDWI, which detects the existence of water and is insensitive to TSS concentration
[33–35], has been widely used in coastline determination [36,37]. Decreasing NDWI indi-
cates sedimentation of the beach, while increasing NDWI implies shore erosion. The five
oyster-growing areas are used again as ‘monitoring areas’. To improve the detection sen-
sitivity, only areas with a mean depth of water <1 m are used, as shown in Figure 11 below.
The seasonal mean NDWI (based on the 54 images) in the five monitoring areas is pro-
vided in Figure 12.
Figure 11. Monitoring areas used to detect changes in coastline conditions using NDWI as a surro-
gate.
Figure 10.
Seasonal variation of (
a
) mean and (
b
) maximum DIN concentration in the five oyster-
growing areas (A–E in Figure 2).
4.3. Temporal Variation of the Coastline Recession
The normalized difference water index (NDWI, given by Equation (1)) is used as a
surrogate for the conditions of the coast. In Equation (1),
Rgreen
is the reflectance at the
visible green band, and RNIR is the reflectance at the near-infrared band.
NDWI =Rgreen −RN I R
Rgreen +RN I R
(1)
NDWI, which detects the existence of water and is insensitive to TSS concentration
[33–35]
,
has been widely used in coastline determination [
36
,
37
]. Decreasing NDWI indicates sed-
imentation of the beach, while increasing NDWI implies shore erosion. The five oyster-
growing areas are used again as ‘monitoring areas’. To improve the detection sensitivity,
only areas with a mean depth of water <1 m are used, as shown in Figure 11 below. The
seasonal mean NDWI (based on the 54 images) in the five monitoring areas is provided in
Figure 12.
Remote Sens. 2023, 15, x FOR PEER REVIEW 11 of 19
Figure 10. Seasonal variation of (a) mean and (b) maximum DIN concentration in the five oyster-
growing areas (A–E in Figure 2).
4.3. Temporal Variation of the Coastline Recession
The normalized difference water index (NDWI, given by Equation (1)) is used as a
surrogate for the conditions of the coast. In Equation (1), 𝑅 is the reflectance at the
visible green band, and 𝑅 is the reflectance at the near-infrared band.
NDWI = 𝑅 −𝑅
𝑅 +𝑅
(1)
NDWI, which detects the existence of water and is insensitive to TSS concentration
[33–35], has been widely used in coastline determination [36,37]. Decreasing NDWI indi-
cates sedimentation of the beach, while increasing NDWI implies shore erosion. The five
oyster-growing areas are used again as ‘monitoring areas’. To improve the detection sen-
sitivity, only areas with a mean depth of water <1 m are used, as shown in Figure 11 below.
The seasonal mean NDWI (based on the 54 images) in the five monitoring areas is pro-
vided in Figure 12.
Figure 11. Monitoring areas used to detect changes in coastline conditions using NDWI as a surro-
gate.
Figure 11.
Monitoring areas used to detect changes in coastline conditions using NDWI as a surrogate.
Remote Sens. 2023,15, 510 12 of 19
Remote Sens. 2023, 15, x FOR PEER REVIEW 12 of 19
Figure 12. Seasonal variation of NDWI in the five monitoring areas (A–E in Figure 9).
4.4. Factors Impacting Water Quality Constituents and Coast Conditions
An important goal of the current study is to determine the relationship between the
land reclamation project and variations in the concentration of water quality constituents
and the conditions of the coast. To achieve this goal, another set of statistical models uti-
lizing multiple regression is derived following the process below. Stepwise regression
with forward selection with TSS concentration, DIN concentration, or NDWI as the de-
pendent variable is used. The variables listed in Table 7 are the initial selection of predictor
(independent) variables. Disturbed areas (active or cumulative) are directly related to the
progress of land reclamation. Tide is known to be the main mechanism to influence sedi-
ment diffusion [6]. The antecedent dry days and 7-day cumulative rainfall depth are re-
lated to the amount of sediment washed off by runoff. To explore potentially significant
interactions between independent variables, all possible two-way interactions are initially
included, but only the main effects are shown in Table 7 for brevity and clarity of the table.
Using forward selection with minimal AIC as a criterion, the statistical models that repre-
sent the relationship are provided in Tables 8–10. Note that the dependent variables are
transformed in the models.
Table 7. List of main-effect predictor variables initially used in the variable selection process.
Variable Definition Unit Symbol
Active disturbed area km
𝐴
Cumulative disturbed area km
𝐴
Tide (categorical: rising/falling at satellite flyover) n/a 𝐼
Dry days before the image date days 𝐷
7-day cumulative rainfall depth mm 𝑅
Table 8. Statistical models to predict the concentration of TSS in the five oyster-growing areas.
Dependent
Variable 𝟏/𝑻𝑺𝑺
Intercept
𝑨
𝒂𝒄𝒕
𝑨
𝒄𝒖𝒎 𝑰 𝑹𝟕𝒅 (
𝑨
𝒂𝒄𝒕 ×𝑹
𝟕𝒅)
**
(
𝑨
𝒄𝒖𝒎 ×
𝑫𝒅𝒓𝒚) ** (𝑹𝟕𝒅 ×𝑰) **
A (R
2
=
0.44)
p-value <0.0001 * - 0.062 0.21 0.049 * - - <0.0001 *
Coeff. 0.14 - −0.0023 −0.018
+
0.0010 - - −0.0022
+
95% CI (0.094,
0.18) - (−0.0048,
0.00012)
(−0.046,
0.010)
(5.52×10
,
0.0021) - -
(−0.0032,
−0.0011)
VIF - - 1.03 1.02 1.18 - - 1.17
Figure 12. Seasonal variation of NDWI in the five monitoring areas (A–E in Figure 9).
4.4. Factors Impacting Water Quality Constituents and Coast Conditions
An important goal of the current study is to determine the relationship between the
land reclamation project and variations in the concentration of water quality constituents
and the conditions of the coast. To achieve this goal, another set of statistical models
utilizing multiple regression is derived following the process below. Stepwise regression
with forward selection with TSS concentration, DIN concentration, or NDWI as the depen-
dent variable is used. The variables listed in Table 7are the initial selection of predictor
(independent) variables. Disturbed areas (active or cumulative) are directly related to
the progress of land reclamation. Tide is known to be the main mechanism to influence
sediment diffusion [
6
]. The antecedent dry days and 7-day cumulative rainfall depth are
related to the amount of sediment washed off by runoff. To explore potentially significant
interactions between independent variables, all possible two-way interactions are initially
included, but only the main effects are shown in Table 7for brevity and clarity of the
table. Using forward selection with minimal AIC as a criterion, the statistical models that
represent the relationship are provided in Tables 8–10. Note that the dependent variables
are transformed in the models.
Table 7. List of main-effect predictor variables initially used in the variable selection process.
Variable Definition Unit Symbol
Active disturbed area km2Aact
Cumulative disturbed area km2Acum
Tide (categorical: rising/falling at
satellite flyover) n/a I
Dry days before the image date days Ddry
7-day cumulative rainfall depth mm R7d
Remote Sens. 2023,15, 510 13 of 19
Table 8. Statistical models to predict the concentration of TSS in the five oyster-growing areas.
Dependent Variable 1/TSS
Intercept Aact Acum I R7d(Aact ×R7d)
**
(Acum ×
Ddry)** (R7d×I) **
A (R2= 0.44)
p-value <0.0001 * - 0.062 0.21 0.049 * - - <0.0001 *
Coeff. 0.14 - −0.0023 −0.018 +0.0010 - - −0.0022 +
95% CI (0.094, 0.18) - (−0.0048,
0.00012)
(−0.046,
0.010)
(5.52×10−6,
0.0021) - - (−0.0032,
−0.0011)
VIF - - 1.03 1.02 1.18 - - 1.17
B (R2= 0.40)
p-value <0.0001 * - 0.091 0.33 0.027 * - - 0.0007 *
Coeff. 0.13 - −0.0024 −0.016 +0.0014 - - −0.0021 +
95% CI (0.085, 0.18) - (−0.0052,
0.00040)
(−0.049,
0.017)
(0.00016,
0.0026) - - (−0.0033,
−0.00094)
VIF - - 1.03 1.02 1.18 - - 1.17
C (R2= 0.51)
p-value <0.0001 * 0.48 0.048 * 0.74 0.013 * 0.035 * 0.016 * 0.0005 *
Coeff. 0.12 0.0015 −0.0026 −0.0047 +0.0014 0.00026 0.00016 −0.0018 +
95% CI (0.070, 0.17) (−0.0028,
0.0059)
(−0.0052,
−2.85
×10−5)
(−0.033,
0.023)
(0.00031,
0.0025)
(1.92 ×10−5,
0.00051)
(3.08 ×10−5,
0.00028)
(−0.0028,
−0.00084)
VIF - 1.32 1.31 1.10 1.58 1.31 1.14 1.22
D (R2= 0.42)
p-value <0.0001 * - 0.17 0.99 0.097 - 0.079 0.0004 *
Coeff. 0.15 - −0.0017 −0.00025 +0.00093 - 0.00011 −0.0019 +
95% CI (0.094, 0.20) - (−0.0040,
0.00071)
(−0.028,
0.027)
(−0.00017,
0.0020) -
(−1.32
×10−5,
0.00023)
(−0.0029,
−0.00088)
VIF - - 1.02 1.03 1.44 - 1.02 1.18
E (R2= 0.22)
p-value <0.0001 * - - 0.69 0.20 - - 0.0085 *
Coeff. 0.14 - - −0.0084 +0.00098 - - −0.0020 +
95% CI (0.087, 0.18) - - (−0.050,
0.033)
(−0.00052,
0.0025) - - (−0.0035,
−0.00055)
VIF - - - 1.00 1.17 - - 1.17
* Statistically significant; ** The actual forms are in center polynomials.
+
Coefficients are negative for rising tides
and positive for falling tides.
Table 9. Statistical models to predict the mean DIN concentration in the five oyster-growing areas.
Dependent Variable 1/DIN
Intercept Aact R7d(Aact×R7d) **
A (R2= 0.44)
p-value <0.0001 * 0.95 0.10 0.019 *
Coeff. 1.59 −0.00042 0.0025 0.00089
95% CI (1.48, 1.70) (−0.013, 0.012) (−0.00052, 0.0054) (0.00015, 0.0016)
VIF - 1.04 1.05 1.09
B (R2= 0.40)
p-value <0.0001 * 0.80 0.084 0.020 *
Coeff. 1.60 −0.0016 0.0025 0.00083
95% CI (1.50, 1.70) (−0.014, 0.011) (−0.00034, 0.0053) (0.00014, 0.0015)
VIF - 1.04 1.05 1.09
C (R2= 0.51)
p-value <0.0001 * 0.69 0.052 0.042 *
Coeff. 1.68 −0.0029 0.0034 0.00088
95% CI (1.55, 1.80) (−0.018, 0.012) (−2.3×10−5,
0.0068)
(3.18 ×10−5,
0.0017)
VIF - 1.04 1.05 1.09
Remote Sens. 2023,15, 510 14 of 19
Table 9. Cont.
Dependent Variable 1/DIN
Intercept Aact R7d(Aact×R7d) **
D (R2= 0.42)
p-value <0.0001 * 0.70 0.039 * 0.034 *
Coeff. 1.67 −0.0029 0.0037 0.00092
95% CI (1.54, 1.80) (−0.018, 0.012) (0.00020, 0.0071) (7.15×10−5,
0.0018)
VIF - 1.04 1.05 1.10
E (R2= 0.22)
p-value <0.0001 * 0.97 0.10 0.021 *
Coeff. 1.60 0.00029 0.0027 0.00097
95% CI (1.48, 1.72) (−0.014, 0.014) (−0.00056, 0.0060) (0.00015, 0.0018)
VIF - 1.04 1.05 1.09
* Statistically significant. ** The actual forms are in center polynomials.
Table 10. Statistical models to predict mean NDWI in the five monitoring areas.
Dep. Variable 1/NDWI
Intercept Aact Acum I R7d(Aact ×
Acum)**
(Aact ×
I)**
(Aact ×
R7d)**
(Acum ×
I)**
(Acum ×
R7d)**
(I×R7d)
**
A (R2=
0.30)
p-value <0.0001 * - - 0.26 0.12 - - - - - 0.0019 *
Coeff. 2.88 - - −0.30 +0.015 - - - - - −0.031 +
95% CI (2.26,
3.50) - - (−0.83,
0.23)
(−0.0039,
0.035) -----(-0.051,
−0.012)
VIF - - - 1.00 1.17 - - - - - 1.17
B (R2=
0.39)
p-value <0.0001 * - - 0.47 0.0031 * - - - - - 0.0043 *
Coeff. 2.40 - - −0.14 +0.021 - - - - - −0.020 +
95% CI (1.96,
2.85) - - (−0.52,
0.24)
(0.0074,
0.035) -----(−0.034,
−0.0067)
VIF - - - 1.00 1.17 - - - - - 1.17
C (R2=
0.18)
p-value <0.0001 * - - 0.45 0.35 - - - - - 0.017 *
Coeff. 2.59 - - −0.17 +0.0074 - - - - - −0.019 +
95% CI (2.07,
3.10) - - (−0.61,
0.27)
(−0.0084,
0.023) -----(−0.035,
−0.0036)
VIF - - - 1.00 1.18 - - - - - 1.18
D (R2=
0.036)
p-value <0.0001 * - - - 0.17 - - - - - -
Coeff. 2.54 - - - 0.0097 - - - - - -
95% CI (2.09,
2.99) ---(−0.0043,
0.024) ------
VIF - - - - 1.00 - - - - - -
E (R2=
0.52)
p-value <0.0001 * 0.61 0.037 * 0.038 * 0.16 0.16 <0.0001 * 0.034 * <0.0001 * 0.0085 * 0.0232 *
Coeff. 3.59 −0.044 0.67 −0.67 +−0.021 −0.0081 −0.25 0.0075 −0.17+−0.0054 −0.026 +
95% CI (2.60,
4.58)
(−0.22,
0.13)
(0.040,
1.31)
(−1.31,
−0.040)
(−0.051,
0.0086)
(−0.019,
0.0032)
(−0.37,
−0.13)
(0.00060,
0.014)
(−0.23,
−0.096)
(−0.0093,
−0.0015)
(−0.049,
−0.0038)
VIF - 3.96 1.10 1.10 2.13 2.70 1.76 1.96 1.79 2.84 1.25
* Statistically significant. ** The actual forms are in center polynomials.
+
Coefficients are negative for rising tides
and positive for falling tides.
5. Discussion
5.1. Overall Trends of TSS Concentration, DIN Concentration, and Coast Conditions
According to Figure 9, the concentration of TSS in the bay in northern Kinmen is
acceptable for oyster growth (significantly below the hazardous level of 188 mg/L for
oysters [
19
]) in the observation period of 2005–2021. No long-term increasing or decreasing
trend was found for either the mean or maximum value. The mean TSS value reaches
Remote Sens. 2023,15, 510 15 of 19
50.58 mg/L in area E on 2/28/2011, and the maximum TSS value is at 96.11 mg/L on the
same day.
The TSS concentration shows great short-term variations for all five oyster-growing
areas. Using area E as an example, which has the highest maximum TSS concentration of
23.82 mg/L with a standard deviation of 18.89 mg/L. Using available data and assuming a
normal distribution for the maximum TSS concentration, the possibility that the maximum
TSS concentration reaches 188 mg/L or greater at area E is less than 1
×
10
−6
%. Therefore,
the oyster-growing industry in the bay in northern Kinmen is unlikely to be jeopardized
even with the presence of the nearby land reclamation project.
Similarly, DIN also exhibits a steady trend for long-term concentration with significant
short-term variations. Taking into account the rapid development in all coastal cities in
China, Figure 10 implies that the semi-enclosed bay in northern Kinmen is hydrodynam-
ically isolated from the major cities to a certain degree [
5
,
38
], and the land reclamation
activity probably did not significantly alter the circulation of nutrients in the bay.
The trend for coast conditions (using NDWI as a surrogate, Figure 12) is somehow
different. No general trend exists, but two time periods exhibit noticeable trends in NDWI,
namely 2008–2013 (sedimentation) and 2018–2021 (erosion). The second period is partic-
ularly significant, which might be causally related to the completion of the final phase
(July 2016 – December 2018) of the land reclamation project. Longer observations and
comprehensive simulations are required to confirm such a trend.
5.2. Factors Influencing TSS Concentration, DIN, and Coast Conditions
The statistical models derived in Tables 8–10 revealed factors that influence TSS
concentration, DIN concentration, and coast conditions, respectively. Moderate R
2
values
limit their use in directly predicting TSS, DIN, or NDWI, but the statistical significance of
the individual predictor variable can still be confirmed. For TSS, the interaction between
the 7-day cumulative rainfall depth and tide direction (
R7d×I
) is the most significant
variable among all five areas. The coefficient for that term suggests that the sediment source
is outside the bay (potentially from rainfall-induced runoff from land reclamation sites) as
R7d
is positively correlated to TSS (note that the dependent variable in the model is 1
/TSS
)
during rising tides (i.e., flowing into the bay).
Area C is the only area with TSS that is potentially influenced by the cumulative area
(
Acum
) or active area (
Aact
) of the land reclamation project. This is intriguing because area
C is located deep in the bay. This finding implies the potential existence of hydrodynamic
connections between the interior of a bay and offshore pollution sources. This is a topic
worth further study.
For DIN, the interaction between the active area and the 7-day cumulative rainfall
depth (
Aact ×R7d
) is significant for all areas. Nevertheless,
Aact
and
R7d
are mostly not
significant. This indicates a “crossover” interaction in which only the two main effects
together have explanatory power, and the effect of either main effect alone would be offset
by the effect of the interaction [
39
]. The result shows an instantaneous connection between
the active area and nutrient concentration; however, more active reclamation area and more
rainfall appear to decrease the concentration of DIN (note that the dependent variable in
the model is 1
/DIN
). One reasonable explanation is that runoff from newly reclaimed land
dilutes the concentration of nutrients in water. The land reclamation project is confirmed
not to have a detrimental influence on nutrient concentration in the bay. Such a conclusion
should be accepted with caution as the validation accuracy for DIN estimation is not
satisfactory (Figure 6b).
The coast condition in the monitoring areas A–D is simply affected by the interaction
between tide and the 7-day cumulative rainfall depth (
R7d×I
), showing the short-term
influence of storms and tides on beach conditions. During rising tides, more antecedent
rainfall induces shore erosion (while sedimentation occurs in falling tides). Area E appears
to be influenced by different mechanisms, however. The cumulative reclaimed area (
Acum
)
and interactions involving
Acum
are significant factors for the coast condition in monitoring
Remote Sens. 2023,15, 510 16 of 19
area E. This coincides with what Lin et al. [
40
] found: coastline progression in northern
Kinmen is a complex process: beach erosion and sedimentation can follow one another at
the same location in the short term.
5.3. Implications for the Integrated Coastal Management of Semi-Enclosed Coastlines
The mechanism of interaction between land reclamation and the water quality of a
semi-enclosed bay can be complicated. In the case of Xiang’an Airport, land reclamation
does not have a direct causal relationship with water quality and coast conditions, whereas
studies in other parts of the world had mixed findings.
For suspended sediment, a study in Palu Bay in Indonesia [
41
] had a result similar to
that of the current study, that the TSS concentration is mostly stable after land reclamation.
However, this is not the case for bays with more restricted external access [
42
]. The relative
alteration of the sea current by land reclamation could be the key factor controlling the
outcome of the TSS concentration. A future study that provides empirical criteria on
whether the concentration of TSS will be impacted after land reclamation based on the
reclaimed area and the geometry of the bay can advance integrated coastal management.
For nutrients, two independent studies conducted by Lyu et al. [
7
] and Zhang et al. [
43
]
both determined that land reclamation contributes significantly to the change of nutrient
concentration and can intensify the effect of land-based pollutant inputs. Therefore, the
current study could be a special case as the semi-enclosed bay lacks significant land-based
pollutant sources. The synergy between land reclamation and land-based pollution is
crucial to effective integrated coastal management.
The current study only observes a noted trend in coastline changes after the land
reclamation project was completed in 2018. Another study in the Pearl River Delta in
China [
44
] with a much longer (about 40 years) observation time frame confirmed that land
reclamation causes long-term geomorphological changes. Therefore, the geomorpholog-
ical effect of Xiang-an Airport is worth close monitoring before it can contribute to our
understanding of integrated coastal management.
The current study also highlights the importance of transboundary corporation in
environmental issues. Due to the lack of key information (such as the actual land recla-
mation schedule) released by the Chinese government, the current study can only use the
best information available on the Internet. An integrated management scheme can only be
achieved with the cooperation of all parties.
6. Conclusions
Utilizing remotely sensed data, this study investigates the two main concerns of
Kinmen residents caused by a nearby land reclamation project, namely the deterioration of
water quality and the change in coastline. TSS and DIN are used to represent the general
trends of the water quality constituents and NDWI for the coast conditions.
The TSS concentration, DIN concentration, and coast conditions do not show a clear
trend for the observation time frame of 2005–2021. From 2005 to 2021, the TSS concentration
is generally low in the bay regardless of the existence of the major land reclamation project.
Statistical analyses gauge the possibility for TSS to achieve a hazardous level (188 mg/L) in
the bay at less than 1
×
10
−6
%. The DIN concentration shows an even more steady trend
despite the rapid urbanization on the Chinese coast. Regarding coast conditions (surrogated
by NDWI), there is a significant rising trend (i.e., coast erosion) after the completion of the
project in 2018 despite the long-term stability, but such a recent trend can only be confirmed
with longer observations in the future.
Factors that influence short-term variations in TSS concentration and coast conditions
are also analyzed by models derived from multiple regression, and neither the actively
reclaimed area (
Aact
) nor cumulative reclaimed area (
Acum
) is among the main influencers.
The 7-day cumulative rainfall depth (
R7d
) positively and significantly influences TSS
only when the tide (
I)
increases, suggesting that the sediment source is outside the bay
(potentially from rainfall-induced runoff from the land reclamation sites). Coast conditions
Remote Sens. 2023,15, 510 17 of 19
are influenced by similar factors, with larger storms followed by shore erosion at rising
tides (while shore sedimentation at falling tides).
DIN concentration is affected by
Aact ×R7d
, but a larger actively reclaimed area
Aact
decreases DIN concentration, not increasing it, suggesting a dilution effect from rainfall-
induced runoff. Newly claimed land is not a source of nutrients in this case. Such a
conclusion should be accepted with caution as the validation accuracy for DIN estimation
is not satisfactory.
In summary, the concentration of TSS, the concentration of nutrient (DIN), and the
conditions of the coast are in satisfactory numerical range and stable. All three indicators
(TSS, DIN, and NDWI) are not negatively affected by the land reclamation project after
utilizing multiple regression models. However, some intriguing findings are found from
the current study and require further investigation in the future:
(1) The concentration of TSS in area C deep in the bay is potentially affected by the
cumulative area (
Acum
) or active area (
Aact
) of the land reclamation project. It might imply
the potential existence of hydrodynamic connections between the interior of a bay and
offshore pollution sources; and
(2) The coast condition in area E is affected not only by the cumulative reclaimed area
(
Acum
) but also by several interactions involving
Acum
. More delicate investigations are
needed to understand the erosion/sedimentation mechanisms in that area.
Author Contributions:
Conceptualization, Y.-c.H.; Methodology, M.-c.T.; Software, M.-c.T. and
Y.-c.H.
; Validation, M.-c.T.; Formal analysis, M.-c.T.; Investigation, M.-c.T. and Y.-c.H.; Resources,
M.-c.T.; Data curation, M.-c.T. and Y.-c.H.; Writing—original draft, M.-c.T.; Writing—review &
editing, M.-c.T.; Visualization, M.-c.T.; Supervision, M.-c.T.; Project administration, M.-c.T.; Funding
acquisition, M.-c.T. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement:
All data available from publicly accessible sources delineated in the article.
Conflicts of Interest: The authors declare no conflict of interest.
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