Long-Term Ecological and Environmental Quality Assessment Using an Improved Remote-Sensing Ecological Index (IRSEI): A Case Study of Hangzhou City, China

Abstract

The integrity and resilience of our environment are confronted with unprecedented challenges, stemming from the escalating pressures of urban expansion and the need for ecological preservation. This study proposes an Improved Remote Sensing Ecological Index (IRSEI), which employs humidity (WET), the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), a standardized Building–Bare Soil Index (NDBSI), aerosol optical depth (AOD), and the comprehensive salinity index (CSI). The IRSEI model was utilized to assess the ecological quality of Hangzhou over the period from 2003 to 2023. Additionally, the random forest model was employed to analyze the factors driving ecological quality. Furthermore, the gradient effect in the horizontal direction away from the urban center was examined using the buffer zone method. Our analysis reveals the following: (1) approximately 95% of the alterations in ecological quality observed from 2003 to 2023 exhibited marginal improvements, declines, or were negligible; (2) the transformations in IRSEI during this period, including variations in surface temperature and transportation networks, exhibited strong correlations (0.85) with human activities. Moreover, the influence of AOD and the comprehensive salinity index on IRSEI demonstrated distinct spatial disparities; (3) the IRSEI remained generally stable up to 30 km outside the city center, indicating a trend of agglomeration in the center and significant areas in the surroundings. The IRSEI serves as a robust framework for bolstering the assessment of regional ecological health, facilitating ecological preservation and rejuvenation efforts, and fostering coordinated sustainable regional development.

1. Introduction

Environmental protection and sustainable development constitute pivotal areas of contemporary research. In the context of economic growth and urban expansion, alterations in land use/cover and the repercussions of human activities have reshaped ecological landscapes and impacted their quality [1]. China has experienced remarkable strides in economic advancement and urbanization over recent decades; however, the consequences of prioritizing rapid development at the expense of ecological integrity and natural assets during initial expansion phases have become evident. Ecological challenges now emerge as pivotal factors influencing and constraining China’s socioeconomic progress [2]. In 2006 and 2015, the Ministry of Environmental Protection issued trial and revised Technical Specifications for the Evaluation of the State of the Ecological Environment, which were presented as industry standards. These specifications proposed an ecological index (EI) encompassing factors such as biological richness, air pollution, water network density, vegetation coverage, land degradation, and related elements. Subsequently, in 2021, the Ministry of Ecology and Environment introduced two trial measures for regional ecological quality evaluation, primarily targeting county and administrative divisions [3]. The evaluation outcomes are based on the overall ecological quality of the region. However, visualizing and comprehending the spatial distribution of ecological quality, as well as analyzing its spatial disparities and changes, remain challenging.

Remote sensing spatial information technology offers rapid, real-time, and large-scale monitoring capabilities, finding widespread application in studying ecological environments [4,5,6,7]. To better manage the urban environment and protect people’s lives, a tool that can effectively evaluate and monitor the status of the urban ecological environment is needed. Therefore, the Remote Sensing Ecological Index (RSEI) was developed by Xu [8,9]. It integrates multiple intuitive indicators to reflect the ecological environment and can realize rapid monitoring and evaluation of the regional ecological environment [10]. The RSEI is based on remote-sensing information and established through principal component analysis by coupling greenness, humidity, dryness, and heat indices that reflect ecological environment status [8,9,10,11]. Through dynamic monitoring and analysis of ecological environment quality in these regions, it can provide a scientific basis for ecological environment protection and restoration. The popularity of RESI has been demonstrated in diverse natural ecosystems (such as forests, farmlands, deserts, and wetlands) [12] and man-made environments (mining areas, cities, and industrial areas) [13,14,15]; however, there are still some limitations when using RSEI. RSEI mainly selects indicators based on ecological environment characteristics, which may ignore the impact of social, economic, cultural, technological, and other factors on the ecological environment. Previous studies have reported a lack of homogeneity in application scenarios, randomness in models, and limited applicability in extreme ecological scenarios such as deserts and land degradation areas, but have ignored air quality in the atmosphere, among other factors [14,16]. Moreover, it fails to reveal what gradient effect RSEI has in the horizontal direction. In future studies, more indicators can be considered for the RSEI evaluation system.

Air pollution, particularly PM2.5, exerts a significant impact on the ecological quality within and surrounding major central cities. Aerosol optical depth (AOD) is extensively utilized to investigate the spatial distribution of PM2.5 and serves as a suitable proxy for an air quality index [17,18,19]. The comprehensive salinity index (CSI), with ecological factors such as air quality and vegetation cover, measures the soil fertility that impacts vegetation growth [20,21]. These effects may further influence local climate and air quality. While salinization may not be a widespread issue in Hangzhou overall, it can occur in specific areas or under certain conditions, significantly affecting soil quality and agricultural production. Remote-sensing technology enables a comprehensive evaluation of Hangzhou’s ecological quality, providing a scientific foundation for land management and agricultural production. Furthermore, air quality is a vital component of ecological quality that directly affects human health and quality of life [22]. As a densely populated and economically developed city, Hangzhou faces considerable concerns regarding air quality [23]. Salinization and air quality are frequently neglected in regional ecological assessments, potentially leading to a distorted understanding of these interactions and the overall impacts on ecological quality. To address the deficiencies of remote-sensing ecological indices in characterizing ecosystems and air quality, this study introduces salinization and air quality indices based on RSEI and develops a comprehensive and Improved Remote-Sensing Ecological Index (IRSEI). Incorporating the salinization factor and air quality into Hangzhou’s remote-sensing ecological quality assessment is crucial for comprehensively and accurately understanding Hangzhou’s ecological quality.

By integrating remote-sensing technology with air quality monitoring data, we can gain a thorough understanding of Hangzhou’s air pollution situation, encompassing the primary pollution sources as well as the distribution and transmission paths of pollutants. This information will facilitate the government in formulating targeted air pollution control measures aimed at enhancing Hangzhou’s air quality. Furthermore, the incorporation of salinization factors and air quality into remote-sensing ecological quality assessments can reveal the interactions and influence mechanisms among various ecological quality elements. The primary objectives of this paper are threefold:

• To develop an integrated IRESI assessment model that incorporates air quality and salinization indicators, providing a comprehensive assessment framework;
• To assess and analyze the ecological environment quality of Hangzhou from 2003 to 2023 using the IRSEI model, with the aim of identifying trends and patterns and exploring the driving mechanisms through the application of the random forest algorithm;
• To evaluate the urban–rural echelon effect of ecological quality in the horizontal direction of the urban central area by constructing buffer zones, in order to gain a deeper understanding of the spatial distribution and changes in ecological quality within the urban landscape.

2. Materials and Methods

2.1. Research Area

Hangzhou City was chosen as the research area for ecological monitoring and evaluation. Hangzhou serves as the capital of Zhejiang Province in southeastern China (refer to Figure 1). The urban area spans from approximately 29°11′ to 30°34′ N in latitude and 118°20′ to 120°44′ E in longitude. As of 2022, the city’s population was 12.4 million. Renowned for its West Lake, Hangzhou’s traditional urban core lies along the northeastern bank of this iconic water body. Over the past two decades, the Qianjiang CBD (also known as Qianjiang New Town) has undergone significant development east of the central city. The research area administers 10 districts, 2 counties, and 1 county-level city, which mainly include Shangcheng District, Gongshu District, Xihu District, Binjiang District, Xiaoshan District, Yuhang District, Linping District, Qiantang District, Fuyang District, Lin’an District, Tonglu County, Chun’an County, and Jiande City. Within the urban land area, mountains and hills, plains, and various water bodies occupy 65.6%, 26.4%, and 8% of the total area, respectively. Situated south of the Qiantang River, the study area is characterized by highly developed regions, cultivated land, and small-scale forested areas. Over the past decade, extensive land-use transformations have occurred, converting farmlands and wetlands into highly developed areas. Furthermore, the region has experienced rapid urbanization, leading to ecological challenges and a decline in ecological quality.

2.2. Data Sources

This study primarily utilized remote-sensing images, Land Use and Land Cover (LULC), population, and economic data. Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) remote-sensing images, along with MCD19A2 data, were predominantly employed to conduct the IRSEI evaluation, while other datasets were utilized for the IRSEI influencing factor analysis. Furthermore, Landsat TM/OLI images with high quality, featuring less than 10% cloud cover, were obtained between 30 June and 30 December of the respective years spanning from 2003 to 2023. In the Northern Hemisphere, the summer period, specifically spanning from June to September, is a crucial time when plant growth reaches its peak. During this phase, vegetation cover exhibits its most luxuriant state, enabling satellite remote-sensing data to capture and reflect the growth status of vegetation with greater precision. The decision to utilize satellite datasets spanning from 30 June to 30 December rather than annual data is primarily based on considerations of seasonal vegetation changes, phenological characteristics, and specific research objectives. Additionally, this selection aids in reducing the complexity of data processing and analysis. Following the acquisition of MCD19A2 data for the entire year, the annual mean AOD from 2003 to 2023 was computed to ensure the comparability of the research findings. The MCD19A2 data product is a gridded Level-2 product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua satellites, utilizing the Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm for land AOD. This product is generated daily with a resolution of 1 kilometer, providing crucial aerosol information for Earth science research. The MCD19A2 product is widely used in various research fields such as aerosol science, atmospheric environment, and climate change.

The dataset preprocessing involved calibration and preprocessing by Google Earth Engine (GEE), utilizing a multi-spectral band grey value or digital number (DN), sensor reflection value conversion, and Spectral Hypercube Rapid Line-of-Sight Atmospheric Analysis (FLAASH). The nearest pixel method was employed in this study to rectify images from different periods by 0.5 pixels. Two polynomials and root-mean-square error analysis were utilized, and the cropped portion of the remote-sensing image was ultimately included within the research scope.

2.3. Research Methods

2.3.1. Research Framework

Humidity (WET), NDVI, Land Surface Temperature (LST), a standardized Building–Bare Soil Index (NDBSI), AOD, and the comprehensive salinity index (CSI) were employed to represent the regional ecological quality. The overall framework for calculating and analyzing the IRSEI consisted of three main steps. Firstly, the IRSEI index of Hangzhou was determined using principal component analysis. Secondly, the ecological indicators of Hangzhou were analyzed considering their spatiotemporal differences and changes. Finally, the factors influencing IRSEI and horizontal changes in ecological quality were studied.

2.3.2. IRSEI Model

The NDVI is typically utilized for monitoring vegetation growth, directly reflecting the quality of the regional ecological environment. LST is closely associated with vegetation growth, crop yield, the surface water cycle, urbanization, other natural phenomena and processes, and human activities. It serves as a heat index, reflecting the surface ecological environment. The Kauth–Thomas transformation method, a linear transformation based on multi-spectral imaging, derives moisture components reflecting soil and vegetation moisture information (WET). The dryness index, indicating soil dryness, can detrimentally impact the ecological environment. Given that urban construction land predominates in our study area, the dryness index was represented by combining the Bare Soil Index (SI) and Construction Index (IBI) into a standardized Building–Bare Soil Index (NDBSI). WET, NDVI, LST, NDBSI, CSI, and AOD indices were utilized to represent the ecological quality of Hangzhou (

Table 1. IRSEI parameter and formula.

IRSEI Indices			Equations	Reference
Humidity index	WET	/	I_wet(TM) = 0.0315ρ(Blue) + 0.2021ρ(Green) + 0.3102ρ(Red) + 0.1594ρ(NIR) − 0.6806ρ(SWIR1) − 0.6109ρ(SWIR2)	[2]
			I_wet(ETM+) = 0.2626ρ(Blue) + 0.2141ρ(Green) + 0.0926ρ(Red) + 0.0656ρ(NIR) − 0.7629ρ(SWIR1) − 0.5388ρ(SWIR2)
			I_wet(OLI) = 0.1511ρ(Blue) + 0.1973ρ(Green) + 0.3283ρ(Red) + 0.3407ρ(NIR) − 0.7117ρ(SWIR1) − 0.4559ρ(SWIR2)
			MNDWI = (ρ(Green) − ρ(SWIR1))/(ρ(Green) + ρ(SWIR1))
Greenness index	NDVI	Normalized Difference Vegetation Index	NDVI = (ρ(NIR) − ρ(Red))/(ρ(NIR)+ρ(Red))	[2]
Heat index	LST	Land Surface Temperature	FVC=(NDVI-NDVI_soil)/(NDVI_veg-NDVI_soil)	[2]
			ε_water = 0.995 (NDVI≤0)
			ε_building = 0.9589 + 0.086 × F(veg) − 0.0671 × F²veg (0 < NDVI < 0.7)
			ε_natural = 0.9625 + 0.0614 × F(veg) − 0.0461 × F²veg (NDVI ≥ 0.7)
			L = g × DN + b
			Tb = K2/ln(K1/L + 1)
			LST = Tb/{1 + [(λTb)/ρ] ln ε } − 273.15
Dryness index	NDBSI	Normalized Difference Built-up and Soil Index	NDBSI = (SI+IBI)/2	[2]
			SI = [(ρ5+ρ(Red)) − (ρ(NIR)+ρ1)]/[(ρ5+ρ(Red)) + (ρ(NIR) + ρ(Blue))]
			IBI = [2ρ(SWIR1)/(ρ(SWIR1) + ρ(NIR)) − (ρ(NIR)/(ρ(NIR) + ρ(Red)) + ρ(Green)/(ρ(Green) + ρ5))]⁄[2ρ(SWIR1)/(ρ(SWIR1) + ρ(NIR)) + (ρ(NIR)/(ρ(NIR)+ρ(Red)) + ρ(Green)/(ρ(Green) + ρ(SWIR1)))]
Salinity index	CSI	Comprehensive Salinity Index	CSI = (SI − T + NDSI + SI3)/3	[2,20]
			SI − T = (ρ(Red)/ρ(NIR)) × 100
			NDSI = (ρ(Red) − ρ(NIR))/(ρ(Red) + ρ(NIR))
			SI3 = Sqrt(ρ²g + ρ²r)
Air index	AOD	Aerosol Optical Depth	The AOD data are collected by the MAIAC (Multi-angle Implementation of Atmospheric Correction) algorithm from MCD19, a new aerosol product of MODIS	[18]

).

• Humidity index ($I_{wet}$)

The humidity index is closely linked to the ecological environment’s quality. Low humidity indicates severe soil degradation, low vegetation coverage, and a poor ecological environment, while high humidity suggests sufficient soil moisture, abundant vegetation cover on the surface, and a favorable ecological environment. In this study, the humidity index was denoted as the I_wet component. Due to the different sensors of the Landsat TM/ETM+ and Landsat OLI images, the extraction formulas for the humidity index varied (Table 1).

2.

Greenness index ($I_{ndvi}$)

The NDVI is closely related to vegetation coverage, biomass, and leaf area index, which are commonly used to monitor vegetative growth. The NDVI was selected to represent the green index using the formula, as shown in Table 1.

3.

Heat index ($I_{heat}$)

Fractional vegetation cover (FVC) refers to the percentage of the vertical projected area of vegetation in the soil relative to the total statistical area. Vegetation coverage was based on Landsat NDVI, and a mixed-pixel binary model was adopted where NDVI is the normalized vegetation index, NDVI_soil is the normalized vegetation index value of the bare land surface, and NDVI_veg is the normalized vegetation index value of complete vegetation cover. NDVI_soil and NDVI_veg selected NDVI_max and NDVI_min with a confidence level above 95%. The g and b represent the offset values of the thermal infrared band; DN is the grey value of the pixel affected by remote sensing; L denotes the radiation brightness; LST is the surface temperature; and K1 and K2 are the calibration parameters, and various sensors use different values; Tb indicates the brightness temperature of the sensor; ε is the specific emissivity; and λ is the central wavelength of the thermal infrared band; ρ is 1.438 × 10⁻² mk.

4.

Dryness index ($I_{dry}$)

In this study, areas from bare soil and buildings were extracted by setting appropriate thresholds, and the NDBSI was then calculated using the area ratio as a weighted reference standard.

5.

Salinity ($I_{csi}$)

The CSI provides a more accurate reflection of the ecological impacts of salinization compared to other indices. Utilizing comprehensive learning, the CSI integrates the salinity index (SI-T), Normalized Difference Built-up and Soil Index (NDBSI), and salinity index 3 (SI3) to enhance the stability and reliability of the detection results. The CSI denotes the comprehensive salinity index. When calculating the CSI, it is necessary to normalize SI-T, NDSI, and SI3 to (0,1) to ensure that the CSI is obtained under the same standard and make the results representative.

6.

Air index ($I_{aod}$)

Aerosols are suspensions of liquid or solid particles dispersed in air or gases that circulate through numerous atmospheric chemical cycles and constitute an essential component of the atmospheric environment. Among the most fundamental optical properties of aerosols, AOD has emerged as a crucial parameter for studying atmospheric turbidity, providing insights into changes in aerosol distribution to a certain extent. The distribution of AOD is typically influenced by geographical factors, population density, and industrial distribution, making it an indicator of atmospheric turbidity and pollution. AOD accurately reflects the air quality concerning particulate matter within a specific area, exhibiting the highest degree of spatial coupling with pollutant emissions. Consequently, AOD was selected as a representative measure of air quality.

2.3.3. Random Forest Algorithm

The random forest algorithm is an ensemble learning method that enhances the generalization ability of a model by combining the predictions of multiple decision trees and incorporating randomness [24]. It is capable of addressing both classification and regression problems. One of its key features is its ability to rank the importance of different features, which aids in identifying the independent variables that contribute the most to model predictions and assessing the influence of each independent variable on the dependent variable. In this study, Python was utilized for machine learning purposes. The random forest regressor code was used to construct the regression model, obtain factor importance, and explain its effect on the dependent variable. The random forest algorithm was employed to analyze the driving forces behind IRSEI changes, with the selected independent variables being elevation (DEM), road conditions, humidity, green capital, heat, dryness, salinity, and air quality. The modeling process was performed using Python 3.12.

2.3.4. Equal Area Buffer Analysis Method

In the process of urbanization, the transformation from rural to urban areas leads to changes in economic, social, and environmental aspects. Therefore, the gradient effect of ecological quality in the horizontal direction between urban and rural areas is subject to the comprehensive influence of various factors such as urban–rural distance. By rationally utilizing the gradient effect, the dynamic balance and coordination of ecological quality between urban and rural areas can be effectively promoted, thereby enhancing the quality of the ecosystem. Taking the urban center of Hangzhou as the starting point of the circle, a circular buffer zone with a radius of 1km is established, and 500 concentric circles with gradually decreasing radii and equal areas are diverged outward, covering the main areas of the main urban area of Hangzhou. This study focuses on the relationship between ecological quality and the distance from the city center in this area from 2003 to 2023, analyzing the gradient effect of ecological quality in the horizontal direction between urban and rural areas.

3. Results and Discussion

3.1. Spatial Distribution Characteristics of Ecological Factors

By comparing the spatial distribution of the six ecological factors in Hangzhou from 2003 to 2023 (refer to Figure 2), we observed that high NDVI and WET values were primarily distributed around the main urban area. The spatial distribution characteristics of NDVI were consistent with land types, with high-value areas primarily consisting of vegetated areas such as forests and grasslands, while low-value areas mainly comprised central urban and residential areas. The spatial distribution of the NDBSI value was affected by the distribution of impervious ground and the degree of dryness of bare soil; hence, NDBSI values around water and forest areas were low, whereas those in cultivated land and human settlement areas were high. The spatial distribution of the LST value was influenced by human activities, the natural environment, and climate, with the highest values observed in the central urban area. AOD reflects the basic air quality situation, with variations in its spatial distribution caused by multiple factors such as human activities, natural climate, and the surrounding region. The results are consistent with other studies [17,18,25]. The CSI reflected changes in salinity and alkalinity, adversely affecting ecosystem structure and functions. The implementation of land management and ecological protection policies in Hangzhou in recent years, such as soil improvement and vegetation restoration, has also reduced the amount of saline–alkaline land. In arid areas, the adverse effects of soil salinization on the dynamic change in ecological quality cannot be ignored, and CSI also shows a correlation. For large areas, the salinity index reflects the regional ecological quality due to the diversity of soil texture and the topographic characteristics of landform [20].

In 2003, the LST values of Chun’an and Jiande were significantly higher than those of the central region, while the AOD values of the central and western regions were significantly higher than those of other regions. By 2008, the central city exhibited the highest NDBSI value, and the northwest of Lin’an District showed the highest AOD value. In 2013, the LST value of the main urban area was high, and the central part of Lin’an District exhibited a high AOD value. By 2018, the CSI in the central part of Fuyang was significantly lower than in 2003 and 2008, with the central part showing a high LST value. In 2023, most areas of Lin’an, Chun’an, and Tonglu exhibited high AOD values.

3.2. Spatiotemporal Distribution of Ecological Quality in Hangzhou

Combined with the spatial distribution characteristics of the ecological indicators (as discussed in Section 3.1) and the weights of each indicator (refer to

Table 2. Principal component analysis.

Ecological Index	PC1
	2003	2008	2013	2018	2023
NDVI	0.534	0.592	0.619	0.591	0.655
WET	0.182	0.146	0.155	0.104	0.116
LST	−0.130	−0.179	−0.282	−0.331	−0.251
NDBSI	−0.586	−0.529	−0.519	−0.484	−0.442
AOD	−0.565	−0.556	−0.485	−0.497	−0.528
CSI	−0.006	−0.068	−0.085	−0.215	−0.136
Eigenvalue	0.265	0.369	0.366	0.517	0.489
Contribution	76.81%	76.49%	83.65%	76.21%	84.33%
RSEI	4.14	4.23	4.25	4.25	4.32

), the spatial distribution characteristics and causes of the IRSEI in 2003, 2008, 2013, 2018, and 2023 were analyzed (refer to Figure 3). The comparatively low IRSEI values in the Gongshu, Shangcheng, West Lake, and Binjiang areas from 2003 to 2023 primarily resulted from the difference between urbanization and LST. In 2003, owing to the influence of AOD and NDBSI, the IRSEI in the central urban area was low. In 2013, the band area between Fuyang, Tonglu, and Jiande had a low IRSEI value, primarily due to the high AOD and poor air quality. In 2018, the IRSEI values in Gongshu and other central urban areas were low because of the high LST, whereas the spatial differences in WET, NDBSI, and AOD led to high IRSEI values in the central and northern parts of the region. Overall, the differences in the spatial distribution of AOD and air quality consistently had a significant impact on the IRSEI. The differences in the spatial distribution of WET and humidity also had a considerable impact on the IRSEI values.

Overall, the spatial distribution of the IRSEI in Hangzhou from 2003 to 2023 reveals that the six central urban areas exhibited significantly lower values than those of the remote urban areas, and the city center had lower values than the surrounding areas. Influenced by urban construction and expansion, the proportion of impervious water surfaces in the six central urban areas was high, resulting in overall low ecological quality (

Table 3. Area and proportion of ecological quality level in Hangzhou (unit: km²/%).

Ecological Quality	2003		2008		2013		2018		2023
Poor	61.67	0.33%	74.28	0.40%	291.23	1.58%	479.41	4.73%	339.82	3.21%
Fair	688.65	3.72%	939.83	5.11%	1186.34	6.42%	1079.32	10.65%	1068.11	10.08%
Moderate	1905.21	10.30%	2026.69	11.01%	1882.38	10.18%	1719.81	16.97%	1735.54	16.39%
Good	9718.29	52.55%	6576.60	35.73%	5358.51	28.99%	5046.68	49.80%	4831.47	45.61%
Excellent	6118.42	33.09%	8787.77	47.75%	9768.68	52.84%	10,134.23	100.00%	10,592.28	100.00%

). From 2003 to 2023, the IRSEI values in Shangcheng, Xiacheng, Jianggan, Gongshu, Xihu, and Binjiang districts were notably lower than those in other districts, with most below 0.40. As a central urban area, Shangcheng District contains a large non-construction area, and its average ecological quality is higher than that of other central urban areas. Junan and Kende had the highest IRSEI values, ranging between 0.55 and 0.85.

From 2003 to 2023, as shown in Figure 3, the overall IRSEI in Hangzhou exhibited a steady changing trend. Hangzhou boasted a large proportion of forest area, with a forest coverage rate as high as 78.63%, indicating rich forest resources. Chun’an, particularly rich in forested wetlands, is home to the artificial Qiandao Lake. The ecological quality of the region’s forestland remained high and stable, strongly influencing the ecological quality of the entire city.

Figure 4 illustrates the proportion of IRSEI grades and their conversions from 2003 to 2023. The grade transformation reveals that IRSEI values remained stable during this period, consistent with previous studies’ conclusions. The areal proportion of grades 0–0.2 was the lowest, fluctuating between 0.33% and 2.55%; that of grades 0.2–0.4 was the second-lowest, fluctuating between 3.68% and 6.30%; the largest proportion belonged to grades 0.8–1.0, fluctuating between 33.18% and 57.05%. The proportions of grades 0.4–0.6 and 0.6–0.8 varied considerably, fluctuating from 9.25% to 52.82%. Between 2003 and 2008, there was a significant improvement in ecological quality, with approximately half of the area in grades 0.6–0.8 transitioning to grades 0.8–1. From 2008 to 2013, ecological quality continued to improve, with the area of grade 0.8–1.0 increasing from 47.84% to 53.05%. However, urban expansion led to significant land consumption, including farmland, forests, and even ecological protection areas for urban construction. This expansion resulted in severe air and water pollution, with emissions from traffic, industry, and residents negatively impacting the ecological environment. Consequently, the proportion of grade 0–0.2 increased from 0.40% to 1.56%. Ecological quality showed slight improvement from 2013 to 2018, remaining stable from 2018 to 2023.

3.3. Influencing Factor Analysis of Ecological Quality Changes in Hangzhou

Table 4. Change in ecological classification in Hangzhou (unit: km²/%).

Factors	AOD	LST	WET	CSI	NDVI	NDBSI	ROAD	DEM
RMSE_noise	0.08	0.08	0.08	0.05	0.06	0.15	0.12	0.07
Contribution	0.16	0.41	0.15	0.07	0.09	0.06	0.04	0.02

presents the degrees of influence of the AOD, CSI, WET, LST, NDVI, NDBSI, ROAD, and DEM variables on IRSEI value changes. The influence of each factor on the IRSEI exhibited significant spatial heterogeneity, with the coefficient of LST being large and the coefficients of AOD and WET being exceedingly small. This indicates that human activities, represented by LST, were the most significant and direct factors affecting ecological quality, while the direct influences of air quality and elevation changes on ecological quality were minor.

For further analysis and understanding of the microscopic characteristics of ecological quality change in Hangzhou, a concentric ring with a radius of 1 km was established in the central urban area of Hangzhou to analyze the spatial horizontal distribution characteristics of areas with significant variations in Hangzhou (Figure 5). The area of these rings is the same, in order to truly reflect the true density of the change in ecological quality from different urban centers. From 2003 to 2008 and from 2008 to 2013, significant variations near the central urban area changed slightly in terms of their horizontal extent; however, after 2008 to 2013, these differences expanded, consistent with the areas of IRSEI variation. Between 2003 and 2023, areas experiencing significant changes in ecological quality conflicted in Hangzhou, distributed within ranges of 1–3 and 5–10 km. This trend suggests that the changes in IRSEI from 2003 to 2023 were closely related to urban expansion, population change, and economic development. Additionally, influenced by the construction of ecological civilization and adjustments in the development structure, the IRSEI remained generally stable up to 30 km outside the city center, indicating a trend of agglomeration in the center and significant areas in the surroundings. Overall, the area within the scope of the ecological quality changes increased as the increase in distance to the center of Nanjing City slowed, and near Nanjing City, land use change is more intense and the ecological quality of change is more obvious, illustrating that the change of land use will have great influence on ecological quality, which is consistent with other studies [2,15,21,26].

Hangzhou’s overall ecological quality was high, demonstrating the positive impact of the municipal government’s close attention to ecological environmental protection and sustainable development. In recent years, Hangzhou has implemented a series of measures to improve and enhance ecological quality, including strengthening pollution controls, promoting greening projects, and optimizing the industrial structure. Additionally, water quality in Hangzhou has significantly improved. The proportion of high-quality water in sections above the municipal control level remained high, with excellent assessment results for cross-administrative river-crossing sections, and centralized drinking water sources above the county level maintained a 100% water quality rating. These achievements are attributed to Hangzhou’s strict supervision of sewage treatment and discharge as well as the implementation of ecological water replenishment and other measures. Remarkable progress has been made in air quality. Through the implementation of an air pollution prevention and control action plan and the strengthening of industrial pollution controls and vehicle exhaust emission controls, Hangzhou’s air quality has significantly improved. The average concentration of PM_2.5 in urban areas has decreased annually, while the air quality rate has remained high. Hangzhou has also focused on ecological protection and restoration. Biodiversity in Hangzhou has been effectively protected through the implementation of ecological protection and restoration projects and the strengthening of the construction and management of ecological functional areas, such as nature reserves and forest parks. Concurrently, Hangzhou has actively promoted green development, optimized its industrial structure, developed a low-carbon and circular economy, and reduced pressure on the ecological environment. IRSEI takes into account various factors affecting the ecological environment more comprehensively by adding indicators such as AOD and CSI as well as utilizing principal component analysis for model construction. This improvement enables IRSEI to more accurately assess the quality of the ecological environment. IRSEI has been practically applied and validated in some areas, such as Hainan Island and the confluence area of the three rivers in Yibin City. These application cases demonstrate that IRSEI can more accurately reflect the quality of the ecological environment and its dynamic changes, providing a decision-making basis for ecological environmental protection and sustainable development.

These changes (Table 3) have primarily occurred because Hangzhou has focused closely on ecological protection and restoration in recent years. Through the implementation of ecological protection and restoration projects and the strengthening of the construction and management of ecological functional areas, such as nature reserves and forest parks, Hangzhou’s biodiversity has been effectively protected. Concurrently, Hangzhou has also actively promoted green development, optimized its industrial structure, developed a low-carbon and circular economy, and reduced pressure on the ecological environment.

3.4. Dynamic Monitoring of Ecological Quality in Hangzhou

To further analyze their spatial differences, the IRSEI changes were divided into seven categories according to the changes in the IRSEI index.

Table 5. Change in ecological classification in Hangzhou (unit: km²/%).

Change	2003–2008		2008–2013		2013–2018		2018–2023		2003–2023
Slight improvement	4396.89	24.02%	3500.90	19.15%	2487.21	13.58%	2518.89	13.69%	5779.39	31.42%
Generally better	66.26	0.36%	83.73	0.46%	135.72	0.74%	171.93	0.93%	194.95	1.06%
Significantly better	3.45	0.02%	2.41	0.01%	13.50	0.07%	22.66	0.12%	15.12	0.08%
No change	11,514.78	62.91%	11,414.47	62.43%	13,381.37	73.06%	13,863.75	75.33%	9905.65	53.85%
Slightly worse	2162.12	11.81%	3010.27	16.46%	2051.84	11.20%	1614.63	8.77%	1996.25	10.85%
Generally worse	147.87	0.81%	221.00	1.21%	192.35	1.05%	161.91	0.88%	399.58	2.17%
Significantly worse	12.65	0.07%	52.44	0.29%	54.83	0.30%	51.64	0.28%	103.64	0.56%

displays the shifts in the ecological quality of Hangzhou City from 2003 to 2023. Over the past two decades, changes in ecological quality have primarily fallen into three categories—slight improvement, no change, and slight deterioration—accounting for 85% of the total change. The proportion of slight improvement surpassed or closely approached 24% from 2003 to 2008, significantly outweighing the proportions of slight, moderate, and significant deteriorations. Between 2008 and 2013, the proportion of slight variations exceeded 16%, with evident improvement accounting for nearly 19.15%. From 2013 to 2018, the proportions of slight improvement and deterioration remained relatively stable. Furthermore, the proportion of evident improvement was 8.3% from 2000 to 2005, markedly higher than that of evident worsening (1.64%). Between 2018 and 2023, as well as from 2003 to 2023, the proportion of slight improvement (13.69% and 31.42%, respectively) notably exceeded that of less slight improvement (8.77% and 10.85%, respectively). This pattern underscores the continuous and steady enhancement of Hangzhou’s regional ecological quality from 2018 to 2023.

An examination of the spatial distribution differences in IRSEI variations (Figure 6) from 2003 to 2023 revealed that IRSEI values generally improved across most regions. Between 2008 and 2013, while some parts of the central urban area experienced improvements in IRSEI values, others witnessed a decline. Moving on to the period between 2013 and 2018, the IRSEI in areas near the north of the central city displayed improvement, whereas those near the south witnessed a slight deterioration, attributable to the predominant direction of urban expansion towards the south during this timeframe. In the interval from 2018 to 2023, several regions exhibited no change in IRSEI, while others demonstrated a balance between improvements and deteriorations.

4. Conclusions

This study evaluated the ecological quality of Hangzhou City from 2003 to 2023. Over the past 20 years, ecological quality fluctuated significantly due to urban expansion. From 2003 to 2008, ecological quality improved, mainly due to enhancements in farmland, forests, and water areas. The average IRSEI in Hangzhou rose gently from 4.13 to 4.32. Changes in Hangzhou’s ecological quality were linked to urban expansion, increased construction land, and reduced ecological land. However, intensified ecological protection, controlled urban expansion, and improved land intercrossing and fragmentation contributed to an overall enhancement in ecological quality. This study’s main findings are as follows:

• Overall, differences in the spatial distributions of AOD and CSI, representing air quality and salinization damage to the ecosystem, had a certain impact on the regional IRSEI but were not dominant factors. The differences in the spatial distributions of LST and NDBSI also significantly impacted the regional IRSEI. Surface temperature, air quality (measured by aerosol optical depth, AOD), and humidity (as denoted by WET) substantially impact the ecological status as indicated by the IRSEI;
• During 2003–2023, the IRSEI in Hangzhou exhibited a steady changing trend, with the area of grade 0–0.2 increasing from 33.18% to 57.05%, significantly improving ecological quality. The changes in the IRSEI from 2003 to 2023, such as surface temperature and traffic networks, were closely related to human activities, and the effects of AOD and CSI on IRSEI were spatially heterogeneous;
• In Hangzhou, areas experiencing drastic changes in ecological quality were located within the ranges of 1–3 km and 5–10 km from the city center, while the IRSEI remained generally stable at distances of at least 30 km from the city center.