Assessment of Ecosystem Service Values of Urban Wetland: Taking East Lake Scenic Area in Wuhan as an Example

Abstract

Urban wetlands represent a significant ecosystem type within urban landscapes. The quantitative assessment of their ecological service value holds great significance in guiding and improving the urban habitat. However, due to the insufficient spatial resolution of traditional low-to-medium resolution remote sensing imagery for surface monitoring, previous studies have conducted relatively limited research on the ecosystem services of urban wetlands. In this paper, based on multi-source data including multi-scale remote sensing data, a spatial-temporal fusion model and multiple ecological parameter inversion models were employed to invert three key ecological parameters at high spatial resolution, thereby assessing the ecosystem service values (ESVs) of urban wetlands. Taking the East Lake Scenic Area (ELSA) in Wuhan as an example, the dynamics of its ecosystem services’ value components were comparatively analyzed. The results indicate that, while the total value of ecosystem services declined slightly in 2015 compared to 2011, there was a notable increase in their value to CNY 3.219 billion by 2019, which represents a doubling of the total value relative to 2011. This trend could be primarily attributed to a significant rise in cultural services within the region. Specifically, the value of tourism services reached CNY 2.090 billion in 2019, representing a threefold increase compared to 2011. This demonstrates that ecosystem services in the ELSA have been significantly optimized and enhanced through associated ecological projects. Further research should investigate the mechanisms by which urbanization affects these crucial ecosystem services, particularly the characterization of cultural services in urban wetlands, and develop more effective strategies to enhance urban resilience and sustainable development.

1. Introduction

Wetlands play a crucial role as natural elements in cities, providing a range of ecosystem services that enhance the built environment and human well-being [1,2,3]. However, the continuous erosion and shrinkage of the wetlands’ ecological space has led to a gradual weakening of the ecosystem function and services of the wetlands [4]. It has been estimated that more than 60% of wetlands have been lost since the 20th century [5]. The main cause of urban wetland loss is urbanization [6,7]. The process of urbanization has resulted in both socio-economic development and a gradual reduction in the area of wetlands within the urban environment [8]. In China, the rate of urbanization-induced wetland loss was 2.8 times higher between 2000 and 2010 than between 1990 and 2000 [9]. Given the significant decline in wetland habitats, it is important to conduct a comprehensive and accurate assessment of the benefits that wetlands provide.

As a crucial bridge between ecosystems and socio-economic systems, ecosystem services have been a popular topic in the fields of ecological economics and urban management [10,11,12,13]. Urban wetlands are the natural ecosystems that are frequently encountered by urban residents, making them an important component of urban environments [14,15]. The ecosystem services classification most commonly recognized in the context of urban wetlands is that proposed by the United Nations in the 2005 Millennium Ecosystem Assessment (MEA). Based on natural ecological processes and functions, the MEA divided ecosystem services into four major types: support services, production services, regulating services, and cultural services [2]. In comparison to natural wetland, urban wetlands exhibit a number of distinctive characteristics [16,17]. They are often characterized by uneven distribution, smaller areas, and low connectivity of wetland patches. Firstly, these are distinguished by a greater degree of habitat fragmentation and pronounced alterations in microclimatic conditions [18]. Secondly, their social service functions have been significantly enhanced [19]. Thirdly, they are frequently subjected to artificial disturbances and governance, which may be either fully artificial or semi-artificial in nature.

The continuous development of remote sensing technology has led to the widespread use of monitoring methods, such as ecological element identification [20,21,22] and ecological physicochemical parameter inversion [23,24,25,26] based on remote sensing data. Various ecosystem services, such as forests, lakes and grasslands, have been extensively and deeply studied at different scales, including global [27,28], regional [29,30,31] and watershed [7,32,33]. Although the selected ecosystem services assessed are largely comparable, the valuation is a multifaceted endeavor with diverse approaches and outcomes due to the inherent complexity of ecosystem processes. This is because their functioning depends on various ecosystem processes at different scales, which means that ecosystem services of different types and at different scales will not be identical in nature [34]. Currently, there are two main methods for valuing ecosystem services: the equivalent factorization method and the functional values method [35]. The former is widely used because it only requires land cover data and a small amount of socio-economic data in the region and is relatively simple to calculate. However, the equivalent factorization method fails to account for spatial heterogeneity within ecosystems, which makes it difficult to identify finer spatial value distributions. The latter method is challenging to generalize due to the large amount of empirical data, socio-economic data, and ecological parameters required for the calculation of various ecosystem services [36].

Urban wetlands differ from natural ecosystems in that they have been significantly impacted by human activities. The declining habitat quality of urban wetlands leads to the degradation of their ecological functions, as well as a reduction in biodiversity and species richness [37,38]. Moreover, the diverse topography of urban areas gives rise to a multitude of intricate and diverse ecosystem features, as well as a plethora of distinct ecosystem services offered by urban wetlands. Scholars have conducted research on the ecological service value of certain typical wetlands, such as Sanyang wetland [14], Kilombero wetland [39], New Zealand wetland [40], South Sudan wetland [41], Poyang Lake [42], and Hangzhou Bay Wetland [43]. However, the monitoring accuracy requirements for urban wetlands using traditional low- and medium-resolution remote sensing imagery are difficult to meet due to their relatively small scale [44]. In addition, existing studies on urban wetlands tend to focus on a particular point in time [45,46], which makes it difficult to investigate how changes in their ecosystem services respond to the urbanization process.

Based on the above context, the following three research questions are addressed in the study: (1) How can the value of ecosystem services provided by urban wetlands be accurately assessed? (2) What is the value composition of the ecosystem services provided by urban wetlands? (3) What is the change trend in the value of ecosystem services within urban wetlands in the context of human activities? To answer the above questions, this paper combines remote sensing (RS) and geographic information system (GIS) methods to calculate three key ecological parameters pertaining to urban wetlands at a fine-grained level, and to assess the ecosystem service values (ESVs) of urban wetlands. Taking Wuhan East Lake Scenic Area (ELSA) as an example, this paper explored the value of ecosystem services and their change trends in ELSA for the three years, 2011, 2015, and 2019, during which several infrastructural projects were implemented in this area and had significant impacts on its ecological environment. This analysis offers a detailed understanding of the changing status of ecosystem services under major human activities and to provide some foundations for their protection and development.

2. Materials and Methods

2.1. Study Area

The Wuhan East Lake wetland is located in the eastern section of Wuhan City, occupying the second-largest urban lake area within the city limits. The area is located in the eastern part of the Jianghan Plain and belongs to the subtropical monsoon climate zone. The total area of the region is approximately 62 km², with the lake’s water area covering about 33 km². The lake comprises several sub-lakes, including Guozheng Lake, Tangling Lake, and Lingjiao Lake, which are connected by culverts [47]. In 1982, it was approved by the State Council as one of the first national scenic spots. In 2013, it was rated as a national 5A-level tourist attraction. Thanks to the unique natural and humanistic landscape of East Lake and its superior geographic location, the East Lake Scenic Area provides urban residents with rich ecological wetland corridors along the lake, and expands the public outdoor activity spaces for residents. Currently, it has formed a comprehensive ecological space with a large natural lake as the core, integrating 5A-level tourist attractions, a national wetland park, and national ecological tourism demonstration areas.

As a typical urban wetland, the Wuhan East Lake wetland has experienced a significant change due to urban expansion and development. In this study, the ELSA was selected based on the actual construction land status and the division of administrative units in the region. As shown in Figure 1, the scope covers an area of 61.85 km² and comprises eight scenic areas around East Lake wetland: Tingtao, Yuguang, Chuidi, Houhu, Luoyan, Moshan, Yujiashan and Baima scenic area.

2.2. Data Collection

Four main categories of data were utilized in this study: basic geographic data, multi-source remote sensing data and products, hydrometeorological data, and socio-economic data. Basic geographic data consisted of study area boundaries, digital elevation model (DEM) data, and other relevant geo-information. Remote sensing data included GF-1 and Landsat-8 image data, land use data, and month-by-month normalized difference vegetation index (NDVI) data. The GF-1 and Landsat-8 image data were obtained from the geospatial data cloud (https://www.gscloud.cn/, accessed on 10 November 2023) with a spatial resolution of 2 m and 30 m. The land use data were obtained from the Landcover land cover dataset of Tsinghua University in 2017 with a spatial resolution of 10 m [21]. The month-by-month NDVI data were selected from MODIS 13Q1 data from Terra satellite data with a spatial resolution of 250 m and a temporal resolution of 16 days. The hydrometeorological data, including annual precipitation, air temperature, total solar radiation, and water quality and water level data, were provided by the National Ecosystem Observation and Research Network [48]. The socio-economic data such as price-related data, number of tourists, average wage of employees, and consumer price index (CPI) were obtained from the Wuhan Statistical Yearbooks [49,50,51], National Standards Documents [52], and related research literature.

2.3. Methods

The study employs a structured approach that consists of three key phases. Figure 2 illustrates the methodology framework of this study. First, a comprehensive review of extensive literature was conducted, along with a rigorous data collection effort. Based on the characteristics of urban wetlands and the availability of data, the types of ecosystem services associated with urban wetlands were identified in the study area. Subsequently, the calculation of three key ecological parameters was performed, upon which the quantity of physical quality and value of each ecosystem service was determined in conjunction with relevant data. Finally, a thorough analysis was performed to examine the dynamic changes that occurred over the three year periods, providing valuable insights into the temporal evolution of the urban wetland ecosystem services.

2.3.1. Key Ecological Parameter Calculations

• Net primary productivity

Due to the different electromagnetic radiation characteristics between terrestrial vegetation and water surface, commonly used remote sensing inversion algorithms have difficulty in accurately estimating the true performance of net primary productivity (NPP) in water bodies, leading to severely underestimated water body vegetation growth conditions. Therefore, in the study, the NPP of terrestrial and aquatic ecosystems was calculated separately.

For the terrestrial vegetation part, the spatial–temporal non-local filtering fusion model (STNLFFM) [53] was used to effectively fuse the spatial and temporal complementary information of multi-source remote sensing data, thereby generating monthly high-resolution NDVI spatial distribution data. Considering that the high-resolution image data used in this study has a resolution of 2 m and the MODIS time-series NPP data has a resolution of 250 m, the resolution gap between the two types of data is too large. Therefore, a spatial fusion resolution of 8 m was ultimately determined. Then combined with regional land cover and meteorological data, an improved CASA model was used to calculate the NPP of terrestrial vegetation [54].

For the aquatic plants part, the research on the coverage area of aquatic plants in East Lake by Landsat image inversion conducted by Jiang et al. [47] was used, combined with the average density parameters of aquatic plants in East Lake measured in the relevant literature for estimation.

2.

Leaf area index

Leaf area index (LAI) is one of the important indicators for evaluating leaf density of the vegetation canopy. Based on the correlation between LAI and remote sensing surface reflectance, the LAI-NDVI regression model established for green areas in Wuhan by Li et al. [55] was used to retrieve LAI in the region. The regression model used is as follows:

$$LAI=8.937-46.371NDVI+78.812{NDVI}^2-35.358{NDVI}^3 (R^2=0.750)$$

(1)

where LAI is the average leaf area index within the pixel, and NDVI is the normalized vegetation index within the pixel.

3.

Vegetation diversity index

Considering the availability of data in ELSA, the Shannon–Wiener diversity index was employed to quantitatively analyze the ecological diversity characteristics of the vegetation in ELSA. Reference to previous research on the inversion of vegetation species diversity [56], the study adopted the Shannon–Wiener inversion method based on Landsat 8 images. The formula for calculating the average species diversity of vegetation in the East Lake Scenic Area is as follows:

$$H_{avg}=\frac{1}{n}\sum _{i=n}^n{H}_i$$

(2)

$$H_i=-0.096NDVI+0.131{TC}_2+0.143{TC}_3+1.673 (R^2=0.637)$$

(3)

where $H_i$ is the Shannon–Wiener diversity index of the i-th pixel, ${TC}_2$ and ${TC}_3$ are the greenness and humidity components based on the Larson–Jorgensen Cap transform of Landsat multispectral remote sensing images.

The value of biodiversity is referenced to the opportunity cost of species loss under different diversity classes in the Specification for the Assessment of Forest Ecosystem Service Functions, as shown in

Table 1. Ranking of different Shannon–Wiener indices and their corresponding values.

Level	Shannon–Wiener Index	Value (CNY/hm2·a)
1	Index ≥ 6	50,000
2	5 ≤ Index < 6	40,000
3	4 ≤ Index < 5	30,000
4	3 ≤ Index < 4	20,000
5	2 ≤ Index < 3	10,000
6	1 ≤ Index < 2	5000
7	Index < 1	3000

.

2.3.2. Ecosystem Service Value Assessment

Based on the classification of ecosystem services in the Millennium Ecosystem Assessment, the assessment indicators of ESVs in ELSA were determined by combining with the actual ecological service characteristics of the study area. Considering that the East Lake, as the main urban wetland, has mainly assumed the functions of ecological environment regulation and social and cultural services in recent years, the original production of aquatic products has lost its corresponding ecological value. At the same time, in the forest ecosystem in ELSA, as an important component of urban green spaces, its timber output is not the main ecological function. Therefore, only the value of water storage in the lakes for the surrounding industrial and agricultural water supply is considered in terms of ecological products of wetland and forest, and the economic value contained in the corresponding aquatic products and forests is not calculated.

Therefore, considering the ecological benefits of the lakes and forest ecosystems in the region, 10 indicators, including water supply, carbon sequestration, oxygen release, climate regulation, flood regulation, dust reduction, water quality purification, biodiversity conservation, education and scientific research and tourism services were selected in the study. Based on the connotation of each indicator, appropriate methods for calculating material and value quantity were selected. The selected indicators and relevant methods are shown in

Table 2. Evaluation indicators and methods of ecosystem service values of ELSA.

Ecosystem Service	Indicator	Calculation Formula	Description	Evaluation Method
Provision	Water supply	$V_{WS}=\sum _{i=1}^n{A}_i\times H_i\times K_{WS}$	$A_i$$: \mathrm{the} \mathrm{area} \mathrm{of} \mathrm{the} i-\mathrm{th} \mathrm{sub}-\mathrm{lake} \left({\mathrm{m}}^2\right); H_i$$: \mathrm{the} \mathrm{depth} \mathrm{of} \mathrm{the} i-\mathrm{th} \mathrm{sub}-\mathrm{lake} \left(\mathrm{m}\right); K_{WS}$: price of industrial water (CNY/m3).	Market value method
Regulation	Climate regulation	$V_{CR}=\left(\frac{C_{TP}+C_{Lake}}{81.8}\times 189\times 24\times 0.7\right)\times 0.573$ $C_{TP}=L_{TR}\times LAI\times A_{veg}\times D$ $C_{Lake}=L_{Eva}\times A_{Lake}\times C\times \delta T$	$L_{TR}$$: \mathrm{Transpiration} \mathrm{heat} \mathrm{uptake} \mathrm{rate} \mathrm{per} \mathrm{unit} \mathrm{area} \mathrm{of} \mathrm{leaf} \mathrm{blade} (\mathrm{J}/{\mathrm{m}}^2·\mathrm{d}); LAI:$$\mathrm{leaf} \mathrm{area} \mathrm{index}; A_{veg}$$: \mathrm{vegetation} \mathrm{area} \left({\mathrm{m}}^2\right); \mathrm{D}: \mathrm{days} \left(\mathrm{d}\right); L_{Eva}$$: \mathrm{evapotranspiration} \left(\mathrm{mm}\right); A_{Lake:}$$\mathrm{lake} \mathrm{area} \left({\mathrm{m}}^2\right); C$$: \mathrm{specific} \mathrm{heat} \mathrm{capacity} \mathrm{of} \mathrm{water}. (\mathrm{J}/\mathrm{kg}·°\mathrm{C}); \delta T$: Difference between annual mean temperature and 100 °C (°C)	Alternative engineering method
	Flood regulation	$V_{FR}=\left[A_{Lake}\times \left(H_{maz}-H_{avg}\right)+\sum _i{A}_{T{P}_i}\times C_{TP\_i}\times P\right]\times K_{AR}$	$H_{maz}$$: \mathrm{annual} \mathrm{maximum} \mathrm{water} \mathrm{level} \mathrm{of} \mathrm{the} \mathrm{lake} \left(\mathrm{m}\right); H_{avg}$$: \mathrm{average} \mathrm{annual} \mathrm{water} \mathrm{level} \mathrm{of} \mathrm{lakes} \left(\mathrm{m}\right); A_{TP\_i}$$: \mathrm{area} \mathrm{covered} \mathrm{by} \mathrm{vegetation} \mathrm{type} i \left({\mathrm{m}}^2\right); C_{TP\_i}$$: \mathrm{rainwater} \mathrm{retention} \mathrm{rate} \mathrm{of} \mathrm{vegetation} \mathrm{type} i; P: \mathrm{total} \mathrm{rainfall} \mathrm{for} \mathrm{the} \mathrm{year} \left(\mathrm{mm}\right); K_{AR}$: construction cost of artificial reservoirs per unit capacity (CNY/m3)	Shadow engineering method
	Water purification	$V_{WP}=V_{TOP}+V_{TON}$ $V_{TOP}=\left(C_{Lake}\times M_{TOP}+C_{AP\_TOP}\right)\times K_{TOP}$ $V_{TON}=\left(C_{Lake}\times M_{TON}+C_{AP\_TON}\right)\times K_{TON}$	$C_{Lake}$$: \mathrm{size} \mathrm{of} \mathrm{lake} \mathrm{storage} \left({\mathrm{m}}^3\right); M_{TOP}$$\mathrm{and} M_{TON}$$: \mathrm{total} \mathrm{phosphorus} \mathrm{and} \mathrm{nitrogen} \mathrm{concentration} \mathrm{in} \mathrm{lake} (\mathrm{g}/{\mathrm{m}}^3); C_{AP\_TOP}$$\mathrm{and} C_{AP\_TON}$$: \mathrm{total} \mathrm{phosphorus} \mathrm{and} \mathrm{nitrogen} \mathrm{content} \mathrm{of} \mathrm{aquatic} \mathrm{plants} \left(\mathrm{g}\right); K_{TOP}$$\mathrm{and} K_{TON}$: treatment costs for total phosphorus and nitrogen pollutants (CNY/g).	Outcome reference method
	Air purification	$V_{AP}=\left(A_{Lake}\times L_{DR}+{\sum }_i{A}_{TP\_i}{LAI}_i{L}_i\right)\times K_{\mathrm{D}\mathrm{R}}$	$A_{Lake}$$: \mathrm{lake} \mathrm{area} \left({\mathrm{m}}^2\right); L_{DR}$$: \mathrm{dust} \mathrm{retention} \mathrm{per} \mathrm{unit} \mathrm{of} \mathrm{lake} \mathrm{water} \mathrm{surface} (\mathrm{g}/{\mathrm{m}}^2); A_{TP\_i}$$: \mathrm{area} \mathrm{covered} \mathrm{by} \mathrm{vegetation} \mathrm{type} i \left({\mathrm{m}}^2\right); {LAI}_i$$: \mathrm{leaf} \mathrm{area} \mathrm{index} \mathrm{of} \mathrm{vegetation} \mathrm{type} i; L_i$$: \mathrm{dust} \mathrm{retention} \mathrm{per} \mathrm{unit} \mathrm{area} \mathrm{of} \mathrm{vegetation} \mathrm{type} i (\mathrm{g}/{\mathrm{m}}^2); K_{DR}$: charges for dust emissions (CNY/g).	Outcome reference method
	Carbon sequestration and oxygen release	$V_{CS}=(A_{TP}{NPP}_{TP}/45\%+M_{AP} )\times 1.63\times K_{CS}$ $V_{OR}=(A_{TP}{\times NPP}_{TP}/45\%+M_{AP} )\times 1.19\times K_{OR}$	${NPP}_{TP}$$: \mathrm{Annual} \mathrm{net} \mathrm{primary} \mathrm{productivity} \mathrm{of} \mathrm{terrestrial} \mathrm{plants} (\mathrm{g}/{\mathrm{m}}^2·\mathrm{a}); M_{AP}$$: \mathrm{Amount} \mathrm{of} \mathrm{aquatic} \mathrm{plant} \mathrm{material} \left(\mathrm{g}\right); K_{CS}$$: \mathrm{carbon} \mathrm{tax} \mathrm{rate} (\mathrm{CNY}/\mathrm{g}); K_{OR}$: industrial oxygen price (CNY/g).	Carbon tax method; shadow engineering method
Support	Biodiversity	$V_{Bio}=A_{Forest}\times K_{DV}$	$A_{Forest}$$: \mathrm{area} \mathrm{of} \mathrm{forest} \mathrm{cover} \left({\mathrm{m}}^2\right), K_{DV}$: opportunity cost of species loss per unit area of vegetation (CNY/m2)	Outcome reference method
Cultural	Tourism	$V_{Tour}=V_{TE}+V_{cs}+V_{TOC}$ $V_{TE}=C_{visitors}\times K_{avg\_cost}$ $V_{cs}=V_{TE}\times 40\%$ $V_{TOC}=C_{visitors}\times T_{visitors}\times K_{income}\times 30\%$	$V_{TE}$: travel expenses; $V_{cs}$: consumer surplus; $V_{TOC}$: time opportunity cost; $C_{visitors}$$: \mathrm{total} \mathrm{number} \mathrm{of} \mathrm{visitors}; K_{avg\_cost}$$: \mathrm{per} \mathrm{capita} \mathrm{travel} \cos \mathrm{ts} \left(\mathrm{CNY}\right); T_{visitors}$$: \mathrm{travel} \mathrm{time} \mathrm{per} \mathrm{capita} \left(\mathrm{h}\right); K_{income}$: hourly payroll costs (CNY/h).	Travel cost method
	Scientific research	$V_{SR}=C_{RP}\times K_{ARC}$	$C_{RP}$$: \mathrm{number} \mathrm{of} \mathrm{academic} \mathrm{research} \mathrm{papers}; K_{ARC}$: average research cost per academic paper (CNY/paper)	Outcome reference method

. Furthermore, in order to facilitate the comparison of value quantities across different years, the monetary value calculations for the three years were aligned to 2019 based on the CPI for each year.

3. Results

3.1. Changes in Land Cover in ELSA from 2010 to 2019

Changes in the ELSA over the years can be observed through the remote sensing image presented in Figure 3. To enhance the visibility of land cover changes, false-color images within the near-infrared (NIR)-red-green spectral band were employed for analysis. In these figures, vegetation was rendered in red, water bodies appeared in darker hues, and the built environment was depicted in lighter colors.

The imagery captured in 2011 highlights a notable phenomenon: the presence of a road traversing the center of the lake, effectively partitioning it into distinct sub-lakes. In 2015, the implementation of the East Lake Tunnel Project is evident. This was executed via the utilization of cofferdam excavation techniques, leading to a distinct demarcation and transformation of East Lake. The observed changes align precisely with the intended direction of the project, spanning from the northwest towards the southeast. By 2019, upon the completion of construction activities, the cofferdam was subsequently removed, and the lakebed was covered with soil. This restoration measure effectively eradicated any visual indications of the previous division created by the project.

3.2. Key Ecological Parameters

3.2.1. Net Primary Productivity

Taking 2019 as an example, combining the October 2019 GF-6 remote sensing imagery, and the NDVI temporal products derived from MODIS 13Q1 data with a spatial resolution of 16 days and a temporal resolution of 250 m, the STNLFFM model was used to temporally and spatially fuse the spatial and temporal complementary information from the multi-source remote sensing data, thereby obtaining the month-by-month NDVI distribution information in 2019. The June 2019 NDVI fusion result is shown in Figure 4; it can be seen that the fused results permit a more detail characterization of the distribution of vegetation in the region.

The improved CASA model was employed to calculate the distribution characteristics of net primary productivity (NPP) in the region. As illustrated in Figure 5, in 2019, the maximum value of NPP is 767.45 gC·m⁻²·a⁻¹, the minimum value is 0 gC·m⁻²·a⁻¹, and the average value is 129.52 gC·m⁻²·a⁻¹.

3.2.2. Leaf Area Index

The high-resolution NDVI distribution calculated in Section 3.2.1 was utilized to perform an inversion of the leaf area index (LAI) in the region. This was accomplished through the application of the LAI-NDVI regression model developed by Li Lu et al. The results of this process are presented in Figure 6, which depicts the range of LAI values observed in the study area, spanning from 0 to 4.84. In conjunction with the land cover characteristics, the statistical characteristics of the leaf area index distribution for different vegetation types, including forests, grasslands, and shrubs, were derived (

Table 3. Statistic results of leaf area index for different land cover types.

Land Cover	Mean	Std
Forest	2.20	0.83
Grassland	1.96	1.65
Shrub	1.48	0.87

).

3.2.3. Vegetation Diversity Index

The diversity index calculation necessitates the utilization of tasseled cap transformation based on multispectral bands. However, the current high-resolution remote sensing imagery is devoid of the requisite band information. Consequently, the Landsat-8 images were employed in this study for the inversion calculation of diversity index. A calculation based on the 2019 Landsat imagery data indicates that the region’s vegetation diversity is between 1.94 and 2.35.

3.3. Changes in ESVs in ELSA from 2010 to 2019

3.3.1. Dynamic Changes in the Total Value of Ecosystem Services

Using the multi-source remote sensing data and socio-economic data for the ELSA for the three periods of 2011, 2015 and 2019, the value of ecosystem services of the ELSA was evaluated to be derived between different years, as shown in Figure 7. It can be seen that the total value of ecosystem services of ELSA was CNY 1.606 billion, CNY 1.588 billion and CNY 3.219 billion for the years of 2011, 2015 and 2019, respectively. The rate of change in its total amount between 2011 and 2015 is −1.13%, with a slight decrease in the total value; the rate of change between 2015 and 2019 is 102.76%, with a significant increase in the total value; and the total rate of change between 2011 and 2019 is 100.47%, with a significant increase in the total value of ecosystem services.

3.3.2. Dynamic Changes in the Value of Individual Ecosystem Services

Table 4. Rates of change in ecosystem service values of ELSA from 2011 to 2019.

Ecosystem Service	Service Indicator	2011–2015	2015–2019	2011–2019
Supply service	Water supply	−4.95	6.71	1.43
Regulation service	Carbon sequestration	38.63	21.98	69.11
	Oxygen release	38.63	21.98	69.11
	Climate regulation	−34.90	48.32	−3.45
	Flood regulation	−3.25	5.09	1.67
	Air purification	−26.41	59.10	17.08
	Water purification	−20.04	9.81	−12.19
Support service	Biodiversity	−5.04	23.37	17.15
Culture service	Scientific research	−59.09	188.89	18.18
	Tourism	42.43	198.51	325.17
Total		−1.13	102.76	100.47

shows that the ESVs of the water provisioning services remained stable over the three study periods. However, there was a slight decrease of −4.95% in 2015. This change may be related to the impacts on the lake surface caused by the construction of the East Lake Greenway and the East Lake Tunnel during this period.

The value of ecosystem services related to carbon sequestration and oxygen release in regulating services increased continuously at a rate of 69.11% from 2011 to 2019. This suggests a significant improvement in the capacity of terrestrial ecosystems, such as forests and grasslands in the ELSA, to sequester carbon and release oxygen. However, in 2015, the levels of climate regulation, flood storage, dust reduction, and water purification decreased in varying degrees. In 2019, these aspects returned to a higher level.

In terms of support services, biodiversity decreased slightly in 2015 but improved significantly in 2019. The value amount for biodiversity increased by 17.15% in 2019 compared to 2011. This may be related to the disturbance of forests, grasslands, and other ecosystems in the region by human activities. Measures need to be taken to maintain the health and stability of the ecosystems.

Regarding cultural services, there were different trends in scientific research and tourism services over the three study periods. In 2015, scientific research services experienced a significant decline with a rate of change of 59.09%. This decline may be attributed to the constraints imposed by the construction of the East Lake Tunnel and the Greenway Project on related scientific research activities. Tourism services increased significantly during the period, indicating that the demand for such services in ELSA by urban residents had not been affected by the aforementioned projects.

3.3.3. Dynamic Changes in the Composition of the Value of Ecosystem Services

Figure 8 shows the proportion of individual ESVs in the total value volume. It is evident from the figure that the composition of the ESVs of ELSA underwent significant changes during the period of 2011–2019. In 2011, climate regulation services had the highest value share in ELSA ecosystem services, followed by recreation, water supply, and flood regulation. This emphasizes the significant importance of climate regulation services. However, in 2015, the value of tourism services increased rapidly, surpassing all other ecosystem services in value share. In contrast, the value share of climate regulation, water supply, and flood regulation decreased accordingly. By 2019, the value volume of tourism services had increased even further, with its value share reaching 65.21%. The results indicate that tourism services have become the most important type of ecosystem services in ELSA. Furthermore, the proportion of services such as climate regulation, water supply, and flood regulation continue to decrease. This reinforces the importance of tourism services in the ELSA ecosystem.

4. Discussion

4.1. The Differences in ESVs between Urban and Natural Wetlands

In this study, multi-source data including remote sensing data, ground observation data, and socio-economic data were used. In order to capture the ecological characteristics of urban wetlands at finer scales, multiple remote sensing data at different scales were integrated for the inversion of high-resolution ecological parameters. In terms of the composition of the ecosystem services, ELSA’s cultural services accounted for a relatively high proportion of ecosystem services in all three time periods, with a peak of 65.21% observed in 2019. In other studies, regulatory and support services are typically identified as the main ecosystem service components on natural wetlands [4,40,43]. This observation further confirms that urban residents have a greater need for cultural services related to urban wetlands compared to natural wetlands.

4.2. Changes in ESVs in the ELSA Region

Based on the above analysis, it can be clearly observed that the value of the ecosystem services of the ELSA has changed significantly between 2011 and 2019. In 2011, its total value was CNY 16.06 × 10⁸, of which the value of climate regulating services accounted for the largest proportion. In 2015, due to the construction of the East Lake Tunnel Project by means of cofferdam open excavation, the catchment area of the East Lake was slightly reduced, resulting in a decrease in the value of some ecosystem services. However, with the completion of the construction project and the implementation of the related ecological restoration project, the ecosystem function of the lake water was effectively restored, leading to an increase in the amount of corresponding ESVs. By 2019, the total ESVs of the ELSA reached CNY 32.19 × 10⁸. Therefore, although the construction of the East Lake Tunnel Project had a certain impact on the lake’s watershed, resulting in a reduction in some ecosystem services, the lake’s ecosystem function has been restored through effective ecological restoration measures, which in turn has increased the total value of ecosystem services.

There was a gradual increase in carbon sequestration and oxygen release over the three periods. Additionally, tourism also showed a similar trend. Although the reduction in water surface area appears to have resulted in a decline in aquatic vegetation, the overall carbon sequestration capacity and oxygen release exhibited a rising tendency, suggesting an enhanced provision of these two ecosystem services by terrestrial vegetation. The growth in the tourism service’s volume is primarily attributed to the annual increase in the number of tourists. This implies that while the construction of the East Lake Tunnel Project may have affected the ecological environment and playing conditions in the lake area of East Lake, the land area’s ecological space may have been the primary attraction for residents during this period. Moreover, the East Lake Greenway project has enhanced the quality of the ecological space in the area, resulting in a doubling in the number of visitors to the region in 2019 compared to 2015. This has led to a significant increase in the volume of cultural services.

4.3. The Culture Service Value in the ELSA Region

Urban wetlands, as the main open space accessible to residents, have garnered extensive scholarly attention for their cultural services, particularly in regard to their impact on resident health and social interactions [57,58]. For instance, studies in several cities have shown that regular visits to urban wetlands by residents positively correlate with improved mental health and reduced stress levels [59]. The green spaces and natural habitats within these wetlands provide residents with a respite from the urban hustle and bustle, fostering a sense of relaxation and well-being. Additionally, these spaces often host community events, festivals, and other gatherings, promoting interactions between residents of diverse backgrounds.

In this paper, the values of tourism services and scientific research were assessed based on the number of tourist visits and the number of published academic papers, respectively. Comparing various ecosystem services between 2011 and 2019, it was shown that tourism services were the fastest-growing service type within the region as the wetlands were constructed and environmentally enhanced. Moreover, the utilization of perceived behavior data derived from the participation GIS and social media platforms has emerged as a research focus for quantifying the value of cultural services [60,61].

Consequently, a deeper examination of cultural services within the ELSA area would be both significant and necessary. Unlike the trade-off relationships in previous studies [62,63,64], ELSA’s ecosystem service was optimized and enhanced through the significant enhancement of cultural services while maintaining the original amount of natural ecosystem services. This process reveals the synergistic relationships of the East Lake region in terms of ecosystem services, further highlighting the interaction between human activities and natural ecosystems. Therefore, it is important to continue focusing on maintaining the stability and health of the natural ecosystem while also fully utilizing the potential of cultural services and promoting their further enhancement in the region.

4.4. Limitations and Future Works

Although this study conducted a refined inversion of ecosystem services in urban wetlands, the type and refinement of ecosystem services still need to be further improved due to the lack of data and the incompleteness of the model. For example, in the inversion of NPP, the terrestrial NPP was accurately inverted in the study, and the NPP of aquatic plants was measured by acquiring the inversion results from related literature, which may lead to the wrong estimation of aquatic plant productivity. Furthermore, due to the lack of data on plant and animal species in the study area, particularly aquatic organisms, the diversity value was calculated based on terrestrial vegetation data, which resulted in an underestimation of the region’s diversity. Additionally, the cultural services indicator can be refined by incorporating specific activities of residents in the region. In future studies, further data collection can be conducted to construct more refined inverse models of ecological parameters. This foundation allows for further investigation into the impact of urbanization on the evolution of various ecosystem services.

5. Conclusions

The assessment of ESVs in urban wetlands is crucial for understanding their ecological and social importance and for informing decision-making in urban planning and management. The present study has provided a comprehensive assessment of the changes in the ESVs of the ELSA in Wuhan, China over a three study periods from 2011 to 2019. The utilization of remote sensing and geographic information system techniques has enabled a fine-grained quantitative analysis of key ecological parameters, thereby revealing the dynamic nature of urban lake wetlands and their associated ecosystem services. The results indicate that, despite a slight decline in the total value of ecosystem services in 2015 compared to 2011, the value experienced a notable increase by 2019, reaching CNY 3.219 billion. This significant growth can be primarily attributed to the remarkable enhancement of tourism services in the ELSA. This outcome highlights the importance of urban wetlands in providing valuable recreational opportunities for city residents.

The optimization and enhancement of the ESVs in the ELSA demonstrate the potential for urban wetlands to serve as valuable natural resources, not only for biodiversity conservation but also for human well-being. The study further highlights the necessity for continued monitoring and management of urban wetlands in order to ensure their sustainability and the optimal utilization of their ecosystem services. In light of the rapid urbanization and growing demand for natural resources, it is important to achieve a balance between development and conservation, ensuring the protection of urban wetlands and the preservation of their ecological functions.