The Changes in Dominant Driving Factors in the Evolution Process of Wetland in the Yellow River Delta during 2015–2022

Abstract

Most of the previous studies exploring the changing patterns of wetland in the Yellow River Delta (YRD) were conducted based on sparse time-series images, which ignored its severe environmental gradient and rapid evolution process of the wetland. The changes in the dominant factors in the evolution of the wetland in the YRD are not clear. This study used the dense time-series Sentinel-2 images to establish a wetland database of the YRD, and then analyzed the spatial distribution characteristics of, and temporal changes in, the wetland during 2015–2022. Finally, the dominant factors of the spatio-temporal evolutions of the wetland were explored and revealed. The results showed the following. (1) During 2015–2022, the wetland in the YRD was dominated by artificial wetland, accounting for 54.02% of the total wetland area in the study area. In 2015–2022, the total wetland area increased by 309.90 km², including an increase of 222.63 km² in natural wetlands and 87.27 km² in artificial wetlands. In the conversion between wetland types, 218.73 km² of artificial wetlands were converted into natural wetlands, and 75.18 km² of natural wetlands were converted into artificial wetlands. The patch density of rivers, swamps, and salt pans increased, showing a trend of fragmentation. However, the overall degree of landscape fragmentation in wetlands weakened. The trend of changes in the number of patches and landscape shape index was the same, while the trend of changes in Shannon’s diversity index and Contagion index was completely opposite. (2) Natural factors, such as precipitation (0.51, 2015; 0.65, 2016), DEM (0.57, 2017; 0.47, 2018; 0.49, 2020; 0.46, 2021), vegetation coverage (0.59, 2019), and temperature (0.48, 2022), were the dominant influencing factors of wetland changes in the YRD. The dominant single factor causing the changes in artificial wetlands was vegetation coverage, while socio-economic factors had lower explanatory power, with the average q value of 0.18. (3) During 2015–2022, the interactions between the natural and artificial factors of the wetland changes were mostly nonlinear and showed double-factor enhancement. The interactions between temperature and sunshine hours had the largest explanatory power for natural wetland change, while interactions between precipitation and vegetation coverage, and between temperature and vegetation coverage, had large contribution rates for artificial wetland change. The interactions among natural factors had the greatest impacts on wetland change, followed by interactions between natural factors and socio-economic factors, while interactions among socio-economic factors had more slight impacts on wetland change. The results can provide a scientific basis for regional wetland protection and management.

1. Introduction

Wetland, the transitional zone between a terrestrial ecosystem and aquatic ecosystem, has abundant wild animal and plant resources, and—together with the ocean and forest—is one of the three major ecosystems of the earth [1]. It plays an important role in regulating the atmosphere, purifying water quality, and protecting biodiversity [2]. Revealing the spatio-temporal change characteristics of wetland and its driving mechanisms is urgently required to understand the evolution of wetland ecosystems [3].

Due to the long-term, unreasonable development and utilization of wetland resources for human activities, a large number of wetlands have been seriously damaged. The loss and degradation of wetlands have aroused widespread concern in the international community. In recent years, international interest in the scientific research of wetlands has continued to heat up. The United States has attached great importance to research in the field of wetlands. By investigating the National Science Foundation’s funding of wetland research projects in recent years, it can be seen that wetland research projects in the United States have mainly focused on ‘carbon cycle and carbon storage changes’ and the ‘interaction between wetlands and environmental climate change’. As the country with the largest wetland area in the world, Canada has also attached great importance to research in the field of wetlands. By investigating the projects funded by the Natural Science and Engineering Research Council of Canada, it can be seen that Canada’s project funding in the wetland field has mainly focused on ‘Wetland Biodiversity Research’ and ‘Biogeochemistry’. The YRD is the most well-preserved estuarine wetland ecosystem in the warm temperate zone, and is also the fastest growing estuarine delta in the world. With its complex biodiversity, it is known as the “national airport of birds”. In 2019, a symposium on the ecological protection and high-quality development of the Yellow River Basin was held, which pointed out that the Yellow River Delta (YRD) was the most complete wetland ecosystem in the warm temperate zone of China, and that it was crucial to protect and improve the health of the wetland ecosystem [4]. Moreover, this symposium emphasized the importance of wetland protection in the YRD from the national strategic level. However, under the combined stress of the sharp decrease in water volume in the lower reaches of the Yellow River, climate warming, and the interferences of human activities, the wetland resources in the YRD have been greatly reduced and its functions and biodiversity have both been seriously affected [5]. To explore the laws that govern the evolution of the wetland ecosystem in the YRD is of great importance for enhancing the carbon sink effects of the wetland, reducing carbon emissions, and finally achieving the goal of “double carbon” [6]. At the same time, it can also provide technical reference and theoretical support for the exploration and research into the laws of wetland development in other native and foreign estuarine deltas.

In 2021, the State Council issued the Outline of the Yellow River Basin Ecological Protection and High-quality Development Plan. As a green ecological corridor in the lower reaches of the Yellow River, the ecological function and eco-environmental health of the wetland in the YRD has become a hot issue, both in China and globally [7], Appendix A. Many scholars have carried out research on wetland restoration [8], the evaluation of wetland ecological functions [9], and the comprehensive management of wetlands [10] in the YRD. In recent years, the monitoring and prediction of wetland changes [11,12,13,14], the driving mechanisms of wetland changes [15,16,17,18], and wetland protection and restoration [19] in the YRD has received increasing attention. The estimation of wetland biomass [20,21] and carbon storage [22,23,24] has gradually become a research hotspot. A grasp of wetland landscape patterns and the laws that determine its change is critical for the ecological evaluation of the YRD’s regional wetland, as well as its restoration and comprehensive management [25]. Recently, studies on the wetland landscape in the YRD which combined remote sensing (RS) and geographic information systems (GIS) have been conducted [26,27,28], aiming to reveal the laws that determine the spatio-temporal changes in the wetland landscape in the YRD. Wu et al. [29] quantitatively evaluated the connectivity of the wetland landscape in the YRD, providing accurate data support for the assessment and management of the regional ecosystem. Zhang et al. [30] extracted wetland information in the YRD based on Sentinel-2 images through multi-feature optimization, providing a new approach for wetland information extraction in their method selection. Based on Landsat remote sensing images, Liu et al. [31] dynamically monitored and analyzed the wetland changes in the YRD from 1991 to 2016, and concluded that the coastal areas had been gradually dominated by artificial wetland. However, most previous studies were conducted using sparse time-series images to explore the change patterns of the wetland in the YRD. Zhang et al. [32] used Landsat images with a spatial resolution of 30 m as the data source to extract remote sensing information of aquatic plants in Honghu Lake; the decision tree classification accuracy was the highest, with a Kappa coefficient of 0.86, followed by the support vector machine, with a Kappa coefficient of 0.74, while the maximum likelihood classification accuracy was the lowest, with a Kappa coefficient of 0.66. By constructing a dense time-series image data cube, Wang et al. [33] conducted remote sensing monitoring and driving force analysis of wetlands in the Yellow River estuary area, and found that the construction of salt pans, ports, and reservoir ponds was the main reason for the reduction in tidal wetlands. Zong et al. [34] quantitatively analyzed the evolution process and driving mechanism of artificial ditches in the YRD, and found that the interaction effect of dry land area and road length on artificial ditches was 99.9%.

With the development of computer technology and remote sensing application technology, scholars have introduced computer programming language and remote sensing technology into the study of wetland information extraction. Unsupervised classification and supervised classification methods based on pixels have emerged as required, such as the maximum likelihood classification, clustering classification, distance classification, transformation classification, composite classification, and rule-based classification methods supported by GIS [35]. Among them, the maximum likelihood classification method was the most widely used because of its simple operation, but this method had the disadvantages of time delay and low accuracy when classifying multi-spectral images [36]. Driven by the development of artificial intelligence and remote sensing satellite technology, machine learning classifications, such as the support vector machine, neural network, random forest, and decision tree, replaced traditional methods to become the mainstream methods for the extraction and classification of wetland information [37]. In addition, due to the fact that the YRD showed a severe environmental gradient and rapid wetland evolution process, most previous studies that were conducted based on sparse time-series with coarse resolution could not reveal the change characteristics and laws of the wetland in the YRD with active land and sea changes. Moreover, the dominant factors influencing the changes in the wetland during different periods were different and unclear under the context of global change. Few studies have investigated the changes in the dominant factors driving the changing evolution of the wetland in the YRD.

Therefore, combined with the random forest algorithm (RF), this study used the dense time series Sentinel-2 images of the YRD from 2015 to 2022 to extract wetland information. On this basis, the spatial distribution characteristics and rules determining the changes in the wetland were analyzed, and then the changes in the dominant factors influencing the changes in the wetland were explored and clarified with GeoDetector. It is of great significance to analyze the wetlands in the YRD in terms of annual spatial and temporal evolution characteristics and driving mechanisms. First of all, in terms of scientific research, the YRD has a dramatic environmental gradient and rapid wetland evolution process. However, current studies have been based mostly on sparse time-series images and a large spatial scale, which had difficulty revealing the characteristics and laws of wetland changes in the YRD with active land and sea changes. In this paper, the annual Sentinel-2 remote sensing images were used as the data source, which could make up for the gap in the study of wetland changes in the YRD under the current fine spatial and temporal scale. In terms of ecological value, finding out the laws of the wetland changes in the study area was the premise of wetland protection and restoration planning in the YRD, and the basis for building a green ecological corridor in the lower reaches of the Yellow River. In terms of social and economic development, the YRD is an important area in the battle against poverty. The economic development of the lower reaches of the Yellow River is lagging behind, and issues affecting flood control, drinking water, and ecological security are widely concerning. It is of great significance to clarify the driving mechanism of wetland change for joint protection and coordinated governance.

In this paper, the YRD was selected as the research area, and the wetlands ecosystem of the YRD was taken as the research object. The Sentinel-2 remote sensing images of the study area from 2015 to 2022 were used as the data source. With the support of RS and GIS technology, the wetland information of the study area was extracted and classified, and the classification system in line with the actual situation of the study area was obtained. On this basis, the evolutionary process of the wetlands ecosystem in the study area was dynamically monitored. Finally, the driving factors of wetland ecosystem evolution in the study area were further discussed.

2. Materials and Methods

2.1. Study Area

The Yellow River Delta is located on the south bank of the Bohai Bay and the west bank of the Laizhou Bay (117°31′–119°18′E, 36°55′–38°16′N). It has a temperate monsoon continental climate with rain and heat synchronization. The annual average temperature ranges from 11 to 12 °C, while the annual average precipitation is 551.6 mm [38]. The modern YRD starts from the Taoer River mouth in the north and ends at the tributary gully mouth in the south. It has the youngest, fastest growing and most active wetland ecosystem in China, which is a habitat and breeding place for rare birds.

2.2. Data Source and Preprocessing

2.2.1. Wetland of the YRD during 2015–2022

Eight Sentinel-2 images (Appendix B 

Table A1. Detailed information of eight Sentinel-2 MSI images.

Data	Task ID	Relative Orbit Number	Splicing Domain Number
13 September 2015	S2A	R032	T50SNG
13 September 2015	S2A	R032	T50SNH
13 September 2015	S2A	R032	T50SPG
13 September 2015	S2A	R032	T50SPH
14 October 2016	S2A	R132	T50SNG
14 October 2016	S2A	R132	T50SPG
14 October 2016	S2A	R132	T50SPH
17 October 2016	S2A	R032	T50SNH
22 September 2017	S2A	R032	T50SNG
12 September 2017	S2A	R032	T50SNH
11 July 2017	S2A	R132	T50SPG
29 September 2017	S2A	R132	T50SPH
29 September 2018	S2B	R132	T50SPG
29 September 2018	S2B	R132	T50SNG
29 September 2018	S2B	R032	T50SNH
29 September 2018	S2B	R032	T50SPH
29 September 2019	S2A	R132	T50SPG
9 September 2019	S2A	R132	T50SNG
29 September 2019	S2A	R132	T50SPH
28 August 2019	S2B	R032	T50SNH
18 September 2020	S2B	R132	T50SPG
1 September 2020	S2B	R032	T50SNG
1 September 2020	S2B	R032	T50SNH
18 September 2020	S2B	R132	T50SPH
8 September 2021	S2A	R132	T50SPG
8 September 2021	S2A	R132	T50SNG
21 September 2021	S2A	R032	T50SNH
8 September 2021	S2A	R132	T50SPH
17 August 2022	S2A	R032	T50SPG
17 August 2022	S2A	R032	T50SNG
17 August 2022	S2A	R032	T50SNH
17 August 2022	S2A	R032	T50SPH

) from 2015 to 2022 were freely downloaded from United States Geological Survey (USGS, https://earthexplorer.usgs.gov/, accessed on 3 October 2022). With the SNAP software, atmospheric correction and resampling were conducted. All bands with spatial resolutions of 20 m and 60 m were automatically resampled to grids with ta resolution of 10 m. After resampling, the images were preprocessed using image mosaic, cropping, and image fusion. The remote sensing images selected in this study were concentrated from August to October because in this period, the hydrological characteristics of the study area were stable and the vegetation and crops were growing well and easy to distinguish, which was more conducive to improving the accuracy of the extraction and classification of wetland information.

According to wetland classification standards, combined with the landscapes of the YRD [39,40,41], the wetland classification system was constructed (Table 1). The process of the extraction and classification of wetland information was mainly divided into the following steps (Figure 1). (1) Feature variable extraction: based on the processed images, five feature variable sets of spectral feature, texture feature, vegetation index, water index, and red-edge index were extracted, including 35 feature variables. (2) Sample selection: combined with Google Earth, according to the constructed wetland classification system (Appendix B 

Table A2. The optimal feature variables combination of wetland classification in the YRD during 2015–2022.

Year	Optimal Combination of Features
2015	PCA_1, PCA_2, PCA_3, GLCM_Variance, RNDVI, GLCM_Mean, MNDWI, MSAVI, RVI, Energy, DVI, MSRre, NDVIre1, NDVIre3, NDre1, NDre2, CIre, NDVI, NDVIre2, GLCM_Correlation
2016	PCA_1, PCA_2, PCA_3, GLCM_Variance, NDVIre1, GLCM_Correlation, GLCM_Mean, MNDWI, MSAVI, DVI, Energy, MSRre, NDVIre3, NDre1, NDVIre1, RNDVI, NDre2, CIre, NDVI, ASM
2017	PCA_1, PCA_2, PCA_3, GLCM_Variance, RNDVI, GLCM_Mean, MNDWI, MSAVI, MSRre, ASM, Homogeneity, Dissimilarity, NDVIre1, NDVIre3, NDre1, NDre2, Cire, NDVI, NDVIre2, GLCM_Correlation
2018	PCA_1, PCA_2, PCA_3, GLCM_Variance, RNDVI, GLCM_Mean, MNDWI, MSAVI, RVI, Energy, MSRre, NDVIre1, NDVIre3, NDre1, NDre2, Cire, NDVI
2019	PCA_1, PCA_2, PCA_3, GLCM_Variance, RNDVI, GLCM_Mean, MNDWI, MSAVI, Entropy, RVI, MSRre, NDVIre2, NDVIre3, NDre1, NDre2, CIre, NDVI, NDVIre1, DVI, GLCM_Correlation
2020	PCA_1, PCA_2, PCA_3, GLCM_Correlation, GLCM_Variance, RNDVI, RVI, GLCM_Mean, MNDWI, MSAVI, Entropy, DVI, NDVIre1, NDVIre3, NDre1, NDre2, CIre, NDVI, NDVIre2, MSRre
2021	PCA_1, PCA_2, PCA_3, GLCM_Variance, ASM, Energy, GLCM_Correlation, MNDWI, MSAVI, NDVIre1, NDVIre3, NDre1, NDre2, CIre, NDVI, NDVIre2, RNDVI, RVI, DVI, Entropy
2022	PCA_1, PCA_2, PCA_3, NDre1, GLCM_Variance, CIre, MSAVI, Entropy, MSRre, ASM, Homogeneity, Dissimilarity, NDre2, NDVIre3, GLCM_Correlation, Contrast, RVI, NDVIre1, RNDVI, MNDWI

), sample selection was carried out, including classification sample sets and verification sample sets [42]. (3) Feature selection and feature variable combination: firstly, based on the recursive feature elimination module in the random forest algorithm, the relationship between the classification accuracy and the number of feature variables was clarified, and the number of feature variables with the best classification effect was determined. Then, based on the principal component analysis method, the spectral characteristics of remote sensing images in the YRD were optimized. In this study, 10 bands of Sentinel-2 remote sensing images were selected to form a spectral feature set. There was a certain information redundancy between the bands of the images with the spectral reflectance of the ground object. The first three bands were selected using principal component analysis. Finally, based on the variable importance discrimination module in the random forest algorithm, the 25 feature variables of the remaining four feature variable sets were ranked in importance. The feature optimization combination composed of 20 feature variables had the best classification effect. The 17 feature variables with the top importance scores were selected and combined with the top 3 spectral bands of principal component analysis to form the feature optimization combination. Based on the above principles, the optimal combination of wetland classification characteristics in the YRD during 2015–2022 was obtained (Appendix B 

Table A3. Descriptions of classification schemes.

Classification Scheme	Feature Combination
A	Spectral feature
B	Spectral feature + Vegetation index + Water index
C	Spectral feature + Red-edge index
D	Spectral feature + Texture feature
E	Spectral feature + Vegetation index + Water index + Red-edge index + Texture feature
F	The optimal feature variables set

). In this study, six classification schemes (Appendix B 

Table A4. The accuracy of wetlands information extraction and classification in 2015–2022.

	2015		2016		2017		2018		2019		2020		2021		2022
	PA	UA	PA	UA	PA	UA	PA	UA	PA	UA	PA	UA	PA	UA	PA	UA
Shallow sea	82.37	98.57	81.66	98.34	98.03	92.98	96.39	92.32	98.65	94.20	96.70	99.10	95.42	96.15	82.37	98.57
River	92.14	95.74	91.95	93.66	88.95	86.28	92.21	88.38	98.96	91.06	89.25	98.77	68.52	90.24	92.14	95.74
Mud flat	95.31	67.96	88.41	71.68	99.36	99.18	99.85	99.75	98.62	98.40	96.59	99.57	96.99	99.23	95.31	67.96
Swamp	93.46	75.76	93.04	76.37	66.67	59.38	75.00	70.31	69.23	56.25	96.83	94.54	85.29	87.88	93.46	75.76
Paddy field	98.65	98.67	98.44	98.94	99.51	99.07	99.22	98.77	99.87	99.93	98.45	96.59	91.51	89.81	98.65	98.67
Salt pans	73.05	66.76	71.17	32.83	30.21	76.95	90.28	93.70	72.30	82.53	79.63	88.68	94.01	90.61	73.05	66.76
Reservoirs and ponds	97.71	95.99	96.54	96.10	94.80	95.71	91.98	95.14	96.01	99.46	99.30	96.14	94.85	88.04	97.71	95.99
Dryland	91.16	94.18	88.08	92.02	88.11	87.65	92.77	91.35	91.97	94.55	89.61	96.24	84.87	84.87	91.16	94.18
Woodland	86.36	78.84	79.50	73.04	77.33	81.19	89.30	90.92	82.49	71.19	91.48	89.56	87.50	89.36	86.36	78.84
Construction land	93.00	86.49	93.54	75.74	86.31	93.11	93.86	94.24	88.88	93.19	89.87	57.39	97.64	86.11	93.00	86.49
OA (%)	93.34		92.23		92.97		93.20		96.51		96.60		90.45		93.34
Kappa	0.91		0.89		0.90		0.90		0.94		0.95		0.89		0.91

) were designed. Setting different classification schemes was mainly used to clarify the contribution rate of different feature variable sets in the process of wetland classification, and then to explore the best way to improve the extraction and classification of wetland information. (4) Classification accuracy evaluation: in the process of the extraction and classification of wetland information, it was very important to evaluate the accuracy of classification results. Verifying the classification accuracy was helpful to improve and perfect the classification method and optimize the classification results. Based on the confusion matrix, this study combined Google Earth images and field survey sampling to evaluate the accuracy of six classification schemes.

Based on six different classification schemes, the extraction and classification of wetland information were performed based on RF with an overall accuracy of 93.58% and a Kappa coefficient of 0.91 (Appendix B Table A4) [43].

Table 1. Wetland classification system and its description in the YRD.

First Classification	Second Classification	Description [44]
Natural wetland	Shallow sea	Permanent waters with a depth of less than 6 m at low tide.
	River	A regular or periodic natural channel concentrated in a surface groove under the action of gravity.
	Mud flat	A shallow beach formed by seawater handling and shelving.
	Swamp	Low-lying water, weedy large mud area.
Artificial wetland	Paddy field	Land that can hold water regularly and can be used to grow aquatic crops such as rice.
	Salt pans	Sites for salt production via the evaporation method.
	Reservoirs and pond	Land with a water storage capacity of less than 100,000 cubic meters (excluding aquaculture water), excavated or naturally formed, below the normal water level of a pond.
Non-wetland	Dryland	Cultivated land with water saving irrigation, development of dry or drought-resistant crops, maximum water storage and maximum water use efficiency.
	Woodland	Patches of land covered by natural, secondary, and planted forests.
	Construction land	Land for building buildings and structures.
	Unused land	Unexploited land, including land that is difficult to use.

Table 1. Wetland classification system and its description in the YRD.

First Classification	Second Classification	Description [44]
Natural wetland	Shallow sea	Permanent waters with a depth of less than 6 m at low tide.
	River	A regular or periodic natural channel concentrated in a surface groove under the action of gravity.
	Mud flat	A shallow beach formed by seawater handling and shelving.
	Swamp	Low-lying water, weedy large mud area.
Artificial wetland	Paddy field	Land that can hold water regularly and can be used to grow aquatic crops such as rice.
	Salt pans	Sites for salt production via the evaporation method.
	Reservoirs and pond	Land with a water storage capacity of less than 100,000 cubic meters (excluding aquaculture water), excavated or naturally formed, below the normal water level of a pond.
Non-wetland	Dryland	Cultivated land with water saving irrigation, development of dry or drought-resistant crops, maximum water storage and maximum water use efficiency.
	Woodland	Patches of land covered by natural, secondary, and planted forests.
	Construction land	Land for building buildings and structures.
	Unused land	Unexploited land, including land that is difficult to use.

2.2.2. Driving Factor Data

Natural factors and socio-economic factors both contributed greatly to the changes in the wetland in the context of global changes (

Table 2. Data sources and processing.

Data Type	Data Name	Resolution (m)	Data Sources	Pretreatment Process
Wetland type data	Wetland type	10	United States Geological Survey (http://www.usgs.gov/, accessed on 9 October 2022)	Extraction and classification of wetland information
Natural factors	Slope	30	Geospatial Data Cloud (https://www.gscloud.cn/, accessed on 11 October 2022)	Resample
	DEM	30		Resample
	Temperature	10	The China Meteorological Data Service Center (http://www.nmic.cn/, accessed on 9 October 2022)	Kriging interpolation
	Sunshine hours	10
	Precipitation	10
	Vegetation coverage (FVC)	10	United States Geological Survey (http://www.usgs.gov/, accessed on 12 October 2022)	Band math, resample
	Distance to river	10	Resource and Environment Science and Data Center (https://www.resdc.cn/, accessed on 9 October 2022)	Euclidean distance
Socio-economic factor	The density of Gross Domestic Product (GDP)	1000	Resource and Environment Science and Data Center (https://www.resdc.cn/, accessed on 5 October 2022)	Resample
	The density of population (POP)	1000		Resample

).

Natural factors: The Digital Elevation Model (DEM) data with a spatial resolution of 30 m was derived from the Geographical Information Monitoring Cloud Platform (http://www.dsac.cn/, accessed on 2 October 2022). The above dataset was resampled into grids with a spatial resolution of 10 m, utilizing the ArcGIS 10.7 tool (Environmental Systems Research Institute, Inc., Redlands, CA, USA). The slope data were extracted from DEM using the ArcGIS 10.7 slope tool. The daily precipitation, daily sunshine hours, and daily temperature during 2015–2022 were obtained from the China Meteorological Data Sharing Network (http://data.cma.cn/, accessed on 6 October 2022). Using the ArcGIS 10.7 geostatistical analysis tool, the above three climate datasets for the years 2015–2022 were interpolated into grids with a spatial resolution of 10 m based on the Kriging interpolation method. Based on the Euclidean distance of the main rivers in Dongying City, the distance to the river was calculated. Based on the Sentinel-2 images of the YRD from 2015 to 2022, the vegetation coverage (FVC) data were calculated.

Socio-economic factors: The datasets of population (POP) intensity and Gross Domestic Product (GDP) density were derived from the Geographical Information Monitoring Cloud Platform (http://www.dsac.cn/, accessed on 9 October 2022), China County Statistical Yearbook and Dongying Statistical Yearbook (http://www.dongying.gov.cn/, accessed on 9 October 2022) for the years 2015–2022.

2.3. Methods

2.3.1. Dynamic Degrees of the Wetland

The dynamic degree of land use [45] indicated the quantitative change in one land use type in a certain study period. It was used to quantitatively analyze the speed of change in the wetland in the YRD, from the perspectives of single and comprehensive dynamic degrees.

The conversion analysis referred to the analysis of the statistical and spatial characteristics of various land use types in the YRD from 2015 to 2022. The statistical characteristic was the area conversion among different wetland types and the spatial characteristic was the spatial distribution characteristic of the conversions among different wetland types [46].

Based on the landscape pattern index, the change process of the wetland landscape in the YRD was investigated and analyzed. The wetland landscape pattern index could better indicate information on the landscape pattern, its structural composition, and some spatial configuration characteristics, which were composed of patch-level index, patch-type-level index, and landscape-level index [47]. Fully considering the characteristics and research objectives of the study area, this study selected four patch-type-level indexes, including patch density (PD), largest patch index (LPI), area-weighted mean fractal dimension index (FRAC_AM), and Patch Cohesion index (COHESION), and four landscape-level indexes, including number of patches (NP), landscape shape index (LSI), Shannon’s diversity index (SHDI), and Contagion index (CONTAG). In this paper, Fragstats 4.2 was applied to calculate the above landscape pattern indexes [48,49].

2.3.2. GeoDetector

The GeoDetector (http://www.geodetector.cn/, accessed on 5 October 2020) was utilized to analyze and reveal the driving mechanisms of wetland changes in the YRD, which is a new statistical method for detecting the spatial distribution consistency of dependent variables and independent variables based on the theory of geographical spatial differentiation [50]. The dependent variable Y represented the area attribute of natural wetland or artificial wetland in each spatial grid. The independent variable X included nine influencing factors, such as DEM, FVC, precipitation, and sunshine hours.

The interaction detector quantitatively determined the interactions between two driving factors of wetland change, mainly including five types of nonlinear weakening and single factor nonlinear weakening (

Table 3. Interaction types of independent variable to dependent variable.

Criterion	Interaction
$q (\mathrm{C}1\cap \mathrm{C}2)<\mathrm{Min}\left(q\left(\mathrm{C}1\right), q\left(\mathrm{C}2\right)\right)$	Nonlinear weakening
$\mathrm{Min}\left(q\left(\mathrm{C}1\right),q\left(\mathrm{C}2\right)\right)<q\left(\mathrm{C}1\cap \mathrm{C}2\right)<\mathrm{Max}\left(q\left(\mathrm{C}1\right),q\left(\mathrm{C}2\right)\right)$	Single-factor nonlinear weakening
$q (\mathrm{C}1\cap \mathrm{C}2)>\mathrm{Max}\left(q\left(\mathrm{C}1\right), q\left(\mathrm{C}2\right)\right)$	Double-factor enhancement
$q\left(\mathrm{C}1\cap \mathrm{C}2\right)=q\left(\mathrm{C}1\right)+q\left(\mathrm{C}2\right)$	Independent
$q\left(\mathrm{C}1\cap \mathrm{C}2\right)>q\left(\mathrm{C}1\right)+q\left(\mathrm{C}2\right)$	Nonlinear enhancement

C1 and C2 represent the influencing factors of wetland change.

).

3. Results

3.1. Spatio-Temporal Distributions of Wetland in the YRD

3.1.1. Spatial Distribution of Wetland in the YRD

The wetlands in the YRD were dominated by perennial wetland, and its spatial distribution was greatly affected by the location of land and sea. Shallow sea and mud flats were mainly distributed in the Hekou District and Kenli District. Reservoirs, ponds, and salt pans were mainly distributed in Hekou District and Kenli District near the Laizhou Bay of the Bohai Sea. Swamp was mainly distributed in the northeast of Kenli District, the Yellow River estuary. Dry land was mainly distributed in inland areas, such as Lijin County. The spatial distributions of wetland during 2015–2022 were shown in Figure 2.

In 2022, the area of wetland in the YRD was 2632.28 km², accounting for 37.42% of the total area in the YRD. The areas of natural wetland and artificial wetland were 1210.22 km² and 1422.06 km², respectively, accounting for 45.98% and 54.02% of the total area of wetland. Mud flats had the largest area, accounting for 32.95% of the natural wetland area, mainly located near the coastline. Rivers had the second largest area, accounting for 32.66%, which were mainly composed of the Yellow River Basin water system, the Haihe River basin water system, and the Huaihe River Basin water system. The Yellow River (138 km) flowed through Dongying City, and its river channel was silted and extended, while the width of the embankment was different. The water system of the Haihe River Basin was located in the north of the YRD, in mostly the north–south direction, which was composed of 14 rivers, including the Chaohe River and Zhanli River. The water system of the Huaihe River Basin was located in the south of the YRD in mostly the east–west direction, which was composed of 25 rivers, including the Xiaoqing River, Guangli River, and Yongfeng River. Shallow sea and swamp had smaller areas, accounting for 19.15% and 15.24% of the natural wetland area, respectively. The natural wetland showed an obvious conversion from sea to land, which was characterized by the incensement of shallow sea, mud flat, swamp, and river. Reservoirs and ponds accounted for 81.90% of the total area of artificial wetland, followed by salt pans, accounting for 12.65%. The above two wetland landscapes were mainly distributed along the coastline, while paddy fields were mainly distributed in areas close to water sources such as rivers, accounting for only 5.45%.

3.1.2. Change Characteristics of Wetland in the YRD

As shown in Figure 3a, the total area of wetland in the YRD showed an increasing trend during 2015–2022. Frequent sea–land changes, such as silt deposition and coastal erosion in the estuary area of the Yellow River, led to dramatic changes in the coastal boundary of the study area from 2018 to 2019. Therefore, the wetland changes in the YRD during 2015–2022 were divided into two stages. The total wetland area increased by 436.19 km² during 2015–2018, and the dynamic degree of comprehensive land use was 14.55%. From 2019 to 2022, the total area of wetland showed a fluctuating, decreasing trend, with a reduction of 19.11 km² and a comprehensive land use dynamic degree of 14.37%. During this period, the total area of wetland in 2021 was the largest, with 2813.15 km², and the natural wetland and artificial wetland had areas of 1239.93 km² and 1573.22 km², respectively, accounting for 44.08% and 55.92%, respectively.

The area changes for natural wetland and artificial wetland during 2015–2022 are shown in Figure 3b. There was a fluctuating, increasing trend during this study period. In 2015–2016, the area of natural wetland increased by 221.75 km² with a change rate of 22.47%, which was mainly caused by an increase in the areas of rivers and mud flats. Rivers contributed to an area increase in natural wetland of 263.36 km², while the contribution of mud flats was 104.56 km². On the contrary, the area of shallow sea and swamp showed a decreasing trend of 129.36 km² and 16.81 km², respectively. In 2016–2019, the area of natural wetland showed a decreasing trend of 50.08 km². In 2016–2017, it was mainly affected by a reduction in mud flats. In 2017–2018, the shallow seas and swamps contributed to a reduction in natural wetland area by 77.02 km² and 48.55 km², respectively. However, the reduction in natural wetland area during 2018–2019 was mainly affected by a decrease in river area. In 2019–2021, the area of natural wetland was increased by 81.27 km² with a change rate of 3.51%. In 2021–2022, the area of natural wetland was decreased by 29.71 km², with a change rate of −2.40%. As shown in Figure 3b, the area of artificial wetland was decreased by 52.29 km² during 2015–2016, with a change rate of −3.92%, which was mainly attributed to a reduction in the area of salt pans and paddy fields. Paddy fields contributed to a reduction in the area of the artificial wetland by 59.98 km², with a dynamic degree of −44.98%, while that of salt pans was 89.75 km², with a dynamic degree of −32.68%.

In 2015–2022, the area change trend of artificial wetland was consistent with that of reservoirs and pond areas, and the dynamic degree of reservoirs and ponds was 2.35%. In 2015–2016, the area of artificial wetland decreased by 52.29 km², with a change rate of −3.92%, mainly due to a decrease in salt pans and paddy fields. Paddy fields contributed 59.98 km² to the decrease in artificial wetland area, and its dynamic degree was −44.98%. Salt pans contributed 89.75 km² to the reduction in the area of artificial wetland, and its dynamic degree was −32.68%. In 2016–2018, the area of reservoirs and ponds in the YRD increased, while the area of salt pans and paddy fields decreased, and dynamic degrees of land use were 14.21%, −20.33%, and−11.20%, respectively.

3.2. Change Characteristics of Wetland Types in the YRD

From 2015 to 2022, the areas of decreased zone, increased zone, and the net increased zone of wetland in the YRD were 318.21 km², 628.11 km², and 309.90 km², respectively. The conversion between wetland and non-wetland was obvious. For the increased wetland zone, 573.54 km² was converted from non-wetland, including 128.84 km² from dry land, 47.67 km² from woodland, 166.48 km² from construction land, and 230.55 km² from unused land. For the decreased wetland zone, 273.70 km² was converted to non-wetland, of which 83.84 km² was converted to dry land, 74.51 km² was converted to woodland, 106.40 km² was converted to construction land, and 8.95 km² was converted to unused land. The area of the conversion zone between different wetland types was 293.91 km², of which 218.73 km² of the artificial wetland was converted into natural wetland and 75.18 km² of natural wetland was converted into artificial wetland (

Table 4. Transfer matrix of wetland changes for YRD during 2015–2022 (km²).

		2022
		Sea	Shallow Sea	River	Mud Flat	Swamp	Paddy Field	Salt Pans	Reservoirs and Ponds	Dryland	Woodland	Construction Land	Unused Land
2015	Sea	--	23.28	13.73	9.07	3.37	0.00	0.08	5.03	0.03	0.00	0.18	0.01
	Shallow sea	39.04	--	55.79	40.33	13.69	0.12	2.85	8.27	1.74	0.75	8.44	0.61
	River	0.01	0.32	--	0.70	7.67	0.18	4.07	17.88	0.54	1.37	0.81	0.08
	Mud flat	3.58	15.71	37.18	--	12.82	0.54	2.95	26.91	9.92	3.94	54.87	3.43
	Swamp	0.00	0.11	10.67	11.27	--	0.01	0.76	10.64	2.28	3.06	1.42	0.18
	Paddy field	0.00	0.05	3.09	0.30	3.83	--	2.49	11.40	52.21	35.80	4.28	1.98
	Salt pans	1.41	3.22	50.40	8.71	4.58	1.04	--	108.19	5.18	5.85	11.74	0.79
	Reservoirs and ponds	0.48	4.65	100.81	30.31	8.79	1.74	55.89	--	11.98	23.75	24.84	1.88
	Dryland	0.00	0.09	14.69	2.84	7.94	40.49	8.43	54.36	--	506.63	78.05	51.33
	Woodland	0.00	0.03	7.89	1.17	3.20	4.55	2.57	28.26	168.13	--	26.54	5.88
	Construction land	0.16	1.37	36.90	25.19	11.06	4.65	9.00	78.32	214.71	108.64	--	70.74
	Unused land	0.00	0.88	26.64	7.80	17.97	6.18	17.10	153.98	235.11	213.35	56.27	--

).

The changes in the wetland’s spatial distribution during 2015–2022 are shown in Figure 4 and

Table 5. The secondary classification coding of wetland types in the YRD.

Number	00	01	02	03	04	05
Landscape types	Sea	Paddy field	Shallow sea	Salt pans	Reservoirs and ponds	Dryland
Number	06	07	08	09	10	11
Landscape types	Woodland	River	Mud flat	Construction land	Swamp	Unused land

The first two digits and the last two digits represent the wetland types shown in Table 5, and the third digit 0 represented the conversion process. For example, 00002 represents the zone of sea converted into paddy field during 2015–2022.

. It was found that the spatial distribution characteristics and its changes for wetland were mainly composed of three aspects. (1) Zones of non-wetland converted into wetland were mainly distributed in the Hekou District, the middle, and northeast of Kenli District. During 2015–2022, 314.91 km² of non-wetland was converted to reservoirs and ponds, including 153.98 km² from unused land and 78.32 km² from construction land, mainly distributed in the northeast of Kenli District and the east of Hekou District. (2) Zones of wetland converted to non-wetland were mainly distributed in the north of Hekou District, the northeast of Kenli District, and the east of Dongying District. A total of 52.21 km² of paddy field was converted into dry land at the estuary of the Yellow River in the northeast of Kenli District, 35.80 km² of paddy field was sporadically converted into woodland in the middle of Kenli District, the south of Hekou District, and the north of Dongying District, and 54.87 km² of mud flat was converted into construction land in the west of Dongying District and the northwest coastline of Hekou District. In addition, zones of wetland converted into construction land were mostly distributed around the original construction land, which were located in the main urban areas of Dongying District and Kenli District. (3) The mutual conversions between different wetland types were mainly distributed in the eastern part of Kenli District and Dongying District, and the northern part of Hekou District, such as the conversion of salt pans into reservoirs and ponds, and the conversion of reservoirs and ponds into river.

3.3. Changes in Wetland Landscape Pattern Index in the YRD

The changes in the landscape pattern index at the patch-type level in the YRD from 2015 to 2022 are shown in Figure 5. The PD of unused land was the largest, followed by that of woodland. In 2015–2022, the PD of rivers, swamps, and salt pans increased, showing a fragmentation trend. The PD of reservoirs and ponds reached its peak value in 2018, which was related to the prevalence of estuarine hairy crab culture in that year. The PD of mud flats decreased, which was closely attributed to the river dredging project. The LPI of dry land, construction land, and reservoirs and ponds were obviously higher than that of other wetland types. These three types were also the main wetland types of the YRD. From 2015 to 2022, the LPI of the river reached its peak value in 2022 and its minimum value appeared in 2015, which was consistent with a change of river area. The LPI of swamps showed a “V”-shaped change trend and its minimum value occurred in 2018, which was consistent with the time when its minimum area value appeared. The FRAC_AM of construction land and dry land were significantly larger than that of other landscape types. The cities and towns in the YRD were sparsely distributed and the staggered distribution of construction land and dry land were relatively obvious. The FRAC_AM of paddy fields was smaller, with its value basically close to 1, indicating that this landscape type was generally distributed regularly. The COHESION of swamps changed slightly in most years except 2018. The COHESION of shallow seas and construction land remained unchanged, while that of rivers, paddy fields, salt pans, woodland, and unused land varied widely. The COHESION of reservoirs and ponds showed fluctuating changes and the change curve had two low peaks, occurring in 2017 and 2019, respectively.

The changes in the landscape pattern index at the landscape-level in the YRD from 2015 to 2022 are shown in Figure 6. At the landscape level, the change trends of NP and LSI were similar from 2015 to 2022, while those of SHDI and CONTAG were completely opposite. In 2021, the NP was the largest, which indicated that the wetland landscape in the YRD was the most fragmented. Meanwhile, the LSI had the largest value, indicating that the landscape shape was most complex in 2021. The CONTAG could indicate the connectivity and the spatial distributions of landscapes [43,44]. In 2016, the CONTAG reached its peak value, which indicated that the connectivity of landscapes was at its largest and that wetland was most evenly distributed. In 2022, the NP and LSI decreased obviously, indicating that the landscape fragmentation of the YRD weakened, and the landscape shape also tended to be simple. During 2021–2022, the SHDI decreased sharply, indicating that the balance of the proportion of different landscape types was broken.

3.4. The Role of Natural Factors in Driving Wetland Change

The total wetland area was negatively correlated with vegetation coverage (R² = 0.92). The main reason was that in 2022, the area of reservoirs and ponds was the largest, followed by mud flats. However, reservoirs and ponds and mud flats were mainly distributed in low vegetation coverage zone in the coastal area. As a result, the total wetland area decreases with the increase in vegetation coverage. The total wetland area had a quadratic polynomial relationship with precipitation (R² = 0.76). When the precipitation was less than 500 mm, the total wetland area did not increase significantly with the increase in precipitation. When the precipitation exceeded 500 mm, the total wetland area increased significantly with the change in precipitation. It was mainly because surface runoff and regional precipitation were the main sources of wetland water supply. When the regional precipitation was small, it was difficult to affect the wetland and non-wetland manifestations in the whole region. When the regional precipitation increased to the saturation of groundwater, it would not only directly recharge the wetland water source, but also indirectly increase the surface runoff, making the total wetland area and precipitation increase. The increasing trend was more obvious.

3.5. Dominant Single Driving Factors of Wetland Change

In this study, nine typical influencing factors of wetland change were found in the YRD. The GeoDetector was then applied to determine the explanatory power of each influencing factor. The nine factors and their specific details are shown in

Table 6. List of driving factors.

Driving Factors	Index	Code Name
Natural factors	FVC	X1
	Precipitation	X4
	Sunshine hours	X5
	Temperature	X6
	Distance to river	X7
	DEM	X8
	Slope	X9
Socio-economic factors	GDP	X2
	POP	X3

.

As shown in Figure 7a, natural factors had more obvious impacts on the changes in natural wetland, while the explanatory power of socio-economic factors was smaller, with a q value of 0.18. The dominant influencing factor on the change in natural wetland in the YRD from 2015 to 2016 was precipitation, with the q value rising from 0.51 to 0.65, followed by FVC, with the q value rising from 0.40 to 0.44. In 2017 and 2018, the dominant factor was DEM, with q values of 0.57 and 0.47, respectively, followed by FVC, with q values of 0.56 and 0.41, respectively. In 2019, FVC and DEM had great explanatory power for the changes in natural wetland in the YRD, with q values of 0.59 and 0.53, respectively. In 2020 and 2021, DEM had the greatest explanatory power for natural wetland changes, with q values of 0.49 and 0.46, respectively. In 2022, the temperature greatly contributed to the changes in natural wetland, with a q value of 0.48, followed by DEM, with a q value of 0.46.

As shown in Figure 7b, FVC was the dominant driving factor for the changes in the artificial wetland in the YRD from 2015 to 2022, and its q value (0.76) reached its peak in 2019. However, in 2016, the explanatory power of FVC on the changes in artificial wetland weakened with a q value of 0.58, which was still the dominant factor. Temperature was another dominant driving factor, reaching its peak q value of 0.43 in 2020.

3.6. Dominant Interactive Driving Factors of Wetland Change

3.6.1. Natural Wetland

The interactions among various driving factors on the natural wetland changes in the YRD from 2015 to 2022 were mostly nonlinear enhancement and double-factor enhancement, and there were no independent interactions and nonlinear weakening factors (Figure 8). The interactive driving factors with greater explanatory power were precipitation and FVC (2016, 0.94), sunshine hours and precipitation (2016, 0.93), temperature and FVC (2019, 0.94), DEM and FVC (2019, 0.93), temperature and sunshine hours (2020, 0.96), and distance to river and sunshine hours (2020, 0.92), indicating that natural environmental factors were the key driving factors in the change process of natural wetland. From 2015 to 2022, many interactive factors had q values greater than 0.90. Among them, in 2015, the interactions between various natural environmental factors and slope had a nonlinear enhancement effect. Additionally, in 2022, the interactions between various natural environmental factors and socio-economic factors, such as GDP, were mutually reinforced.

In 2015, the interactions among various factors on the changes in natural wetland were mostly enhanced by two factors, while that between FVC and slope, distance to river, sunshine hours, temperature and POP, slope and precipitation, sunshine hours, temperature, distance to river, and DEM were all enhanced nonlinearly. During 2015–2022, the double-factor enhancement interactions were greater than that of non-linear enhancement among the factors affecting the changes in natural wetland. In 2022, the interactive factors with partially nonlinear enhancement interactions mainly included FVC and GDP, POP, precipitation, sunshine hours, etc., indicating that interaction enhancement was the main interaction type between natural environmental factors and socio-economic factors. The expansion of natural wetland was mainly the result of the comprehensive impacts of natural environmental and socio-economic factors.

3.6.2. Artificial Wetland

The interactions among various driving factors on the changes in artificial wetland in the YRD from 2015 to 2022 were mostly nonlinear enhancement and double-factor enhancement (Figure 9). The interactive driving factors with greater explanatory power were precipitation and FVC (2015, 0.98), slope and FVC (2017, 0.93), temperature and FVC (2019, 0.98) and DEM and FVC (2019, 0.95), indicating that the interactions between natural environmental factors had greater effects on the change process of artificial wetland in the YRD than that between other factors. The interaction between factors in 2016 generally had a small effect on the changes in artificial wetland area, with the largest effect being FVC and sunshine hours, with a q value of 0.85, followed by FVC and DEM, with a q-value of 0.84. In 2015–2022, many interactive factors had q values greater than 0.90. Among them, the interactions between slope and other influencing factors in 2015 showed a nonlinear enhancement effect. Moreover, the interactions between FVC and GDP, POP, precipitation, sunshine hours, temperature, etc. in 2022 had a mutual enhancement effect.

In 2015, the interactions among various factors on the changes in the artificial wetland were mostly enhanced by two factors, while those between the distance to the river and sunshine hours, temperature, DEM, slope, sunshine hours, and GDP were nonlinearly enhanced. During 2015–2022, the change process of the artificial wetland was greatly influenced by the enhanced interactions between two factors. In 2022, the interactions were partially nonlinear, mainly including sunshine hours, GDP, POP, etc. The explanatory power of the interactions between the socio-economic factors and the natural factors was significantly greater than that between the socio-economic factors, indicating that the changes in the artificial wetland were mainly the results of the comprehensive impacts of the natural environmental and socio-economic factors.

4. Discussion

4.1. Reasons for the Spatio-Temporal Change Patterns of the Wetland

The area of natural wetland increased by 214.97 km² from 2015 to 2018, which was mainly attributed to the siltation of the lower reaches of the river and the delta coast caused by the increase in the sediment content of the Yellow River. In 2018–2019, a large amount of sand came from the middle and upper reaches of the Yellow River, and the coastline greatly expanded to the sea in the process of siltation and erosion. The sediment reclaimed from the sea formed the new land, resulting in the development and extension of the newly opened sand spit and river mouth [51]. The natural wetland area showed an increasing trend of 51.56 km² from 2019 to 2022, which was mainly affected by policy implementations of the ecological protection and high-quality development of the Yellow River Basin. The strengthening control of wind prevention and sand fixation in key regions in the upper reaches of the Yellow River achieved remarkable results, and the phenomena of water and soil loss were comprehensively improved [52,53]. In the middle reaches of the Yellow River, forest and grass protection had been vigorously implemented and the soil and water conservation capacity of the Loess Plateau had been enhanced, promoting wetland protection and ecological management in the lower reaches of the Yellow River [54]. The river sedimentation was reduced and the river system at the mouth was connected [55]. In addition, the measures of returning ponds to rivers, farmland to wetland, and farmland to beaches had been vigorously implemented, which greatly promoted the restoration of wetland. During 2015–2018, the area of artificial wetland was increased by 221.22 km². The main reason was that large numbers of ponds were constructed to promote economic development, resulting in a sharp increase in artificial wetland area [56]. The area of artificial wetland was decreased by 70.70 km² from 2019 to 2022, the main reason being that the measures of returning ponds to rivers had been extensively carried out to protect and repair the natural wetland in the YRD, which largely reduced the impacts of economic activities such as reclamation and aquaculture on the wetland ecosystem [57].

4.2. Reasons for the Changes in the Dominant Factors of the Wetland

In 2015–2022, the changes in the dominant factors of natural wetland were as follows: precipitation (2015, 2016) → DEM (2017, 2018) → FVC (2019) → DEM (2020, 2021, 2022). In addition, the dominant factor of artificial wetland was mainly FVC. The process of the wetland’s changes and its spatial distribution in the YRD was the result of the comprehensive actions of various driving factors [58,59]. There were three dominant factors for the wetland changes in the YRD, namely precipitation, FVC, and DEM. Precipitation had both direct and indirect effects on the changes in the wetland [60]. Surface runoff and regional precipitation were the main sources of wetland water supply. When the regional precipitation was increased to the saturation of groundwater, it not only directly recharged the wetland water source, but also indirectly increased the surface runoff, leading to an increased total wetland area. Water was the key factor for the formation and sustainable existence of the wetland, and sediment was the material basis for the formation of delta coastal wetland [61]. Therefore, increased precipitation, abundant water resources, and dense river networks were all conducive to the formation and evolution of natural wetland in the YRD during 2015–2016. The contributions of FVC to wetland changes were indirect [62]. The improvement of FVC was conducive to enhancing the water and soil conservation capacity of the Yellow River Basin, reducing the sediment content of the river, which greatly inhibited the sediment deposition process in the delta. Additionally, the above ecological process largely affected the changes in natural wetland. Secondly, vegetation had the function of regulating the regional climate, such as by reducing the temperature [63]. The temperature affects the evaporation of vegetation and surface soil, which indirectly influences the changes in the natural wetland and artificial wetland. The influence of DEM on wetland changes was indirectly affected by surface runoff and FVC [64].

4.3. Suggestions for the Protection and Development of Wetlands in the YRD Based on the Results

Strictly abide by the permanence of basic farmland, and improve the farming rate of saline-alkali land. The results showed that the area of artificial wetland in the study area decreased during 2015–2022. On the one hand, the expansions of construction land, reservoirs, and ponds were large. On the other hand, the amount of farmland transferred was small and the supplementary sources were insufficient. Therefore, it was necessary to strictly implement the protection measures of permanent basic farmland, strengthen the comprehensive development and utilization of saline-alkali land while ensuring that high-quality cultivated land was not occupied, and accelerate the development of efficient cultivation techniques for saline-alkali land.

4.4. The Uncertainty of Dominant Driving Factors in the Evolution Process of the Wetland in the YRD

The uncertainty of this study mainly refers to the selection of data sources, the use of research methods, and the implementation process. In terms of data source selection, this study extracted and classified wetland information based on the random forest algorithm. Due to the lack of the multi-temporal characteristics of ground objects, the reservoir ponds and salt pans were misclassified, which made the characteristics and laws of wetland evolution deviate. In terms of the research methods and the implementation process used, the selection of driving factor data, and analysis methods on the factor detection results during the wetland evolution process, the differences included the construction of fishing nets, the selection of driving factors, and analysis methods. The construction of fishing nets was the basis of driving factor detection. The quality of fishing nets directly affected the accuracy of factor detection results, while the small number of fishing nets limited the representativeness of data, which in turn affects the final detection results. At the same time, due to the differences in topographic features (DEM, slope), climatic conditions (precipitation, temperature, sunshine hours) caused by the heterogeneity of the regional natural environment, and the influence of human activities, the distribution of wetland types in different districts and counties was obviously different. This feature makes the construction of fishing nets one of the limiting factors for accurate detection.

5. Conclusions

Based on the dense time-series Sentinel-2 images from 2015 to 2022, the wetland information in the YRD was extracted and classified, and then the change patterns of the wetland were analyzed and revealed. Finally, its driving mechanisms were explored and clarified with GeoDetector. The main conclusions were as follows:

(1) Affected by the frequent sea–land change activities of the Yellow River estuary, the coastline of the YRD migrated from 2018 to 2019, and the changing characteristics of the wetland area were divided into three stages. From 2015 to 2022, the total area of the YRD wetland increased by 309.90 km². This change was mainly into new wetland. The conversion between the wetlands mainly occurred at the estuary of the Yellow River and the eastern and northern coastal areas of the study area. The landscape patterns of level, river, swamp, and salt pans showed a trend of patch fragmentation, and the distribution of rivers was regular. At the landscape level, the landscape heterogeneity decreases. In 2021, the wetland landscape had the highest degree of fragmentation, the most complex landscape shape, and small landscape connectivity, but the landscape richness was high.

(2) Natural factors and socio-economic factors were selected to analyze the driving mechanism of wetland changes in the YRD. Based on the geographical detector, the dominant single factor and the dominant interaction factor of wetland changes were detected and analyzed. The dominant single factors that affected natural wetland changes during different periods had significant differences, including precipitation (2015, 2016), DEM (2017, 2018), FVC (2019), DEM (2020, 2021), and temperature (2022). In the process of natural wetland and artificial wetland changes, the interactions between factors were mainly nonlinear enhancement and two-factor enhancement, and there was no independent interaction and nonlinear weakening factor.

There were many limitations in this study, such as the short time-series of data sources, the lack of multi-temporal features in wetland information extraction, and the small scope of the driving factors involved. In the process of continuing research, multi-source and multi-temporal remote sensing data could be selected as data sources to achieve the purpose of extending time series and extracting multi-temporal features. Combined with the real situation of the YRD wetlands, the data of driving factors, such as the sediment content and water flow of the Yellow River, could be selected for more perspectives.