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\begin{document}

\title{Extracting spatiotemporal characteristics and succession pattern of
native and invasive plant species using plant phenophase and
multi-source remote sensing images in the Yellow River delta, China}



\author[1]{Zhaoning Gong}%
\author[1]{Cheng Zhang}%
\author[1]{Lei Zhang}%
\author[2]{Junhong Bai}%
\author[1]{Demin Zhou}%
\affil[1]{Capital Normal University}%
\affil[2]{Beijing Normal University}%


\vspace{-1em}



  
  \date{March 14, 2025}


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\selectlanguage{english}
\begin{abstract}
The native and invasive species in the Yellow River Delta were examined
for their spatiotemporal characteristics and succession pattern. First,
the appropriate Sentinel-2 and Landsat-8 images from 2018 were selected
according to phenological characteristics. A random forest algorithm was
used to verify the image spectral band significance and separability
using selected images to determine the native and invasive species.
Then, the spatiotemporal variation of habitat structure of native and
invasive species is discussed in depth from the perspective of landscape
ecology. Finally, the expansion direction and expansion mode of S.
alterniflora were further analyzed, and main results were obtained as
follows. (1) At the medium-high resolution multi-spectral image level,
the accuracy of different vegetation community extractions can be
improved by taking into consideration both the vegetation phenology and
the spectral features of remote sensing images. (2) Sentinel-2 images
with red edge bands have obvious advantages in vegetation community
extraction as compared to Landsat-8 images (Sentinel-2, OA=82.86\%,
Kappa coefficient=0.79; Landsat-8, OA=78.77\%, Kappa coefficient=0.74).
(3) The expansion pattern of the S. alterniflora community became
spatially continuous, more regularized and aggregated overtime. (4) The
expansion in the north shore mainly faces to the sea, and the south bank
mainly faces to the land, and this phenomena is closely related to the
sedimentation of the Yellow River Delta. Marginal and external expansion
both occurred, but marginal expansion predominated. The results from
this study have important theoretical and scientific value for the
environmental protection and sustainable development of the entire
Yellow River Delta.%
\end{abstract}%



\sloppy


\textbf{Extracting spatiotemporal characteristics and succession pattern
of native and invasive plant species using plant phenophase and
multi-source remote sensing images in the Yellow River delta, China}

\textbf{Zhaoning Gong \textsuperscript{1, 2, 3, 4}, Cheng
Zhang\textsuperscript{1, 2, 3, 4}, Lei Zhang \textsuperscript{1, 2, 3,
4}} 11\textsuperscript{*} Corresponding authors.\emph{E-mail}
\emph{addresses:} zhangleigis@outlook.com\textbf{, Junhong
Bai\textsuperscript{5}, Demin Zhou\textsuperscript{1, 2, 3, 4}}

\textsuperscript{1} College of Resources, Environment and Tourism,
Capital Normal University, Beijing 100048, China

\textsuperscript{2} Key laboratory of 3D Information Acquisition and
Application of Ministry, Beijing 100048, China

\textsuperscript{3} Beijing Key Laboratory of Resources Environment and
GIS, Beijing, 100048, China

\textsuperscript{4} Beijing Laboratory of Water Resources Security,
Beijing, 10048, China

\textsuperscript{5} College of Environment, Beijing Normal University,
Beijing, 100875, China

Abstract: The native and invasive species in the Yellow River Delta were
examined for their spatiotemporal characteristics and succession
pattern. First, the appropriate Sentinel-2 and Landsat-8 images from
2018 were selected according to phenological characteristics. A random
forest algorithm was used to verify the image spectral band significance
and separability using selected images to determine the native and
invasive species. Then, the spatiotemporal variation of habitat
structure of native and invasive species is discussed in depth from the
perspective of landscape ecology. Finally, the expansion direction and
expansion mode of \emph{S. alterniflora} were further analyzed, and main
results were obtained as follows. (1) At the medium-high resolution
multi-spectral image level, the accuracy of different vegetation
community extractions can be improved by taking into consideration both
the vegetation phenology and the spectral features of remote sensing
images. (2) Sentinel-2 images with red edge bands have obvious
advantages in vegetation community extraction as compared to Landsat-8
images (Sentinel-2, OA=82.86\%, Kappa coefficient=0.79; Landsat-8,
OA=78.77\%, Kappa coefficient=0.74). (3) The expansion pattern of
the\emph{S. alterniflora} community became spatially continuous, more
regularized and aggregated overtime. (4) The expansion in the north
shore mainly faces to the sea, and the south bank mainly faces to the
land, and this phenomena is closely related to the sedimentation of the
Yellow River Delta. Marginal and external expansion both occurred, but
marginal expansion predominated. The results from this study have
important theoretical and scientific value for the environmental
protection and sustainable development of the entire Yellow River Delta.

Key words: multi-source remote sensing images; plant phenophase;
temporal and spectral characteristics; invasive species; expansion
analysis; the Yellow River Delta wetland

\section*{1. Introduction}

{\label{introduction}}

Invasive species are considered to be the second largest factor in
global biodiversity reduction after habitat destruction, have
significant advantages in terms of growth, reproduction, and
competition, and can pose a great threat to local ecosystems (Liu et
al.2015). The estuary wetland, located at the sea-land junction of the
river and the sea, is an ecological marginal zone formed by two distinct
ecosystems. This ecosystem is so sensitive and fragile that it is a
frequent area for biological invasion (Wang 2005). The Yellow River
Delta Wetland, as a typical estuary wetland in northern China, appeared
in the ``List of Ramsar wetlands of international importance'' by the
Ramsar Convention on Wetlands in 2013. It is not only one of the most
representative estuarine wetlands in the world, but it is also the most
complete and the vastest, youngest wetland ecosystem in the warm
temperate zone of China (Gong et al.2016; Wen et al.2011).

Smooth cordgrass (\emph{Spartina alterniflora} ) is an invasive plant
that was artificially introduced. It exerts a certain degree of wind
protection, berm protection, beach preservation, and siltation in the
estuary wetland in the early stage. \emph{S. alterniflora} has been
introduced into many countries throughout history, including the west
coast of North America, Europe, New Zealand, and China, and it has
spread rapidly at all these introduction sites(Chen, et al., 2004; Deng
et al., 2006). \emph{S. alterniflora} was introduced into China as an
ecological project in 1979. However, \emph{S. alterniflora} has strong
salt and reproductive capacity. At the same time, it lacks competing
species and natural enemies along the coast of China, which has resulted
in a great expansion of \emph{S. alterniflora} in China's coastal
areas.. It has gradually encroached on the living space of the native
marsh plant species, which consists of seepweed (\emph{Suaeda salsa} )
and the common reed (\emph{Phragmites australis} ), and it has caused
serious damage to the original ecosystem of the Yellow River Delta (Zhou
et al.2017). Therefore, it is urgent to use advanced technology to
timely monitor the distribution patterns and trends of the native and
invasive plant species in the Yellow River Delta, which will provide a
scientific basis for the protection and rational use of wetland
resources in the future.

Compared with field research, remote sensing (RS) technology has the
advantages of wide observation range, flexible monitoring period, and
large amount of information. These technologies were widely used in many
studies in China. For example, saltmarsh vegetation types were mapped
and analyzed based on remote sensing images with an ideal precision
(Huang et al. 2007; Sun et al. 2016;Ai et al. 2016). Combined with
vegetation phenological characteristics, Wu et al. (2012) used Landsat
and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data
to obtain vegetation indexes to track the expansion of \emph{S.
alterniflora} in the coastal area of the southern Yellow Sea. Wan et al.
(2010) collected HJ-1 charge-coupled device (CCD) images and
high-resolution unmanned aerial vehicle (UAV) images in the Guangxi
Beihai area, and extracted the distribution pattern of \emph{S.
alterniflora} . The results indicated that the UAV image can provide
detailed information on the distribution range and expansion process
of\emph{S. alterniflora} . Under the support of 3S (GIS, GPS, and RS)
technology, Wang et al. (2018) analyzed the spatial distribution
characteristics of \emph{S. alterniflora} in Jiangsu Yancheng National
Nature Reserve from 2006 to 2015 through the landscape index and
centroid change, and then they used Gaofen-2 (GF-2) satellite images to
identify the expansion pattern of \emph{S. alterniflora} and predict the
expansion trend. The above research on \emph{S. alterniflora} has been
conducted mainly in southern China. Since 2010, \emph{S.
alterniflora}has invaded beaches and expanded its range, but there are
few studies on the growth scale, habitat structural changes, and
expansion pattern of\emph{S. alterniflora} in the Yellow River Delta.

With the improvement in Earth observation systems in the
21\textsuperscript{st} century in various countries and regions, an
increasing number of remote sensing images have surpassed the limitation
of the number of bands, spatial resolution, and revisiting cycles (Zheng
et al. 2017). The long-sequence terrestrial satellite Landsat has become
one of the common sources of multi-source imagery, and the twin
Sentinel-2 satellites launched by the European Space Agency (ESA) are
also becoming widely used in research. Tesfamichael et al. (2017)
compared multi-band remote sensing images (Landsat-8, Sentinel-2A,
Spot-6, Pleiades-1B, and WorldView-3) to distinguish three intrusions of
shrub plants from native plants that are symbiotic with shrub plants.

Compared to the Landsat series of data, the two Sentinel-2 satellites
are equipped with a Multispectral Instrument (MSI), which provides
remote sensing images with multi-scale, medium-high spatial resolution
(10 m, 20 m, and 60 m) from visible and near-infrared to short-wave
infrared (13 bands). Moreover, the Sentinel-2 satellites specifically
monitor three bands in the red edge region (670-760 nm) of the
vegetation spectrum, which is very effective for monitoring vegetation
growth information and its health status (Shoko et al. 2017; Delegido et
al. 2011; Korhonen et al. 2017). At the same time, the revisiting cycle
of the two Sentinel-2 satellites was shortened to 5 days, which greatly
enhances the capability for Earth observation (Zheng et al. 2017).
Therefore, how to exploit the advantages of the two Sentinel-2
satellites in monitoring the surface of the Earth is bound to become the
focus of future research in global scientific research.

In view of this, it is imperative that we build a capacity to monitor
timely the spatial and temporal characteristics of the invasive plant
species and their dynamic changes in order to restore ecological
conditions and protect the environment in the Yellow River Delta. The
main objectives of this study are to characterize the spatial
distribution and spatiotemporal variation of native/invasive species via
remote sensing and to understand the expansion mechanism of \emph{S.
alterniflora} in the Yellow River Delta, which are the key point in the
restoration and reconstruction of the estuary wetland ecosystem.

The following analyses were conducted to meet the objectives of this
study:

(1) Conducting spectral feature selection using Sentinel-2 images and
Landsat-8 images acquired at different phenological stages of the native
and invasive species in 2018 in the Yellow River Delta. The selection
process was accomplished by using a random forest algorithm.

(2) Based on selected spectral bands of Sentinel-2 and Landsat-8 images
of 2018, the native and invasive species of the Yellow River Delta were
extracted and mapped using a random forest classifier and results were
validated using reference data.

(3) Based on results from step (1) and (2), the temporal and spatial
variations of native and invasive species were quantified and analyzed,
and the main expansion directions and expansion patterns of the \emph{S.
alterniflora} community were examined from the perspective of landscape
ecology.

\section*{2. Materials and Methods}

{\label{materials-and-methods}}

\subsection*{2.1 Study area}

{\label{study-area}}

The Yellow River Delta (118\selectlanguage{ngerman}°32.981\selectlanguage{english}'E---119deg20.450\selectlanguage{english}'E and
37deg34.768\selectlanguage{english}'N---38deg12.310\selectlanguage{english}'N), which is located at the estuary of the
Yellow River in Dongying City, Shandong Province, China (Fig. 1a-c), is
one of China's four largest river deltas. As a national nature reserve,
it was officially listed on the international list of important wetlands
in 2013. The delta, bounded by the Bohai Sea in the north and the
Laizhou Bay in the east, has a typical temperate monsoon climate with
four distinct seasons, and rain and heat over the same period. The
average annual temperature is 12.1degC, the precipitation is 530--630 mm,
and the evaporation is 1900--2400 mm in the study area (Liu et al. 2017;
Shandong Yellow River Delta National Nature Reserve Administration
2016). Despite the flat topography, the ecological pattern varies with
time and space, and the types of wetlands are diverse in this region.

\subsection*{2.2 Data acquisition}

{\label{data-acquisition}}

\subsubsection*{2.2.1 Remote sensing data and image
preprocessing}

{\label{remote-sensing-data-and-image-preprocessing}}

Due to the complex habitat information and seasonal succession of
estuary wetlands, three Sentinel-2 images and three Landsat-8 images
taken under clear weather conditions were selected to test their ability
in discriminating between native species and invasive species. The
Sentinel-2 satellite images used in this study were recorded on May 27,
2018, September 09, 2018, and October 19, 2018. In order to reduce the
differences caused by different times, Landsat-8 satellite images with
times similar time to those of the Sentinel-2 images were selected, and
images were used that were produced on May 28, 2018 (Sentinel-2A),
September 17, 2018 (Sentinel-2B), and October 19, 2018 (Sentinel-2B).
The data is introduced in Surppporting Imformation part2(S2).

\subsubsection*{2.2.2 Sample data and validation
data}

{\label{sample-data-and-validation-data}}

The quality of the sample data is directly related to the accuracy of
the native and invasive species extraction (Liu et al. 2017). A typical
and representative pure pixel was selected as the sample. The data in
this study mainly come from on-site field surveys and visual
interpretation based on high-resolution images such as those from Google
Earth. From 2017 to 2018, the experimental group conducted a detailed
survey of the Yellow River Delta Nature Reserve, using high-precision
GPS to locate, record, and photograph different vegetation communities.
At the same time, a part of the native and invasive species was visually
interpreted indoors through Google Earth software and the MAP WORLD
website. The above two parts of the data constitute the sample data and
verification data for the establishment of the classifier and the
accuracy verification. The distribution and specific sources of sample
data and verification data are shown in Table 1.

*Methods are introduced in the S3.

\section*{3. Results and Analyses}

{\label{results-and-analyses}}

\subsection*{3.1 Selection of feature
bands}

{\label{selection-of-feature-bands}}

The band importance index was extracted by the random forest algorithm
(Fig. 2). Sentinel-2 images have approximately four to five bands of
higher importance in all three months, particularly, the red-edge band
(B06, B07) and near-infrared band (B08A) are more important. Compared to
Sentinel-2 images, Landsat-8 images have approximately two to three
bands of higher importance in all months, especially the red-band (B04)
and near-infrared band (B05). The importance of the near-infrared band
in May is particularly prominent.

The importance index of the band can only determine the contribution of
different bands. In order to reduce the noise generated by the band with
a lower contribution rate, we determined for each image if there are any
important bands for species extraction. The bands of the top n
importance at different months were selected as the basis for separating
different species. If the importance of a band at the given time is
ranked as the top one, but its separability is poor, other bands from
the same image with a lower importance index can be considered as a
noise band that should be discarded. The bands that are selected with
higher separability are those that will be used for final extraction.

Figure 3 shows the spectral separability of the n significant bands of
Sentinel-2 images for different months. For example, during the
corresponding phenological stage, the red edge band (B06, B07) and the
near-infrared band (B08A) of the vegetation can be used to effectively
distinguish between \emph{S. salsa} , \emph{P. australis} , and \emph{S.
alterniflora.} Among them, 09\_B06, 09\_b07, 09\_B08A, and 10\_B08A can
basically achieve separation between the three species. We selected
multi-temporal bands with good separability from the Sentinel-2 source
data (05\_B06, 05\_B07, 05\_B08A, 05\_B12, 09\_B08A, 09\_B07, 09\_06,
09\_02, 09\_B04, 10\_B06, 10\_B08A, 10\_B12, 10\_B11, 10\_B04, 10\_02,
and 10\_07) to extract the native and invasive species of the study
area.

The Landsat-8 image spectral separability band is mainly concentrated in
the red band (B04) and the near-infrared band (B05) (Fig. 4). From the
importance and separability analysis of the bands, it was found that the
Landsat-8 image in September had the best separability and the most
bands (09\_B04, 09\_B05, 09\_B01, 09\_B02, and 10\_B04) for \emph{S.
salsa} ; however, there were only two separable bands for \emph{P.
australis} (05\_B05, 05\_B07) and one for \emph{S.
alterniflora}(10\_B05).

The above analysis shows that the multi-temporal Landsat-8 and
Sentinel-2 images can distinguish \emph{S. salsa} , \emph{P. australis}
, and \emph{S. alterniflora} . The spectral separability bands were
mainly concentrated in the red band (630 nm-680 nm, Landsat-8),
near-infrared band (845 nm-885 nm, 848 nm-880 nm, Landsat-8,
Sentinel-2), and vegetation red edge band (739 nm-749 nm, 768 nm-796 nm,
Sentinel-2). Besides, some studies have shown that the multi-temporal
vegetation index (such as NDVI) of the Landsat-5 image can also
distinguish between the three types of vegetation (Wang et al. 2013).

\subsection*{3.2 Classification and accuracy
analysis}

{\label{classification-and-accuracy-analysis}}

Figure 5 shows the native and invasive species extracted from Landsat-8
images and Sentinel-2 images. It can be seen from the figure that the
distribution patterns of \emph{S. salsa} , \emph{P. australis} ,
and\emph{S. alterniflora} shown in the two images are almost the
same.\emph{P. australis} is predominant on the beaches on both sides of
the Yellow River and the artificially restored recovery areas. \emph{S.
salsa} is mainly distributed in the beaches and areas of high salinity
in the Yellow River Delta. It is a pioneer plant in muddy tidal flats
and heavy saline-alkaline areas. \emph{S. alterniflora} mainly grows in
the lower part of the intertidal zone to the lower part of the middle
tide zone.

The overall accuracy of data extraction based on multi-temporal
Sentinel-2 is 82.86\%, with a Kappa coefficient of 0.79; the overall
accuracy of data extraction based on multi-temporal Landsat-8 is
78.77\%, with a Kappa coefficient of 0.74 (Tab. 2). Regardless of the
image, the producer accuracy and user accuracy of \emph{S. salsa}
and\emph{S. alterniflora} are generally higher than those of \emph{P.
australis} , which is caused by many factors, such as plant height,
plant density, and the type of underlying surface. However, \emph{P.
australis} and \emph{S. alterniflora} exhibit a mixed phenomenon, and
the extraction accuracy is low in the areas with more human activities.
When the Landsat-8 image is used for classification, the \emph{P.
australis} growth is misclassified into that of \emph{S. alterniflora}in
the marginal areas where the reed grows, but this problem does not occur
in Sentinel-2 images.

\subsection*{3.3 Analysis of landscape dynamics related to native and
invasive
species}

{\label{analysis-of-landscape-dynamics-related-to-native-and-invasive-species}}

The results of the extraction of native and invasive species in the
Yellow River Delta wetlands for 2010, 2013, 2016, and 2018 are shown in
Figure 7. The growth area for \emph{P. australis} is 66.96
km\textsuperscript{2}, 63.84 km\textsuperscript{2}, 76.26
km\textsuperscript{2}, and 72.73 km\textsuperscript{2} for 2010, 2013,
2016, and 2018, respectively, and the area change shows a steady upward
trend. The area where \emph{S. salsa} is found covers 47.6
km\textsuperscript{2}, 27.48 km\textsuperscript{2}, 39.61
km\textsuperscript{2}, and 35.04 km\textsuperscript{2} in the same four
time periods, respectively, which shows a downward trend. \emph{S.
alterniflora} greatly expanded its range from 2010 to 2016, rapidly
increasing from 1.14 km\textsuperscript{2} to 30.77
km\textsuperscript{2}. The expansion trend slowed from 2016 to 2018, but
the area still reached 40.53 km\textsuperscript{2} in 2018.

Due to the influence of tidal currents, large areas of beaches are
flooded during the high tide, which results in the death of some earlier
growth of\emph{S. salsa} . After the ebb tide, the seeds of\emph{S.
salsa} sprout, and new seedlings are re-grown. The \emph{S.
salsa}community in the adjacent part of the sea reciprocates with the
tide period, and poor growth is exhibited mainly in the decreased
density and short size of the plants. Therefore, the patch density and
splitting index of \emph{S. salsa} were significantly higher than that
of \emph{P. australis} and \emph{S. alterniflora} , and the aggregation
index is the lowest (Fig. 2), which indicates that the spatial
distribution of\emph{S. salsa} is irregular and discrete. With the
passage of time, the patch density of \emph{S. salsa} is decreased, but
the splitting index is always at a high level, which indicates that the
area of \emph{S. salsa} in the study location is gradually decreasing,
and the poor growth conditions lead to severe fragmentation of the
spatial distribution.

Over the past 10 years, the landscape indices of the\emph{P. australis}
community have been relatively stable, without a sharp change. The patch
density was low, the largest patch index was the highest among the three
vegetation communities, and the aggregation index was also stable at
approximately 90. This occurred because a large number of wetland
ecological restoration projects have been carried out in the Yellow
River Delta region, and \emph{P. australis} gradually formed a
contiguous distribution pattern based on its rapidly expanding
reproductive capacity, becoming an emergent aquatic plant community of a
single dominant species.

Growth of \emph{S. alterniflora} was recorded in the estuary of the
Yellow River at the beginning of the 21\textsuperscript{st} century. Due
to the impact of the tidal runoff and the Yellow River diversion,
initially, there was no large-scale expansion of\emph{S. alterniflora} .
At present, under the influence of water and sediment adjustment in the
upper reaches of the Yellow River, the area of the \emph{S.
alterniflora}community has been rapidly expanding. Since 2010, the patch
density, largest patch index, and aggregation index of \emph{S.
alterniflora}have shown an upward trend, with the splitting index
showing a significant downward trend, which highlights that the area of
occupation for \emph{S. alterniflora} is gradually increasing. The
spatial distribution of the community gradually shows a pattern of
continuous, regularized, and integrated growth. A number of previous
researchers have reported that \emph{S. alterniflora} currently appears
only on both sides of the Yellow River estuary. An area of \emph{S.
alterniflora}growth was found in the southern tidal flat for the first
time in this study. There was only a sporadic distribution in the 2017,
but a pattern of small-scale contiguous distribution was observed in
2018 (Fig. 7).

\subsection*{3.4 Analysis of expansion of S. alterniflora
community}

{\label{analysis-of-expansion-of-s.-alterniflora-community}}

\subsubsection*{3.4.1 Expansion direction
analysis}

{\label{expansion-direction-analysis}}

To study expansion direction of \emph{S. alterniflora} from 2010 to
2018, the relevant parameters of the standard deviation ellipse
of\emph{S. alterniflora} were obtained. There were great changes in the
spatial distribution pattern of \emph{S. alterniflora} on the north bank
of the Yellow River estuary. The movement path underwent a change
consisting of ``Northwest (2010-2013)---Northeast
(2013-2016)---Northwest (2016-2018),'' which generally shows a trend of
moving to the northwest. The standard deviation of the elliptical
distribution range of \emph{S. alterniflora} showed an expanding trend
from 2010 to 2018. The long semi-axis and the short semi-axis increased
from 0.52 km and 0.12 km in 2010 to 83.86 km and 19.17 km in 2018,
respectively. Combining the change of the long and short half-axis and
the distribution pattern of the standard deviation ellipse shows that
the distribution of \emph{S. alterniflora} on the north shore is
dominated by movement in the east-west direction. During the study
period, the standard deviation of the elliptical deflection angle of the
north shore ranged from 89.35deg to 108.81deg.

The center of gravity of \emph{S. alterniflora} on the south bank of the
Yellow River is mainly north-south, with small movements from east to
west. It moved mainly to the south in 2010--2013 and moved northward in
2013--2018. The long semi-axis and the short half-axis increased from
0.66 km and 0.11 km in 2010 to 39.01 km and 15.66 km in 2018,
respectively. The distribution of the \emph{S. alterniflora} community
on the south bank is mainly in the north-south direction. Compared with
the north shore, the deflection angle of the south bank has been
decreasing, from 75.81deg in 2010 to 2.78deg in 2018, which indicates that
the \emph{S. alterniflora} community on the south bank is mainly
oriented towards land expansion.

\subsubsection*{3.4.2 Analysis of Expansion
mode}

{\label{analysis-of-expansion-mode}}

Zhang et al.(2018) studied the diffusion mechanism of \emph{S.
alterniflora} from the perspective of genetic evolution. The study
showed that all the \emph{S. alterniflora} communities in this study
area are from the No. 5 pile area, rather than gradually expanding from
the north bank of the Yellow River estuary to the south bank, which
indicates that the genetic distance of\emph{S. alterniflora} has no
relationship with the geographical distance. The seeds of \emph{S.
alterniflora} can be transported over long distances by seawater for
plant propagation. After determining the source of the expansion
of\emph{S. alterniflora} , subsequent studies herein mainly analyzed the
different expansion patterns after \emph{S. alterniflora} colonization.

The multi-year expansion pattern obtained by using the LEI index shows
that from the expansion area, the main expansion mode of the \emph{S.
alterniflora} community in the past 10 years is edge expansion. This
mode of expansion is characterized by the outward expansion of the
marginal region of the patches extracted from the previous phase, which
is adjacent to the native patches. The total area of edge expansion is
33.97 km\textsuperscript{2}, and the expansion area is larger than the
original block area (0\selectlanguage{english}[?]LEI\textless{}1). The external expansion area is
small, with a total area of 5.42 km\textsuperscript{2}, and the
new\emph{S. alterniflora} patches are not adjacent to other \emph{S.
alterniflora} patches, which demonstrates a discrete distribution state.
However, from the number of patches, the number of edge expansion
plaques in the \emph{S. alterniflora} community is slightly less, only
73, and the total number of external expansion patches is 339, which
also indicates that the growth scale of \emph{S. alterniflora} community
is continuous, regularized, and integrated.

\section*{4. Discussion}

{\label{discussion}}

4.1 Image Analyses and Species Classification

Based on the multi-temporal Sentinel-2 imagery and Landsat imagery, the
extraction of the dominant species in the Yellow River Delta can be
achieved by the more important bands optimized by the random forest
algorithm. There were significantly more bands with more importance from
Sentinel-2 images than those from Landsat images, which is mainly due to
the fact that the three unique red-edge bands of Sentinel-2 images
provide more data support for the spectral separability of different
species. This resulted in an overall accuracy of the Sentinel-2 image
extraction results that was slightly higher than that for the Landsat
images. Both types of data have advantages and disadvantages when
extracting native and invasive species. Sentinel-2 data can improve the
classification accuracy by the advantages of band and spatial
resolution. The Landsat data can make up for the shortcomings of
Sentinel-2 data in long-term sequences. The combination of the two
images enabled us to analyze the temporal and spatial variation of
native and invasive species in the study area.

For extracting \emph{S. salsa} in current study area, remote sensing
image of September was selected because of the following reasons: (1)
Studies have shown that the key substance for the discoloration
of\emph{S. salsa} is salt. When \emph{S. salsa} grows in a low salt
environment (0.3--1\%), the vacuole tissue of its leaf cells is mainly
chlorophyll. However, in strong salinity (1--1.6\%), betaine becomes the
main vacuole structure, which results in leaves that appear red. (2) In
September, it is in the middle growth stage of\emph{S. salsa} , with
high biomass and a wide distribution area. At the beginning of
September, the Chinese rain belt begins to rapidly withdraw south, with
cold air remaining in the north. The rainy season in North China ends,
the amount of water coming from the Yellow River decreases, the
groundwater depth subsequently decreases, and the soil salinity
increases. The various factors mentioned above lead to the change of
color of \emph{S. salsa}from reddish to fuchsia in September, which can
better highlight the difference in remote sensing images between
\emph{S. salsa} and other vegetation.

As can be seen from Figure 7 that the distribution pattern of native and
invasive species has obvious gradients. The main driving forces for this
distribution and succession are water (groundwater depth), salt (soil
salinity), and human activities. The study area is located where river
freshwater and seawater interact. From the river bank highland to the
intertidal zone, the freshwater wetland is gradually transformed into
saltwater wetland, and the water and salt distributions have obvious
gradient differentiation. Since the \emph{S. salsa} , \emph{P.
australis} , and \emph{S. alterniflora} communities can only survive
under certain water and salt conditions, the existence of water and salt
gradients leads to the distribution and succession of the three
communities with obvious zonal distribution. At the same time, the
ecological restoration projects of the constructed wetland in the
protected area will funnel the freshwater of the Yellow River into the
ecological restoration area, which will increase the groundwater depth
in this area. The salt washing and salt discharging actions by fresh
water will reduce the degree of soil salinization. These conditions were
suitable for the reed community, and promoted its growth so that it
became the dominant species in the ecological restoration area.

Regardless of the landscape index or expansion analysis, the expansion
of the \emph{S. alterniflora} community has become an indisputable fact.
According to the current research results, the expansion of the north
shore is mainly manifested as expansion to the sea. This is because
rivers flowing eastward in the northern hemisphere are affected by the
Earth's rotation, which will cause washing of the south bank of a river.
However, sediment will be deposited due to the slower water flow in the
north shore. It is the sedimentation on the north shore that provides
habitat conditions for the rooting and wild growth of \emph{S.
alterniflora} . The data provided by the Lijin hydrological station (the
nearest hydrological station in the Yellow River Delta) show that the
artificial water and sediment adjustment occurring in the upper reaches
of the Yellow River in 2010 and 2013 resulted in a large amount of
sediment deposited on the north bank of the Yellow River estuary, which
led to a significant increase in the area of the \emph{S.
alterniflora}community on the north shore in 2013 and 2016.

The data show that water and sediment adjustment has increased in 2018,
and sedimentation in the northern part of the Yellow River estuary can
be visualized from remote sensing images. It is foreseeable that in the
next two years, the expansion of \emph{S. alterniflora} on the north
bank of the Yellow River estuary will still be considered to be on the
sea side. There is less sedimentation in the south bank, and therefore,
the expansion of \emph{S. alterniflora} occurred mainly towards the
land. The expansion pattern indicates that the \emph{S.
alterniflora}community is dominated by marginal expansion. This
expansion relies mainly on underground roots for tillering, while the
highly developed and aerated tissue of \emph{S. alterniflora} provides
sufficient oxygen to its roots to facilitate the growth of adjacent
\emph{S. alterniflora}plants. The external expansion area is small, but
the number of patches is high. This type of expansion is very important
for the development of plant growth in a new environment.

4.2 Biological and Ecological Impacts

The reproductive ability, high tolerance and adaptability to tidal flat
environmental stress of \emph{S. alterniflora} determine its competitive
advantage in the intertidal zone. Due to its high tolerance to flooding
and salinity, \emph{S. alterniflora} has an absolute competitive
advantage on the coastal side. When external factors such as tide carry
the seeds of \emph{S. alterniflora} to a new habitat, \emph{S.
alterniflora} colonizes in a new habitat, depending on sexual
reproduction, and begins the external expansion. Because of its low
productivity in the early stage of colonization, its patch area is
relatively small and scattered. After the successful establishment
of\emph{S. alterniflora} , the seedlings are difficult to survive due to
the low light intensity under the canopy of the community.
Therefore,\emph{S. alterniflora} starts a rapid marginal expansion
relying on asexual reproduction, forming a large area of single-spices
community with high density and productivity, which prevents other
vegetation from growing in its distribution area (Wang Q, 2006).

The external expansion patches in the southernmost tidal flat may be
caused by the spread of seawater or ships and birds. It is expected
that\emph{S. alterniflora} will gradually occupy the southern light
beach in the next few years. The tide is one of the carriers of \emph{S.
alterniflora} seeds. The development of tidal creek can promote the
external expansion of \emph{S. alterniflora} . \emph{S.
alterniflora}seeds drift with the tide and germinate and settle on the
edge of the tidal creek, and invades along the tide to the land side. In
addition, due to the strong shore-fixing ability of \emph{S.
alterniflora} , the erosion of the tide creek by the tide has changed
from horizontal to vertical, forming a narrow and deep tide creek (Shen
Y M, 2003\selectlanguage{english};Zhaoning Gong, 2019). \emph{S. alterniflora} has a certain
wave-slowing effect, which can promote bank consolidation, but it also
affects the development of tidal creek and is not conducive to
hydrological connectivity. In addition, the invasion of \emph{S.
alterniflora} has reduced the distribution of native species, endangered
biodiversity, occupied the light beach, and caused the loss of bird
habitats and food sources, such as rare birds --- red-crowned cranes and
black-billed gulls that depend on the habitat of Suaeda salsa(Gallardo
B,2016; Callaway J C,1992; Guy-Haim T,2018). Prevention and management
measures should be taken in time to suppress the invasion of \emph{S.
alterniflora} .

\section*{5. Conclusions}

{\label{conclusions}}

The results from this study demonstrate that it is feasible to extract
native and invasive species in the Yellow River Delta region based on
phenological characteristics of vegetation and spectral features of
satellite remote sensing images. May and October are important periods
for extracting the \emph{P. australis} community and the \emph{S.
alterniflora} community, respectively, in the Yellow River Delta or in
the coastal wetlands in North China. The landscape characteristics and
spectral characteristics of the two communities are not much different
and are difficult to distinguish.

The accuracy of different vegetation community in this area can be
improved by taking into consideration both the vegetation phenology and
the spectral features of remote sensing images. The Sentinel-2 images
with red edge bands have obvious advantages in vegetation community
extraction as compared to Landsat-8 images. The \emph{P.
australis}community in the study area is the dominant species, and its
multi-year landscape index is stable. The spatial distribution of
\emph{S. salsa}is discrete, the degree of aggregation is low, and the
area is decreasing. The landscape index of the \emph{S. alterniflora}
community showed a trend of increasing year by year, but the degree of
fragmentation decreased, indicating that the expansion pattern of
the\emph{S. alterniflora} community became spatially continuous, more
regularized and aggregated overtime.

The findings from this study advanced understanding of the spatial
distribution and spatiotemporal variation characteristics of native and
invasive plant species, and the expansion mechanism of \emph{S.
alterniflora} and other species in the study area, and provide a basis
for making scientifically sound decision for management and the
control\emph{S. alterniflora} invasion. It will contribute to the
ecological restoration and the protection of species diversity of the
entire Yellow River Delta in China. In addition, the research findings
have important theoretical and scientific value for the ecological
environment protection and sustainable development of the worldwide
estuary wetland ecosystem.

\section*{Acknowledgements}

{\label{acknowledgements}}

This work was supported by the Project of National Keypoint Research and
Invention Program of ''13th Five Year'' Period (2017YFC0505900) . The
authors greatly thank Limin Yang (Guest Professor, Capital Normal
University, Beijing, China) ) for revising and editing the contents of
the paper in English.

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\textbf{Table 1.} The number of sample data and verification data\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Class} & \textbf{Sample Data} & \textbf{Sample Data} &
\textbf{Sample Data} & \textbf{Verification Data} & \textbf{Verification
Data} & \textbf{Verification Data}\tabularnewline
\midrule
\endhead
& from on-site field & from high-resolution images & totally & from
on-site field & from high-resolution images & totally\tabularnewline
P. australis & 105 & 145 & 250 & 270 & 230 & 500\tabularnewline
S. salsa & 120 & 130 & 250 & 390 & 110 & 500\tabularnewline
S. alterniflora & 150 & 100 & 250 & 200 & 300 & 500\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} The statistics of classification accuracy\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Species} & \textbf{Landsat-8} & \textbf{Landsat-8} &
\textbf{Sentinel-2} & \textbf{Sentinel-2}\tabularnewline
& PA/\% & UA/\% & PA/\% & UA/\%\tabularnewline
\emph{P. australis} & 73.80 & 70.83 & 81.60 & 76.55\tabularnewline
\emph{S. salsa} & 72.80 & 70.82 & 79.20 & 79.04\tabularnewline
\emph{S. alterniflora} & 76.60 & 79.79 & 82.20 & 83.37\tabularnewline
OA/\% & 78.77 & 78.77 & 82.86 & 82.86\tabularnewline
Kappa & 0.74 & 0.74 & 0.79 & 0.79\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3.} Landscape index of different vegetation communities
during different years\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
\textbf{Species} & \textbf{CA} & \textbf{CA} & \textbf{CA} & \textbf{CA}
& \textbf{PD} & \textbf{PD} & \textbf{PD} & \textbf{PD} & \textbf{LPI} &
\textbf{LPI} & \textbf{LPI} & \textbf{LPI}\tabularnewline
\midrule
\endhead
& 2010 & 2013 & 2016 & 2018 & 2010 & 2013 & 2016 & 2018 & 2010 & 2013 &
2016 & 2018\tabularnewline
\emph{S. salsa} & 47.60 & 27.48 & 39.61 & 35.04 & 23.21 & 27.07 & 13.13
& 11.81 & 2.85 & 1.70 & 3.70 & 2.11\tabularnewline
\emph{P. australis} & 66.96 & 63.84 & 76.26 & 72.73 & 5.84 & 4.64 & 3.93
& 5.24 & 21.47 & 32.68 & 38.84 & 28.58\tabularnewline
\emph{S. alterniflora} & 1.14 & 15.15 & 30.77 & 40.53 & 0.22 & 5.60 &
2.37 & 4.17 & 0.72 & 8.15 & 8.10 & 9.87\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{Species} & \textbf{SPLIT} & \textbf{SPLIT} & \textbf{SPLIT} &
\textbf{SPLIT} & \textbf{AI} & \textbf{AI} & \textbf{AI} &
\textbf{AI}\tabularnewline
\midrule
\endhead
& 2010 & 2013 & 2016 & 2018 & 2010 & 2013 & 2016 & 2018\tabularnewline
\emph{S. salsa} & 264.00 & 942.74 & 271.18 & 538.26 & 73.35 & 63.71 &
75.62 & 74.25\tabularnewline
\emph{P. australis} & 12.34 & 8.60 & 6.38 & 11.26 & 91.96 & 90.23 &
92.55 & 88.53\tabularnewline
\emph{S. alterniflora} & 181.80 & 139.83 & 87.79 & 56.55 & 88.52 & 87.95
& 92.73 & 92.14\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4.} Standard deviation of elliptical parameters of spatial
expansion of the S. alterniflora community in 2010-2018\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& \textbf{North Bank of the Yellow River Delta} & \textbf{North Bank of
the Yellow River Delta} & \textbf{North Bank of the Yellow River Delta}
& \textbf{South Bank of the Yellow River Delta} & \textbf{South Bank of
the Yellow River Delta} & \textbf{South Bank of the Yellow River
Delta}\tabularnewline
\midrule
\endhead
& Long semi-axis length (km) & Short semi-axis length (km) & Deflection
angle (\selectlanguage{ngerman}°) & Long semi-axis length (km) & Short semi-axis length (km) &
Deflection angle (\selectlanguage{ngerman}°)\tabularnewline
2010 & 0.52 & 0.12 & 100.41 & 0.66 & 0.11 & 75.81\tabularnewline
2013 & 55.41 & 0.90 & 108.81 & 39.25 & 13.27 & 36.02\tabularnewline
2016 & 63.30 & 22.40 & 89.35 & 36.25 & 17.51 & 8.32\tabularnewline
2018 & 83.86 & 19.17 & 97.11 & 39.01 & 15.66 & 2.78\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 5.} Quantitative analysis of the spatial expansion pattern
of S. alterniflora\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Expansion Mode} & \textbf{LEI range} & \textbf{Number of
patches} & \textbf{Number of patches} & \textbf{Number of patches} &
\textbf{Area (km\textsuperscript{2})} & \textbf{Area
(km\textsuperscript{2})} & \textbf{Area
(km\textsuperscript{2})}\tabularnewline
\midrule
\endhead
& & \textbf{2010-2013} & \textbf{2013-2016} & \textbf{2016-2018} &
\textbf{2010-2013} & \textbf{2013-2016} &
\textbf{2016-2018}\tabularnewline
Edge expansion & -1\textless{}LEI\textless{}0 & 0 & 20 & 25 & 0 & 3.96 &
1.47\tabularnewline
& 0[?]LEI\textless{}1 & 2 & 13 & 13 & 10.14 & 10.88 & 7.52\tabularnewline
External expansion & LEI=1 & 124 & 119 & 96 & 3.87 & 0.78 &
0.77\tabularnewline
\bottomrule
\end{longtable}



\section*{Supporting Information}

{\label{supporting-information}}

\subsection*{1.Characteristics of invasive
species}

{\label{characteristics-of-invasive-species}}

The spatial and temporal distribution of wetland vegetation communities
are affected by various factors, such as climate, hydrology, soil,
topography, and the biological characteristics of vegetation itself. The
distribution and growth situation of typical dominant communities in the
Yellow River Delta wetland are as follows:

\emph{S. salsa} (Fig. 1d) is a poly-salt and annual herbaceous plant
with a flowering and fruiting period from July to October, and the
entire plant turns red in summer and autumn. Because of its bubbles used
for storing salt, it has strong salt tolerance and is the main coastal
halophyte that is distributed in beach areas, without associated
species. The height of the plant is in the range of 15--80 cm. Due to
the influence of soil salinity and groundwater depth, the vegetation
coverage of the community is significantly different.

\emph{P. australis} (Fig. 1e) is a perennial herbaceous plant that is
mainly propagated by rhizomes. In early and mid-April, \emph{P.
australis} sprouts from underground rhizomes, with intense growth from
April--July. The flowering and fruiting period is from August to
September, and then the plant becomes dry and withered in October. The
ecological range of \emph{P. australis} is extremely wide, being
distributed in the inland fresh water, coastal salt water, and brackish
water interaction areas of the Yellow River Delta. With more than 50\%
coverage, the height of \emph{P. australis} ranges from 0.5 to 3.0 m,
with some plants exceeding 4 m.

\emph{S. alterniflora} (Fig. 1f) is a perennial herbaceous plant that is
salt-tolerant and resistant to wind and waves, is mainly distributed in
the intertidal zone. The seeds can spread with the wind and waves. The
roots are developed and often densely distributed in the soil layer to a
depth of 30 cm. One plant can reproduce and yield dozens or even
hundreds of plants within one year. The growth period of \emph{S.
alterniflora} is related to its geographical distribution. The jointing
period of \emph{S. alterniflora} in the study area is in early May, and
the flowering period is from mid-August to early September. The seeds
mature in mid- to late October, with the plant then entering the late
growth stage and finally dying in November. With the height generally
between 0.6--2.0 m and more than 90\% coverage, the community
composition of S\emph{. alterniflora} is extremely simple, and it is an
important invasive species in the Yellow River Delta Nature Reserve (Shi
et al. 2009).

\subsection*{2.Data introduction}

{\label{data-introduction}}

The Landsat-8 data and Sentinel-2 data used in this study were
downloaded from the US Geological Survey official website
(https://glovis.usgs.gov/) and the European Space Agency data sharing
website (https://scihub.copernicus.eu/), respectively. The Landsat-8
image consisted of 8 bands at 30 m spatial resolution (Fig.S1). The area
under study was covered by a single Landsat-8 image with path and row of
121/34. Atmospheric correction was performed for the Landsat-8 OLI image
using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube
(FLAASH) model and Environment for Visualizing Images (ENVI) software
(Shoko et al. 2017). The Sentinel-2 mission carries a multispectral
instrument (MSI) with 13 spectral bands spanning from the visible
spectrum (VIS) and near-infrared (NIR) to shortwave infrared (SWIR) at
spatial resolutions of 10, 20, and 60 m (Yang et al. 2017; Yang et al.
2018) (Fig. 2). The Sentinel-2 dataset we downloaded was the standard
product of the top of atmosphere (TOA) reflectance. Therefore, the
atmospheric correction was preprocessed by the Sen2Cor model, which was
utilized to transfer the TOA reflectance values to the bottom of
atmosphere (BOA) reflectance values in the official software Sentinel
Application Platform (SNAP) (Erinjery et al. 2018). After atmospheric
correction, 20-m resolution bands, including bands 2-7, 8A, 11, and 12,
were adopted for later extraction of native and invasive species.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure S1.} Spectral and spatial information for Sentinel-2 MSI
and Landsat-8 OLI (Changed from Sentinel-2 MSI Level 2A Products
Algorithm Theoretical Basis Document)

\subsection*{3.Method}

{\label{method}}

\subsubsection*{3.1Random Forest}

{\label{random-forest}}

As a relatively new machine learning model, random forest can predict
the role of up to several thousand variables and is regarded as one of
the best machine learning algorithms. The random forest classification
algorithm is an algorithm based on the classification and regression
tree (CART) invented by Breiman et al., which integrates multiple
decision trees through the idea of integrated learning. Its basic unit
is the decision tree. If the CART decision tree is seen as an expert in
the classification task, random forests are the experts that classify a
task together (Iverson et al. 2008; Breiman 2001).

The steps for establishing a random forest are as follows:

(1) In the original sample, N training samples are randomly and
regressively extracted, which is called the bootstrap method. This
method is used to form a training sample set, and the data for each
training sample set are approximately two-thirds of the original sample
data set.

(2) Based on the extracted training sample set, N CART decision trees
are constructed to form a random forest. During the decision tree growth
process, m features are randomly selected at each node of each tree (the
total number of features is M, m[?]M). According to the principle of
minimum Gini coefficient, a feature with the most classification ability
was selected to perform node splitting within the decision tree.

(3) Multiple decision trees are generated to form a random forest
classifier, which is used to classify remote sensing images and
determine the category by voting.

The random forest algorithm not only enables the classification of
remote sensing images, but it also plays an important role in feature
selection and dimensionality reduction. Since approximately one-third of
the original sample data is not extracted during the sampling process,
this part of the data is called the Out-Of-Bag data (OOB data).
Out-Of-Bag-Error (OOB Error) generated by OOB data evaluates
classification accuracy and also calculates the importance of different
feature variables (Variable Important, VI) for feature selection (Genuer
et al. 2010; Sandri et al. 2012). The characteristic variable importance
assessment model is as follows:

\(\text{VI}\left(M_{A}\right)=\frac{1}{N}\sum_{t=1}^{N}{(B_{n_{t}}^{M_{A}}-B_{O_{t}}^{M_{A}})}\)\emph{,}

where \emph{VI} indicates the importance of the characteristic
variable,\emph{M} is the total number of features of the sample,
\emph{N} is the number of trees in the generated decision tree,
\(B_{O_{t}}^{M_{A}}\) is the OOB error of the t-th decision tree when any
eigenvalue \(M_{A}\) is not added with noise interference, and
\(B_{n_{t}}^{M_{A}}\) is the OOB error of the t-th decision tree when any
eigenvalue \(M_{A}\) is added with noise interference. If a
certain feature \(M_{A}\) is randomly added with noise, and the
accuracy of the OOB data is greatly reduced, this indicates that the
feature \(M_{A}\) has a great influence on the classification
result, and it also indicates that its importance is relatively high.

In the current study, the EnMAP-BOX tool developed by the German
environment mapping and analysis program project team was used for band
optimization and native and invasive species extraction. There are two
important parameters in the process of constructing the random forest
algorithm, namely, the number N of decision trees in the random forest
and the number m of features extracted during the node-splitting
process. When extracting feature variables, we selected the arithmetic
square root of the total number of features in the EnMAP-BOX tool as the
number of features. In theory, the greater the number of decision trees
N, the higher the classification accuracy, but the higher the time cost.
Based on the determination of the extracted feature m, we found that
when the number of decision trees is N[?]20, the OOB error gradually
converges and tends to be stable. Therefore, we chose N=20 as the number
of generated decision trees.

\subsubsection*{3.2Accuracy assessment of species
classification}

{\label{accuracy-assessment-of-species-classification}}

The confusion matrix is also called the error matrix. It is mainly used
to compare the degree of confusion between the classification result and
the actual measured value for accuracy evaluation. In the current study,
the overall accuracy (OA), Kappa coefficient, producer accuracy (PA),
and user accuracy (UA), which are commonly used at present, were
selected as evaluation indicators to evaluate the classification results
of different remote sensing images.

\subsubsection*{3.3Landscape index}

{\label{landscape-index}}

The landscape index refers to the highly concentrated landscape pattern
information, which reflects the simple quantitative indicators of its
structural composition and spatial configuration, and is suitable for
quantitative spatial analysis of the relationship between landscape
pattern and ecological process. Due to the large number of landscape
indices, previous research was consulted, and the class area (CA), patch
density (PD), largest patch index (LPI), splitting index (SPLIT), and
aggregation index (AI) were selected for obtaining the spatial and
temporal changes in the habitat pattern of native and invasive species
(Zhen et al. 2012; Liu et al. 2017).

\textbf{Table S1.} Description of landscape index\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Landscape Index} & \textbf{Mathematical Model} &
\textbf{Explanation} & \textbf{Explanation}\tabularnewline
\midrule
\endhead
Class Area & \(N=\sum_{i=1}^{n}a_{i}\) & \(N=\sum_{i=1}^{n}a_{i}\) & Total area of a
certain type of class.\tabularnewline
Patch Density & \(PD=N/A\) & \(PD=N/A\) & Indicates the
number of patches per unit area.\tabularnewline
Largest Patch Index & \(LPI=Max\ (a_{1}\ldots a_{n})\ /A*100\) & \(LPI=Max\ (a_{1}\ldots a_{n})\ /A*100\) &
Quantifies the percentage of total landscape area comprised by the
largest patch. As such, it is a simple measure of
dominance.\tabularnewline
Splitting Index & \(SPLIT=A^{2}/\sum_{1}^{m}a_{i}^{2}\) & \(SPLIT=A^{2}/\sum_{1}^{m}a_{i}^{2}\) & Indicates
the degree of dispersion and aggregation between patches of the same
landscape type. The greater the value, the more separated the landscape,
and the greater the number of small patches in the
landscape.\tabularnewline
Aggregation Index & \(AI=\left[\sum_{i=1}^{m}{(g_{\text{ii}}/maxg_{\text{ii}})p_{i}}\right]*100\) & \(AI=\left[\sum_{i=1}^{m}{(g_{\text{ii}}/maxg_{\text{ii}})p_{i}}\right]*100\) & Reflects
the proximity of the same type of plaque. When there is no common
boundary between all plaques, the degree of aggregation is the
lowest.\tabularnewline
\bottomrule
\end{longtable}

\emph{N} is the total number of patches of the landscape type;
\emph{A}is the total area of the landscape
type;\(Max\ (a_{1}\ldots a_{n})\ \)represents the area of the largest
plaque;\emph{E} represents the total length of the landscape
edge;\emph{a\textsubscript{i}} represents the area of the \emph{i-th}
plaque.\(g_{\text{ii}}\): Number of like adjacencies (joins) between
pixels of patch type (class) \emph{i} based on the single-count
method.\(\max g_{\text{ii}}\): Maximum number of like adjacencies (joins)
between pixels of patch type (class) \emph{i} based on the single-count
method. \(p_{i}\): Proportion of the landscape comprised of
patch type (class) \emph{i} .

\subsubsection*{3.4Metrics for analysis of S.alterniflora
expansion}

{\label{metrics-for-analysis-of-s.alterniflora-expansion}}

\subsubsection*{3.4.1 Standard deviational
ellipse}

{\label{standard-deviational-ellipse}}

The current study used the standard deviation ellipse to analyze the
spatial variation characteristics of invasive species. This is a spatial
statistical method that is used to quantitatively describe the spatial
distribution characteristics of geographic elements, and is widely used
in the fields of disease transmission, urban expansion, and pollution
diffusion. The standard deviation ellipse parameters include the center
of gravity coordinates, the deflection angle, and the length of the long
and short half axes, which indicate the relative position of the spatial
distribution pattern of geographic elements, the dominant trend
direction of geographic element development, and the degree of
dispersion in the primary and secondary directions, respectively. The
calculation of key parameters can be found in previous studies (Wong
1999; Scott et al. 2010; Qiao et al. 2017).

\subsubsection*{\texorpdfstring{3.4.2 Metric for analysis of the spatial
expansion of \emph{S.
alterniflora}}{3.4.2 Metric for analysis of the spatial expansion of S. alterniflora}}

{\label{metric-for-analysis-of-the-spatial-expansion-of-s.-alterniflora}}

The pattern of landscape expansion determines the spatial combination of
landscape elements, and the spatial combination of landscape elements
has an important impact on various ecological processes. Therefore, the
landscape expansion mode is of great significance for understanding the
process of landscape expansion. The landscape expansion index (LEI) was
adopted to quantitatively realize the identification and analysis of
landscape spatial expansion patterns (Liu et al. 2010; Wu et al. 2012).
Its expression is as follows:

\(LEI=\frac{A_{p}-A_{0}}{A_{p}+A_{0}}\),

\(\text{where\ }\ A_{p}\ \)is the area of the expanded plaque,
and\(\text{\ A}_{0}\) is the area of the plaque adjacent to the expanded
plaque, which can be understood as the original plaque. The range of the
LEI is from -1 to 1 (including 1). When the LEI value is in the interval
(-1, 1), we can call it edge expansion. When LEI\textless{}0, it
indicates that the expansion area is smaller than the original plaque
area. When LEI[?]0, it indicates that the expansion area is greater than
or equal to the original plaque area. When LEI=1 (\(A_{0}\)=0),
the original plaque does not exist, and the spatial expansion pattern of
the\emph{S. alterniflora} community is that of external expansion.

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{\label{references-1}}

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