Search Results (139)

Fine-scale evidence remains scarce regarding where ecological quality has improved or deteriorated in the Yangtze River Delta (YRD) and which landscape conditions are associated with these trajectories. We developed a 1 km² hexagon-based diagnostic framework integrating the Remote Sensing Ecological Index (RSEI), Sen–Mann–Kendall trend analysis, Local Moran’s I clustering, recurrence-based ecological stress typology, and XGBoost–SHAP interpretation for 2000–2025. Annual RSEI was standardized by year to capture relative trajectories of ecological quality rather than absolute change under a fixed loading system. The regional mean RSEI fluctuated markedly and declined only slightly, from 0.639 in 2000 to 0.632 in 2025, suggesting that long-term ecological change was nonlinear. At the hexagon scale, 64.77% of valid units showed positive RSEI trends, with significant improvement covering 15.08% of units and significant degradation covering 5.47%. Local Moran’s I identified distinct High–High and Low–Low clusters; persistent low-quality clusters and stable high-quality areas accounted for 10.0% and 7.8% of valid hexagons, respectively. XGBoost–SHAP results indicated statistical associations between RSEI trends and soil moisture, elevation, impervious surface change, and nighttime light change, rather than direct causal effects. This framework provides a spatially explicit basis for identifying priority monitoring areas, ecological stress zones, and differentiated land management units across rapidly urbanizing megaregions. Full article

This study investigates the spatiotemporal evolution of ecological environment quality and its driving mechanisms in the Sichuan section of the Yellow River Basin using Landsat imagery from 2000 to 2023. The Remote Sensing Ecological Index (RSEI) was constructed on the Google Earth Engine platform, and a comprehensive evaluation model was developed using principal component analysis. Sen’s slope, the Mann–Kendall test, and the Hurst exponent were applied to assess temporal trends and future persistence, while the optimal parameter-based Geodetector model was used to identify the driving factors of spatial differentiation. Results show that: (1) ecological environment quality exhibits a fluctuating but overall increasing trend, with a multi-year mean RSEI of 0.58, indicating a transition from “moderate” to “good–excellent” conditions; (2) spatially, ecological quality demonstrates significant heterogeneity and clear altitudinal gradients, with better conditions in the northwest than in the southeast, where low- and mid-altitude areas show higher ecological quality and stronger improvement, whereas high-altitude areas remain relatively poor due to strong natural constraints; (3) the spatial differentiation is jointly driven by multiple factors, among which precipitation and temperature are dominant, elevation exerts a fundamental constraint, and human activity plays a relatively minor role, while the interaction between climate and topographic factors shows the strongest explanatory power. These findings provide insights into the evolution and drivers of ecological environment quality in high-altitude regions and support ecological protection and regional management in the upper Yellow River Basin. Full article

Siziwang Banner in Inner Mongolia is a typical arid and semi-arid grassland region where ecological environmental quality is highly sensitive to climate variability and land use and land cover change (LULCC). Clarifying the long-term coupling relationship between LULCC and ecological environmental quality is essential for regional ecological protection and sustainable land management. Based on the Google Earth Engine (GEE) platform, this study integrated multi-temporal Landsat imagery and CLCD-based land use datasets, including an updated 2024 land use layer, to construct a Remote Sensing Ecological Index (RSEI) using standardized and direction-corrected principal component analysis. land use transition matrix analysis, spatial autocorrelation analysis, ecological contribution rate calculation, and GeoDetector were further applied to reveal the spatiotemporal evolution patterns, ecological effects, and driving mechanisms of LULCC in Siziwang Banner from 2000 to 2024. The results showed that: (1) grassland was consistently the dominant land use type, accounting for more than 90% of the total area. The overall land use pattern was characterized by stable grassland dominance, decreasing farmland and unused land, and slight increases in grassland and construction land; forestland showed a high relative growth rate but remained very small in absolute area. (2) The regional ecological environmental quality remained at a lower-to-medium level, with mean RSEI values ranging from 0.27 to 0.47. RSEI showed a phased pattern of initial improvement, subsequent decline, and partial recovery; the marked decline around 2015 was associated with the combined effects of drought stress and land use degradation rather than a single driving factor. RSEI exhibited significant positive spatial autocorrelation, with Moran’s I values ranging from 0.898 to 0.993. High-value clusters were mainly distributed in the southern region, whereas low-value clusters were concentrated in the central and northern regions. (3) Different land use transitions produced differentiated ecological effects. The conversion of unused land to grassland contributed positively to ecological restoration, while grassland degradation and construction land expansion exerted negative effects. The positive RSEI response of some grassland-to-farmland transitions should be interpreted cautiously in relation to local irrigation and intensive farmland management. (4) GeoDetector results indicated that land use type and DEM were the dominant factors controlling the spatial differentiation of RSEI, with average q values of 0.7188 and 0.6178, respectively. The interaction between DEM and land use type showed the strongest explanatory power, indicating that ecological quality was jointly shaped by land use structure and natural background conditions. This study provides a scientific basis for grassland protection, unused-land restoration, farmland management, and spatially differentiated ecological restoration in Siziwang Banner and similar ecologically fragile arid and semi-arid grassland regions. Full article

The human–environment interaction on the Qinghai–Xizang Plateau determines the direction of global human sustainable development, making it necessary to propose a refined prediction for this relationship. Currently, there is a lack of a predictive method for human–environment relationships, especially at the grid scale. This study focuses on Qinghai Province and proposes a human–environment relationship simulation method based on cellular automata (CA), utilizing land-use data and a remote sensing-based ecological (RSEI) index. The method enables grid-scale explicit predictions of human–environment relationships. The results show that by 2030, the human–environment relationship in Qinghai Province will become more diverse, with the coordination ratio rising to 11% and the degradation ratio to 7%. The ecological protection scenario serves a defensive role, preventing 3835 km² of land from degradation. In contrast, the urban development scenario plays a revitalizing role, achieving a coordinated area 2% larger than the business-as-usual scenario. By 2030, about 8956 km² of land in Qinghai will be suitable for agricultural revitalization, and 54,340 km² must be reserved for ecological protection. Due to the high-altitude environment, the human–environment relationship aligns only with the right half of the Environmental Kuznets Curve, namely, development brings greater harmony. We further discover the lag in the natural system’s response, for artificially increasing vegetation cover will not quickly improve habitat quality. Likewise, leapfrogging expansion in the urban development scenario may conceal long-term ecological risks behind short-term coordination. For stakeholders and policymakers, this study provides refined and differentiated governance measures at the grid scale, while highlighting the need to focus on underdeveloped regions and remain vigilant about the lag in human–environment relationship responses. Full article

Rapid urban expansion has intensified interactions between human disturbance and urban ecological processes, creating an urgent need for robust and urban-sensitive assessment tools. To improve the applicability of conventional remote sensing ecological evaluation in cities, this study develops an Urban Remote Sensing Ecological Index (URSEI) by incorporating an Urbanization Index (UI) into the RSEI-based PCA framework. Multi-temporal Landsat observations acquired during the peak vegetation season were used to construct annual ecological indicators, thereby improving the temporal representativeness of ecological assessment. Taking Hangzhou, China, as a case study, URSEI was applied to examine ecological quality dynamics inside and outside the Ecological Conservation Redline (ECR) from 2010 to 2024, together with temporal trend characteristics, indicative persistence patterns, and meteorological associations. The results show that URSEI generally achieved higher first principal component contribution rates than RSEI, suggesting stronger integration of ecological information within the PCA framework. UI exhibited the strongest negative correlation with URSEI among the stress-related indicators, highlighting the importance of explicitly representing urbanization-related disturbance in urban ecological assessment. Citywide ecological quality displayed a fluctuating but weakly improving tendency over the study period, while the ECR consistently maintained higher URSEI values than the overall urban area. However, most detected temporal changes were statistically non-significant, indicating that ecological conditions remained broadly stable rather than showing pronounced improvement or degradation. Temperature-related thermal conditions were predominantly negatively associated with URSEI, whereas precipitation showed mainly positive relationships and a stronger association with URSEI among the climatic variables examined. Overall, URSEI provides an urbanization-aware framework for long-term ecological monitoring and offers a useful basis for ecological management and sustainable planning in rapidly urbanizing regions. Full article

Arid inland river basins are constrained by severe water scarcity and fragile ecosystems. Although large-scale water transfer projects are critical interventions, studies of their comprehensive impacts on eco-socioeconomic systems remain limited. To address this gap, this study proposes an integrated assessment framework. A global Remote Sensing Ecological Index (gRSEI) was developed by incorporating a salinity indicator, employing optimal indicator selection, and utilizing a full-period global normalization strategy. A Gridded Socioeconomic Index (GSEI) was constructed by integrating nighttime light (NTL), population (POP), and gross domestic product (GDP) data. The coupling coordination degree (CCD) model, spatial autocorrelation analysis, and the optimal parameters-based geographical detector (OPGD) were applied to analyze spatial patterns across subregions. Focusing on the Shiyang River Basin (SYRB), this study analyzed the spatiotemporal responses and coupling coordination of the eco-socioeconomic system to the 2001 Jingdian Phase II Water Transfer Project. Results indicate that ecological quality improved significantly after the water transfer, with gRSEI increasing from 0.225 to 0.334. Socioeconomic development also improved overall. The eco-socioeconomic system exhibited high coupling but moderate coordination. The coupling degree (C) and coordination degree (D) increased from 0.824 and 0.370 to 0.852 and 0.442, respectively, with clear regional heterogeneity. The water transfer project shifted the dominant driver of coordinated development from water-related factors to land cover. This study provides a practical framework for assessing ecological and socioeconomic dynamics and their interactions in arid basins under major water transfer project interventions. Full article

To better capture ecological quality under aerosol pollution stress, an AOD-integrated Remote Sensing Ecological Index (ARSEI) was developed for the Shenyang Metropolitan Area (2000–2025). Using Google Earth Engine, multi-source MODIS products were compiled to generate an annual growing-season ARSEI through PCA, combining PC1 and PC2 by variance-weighted contributions. Long-term trends were assessed with Theil–Sen slope estimation and the Mann–Kendall test, future persistence with the Hurst index, and drivers with an optimal parameter geographical detector. ARSEI closely matched conventional RSEI in multi-year pixel means (R² = 0.98, p < 0.001) but identified larger “poor” (+0.4%) and “moderate” (+3.4%) areas from 2000 to 2025, indicating higher sensitivity to pollution-related stress. Ecological quality improved overall, with high grades in eastern mountainous forests and low grades in the central built-up core and surrounding croplands. Improvement was dominant (31.08% significant, 38.27% slight), while degradation was limited (4.27% significant, 13.92% slight) and concentrated in peri-urban expansion belts. Elevation was the strongest natural control, whereas land use and population were the leading socioeconomic drivers with increasing influence over time. Finally, we delineated differentiated management zones based on current status and projected trajectories to support targeted regional governance. Full article

Satellite-observed greening in arid regions is often interpreted as ecological restoration success, yet this assessment may conflate natural recovery with agricultural expansion. We developed an Arid Remote Sensing Ecological Index (ARSEI) incorporating a Comprehensive Salinity Index (CSI) to address systematic biases in the traditional RSEI when applied to irrigated drylands. ARSEI scores were validated against MODIS Net Primary Production (NPP) ($R^2>0.75$ at the regional scale), confirming its reliability in capturing ecosystem productivity, while CSI effectively maps the upper-bound of surface salinization potential dictated by intrinsic soil properties. Applied to China’s Mu Us Sandy Land (2000–2024), the ARSEI reveals that 2327 km² of sandy land—54% of current cropland—was converted to agriculture, creating “assessment-induced false greening” signals. While the traditional RSEI increased monotonically (+135%), the ARSEI shows a nuanced pattern with plateau (2010–2015) and decline (2015–2020) phases, reflecting salinization risks masked by high crop NDVI. Optimal Parameters-Based Geographical Detector analysis demonstrates that Land Cover × Precipitation interactions (q = 0.28) drive spatial heterogeneity through irrigation-mediated water redistribution. The ARSEI provides a dialectical evaluation framework: acknowledging agricultural greening’s economic benefits while monitoring subsurface degradation risks. This study offers a critical methodological advance for sustainable land assessment in global drylands undergoing agricultural intensification. Full article

Against the backdrop of global climate change and the “carbon neutrality” target, the ecological quality improvement of the Loess Plateau—a key region for ecological restoration in China—and its impact on vegetation carbon sources hold significant importance for regional carbon balance and ecological security. Based on MODIS and meteorological reanalysis data from 2002 to 2024, this study constructed the Remote Sensing Ecological Index (RSEI). Combined with a carbon source/sink model, it systematically assessed the spatiotemporal coupling evolution characteristics of ecological environment quality and vegetation carbon storage capacity in the Loess Plateau, and explored the synergistic driving mechanisms of major hydrothermal and surface factors. The results indicate the following: (1) From 2002 to 2024, the ecological environment of the Loess Plateau improved significantly, with the RSEI rising from moderate to good. This improvement was accompanied by a marked decrease in surface dryness, an increase in surface wetness, and notable growth in vegetation cover, revealing a positive coupling relationship characterized by “reduced surface dryness—increased surface wetness—enhanced vegetation restoration.” (2) Regional vegetation carbon storage capacity strengthened markedly. Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and Net Ecosystem Productivity (NEP) all showed significant increasing trends, and the proportion of area classified as carbon sink increased substantially. (3) Spatially, carbon sink distribution exhibited a pattern of “higher in the southeast, lower in the northwest.” Sub-regions A and D were identified as core areas with higher ecological quality and carbon sink capacity, whereas sub-regions B and C were more ecologically fragile and served as primary carbon source areas. (4) The implementation of soil and water conservation measures on the Loess Plateau has effectively enhanced regional carbon storage capacity. Vegetation restoration, improved water conditions, and reduced surface dryness have jointly driven the transition of the Loess Plateau ecosystem from a “vulnerable type” to a “recovering type”, while ecological restoration projects have played a certain role in enhancing the carbon sink. This study provides a theoretical basis and scientific–technological support for ecological protection and high-quality development in the Yellow River Basin. Full article

The Maceió Metropolitan Region (MMR) has undergone significant changes due to public policies that promote urban growth. This has intensified environmental impacts, adversely affecting local communities. The Remote Sensing Ecological Index (RSEI), a remote sensing-based metric, was used to evaluate ecosystem quality. The study assessed annual ecosystem quality in the MMR, Alagoas, using RSEI values from MODIS data spanning 2000 to March 2024/2025. To ensure data quality and reliable results, all MODIS data underwent rigorous quality control, including the exclusion of pixels affected by cloud cover, shadows, and missing values. Only data points meeting established MODIS quality assurance standards were used. Annual RSEI values varied considerably, from 0.449 in 2005 to 0.636 in 2014. Most areas in the MMR are classified as moderate quality (0.4 < RSEI < 0.6), particularly in central and eastern sectors. The lowest-quality regions (0 < RSEI < 0.4) are concentrated in the east—including Maceió, the hub city—and the west, largely due to high population density. The Sen-Slope Estimator and trend analysis revealed significant trends in the hub city, with positive trends in the northeast. Urban expansion has led to the loss of native vegetation, including sugarcane fields and remnants of the Atlantic Forest. The Pettitt test identified a structural change in 2018, likely linked to environmental violations related to the Braskem petrochemical industry and salt extraction in Maceió. Full article

Traditional field-based ecological surveys are inefficient in mountainous regions with steep slopes and deep valleys, highlighting the need for new quantitative remote sensing–based approaches. To account for complex terrain, four representative topographic factors (slope, relief, dissection, curvature) were selected via Digital Elevation Model (DEM) analysis to develop a Terrain Complexity Index (TCI), replacing the dryness component in the Remote Sensing Ecological Index (RSEI). Combined with greenness, wetness, and heat factors from Landsat 8, TCI was integrated using principal component analysis to form a Terrain-Adjusted RSEI (TARSEI), extending ecological assessment from two to three dimensions. In a mountainous case study in Huzhou City, Zhejiang, China, TARSEI showed a marked 34.2-percentage-point improvement over the original RSEI. Its high-value areas captured 82.3% of ecotourism points of interest, versus 48.1% for RSEI, demonstrating its enhanced accuracy for terrain-specific analysis. TARSEI further identified 28 new ecotourism resource clusters totaling 520.1 km² (8.9% of the city area), with a 98.5% overlap with high TARSEI zones. These results confirmed TARSEI’s effectiveness and provided robust scientific support for sustainable ecotourism development and spatial planning. With its high accuracy, stability, and universality, TARSEI is a promising and transferable tool for ecotourism resource assessment and spatial planning and management in complex terrain regions. Full article

Ecological quality (EQ) in the Yangtze River Economic Belt (YREB) has been profoundly reshaped by rapid urbanization and intensive ecological restoration over the past two decades. This study aimed to reveal the long-term spatiotemporal patterns of EQ and their driving forces at the basin scale. We constructed a 1 km, 25-year (2000–2024) Remote Sensing Ecological Index (RSEI) series using MODIS data and applied Sen’s slope, the Mann–Kendall and Hurst tests, and Geographically Weighted Ridge Regression (GWRR) to quantify trends, persistence, and spatially non-stationary driver effects. Results showed a significant overall improvement: by 2024, 69.6% of the YREB is classified as Good or Excellent EQ, with 34.6% of land showing continuous improvement and 6.4% faced persistent degradation risks. Forest and grassland cover exerted stable positive effects, while built-up expansion, population density, and GDP increasingly contribute to EQ decline, and the area dominated by urbanization-related negative coefficients expanded to 84.6% of the middle and lower reaches. The GWRR model achieved high average local $R^2$ (>0.92) and revealed pronounced spatial heterogeneity and multicollinearity-robust driver estimates. This study illustrates the potential of GWRR-based EQ diagnosis to support differentiated ecological governance strategies tailored to the upper, middle, and lower reaches of the YREB. Full article

With the intensification of environmental degradation, it is crucial for environmental protection to monitor and evaluate the ecological environmental quality (EEQ) in a timely and accurate manner based on remote sensing technology. However, current remote sensing EEQ evaluation methods suffer from deficiencies with regard to the indicator system and the EEQ quantification, reducing the accuracy of EEQ evaluations. Therefore, a new EEQ evaluation method is proposed in this study. Remote sensing indicators used in the pressure–state–response (PSR) framework are selected based on the traditional EEQ evaluation system, and deep neural networks (DNNs) are used to quantify EEQ. The results show that the proposed method has a significantly higher EEQ estimation accuracy with NRMSE of 13.61% and R² of 0.75 than the commonly used remote sensing ecological index (RSEI) method with NRMSE of 19.13% and R² of 0.51. This study suggests that the proposed method is suitable for the estimation of EEQ in a city. Full article

Understanding the evolutionary dynamics of land use and habitat quality (HQ) under climate change scenarios is pivotal for formulating science-based biodiversity conservation policies and promoting climate-resilient urban development in arid regions. By integrating the SD–PLUS–InVEST framework with SPEI-driven drought scenarios, this study introduces a novel coupling mechanism that links climate variability, land-use transitions, and HQ evolution in the Northern Slope of the Tianshan Mountains (UANSTM) under SSP–RCPs scenarios. The HQ assessment was validated using the Remote Sensing Ecological Index (RSEI). Simultaneously, the Optimal Multivariate-Stratification Geographical Detector (OMGD) was applied to identify scale-optimized drivers of HQ changes. The results indicated the following: (1) From 2000 to 2020, cultivated and construction land in the UANSTM expanded, while forest and water areas declined, with unused land remaining dominant from 2000 to 2020. (2) HQ decreased from 0.36 to 0.33 (2000–2020), significantly correlating with RSEI (Pearson r = 0.329, Spearman ρ = 0.446, p < 0.001), with climatic, vegetation, and coupled natural-social factors remaining the dominant drivers. (3) From 2020 to 2050, under all climate scenarios, the areas of farmland, grassland, and construction land are expected to grow, while HQ is projected to improve through the conversion of low-quality areas into moderate- and high-quality habitats (greatest under SSP119, least under SSP585). The framework advances predictive insights for arid-region ecological planning, supporting practical applications in habitat management and sustainable land-use planning, while providing a methodological paradigm for dryland habitat resilience assessment. Full article

The sustainable development of cities urgently requires an understanding of the interaction between social equity and ecological quality, especially in the peri-urban areas that traditional environmental justice research has paid less attention to. Taking Outer London as an example in this study, the Comprehensive Social Equity Index (CSEI) and the Remote Sensing Ecological Index (RSEI) were constructed to explore the social–ecological coupling relationship and spatial heterogeneity. Four types of socio-ecological coupling were identified through the four-quadrant model, ordinary least squares (OLS), and multi-scale geographically weighted regression (MGWR). The results reveal the characteristics of nonlinear coupling: in addition to the dual disadvantages and advantages of society and ecology, there are also regional patterns where social conditions are advantageous, but ecology is degraded, and where society is weak, but ecology is rich. This indicates that there is a complex spatial dislocation relationship between society and ecology in the peri-urban. The research proposes a scale-sensitive governance strategy based on location, emphasizing the coordinated countermeasures of social reinvestment and ecological restoration, providing a new perspective for environmental justice and sustainable planning in the peri-urban areas of the UK. Full article