Search Results (240)

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

High-quality ecological environments are vital for sustainable agro-pastoral development. This study evaluated the spatiotemporal dynamics of ecological environment quality (EEQ) in Zalait Banner from 2000 to 2022 using the Remote Sensing Ecological Index (RSEI) and explore the correlation between various factors and EEQ via Geodetector. Results show a fluctuating upward RSEI trend over 22 years. EEQ hit a low in 2004, with “poor” areas peaking at 30.77%, followed by a significant recovery between 2009 and 2013. Spatially, the region exhibits a “high in the west/northeast, low in the central-south/southeast” pattern. Notably, the central-south region, despite early recovery, has shown continuous deterioration since 2009, requiring urgent remediation. Geodetector analysis revealed that land surface temperature (LST) is the dominant single factor (q = 0.87) influencing EEQ, followed by land use/cover (LULC) and air temperature. Interaction analysis indicates that the synergy between RSEI’s four components (NDVI, WET, NDBSI, and LST) provides the highest explanatory power, while socioeconomic factors (GDP, population) and topography show weaker effects. These findings could provide a scientific basis for local ecological management, with future research planned for the Qinghai–Tibet Plateau. Full article

Long-term ecological monitoring is essential for sustainable management in fragile regions. This study assessed four decades (1986–2024) of ecological evolution in the Ji River Basin—a 1276.64 km² transitional loess–gully ecosystem in China’s Yellow River Basin—using the Remote Sensing Ecological Index (RSEI). We integrated multi-temporal Landsat images via Google Earth Engine to construct a 40-year RSEI time series. The index couples greenness (NDVI), wetness (WET), heat (LST), and dryness (NDBSI) through principal component analysis, with PC1 explaining > 82% of the variance. Three evolutionary phases were identified: initial degradation (1986–1996), driven by slope cropland expansion; stabilization (1996–2006), coinciding with early ‘Grain for Green’ policies; and sustained recovery (2006–2024), characterized by the expansion of high-quality zones. We developed a novel resilience zoning framework integrating local spatial consistency, terrain constraints, and functional state (mean RSEI 2016–2024), which delineated three zones: high-resilience refugia (19.37%), moderate-resilience matrix (75.54%), and low-resilience corridors (5.09%). Mid-slope positions (TPI: 1.220–1.510) within moderate-resilience zones demonstrated optimal restoration efficiency, challenging conventional uniform approaches. The findings advocate spatially differentiated strategies—investing in transitional zones, retrofitting degraded corridors, and monitoring stable refugia—to advance the implementation of Sustainable Development Goal 15 in semi-arid regions globally. Full article

The Yellow River Basin is among the regions in China most severely affected by soil erosion. Elucidating the evolution trend of its ecological environment quality and identifying the key driving factors can provide a theoretical basis for the management and protection of the ecological environment in the Yellow River Basin. In this study, an improved remote sensing ecological index (ARSEI) was constructed by incorporating the soil erosion factor (A) into the original remote sensing ecological index (RSEI). Subsequently, the Theil–Sen slope estimator, Mann–Kendall trend test, coefficient of variation, Hurst index and Geodetector were employed to analyze the spatiotemporal evolution trend and driving factors of the ecological environment quality in the basin from 2002 to 2022. The results were as follows: (1) During the study period, the mean ARSEI of the basin increased from 0.518 to 0.568, representing an increase of 9.65%, with a spatial pattern of “poor in the north and excellent in the south.” (2) 62.12% of the basin exhibited improved ecological quality, 75.74% of the area showed medium or lower fluctuation levels, and 35.12% of the region is projected to be at risk of degradation in the future. (3) Annual precipitation was identified as the dominant factor influencing the spatial variation in ARSEI (q = 0.428), followed by land use type (q = 0.299). All interactions between factors exhibited either nonlinear enhancement or bi-factor enhancement. Specifically, the interaction between annual precipitation and land use type was the strongest, with a maximum q-value of 0.693. This study provides a novel approach for assessing the ecological environment quality in regions severely affected by soil erosion. Full article

Under multiple stresses such as an arid climate, water scarcity, and desertification, inland river basins in arid regions represent a typically fragile ecosystem worldwide, and their ecological security faces increasingly complex and severe challenges. To address the limitations of traditional assessment methods characterized by single-perspective approaches, difficulties in quantifying indicators, and lack of a systematic framework for arid basins, this study constructed an innovative Health–Service–Risk–Sensitivity (HSRS) framework. Taking the Tarim River Basin (TRB) as a case study, the validity and necessity of this framework were validated through the Remote Sensing Ecological Index (RSEI) and correlation analysis. Furthermore, the XGBoost–SHAP model was further integrated to identify key threshold responses of multidimensional driving factors within the basin. The findings indicate that the ecological security of the TRB progressively improved, with approximately 11.64% of the area showing significant enhancement. The four most influential driving factors were land use, NDVI, human activity intensity, and soil moisture. Notably, the study identified critical environmental thresholds: when DEM ranged from 1500 to 3000 m and slope from 2° to 30°, constraining effects on the Comprehensive Ecological Security Index (CESI) increased. When annual precipitation exceeded 150 mm, NDVI was greater than 0.35, and soil moisture content exceeded 0.14 m³/m³, the constraint effect was further strengthened. Overall, the integration of the HSRS framework and the XGBoost-SHAP model offers a novel and effective approach for ecological security assessment in arid inland basins. Moreover, this approach has substantial practical implications for achieving precise coordination between regional ecological protection and sustainable development. Full article

As a newly designated international wetland city, Hangzhou (China) is currently exploring pathways for high-quality, sustainable development as a habitable city. It is necessary to reveal the baseline status of ecological quality scientifically and rationally whilst tracing its historical changes to support future detailed urban development planning. This study employs the GEE platform, utilizing remote sensing images of Hangzhou from 2013 to 2025. The RSEI index is constructed using four indicators directly perceptible to humans: dryness, heat, wetness, and greenness. The RSEI, coefficient of variation, and Sen-trend analysis were applied to evaluate patterns in ecological quality changes within Hangzhou. Results indicate that during the study period, Hangzhou exhibited minimal variation in RSEI values and Sen indices, reflecting overall ecological stability. Areas classified as “good” ecological grade increased, while other grades decreased. Ecological improvement primarily occurred in early-developed central districts like Xihu and Gongshu, demonstrating Hangzhou’s commitment to refined urban ecological management. This study validates the feasibility of RSEI for environmental assessment in Hangzhou, effectively guiding the city’s pursuit of refined development during late-stage urbanization to enhance the residents’ well-being. Furthermore, it provides a case study for ecological and environmental monitoring in megacities with similar characteristics to Hangzhou, offering significant demonstration value and implications. Full article

Xinjiang is an arid and semi-arid region where ecosystems are fragile, and monitoring how its ecology changes over time is critical for its sustainable development. In this study, a Remote Sensing Ecological Index (RSEI) was established for Xinjiang from 2000 to 2025. To understand temporal and spatial changes in ecological quality, we conducted spatial autocorrelation analysis, Theil–Sen median trend analysis, a Mann–Kendall trend test, and Hurst exponent analysis. We also used Geodetector to determine which factors affect the RSEI. The main results were as follows: (1) The RSEI in Xinjiang remained low, with a mean value between 0.285 and 0.336. Mountainous areas had higher values, basins had lower values, and spatial clustering was strong (Moran’s I index: 0.81–0.86). (2) H-H clusters expanded and then shrank, while L-L clusters grew after 2015. Areas with excellent ecological grades increased, but so did areas with poor grades, indicating that improvement and degradation both exist. (3) Most areas were stable, but 19.13% showed persistent degradation, indicating that these areas need more attention. (4) Land surface temperature (q = 0.624) and land cover (q = 0.576) were the main driving factors, and factor interactions showed enhanced effects. The results of this study could provide a scientific basis for ecosystem protection and restoration in Xinjiang. Full article

Rapid urbanization has significantly affected urban ecological environments, necessitating accurate and scientific quality assessments. In this study, we develop an enhanced remote sensing ecological index (WRSEI) for water network cities using Linyi City, China, as a case study. Key innovations include (1) introducing a water–vegetation index to better represent aquatic ecosystems; (2) incorporating nighttime light data to quantify the intensity of human activity; and (3) employing hierarchical PCA to rationally weight ecological endowment and stress indicators. The model’s effectiveness was rigorously validated using independent land use data. The results show that (1) the WRSEI accurately captures Linyi’s “water–city symbiosis” pattern, increasing the assessed ecological quality of water bodies by 15.78% compared to the conventional RSEI; (2) hierarchical PCA provides more ecologically reasonable indicator weights; and (3) from 2000 to 2020, ecological quality exhibited a pattern of “central degradation and peripheral improvement”, driven by urban expansion. This study establishes a validated technical framework for ecological assessment in water-rich cities, offering a scientific basis for sustainable urban management. Full article

The Arid Region of Northwest China (ARNC) functions as a critical ecological barrier for the Eurasian hinterland. To clarify the non-linear drivers of eco-environmental dynamics, a long-term (2000–2024) Remote Sensing Ecological Index (RSEI) time series was constructed and analyzed using an interpretable machine learning framework (XGBoost-SHAP). The analysis reveals pronounced spatial asymmetry in ecological evolution: improvements are concentrated in localized, human-managed areas, while degradation occurs as a diffuse process driven by geomorphological inertia. The ARNC exhibits low-level stability (mean RSEI 0.25–0.30) and marked unbalanced dynamics, with significant degradation (19.9%) affecting more than twice the area of improvement (6.5%). Attribution analysis identifies divergent driving mechanisms: ecological improvement (R² = 0.559) is primarily anthropogenic (58.3%), whereas degradation (R² = 0.692) is mainly governed by natural constraints (58.4%), particularly structural topographic factors, where intrinsic landscape vulnerability is exacerbated by human activities. SHAP analysis corroborates a “Greenness-Quality Paradox” in stable agroecosystems, where high vegetation cover coincides with reduced evaporative cooling and secondary salinization from irrigation, resulting in declining Eco-Environmental Quality (EEQ). A zero-threshold effect for grazing intensity is also identified, indicating that any increase beyond the baseline immediately initiates ecological decline. In response, a Resist-Accept-Direct (RAD) framework is proposed: direct salt-water balance regulation in oases, resist hydrological cutoff in ecotones, and accept natural dynamics in the desert matrix. These findings provide a scientific basis for reconciling artificial greening initiatives with hydrological sustainability in water-limited regions. Full article

Driven by the globalization tide, urbanization and cross-border economic cooperation have intensified challenges to ecological conservation, with border regions increasingly confronting irreversible habitat degradation risks. As a globally recognized biodiversity hotspot, Xishuangbanna acts as a strategic hub for cross-border ecological security between China and Southeast Asia, having long been confronted with dual pressures from economic development and ecological conservation. By analyzing the spatiotemporal evolution of the Remote Sensing Ecological Index (RSEI) during 2003–2023, this study simulates its multi-scenario dynamics, develops the “RSEI-ESP-PLUS” framework, presents a novel assessment mechanism for ecological security patterns (ESP), and provides a scientific basis for regional sustainable development. Results indicate that integrating RSEI improves the accuracy of ecological source identification. Over the past two decades, regional Ecological Environmental Quality has exhibited an overall improvement trend, yet persistent ecological pressures remain—including vegetation degradation and climate warming. Concurrently, high-quality ecological areas have contracted while moderate-quality ones have expanded. In the 2033 simulation, the ecological conservation scenario delivered the most favorable ecological network assessment outcomes, identifying 16 stable and 15 potential ecological sources. Accordingly, this study establishes an ecological security pattern centered on the core structure of the “One Axis, Two Corridors, and Three Zones”, which provides a spatial planning scheme for regional sustainable development. Full article

The previous studies have suggested that the cap-and-trade carbon emissions trading scheme (ETS) was effective in reducing greenhouse gas emissions and atmospheric pollution. Are there other environmental benefits of this policy? This research question remains unanswered in the literature. Our study reports that China’s carbon ETS significantly improved the land surface ecological quality (LSEQ). The study analyzes the data of 328 Chinese cities during 2005–2020. A difference-in-differences (DID) regression model is used for quantitative policy evaluation. The land surface ecological quality is measured by a synthetic indicator of the remote sensing ecological index (RSEI). There are three main findings. (1) On average, the carbon ETS improved the land surface ecological quality index by 0.0113, which contributed 51% of the ecological quality improvement in ETS-implementing regions in the post-policy period. The positive effect of the policy increased over time. (2) The implementation of the carbon ETS reduced pollution emissions, promoted green innovation, and expanded the share of land with natural vegetation coverage. These phenomena provide explanations for why the policy improved the land surface ecological quality. (3) The policy effect exhibited some heterogeneities contingent on local climatic conditions. The effect was stronger in regions with more precipitation, shorter sunlight duration, and higher temperature. Full article

The Chang–Zhu–Tan Metropolitan Area, the first national-level metropolitan region in central China, faces a prominent conflict between urban expansion and the quality of the ecological environment (EEQ) amid rapid urbanization. Investigating the ecological evolution of this area holds both significant scientific and practical value. This study leverages the Google Earth Engine (GEE) platform and long-term Landsat remote sensing imagery to explore the spatiotemporal variations in EEQ in the Chang–Zhu–Tan Metropolitan Area from 2002 to 2022. A modified remote sensing ecological index (MRSEI) was developed by incorporating the Air Quality Difference Index (DI), and changes in EEQ were analyzed using Sen slope estimation and the Mann–Kendall test. Apart from that, using 2022 data as an example, the Optimal Parameter Geodetector (OPGD) was employed to evaluate the impacts of multifarious driving factors on EEQ. The main findings of the study are as follows: (1) In comparison with the traditional remote sensing ecological index (RSEI), MRSEI can more effectively reflect regional differences in EEQ. (2) The overall EEQ in the region is relatively good, with over 60% of the area classified as “excellent” or “good”. The spatial distribution follows a pattern of “higher at the edges, lower in the center”. (3) The EEQ trend in the study area generally suggests reinforcement, though central areas such as Kaifu District and Tianxin District exhibit varying degrees of degradation. (4) Human factors have a greater impact on EEQ than natural factors. Land Use and Land Cover Change (LUCC) is the primary driver of the spatial differentiation in the regional ecological environment, with the interaction of these factors producing synergistic effects. The results of this study strongly support the need for ecological protection and green development in the Chang–Zhu–Tan Metropolitan Area, offering valuable insights for the sustainable development of other domestic metropolitan regions. Full article

Ancient walnut trees (Juglans regia L.), revered as “cultural heritage in motion,” have coexisted harmoniously with dense clusters of Tibetan traditional villages for centuries. However, accelerating climate change and expanding human activities along the middle reaches of the Yarlung Tsangpo River have increasingly threatened their survival. To quantitatively evaluate the health of these ancient trees and identify the underlying driving mechanisms, this study developed a remote sensing-enhanced Structural Equation Model (SEM) that integrated satellite-derived ecological indices, land-use intensity, and field-measured morphological and physiological indicators. A total of 135 ancient walnut trees from villages such as Gamai in Jiacha County, Tibet, were examined. Key findings: (1) The SEM demonstrated an excellent model–data fit (Minimum Discrepancy Divided by Degrees of Freedom (CMIN/DF) = 1.372, Root Mean Square Error of Approximation (RMSEA) = 0.053, Tucker–Lewis Index (TLI) = 0.956, and Comparative Fit Index (CFI) = 0.962), confirming its robustness. (2) Among the latent variables, overall condition exerted the strongest influence (weight = 0.360), whereas foliage condition contributed least (0.289). (3) Approximately 35.56% of trees were healthy or sub-healthy, while 61.48% showed varying levels of decline. (4) Tree health was jointly shaped by intrinsic and extrinsic factors, with intrinsic drivers exhibiting stronger explanatory power. Externally, human disturbance negatively affected health, whereas ecological quality was positively associated. These results highlight the effectiveness of integrating remote sensing and SEM for ancient tree assessment and underscore the urgent need for long-term monitoring and adaptive conservation strategies to enhance ecological resilience. 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

The Semipalatinsk Test Site (STS), one of the most heavily contaminated nuclear test sites globally, presents critical challenges for ecological monitoring and restoration due to long-term radioactive pollution and soil degradation. This study applied the Remote Sensing Ecological Index (RSEI) model to systematically evaluate the spatiotemporal changes in ecological quality at STS from 2003 to 2023. The RSEI model integrated multi-indicator data, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), WET (Wetness), and NDBSI (Normalized Difference Built-up and Soil Index), enabling a comprehensive assessment of ecological dynamics. Results demonstrated a significant improvement in ecological quality, with the RSEI increasing by 29.59% (from 0.345 in 2003 to 0.447 in 2023). PCA results indicated that ecological recovery was primarily influenced by surface temperature, vegetation cover, and soil moisture, with radioactive residues further hindering recovery in severely contaminated zones. The proportion of “Poor” areas declined from 14.99% to 0.61%, while “Moderate” and “Good” areas expanded to 55.76% and 8.87%, respectively. Peripheral regions showed faster recovery due to effective natural and management interventions, while core high-contamination zones (Sary-Uzen) exhibited slower recovery due to persistent radioactive residues. This study highlights the applicability of RSEI for assessing ecological recovery in nuclear test sites and emphasizes the need for targeted remediation strategies. These findings provide valuable insights for global ecological management of nuclear test sites, supporting sustainable restoration efforts. Full article