The CZT-GH (112.89°E–113.30°E, 27.72°N–28.09°N) is situated at the geographic intersection of Changsha, Xiangtan, and Zhuzhou, functioning as a vital connection between these three cities (Figure 1a). The region experiences a subtropical monsoon climate, with annual temperatures ranging from 3.2 °C to 31.7 °C and average yearly precipitation of 1,450 mm. The topography comprises low hills, mountains, and plains. The dominant land use categories consist of farmland, forest, and construction areas. The non-crop vegetation includes evergreen broadleaf, evergreen needleleaf, and deciduous broadleaf forests, alongside wetlands. The CZT-GH contains abundant natural resources, establishing it as a significant biodiversity conservation area and wildlife habitat. As of 2018, the CZT-GH had a population of 3.32 million, distributed as 42.3% rural and 57.7% urban. The GDP attained 35.11 billion RMB, with the tertiary sector comprising the largest portion at 53%. The government in Hunan Province revised the GH policy in 2013, which legally safeguards the GH with a focus on establishing ecological barriers, maximizing ecosystem services, promoting high-end primary and tertiary industries, and optimizing land use structures for sustainable regional development (Cao et al., 2023).

In this research, we utilized Landsat Collection two imagery from GEE, a cloud-based platform providing access to global surface reflectance and LST products with enhanced geolocation accuracy (Table 1). Remote sensing data, including annual Landsat 5 TM images from 1990 to 2011, Landsat 7 ETM+ image in 2012, and annual Landsat 8 OLI/TIRS images from 2013 to 2020, were selected during the vegetation growth period between April and September in the study area (Zhang Y. et al., 2022). These images were mosaicked to minimize cloud cover and atmospheric interference and processed with GEE at a 30-m spatial resolution. The preprocessing steps comprised data filtering, radiometric calibration, cloud and water masking, atmospheric adjustment to surface reflectance, and geometric alignment of the images. These steps ensure spatial consistency and high-quality data for accurate analysis of EEQ trends throughout the study period (Fu et al., 2024). A median synthesis was applied to cloud-masked scenes to merge them into a final image, reducing residual cloud shadows and noise while preserving natural surface reflectance and avoiding extreme value bias. Additionally, the water body mask was derived from the JRC/GSW1_3/Yearly History, which provides surface water location and time data from 1990 to 2020. This dataset effectively minimizes water body impact on the RSEI calculation. We applied image fusion and normalization methods to eliminate biases caused by temporal differences in sensor data from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI, mapping the data from different sensors to a unified scale, ensuring temporal consistency and accuracy across the data (Mancino et al., 2020; Wachmann et al., 2024). Due to the failure of the Landsat seven sensor’s Scan Line Corrector, the images exhibit striping gaps. Missing pixels were filled using the focal statistics function, with focalMean and blend functions applied (Yan et al., 2024). The parameters of the focalMean function are detailed in Supplementary Table S1. This function calculates the average of input pixels within a defined neighborhood and assigns it to fill the missing pixels, effectively completing the imagery gaps (Huang et al., 2025). Due to the 16-day revisit period and limited coverage, annual composite imagery was used.

The DEM data were obtained from the Geospatial Data Cloud (https://www.gscloud.cn) utilizing the GDEMV2 dataset with a 30 m spatial resolution. Slope and slope aspect data were derived from the DEM data. Vegetation data were acquired from the Resource and Environment Science and Data Center (https://www.resdc.cn), providing spatial distribution data of China’s vegetation types. Land use data were obtained from the Geographical Information Monitoring Cloud Platform (1990–2020), including classifications such as cropland, water areas, forest, grassland, urban zones, and unutilized land. Both road and village boundary datasets were extracted from Open Street Map (https://www.openstreetmap.org), incorporating national road networks and administrative boundaries at the village level in the Hunan Province. The details of data types and sources for GH are detailed in Table 2.

According to the spatial planning schematic from the “Comprehensive Strategy for the GH Zone in the Changsha-Zhuzhou-Xiangtan Metropolitan Region”, GH is divided into three regions: prohibited development area, restricted development area, and controlled construction area (Figure 1b). The prohibited development area, encompassing 275.89 km² (52.22% of the total area), comprises areas of extreme and high ecological sensitivity, ecological reserves, mountains with slopes exceeding 25°, and contiguous farmlands and wetlands. The restricted development area encompasses 191.30 km² (36.21%), including regions of moderate and low ecological sensitivity, areas surrounding the Xiangjiang River and its main tributaries, and elevated terrains with slopes ranging from 15° to 25°. The controlled construction area spans 61.13 km² (11.57%), incorporating contiguous development areas, non-sensitive ecological zones, and regions with high development potential. Based on the environmental variable similarities between the 3 km buffer zone and the GH, and considering spillover effects at PA edges from previous research (Chiu et al., 2016; Shen Y. et al., 2022), a 3 km buffer zone of GH was established using the Analyst Toolbox in ArcGIS 10.8 (Figure 1b).

RSEI is a comprehensive ecological index that analyzes EEQ spatiotemporal dynamics with remote sensing images (Xu et al., 2019). The construction of the RSEI requires four indicators: greenness, wetness, dryness, and heat (Supplementary Table S2). The formula is presented in Equation 1. This comprehensive approach enables an objective and impartial evaluation of EEQ. R S E I = f G r e e n e s s , W e t n e s s , D r y n e s s , H e a t (1)

Where greenness, wetness, dryness, and heat represent the four remote sensing metrics-the Normalized Difference Vegetation Index (NDVI), Wetness (WET), Normalized Difference Bare Soil Index (NDBSI), and Land Surface Temperature (LST), respectively (Yang and Li, 2023). Furthermore, to prevent water’s influence on principal component loadings and account for variance in indicator scales, the modified normalized difference water index (MNDWI) is applied for each indicator to mask the water body before PCA analysis (Du et al., 2016). The formulas for normalization and water mask are presented in Equations 2,3 . N I = M − M min M max − M min (2)

Where M is the initial index, NI stands for the Normalized Index, and M_min and M_max denote the lowest and largest values of the indicator, respectively. M N D W I = G r e e n − S W I R 1 G r e e n + S W I R 1 (3)

Where Green and SWIR1 denote the reflectance of Band two and Band five in Landsat 8, and Band 3 and Band 4 in Landsat 5/7, respectively.

Then, to calculate the initial Remote Sensing Ecological Index (RSEI₀), we first normalize the four indicators mentioned above to the range [0, 1] to ensure comparability and avoid unbalanced weighting due to differing dimensions. These normalized indicators are then processed using PCA to extract the first principal component (PC1), which captures the main variance in the data. To align the higher RSEI values with better ecological quality, we subtract PC1 from 1, as shown in Equation 4: R S E I 0 = 1 − P C 1 f N D V I , W e t , L S T , N D B S I (4)

Where the RSEI value lies within the range of [0, 1], with higher values reflecting superior ecological health (Pariha et al., 2021; Yuan et al., 2021), and PC1 denotes the first principal component of indicators, and f represents the normalized form of each indicator. To demonstrate the protective effectiveness of GH policies, the timeline was divided into two phases: 1990–2013 and 2013–2020, using the implementation year of GH policy as the dividing line. By comparing data from these two periods, a more comprehensive assessment of the area’s ecological shifts can be obtained.

The study’s methodology is depicted Figure 2. Initially, four remote sensing metrics-NDVI, WET, NDBSI, and LST-were extracted from the synthesized Landsat imagery on the GEE platform. PCA was employed to generate annual spatiotemporal maps of RSEI for each CZT-GH subarea from 1990 to 2020. Subsequently, Theil-Sen-Mann-Kendall (MK) was applied to analyze the EEQ trends in GH. Lastly, PSM and Wilcoxon methods were utilized to assess and evaluate the effectiveness of the GH policy.

A PCA on the RSEI of CZT-GH (1990–2020) reveals that the cumulative contribution rate of the main components (PC1) in GH is 63.7% ± 2.4% (mean ± SD) (Table 3). The majority of characteristics of the NDVI, WET, LST, and NDBSI can be represented by these first principal components (mean ± SD). According to their contribution rates to RSEI, the NDVI and WET contribute positively to ecology, while NDBSI and LST demonstrate adverse ecological impacts.

During the three monitoring periods, the overall RSEI levels predominantly fall into the “Moderate” and “Good” categories, with areas of low values primarily located in the controlled construction area and the southeastern part of the buffer zone (Supplementary Figure S2a–c). Higher RSEI values are predominantly observed in the prohibited development area and restricted development area, along with their adjacent buffer zones. The southern part of the CZT-GH demonstrated a notable increase in RSEI from 2013 to 2020 (Supplementary Figure S2b). This improvement is attributed to GH policy, which prioritizes protecting the natural environment and controlling large-scale economic development.

As illustrated in Supplementary Figure S2c, the “Poor” category comprises the smallest area at 6.00% of the total, followed by “Fair” at 11.22%, “Moderate” category at 19.79%, “Good” category at 37.02%, and “Excellent” category at 25.98%. The percentages of each category demonstrate fluctuations (Figure 3). The “Fair” “Poor” and “Good” categories exhibit a relative fluctuation trend, the “Moderate” category shows a significant reduction, and the “Excellent” categories display clear growth. Before (1990–2013) and after (2013–2020) policy implementation, the average proportion of areas with the “Moderate”, “Fair”, and “Poor” categories decreased from 22.36% to 10.17%, while the share of “Good” and “Excellent” categories increased from 77.15% to 89.69%.

The RSEI in the area exhibited a fluctuating upward trend, indicating an improvement in the overall EEQ in the CZT-GH (Figure 4). The annual RSEI across the entire region has increased by 0.0038 over the study period. The growth rate from 1990 to 2013 was slightly lower than that from 2013 to 2020, with rates of 0.0045 years^-1 and 0.0089 years^-1, respectively.

The β_RSEI analysis using the Theil-Sen-MK method revealed distinct spatial variations in EEQ changes across the study area from 1990 to 2020 (Figure 5). The “No change” category decreased substantially from 75.49% to 29.16% between the periods 1990–2013 and 2013–2020. Simultaneously, areas showing “Slight improvement” and “Strong improvement” categories in EEQ increased significantly from 23.86% to 59.38% during these periods. As shown in Figures 5a,b, the dominant EEQ trend spatially transitioned from “No change” to “Slight improvement,” becoming widely distributed across various subareas. Regions exhibiting “Serious degradation” and “Slight degradation” categories increased from 0.97% to 12.24%, shifting from the northern part of GH to the southeastern and southern buffer zones between 1990–2013 and 2013–2020.

Throughout 1990–2020, roughly 12% of the total study area demonstrated “Slight improvement” and “Strong improvement” categories in EEQ change trends (Figure 5c), mainly concentrated in the northern GH and southwestern buffer zones. The “Slight improvement” category constituted 8.7% of this change. The predominant EEQ change trend remained “No change,” comprising approximately 83% and primarily distributed across central GH and the eastern buffer zone. The areas classified under the “Slight degradation” and “Serious degradation” categories represented only 2.24% and 0.94% of the total area, respectively.

In the PSM results, the prohibited development area and restricted development area yielded 172 and 174 matched data points, respectively (Table 4). Within the Buffer-P and Buffer-R, the matched data locations demonstrated a high overlap rate, predominantly situated in the southern, eastern, and western regions (Figures 6c,e). The propensity score matching results in the controlled construction area indicated near-complete fulfillment of the matching criteria (Figure 6b).

Following policy implementation (2013–2020), the β_RSEI of the GH buffer zone and subareas exhibited distinct variations (Figures 7d–f). The prohibited development area showed an average β_RSEI of 0.0079, significantly exceeding the Buffer-P (0.0054, P < 0.001). The restricted development area displayed an average β_RSEI of 0.0082, notably higher than the Buffer-R (0.0049, P < 0.0001). The controlled construction area demonstrated an average βRSEI of 0.0084, compared to the Buffer-C’s 0.0055, indicating a significant difference (P < 0.01). However, before GH policy implementation (1990–2013), no significant differences were observed between the matched buffer zones and GH subareas, including prohibited development, restricted development, and controlled construction areas (Figures 7a–c).

The average β_RSEI values for the matched buffer zones of each GH subarea after policy implementation were significantly higher than those of the corresponding areas before policy implementation (Supplementary Figure S3). The implementation of the GH policy resulted in notable positive conservation outcomes within the 3 km buffer zone.

Before (1990–2013) and after (2013–2020) policy implementation, the RSEI of the CZT-GH within the “Good” (0.6–0.8) and “Excellent” (0.8–1.0) categories demonstrate an upward trend, increasing from 77.15% to 89.69% (Figure 3), and primarily concentrated in the central and north-east regions of GH (Supplementary Figure S2). This aligns with previous research that reported over 60% of the RSEI in the Changsha-Zhu-Xiangtan urban agglomeration ranged between 0.65 and 1 during 1999–2020, indicating an overall improvement in EEQ (Li G. et al., 2023).

The RSEI growth rate of CZT-GH from 2013 to 2020 was 0.0089 years^-1, which exceeded the rate of 0.0038 years^-1 observed from 1990 to 2013 (Figure 4). This increase can be attributed to the rapid economic development and land expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration from 2000 to 2010 (Deng et al., 2020). Furthermore, anthropogenic activities such as deforestation and construction adversely impacted EEQ. Li J. et al. (2022) observed that decreased vegetation cover directly correlates with declining regional EEQ in areas experiencing frequent construction and deforestation within CZT-GH from 2008 to 2013. Since 2001, urban expansion has emerged as a significant factor in reducing vegetation cover in the CZT-GH (Shunshi et al., 2019).

The GH and the 3 km buffer zone categorized as “Slight improvement” and “Strong improvement” in the β_RSEI demonstrated substantial increases with spatial heterogeneity (Figure 5). The enhancement in GH stems from increased vegetation cover, stricter regulations on environmentally risky projects, and the establishment of environmentally conscious industries (Deng et al., 2020). The improvement in the 3 km buffer zone results from the development of ecological corridors, which enhanced connectivity with the GH, strengthening ecological stability (Qu et al., 2024). This EEQ improvement pattern in CZT-GH aligns with previous research findings (Dieleman and Musterd, 2013). In comparable climate regions, such as Chengdu and the Yangtze River Delta, green spaces have enhanced EEQ through greenway network construction and green development policies, emphasizing connectivity and balanced conservation with sustainable land use (Wang J. et al., 2023; Zhong et al., 2020).

GH policy has demonstrated positive outcomes, facilitating a comprehensive recovery of EEQ in the region from 2013 to 2020 (Figure 5b). The average β_RSEI of the GH subareas is significantly higher than that of buffer zone. The average β_RSEI values for the prohibited, restricted and controlled area are 0.0079, 0.0082 and 0.0084, while the corresponding average value for the buffer zone are 0.0054 (P < 0.001), 0.0049 (P < 0.0001) and 0.0055 (P < 0.01), respectively (Figure 7). The EEQ of CZT-GH primarily fell within the “Moderate”, “Good”, and “Excellent” categories in 2018, with cropland (26.61%) and forest land (48.03%) as the predominant land use types (Li J. et al., 2022). GH policy, serving as a comprehensive framework for ecological protection and urban management, has implemented measures including increased ecological construction, reduced human disturbances, and regular ecological monitoring to maintain ecosystem stability (Li T. et al., 2023; Liang et al., 2024). In the Netherlands, GH policy significantly improved air quality by approximately 20%, diminished urban heat island intensity, and enhanced regional ecological resilience by 30% from 2000 to 2020 (Dieleman and Musterd, 2013; Roodbol-Mekkes and Van Den Brink, 2015). Stockholm’s “Green Wedges” planning has driven spatial development, increasing the city’s green space by 30% and enhancing biodiversity through expanded green corridors since its inception in 1947 (Grădinaru and Hersperger, 2019). Similarly, the CZT-GH significantly enhances biodiversity by establishing ecological corridors that protect native species and key habitats for wildlife (Qu et al., 2024).

During the past decade, GH policy has established a networked ecological pattern centered on forest green spaces, supported by wetlands and farmlands, and interconnected by rivers, streams, and transport corridors. This network has strengthened ecosystem stability and improved EEQ (Wang et al., 2021). GH policy has also designated prohibited, restricted, and controlled development area to ensure sustainable resource utilization, minimize environmental damage, and increase forest cover (Li C. et al., 2023). The controlled construction area maintains EEQ through the protection of ecological patches and corridors, improving connectivity and enhancing ecosystem service efficiency (Unnithan Kumar et al., 2022; Zhang et al., 2024a). Both the controlled construction area and the 3 km buffer zone feature extensive, uninterrupted development zones, characterized by high urbanization levels, similar land use and vegetation types, and dense populations and road networks, which create comparable covariate conditions across most areas of the controlled construction area. The prohibited and restricted development areas boost EEQ through restored forests and wetlands, establishing multi-level ecological redlines and strictly controlling land use to create a comprehensive ecological barrier (Hunan, 2025).

While the EEQ of GH and its buffer zones has exhibited varying degrees of change following policy implementation (Figure 5b), it remains vulnerable to urbanization impacts. Moving forward, the CZT-GH should enhance policies for subareas. Prohibited development area should prioritize ecological and landscape protection, with strict enforcement of ecological redlines and routine satellite monitoring. Restoration efforts should focus on native vegetation and habitat reconstruction to recover ecosystems, including wetlands and forests (Valente et al., 2021). Moreover, restricted development areas should adopt protection-first and moderate development strategies while promoting advanced primary sectors and supporting green tertiary sectors such as eco-agriculture and rural tourism (Wang et al., 2022). These areas should control land use and integrate ecological restoration to balance development with conservation (Li Q. et al., 2022). Additionally, controlled construction areas must strictly define development boundaries and utilize land efficiently to maintain ecological corridor connectivity. Green infrastructure requires reinforcement, urban expansion needs rational planning, and sprawl-driven ecological degradation should be prevented (Wu et al., 2020).

Balancing economic growth and ecological integrity in the CZT-GH relies on monetizing ecosystem services through provincial horizontal ecological compensation, which has effectively promoted inclusive green development (Li J. et al., 2022). The government directs capital toward high-end primary industries and eco-tourism within restricted and controlled zones, harmonizing local livelihood strategies with conservation objectives (Liu X. et al., 2024). GH requires a remote-sensing and connectivity-informed ecological security pattern to constrain urban expansion, maintain landscape linkages, and minimize patch fragmentation (Tang et al., 2023).

Between 2013 and 2020, substantial positive conservation effects on EEQ were detected between the matched buffer zones and GH subareas (Figure 7; Supplementary Figure S3). The establishment of a multi-scale ecological corridor network enhances landscape connectivity, protects ecological sources, reduces resistance, and enables species and energy movement. This consequently improves the EEQ of GH and its buffer zone (Ouyang et al., 2024). Additionally, the government limits overdevelopment within GH and implements arable land and forest protection planning, facilitating vegetation growth and restoration (Wu and Wang, 2023). These strategies mitigate urban sprawl’s ecological impact, improving regional EEQ (Li C. et al., 2023; Shao et al., 2024).

Prior studies have established that buffer zones are essential for effective ecosystem protection. The buffer zones at 0–2 km, 2–6 km, and 6–10 km in the Wuyishan PA exhibited significant conservation effects, suggesting a 0–10 km width effectively balances ecosystem preservation and controlled human activity, thereby reducing urban development impacts (Zhang et al., 2023b). Chen et al. (2017) revealed that the 0–10 km buffer zone for the Cangshan nature reserve demonstrated a significant positive spillover effect on forest cover. The 2 km buffer along the Weihe River in Shaanxi effectively minimized ecological risks and maintained ecosystem service values. The targeted zoning approach successfully mitigated ecological risks from land-use changes and protected the ecosystem (Xie et al., 2024).

The GEE platform enabled historical monitoring and analysis of EEQ spatiotemporal dynamics in CZT-GH. The PSM approach assessed EEQ variations before and after GH policy implementation. This study presents a replicable framework for evaluating the conservation effectiveness of protection policies in similar urban areas, using RSEI and PSM. It contributes to assessing policy effectiveness and facilitating further improvements.

This study primarily focuses on the spatiotemporal dynamics of EEQ in the CZT-GH and its 3 km buffer zone from 1990 to 2020, and evaluates the conservation effectiveness of the GH policy. However, this study is unable to assess the long-term impact of recent urban renewal initiatives (e.g., sponge city projects), as it concluded in 2020. Future research should integrate post-2020 data to explore the synergies or trade-offs between ecological protection and urban development. Additionally, it should also enhance the policy implementation mechanism, establish transferable indicators for cross-scenario comparisons, and combine ecological assessments with economic cost evaluations. Furthermore, ecosystem services (e.g., carbon sequestration, pollination, and flood regulation) in the CZT-GH should be further emphasized in future work to expand the scope of our analysis, while also highlighting the hydrological connectivity between upstream and downstream areas and their impact on EEQ.