Search Results (151)

The traditional remote sensing ecological index (RSEI), based on principal component analysis (PCA) to integrate four evaluation indexes: greenness (NDVI), humidity (WET), dryness (NDBSI), and heat (LST), is insufficient to comprehensively consider the influence of each eco-environment evaluation index on eco-environment quality (EEQ). In this research, a new comprehensive remote sensing ecological index (CRSEI) based on the quaternion Copula function is proposed to comprehensively characterize EEQ responded by integrating four eco-environment evaluation indexes. Additionally, the spatiotemporal variation of EEQ in Henan Province is evaluated using monthly CRSEI data from 2001 to 2020. The results show that: (1) The applicability and monitoring accuracy of CRSEI are better than that of RSEI, which can be used to assess the EEQ. (2) The EEQ of Henan Province declined between 2001 and 2010 but significantly improved and rebounded from 2011 to 2020. During this period, CRSEI values were higher in West and South Henan and lowest in central Henan, with West Henan consistently showing the highest values across all seasons. (3) The EEQ in Henan Province exhibited a tendency of deterioration from the central cities outward, followed by improvement from the outer areas back towards the central cities. In 2010, regions with poor EEQ made up 68.3% of the total area, whereas by 2020, regions with excellent EEQ accounted for 74% of the total area. (4) The EEQ was significantly negatively correlated with human activities, while it was positively correlated with precipitation. The research provides a reference and guidance for the scientific assessment of the regional eco-environment. Full article

The evaluation of ecological environment quality (EEQ) is an important method to measure the quality of ecosystem services. Therefore, the EEQ of Changting County, located in the red soil region of southern China, was assessed by using the remote sensing ecological index (RSEI) based on Landsat images from 1995 to 2019, and its spatiotemporal variability was identified by using the Global Moran’s I index, standard deviational ellipse, and kernel density estimation. The results showed that, firstly, the EEQ degraded from 1995 to 2000, then improved from 2000 to 2019; secondly, the spatial distribution of the RSEI for each study year was not random and had a strong positive correlation; thirdly, the directional distributions of the RSEI for all the grades were almost in the direction of southwest to northeast, and the spatial discrete characteristics of the moderate- and good-grade areas were almost consistent from 1995 to 2019; fourthly, the kernel density distribution of the moderate- and good-grade EEQ was located in towns within the Tingjiang River Basin and in the surroundings of the study area, respectively. This study can help managers to better understand the spatial–temporal variations in the EEQ in the study area, supporting the government in formulating a better ecological restoration strategy. Full article

The ecosystems within the Qilian Mountain National Nature Reserve (QMNNR) and its surrounding areas have been significantly affected by changes in climate and land use, which have, in turn, constrained the region’s socio-economic development. This study investigates the regional characteristics and application requirements of the ecological environment in the arid and semi-arid zones of the reserve. In view of the saturated characteristics of NDVI in the reserve and the high-altitude saline-alkali environmental conditions, this study proposed a Modified Remote Sensing Ecology Index (MRSEI) by introducing the kernel NDVI and comprehensive salinity index (CSI). This approach enhances the applicability of the remote sensing ecological index. The temporal and spatial dynamics of ecological and environmental quality within the QMNNR from 2000 to 2022 were quantitatively assessed using the MRSEI. The effect of land use on ecological quality was quantified by analyzing the MRSEI contribution rate. The findings in this paper indicate that (1) in arid and semi-arid regions, the MRSEI provides a more precise representation of surface ecological environmental quality compared to the remote sensing ecological index (RSEI). The high correlation (R² = 0.908) and significant difference between MRSEI and RSEI demonstrate that MRSEI enhances the accuracy of evaluating ecological environmental quality. The impact of land use on ecological quality was quantitatively assessed by analyzing the contribution rate of the MRSEI. (2) The ecological quality of the QMNNR exhibited an upward trend from 2000 to 2022, with an increase rate of 1.3 × 10⁻³ y⁻¹. The area characterized by improved ecological and environmental quality constitutes approximately 53.68% of the total area. Conversely, the ecological quality of the degraded areas accounts for roughly 28.77%. (3) Among the various land use types, the improvement in ecological environmental quality within the reserve is primarily attributed to the expansion of forest and grassland areas, along with a reduction in unused land. Forest and grassland types account for over 90% of the total area classified with “good” and “excellent” ecological grades, whereas unused land types represent more than 44% of the total area classified with “poor” ecological grades. Overall, this study provides a valuable framework for analyzing ecological and environmental changes in arid and semi-arid regions. Full article

The Remote Sensing Ecological Index (RSEI) model is widely used for large-scale, rapid Ecological Environment Quality (EEQ) assessment. However, both the RSEI and its improved models have limitations in explaining the EEQ with only two-dimensional (2D) factors, resulting in inaccurate evaluation results. Incorporating more comprehensive, three-dimensional (3D) ecological information poses challenges for maintaining stability in large-scale monitoring, using traditional weighting methods like the Principal Component Analysis (PCA). This study introduces an Improved Remote Sensing Ecological Index (IRSEI) model that integrates 2D (normalized difference vegetation factor, normalized difference built-up and soil factor, heat factor, wetness, difference factor for air quality) and 3D (comprehensive vegetation factor) ecological factors for enhanced EEQ monitoring. The model employs a combined subjective–objective weighting approach, utilizing principal components and hierarchical analysis under minimum entropy theory. A comparative analysis of IRSEI and RSEI in Miyun, a representative study area, reveals a strong correlation and consistent monitoring trends. By incorporating air quality and 3D ecological factors, IRSEI provides a more accurate and detailed EEQ assessment, better aligning with ground truth observations from Google Earth satellite imagery. Full article

Exploring the response of spatial and temporal characteristics of ecological quality change to aridity on the Qinghai–Tibet Plateau (QTP) can provide valuable information for regional ecological protection, water resource management, and climate change adaptation. In this study, we constructed the Remote Sensing Ecological Index (RSEI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on the Google Earth Engine (GEE) platform with regional characteristics and completely analyzed the spatial and temporal variations of aridity and ecological quality on the QTP in the years 2000, 2005, 2010, 2015, and 2020. Additionally, we explored the responses of ecological quality to aridity indices at six different time scales. The Mann–Kendall test, correlation analysis, and significance test were used to study the spatial and temporal distribution characteristics of meteorological aridity at different time scales on the QTP and their impacts on the quality of the ecological environment. The results show that the ecological environmental quality of the QTP has a clear spatial distribution pattern. The ecological environment quality is significantly better in the south-east, while the Qaidam Basin and the west have lower ecological environment quality indices, but the overall trend of environmental quality is getting better. The Aridity Index of the QTP shows a differentiated spatial and temporal distribution pattern, with higher Aridity Indexes in the north-eastern and south-western parts of the plateau and lower Aridity Indexes in the central part of the plateau at shorter time scales. Monthly, seasonal, and annual-scale SPEI values showed an increasing trend. There is a correlation between aridity conditions and ecological quality on the QTP. The areas with significant positive correlation between the RSEI and SPEI in the study area were mainly concentrated in the south-eastern, south-western, and northern parts of the QTP, where the ecological quality of the environment is more seriously affected by meteorological aridity. Full article

The interaction between the natural environmental and socioeconomic factors is crucial for assessing the dynamics of plateau ecosystems. Therefore, the remote sensing ecological index (RSEI) and CatBoost-SHAP model were employed to investigate changes in the ecological quality and their driving factors in the Diqing Tibetan Autonomous Prefecture, China, from 2001 to 2021. The results showed an increase from 0.44 in 2001 to 0.71 in 2021 in the average RSEI for the Diqing Prefecture, indicating an overall upward trend in the ecological quality. Spatial analysis shows the percentage of the area covered by different levels of RSEI and their temporal changes. The results revealed that “good” ecological quality accounted for the largest proportion of the study area, at 42.77%, followed by “moderate” at 21.93%, and “excellent” at 16.62%. “Fair” quality areas accounted for 16.11% and “poor” quality areas only 2.57%. The study of ecological and socioeconomic drivers based on the CatBoost-SHAP framework also indicated that natural climate factors have a greater impact on ecological quality than socioeconomic factors; however, this effect differed significantly with altitude. The findings suggest that, in addition to strengthening climate monitoring, further advancements in ecological engineering are required to ensure the sustainable development of the ecosystem and the continuous improvement of the environmental quality in the Diqing Prefecture. Full article

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran’s I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran’s I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems. Full article

In the face of persistent global environmental challenges, evaluating ecological environment quality and understanding its driving forces are crucial for maintaining the ecological balance and achieving sustainable development. Based on a case study of Changle District in Fuzhou, China, this research employed the Remote Sensing Ecological Index (RSEI) method to comprehensively assess ecological environment quality and analyze the impact of various driving factors from 2000 to 2020. Based on the GeoSOS-FLUS model, this study simulated and predicted land use classifications if maintaining the RSEI factors. The results reveal an overall improvement in the southern and southwestern regions, while the northwest and eastern areas face localized degradation. The RSEI index increased from 0.6333 in 2000 to 0.6625 in 2022, indicating significant ecological shifts over the years. The key driving factors identified include vegetation coverage, leaf area index, and aerosol levels. Industrial emissions and transportation activities notably affect air quality, while land use changes, particularly the expansion of construction land, play a critical role in altering ecological conditions. If maintaining the current RESI factors without any improvement, Changle District will experience continued urbanization and development, leading to an increase in built-up areas to 32.93% by 2030 at the expense of grasslands. This study offers valuable insights for policymakers and environmental managers to formulate targeted strategies aimed at reducing industrial and traffic emissions, optimizing land use planning, and enhancing ecological sustainability. The methodology and findings provide a robust framework for similar assessments in other rapidly urbanizing regions, contributing to the broader discourse on sustainable land use and ecological conservation. By advancing the understanding of ecological environment quality and its driving forces, this research supports the development of informed environmental protection and sustainable development strategies for coastal regions in developing countries globally. Full article

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning. Full article

The upper reaches of the Yellow River are critical ecological barriers within the Yellow River Basin (YRB) that are crucial for source conservation. However, environmental challenges in this area, from Longyangxia to Lijiaxia, have emerged in recent years. To assess the ecological environment quality (EEQ) evolution from 1991 to 2021, we utilized remote sensing ecological indices (RSEIs) on the Google Earth Engine (GEE) platform. Spatial autocorrelation and heterogeneity impacting EEQ changes were examined. The results of this study show that the mean value of the RSEIs fluctuated over time (1991: 0.70, 1996: 0.77, 2001: 0.67, 2006: 0.71, 2011: 0.68, 2016: 0.65, and 2021: 0.66) showing an upward, downward, and then upward trend. The mean values of the overall RSEI are all at 0.65 and above. Most regions showed no significant EEQ change during 1991–2021 (68.59%, 59.23%, and 55.78%, respectively). Global Moran’s I values (1991–2021) ranged from 0.627 to 0.412, indicating significant positive correlation between EEQ and spatial clustering, and the LISA clustering map (1991–2021) shows that the area near Longyangxia Reservoir shows a pattern of aggregation, dispersion, and then aggregation again. The factor detection results showed that heat was the most influential factor, and the interaction detection results showed that greenness and heat had a significant effect on regional ecosystem distribution. Our study integrates spatial autocorrelation and spatial heterogeneity and combines them with reality to provide an in-depth discussion and analysis of the Longyangxia to Lijiaxia Basin. These findings offer guidance for ecological governance, vegetation restoration, monitoring, and safeguarding the upper Yellow River’s ecological integrity. Full article

Exploring eco-environmental quality dynamics in the Daihai Lake Basin has significant implications for the conservation of ecological environments in the semi-arid and arid regions of northern China. Based on the Google Earth Engine (GEE) platform, the remote sensing ecological index (RSEI) was constructed by coupling Landsat SR remote sensing data from 1985 to 2022. The spatial significance of the RSEI was analyzed using linear regression equations and an F-test. The spatial correlation, distribution characteristics, and driving factors behind the RSEI were explored using Moran’s index and a geodetector. The results indicated that (1) the RSEI was appropriate for evaluating eco-environmental quality in the Daihai Lake Basin. (2) From 1985 to 2022, the eco-environmental quality of the Daihai Lake Basin exhibited a positive trend but remained subpar. (3) A positive spatial autocorrelation was demonstrated for eco-environmental quality with increasing spatial aggregation. (4) Significant eco-environmental quality degradation (slope < 0) occurred primarily in Sanyiquan Town in the northeastern region of the basin and in Tiancheng Township in the southeastern region. Conversely, a notable improvement (slope > 0) was predominantly observed in Yongxing and Liusumu in southwestern Daihai. (5) The improvement in the ecological environment of the Daihai Lake Basin was primarily attributed to an increase in NDVI and WET and a decrease in NDBSI and LST. The interaction between NDVI and LST had the greatest explanatory power for the ecological environment. Among the external driving factors, DEM (elevation) was the dominant factor in the RSEI and had the strongest explanatory power. The interaction between DEM and LST was the most significant, and the driving factors were enhanced. This study provided a theoretical basis for the sustainable development of the Daihai Lake Basin, which is crucial for the local ecological environment and economic development. Full article

The Sanjiangyuan Nature Reserve of China (SNRC) is recognized as one of the most fragile and sensitive terrestrial ecosystems in China, posing challenges for obtaining reliable and complete Moderate Resolution Imaging Spectro Radiometer (MODIS) data for ecological environment quality (EEQ) monitoring due to adverse factors like clouds and snow. In this study, a complete high-quality framework for MODIS time-series data reconstruction was constructed utilizing the Google Earth Engine (GEE) cloud platform. The reconstructed images were used to compute the Remote Sensing based Ecological Index (RSEI) on a monthly scale in the SNRC from 2001 to 2020. The results were as follows: The EEQ of the study area exhibited a “first fluctuating decline, then significant improvement” trend, with the RSEI values increasing at a rate of 0.84%/a. The spatial pattern of the EEQ displayed significant spatial heterogeneity, characterized by a “low in the west and high in the east” distribution. The spatial distribution pattern of the RSEI exhibited significant clustering characteristics. From 2001 to 2020, the proportion of “high–high” clustering areas exceeded 35%, and the proportion of “low–low” clustering areas exceeded 30%. Poor ecological conditions are mainly associated with population agglomerations, cultivated land, unutilized land, and bare ground, while grasslands and forests have higher RSEI values. The result of the trend analysis revealed a significant trend in RSEI change, with 62.96% of the area significantly improved and 6.31% significantly degraded. The Hurst Index (HI) results indicated that the future trend of the RSEI is predominantly anti-persistence. The proportion of areas where the EEQ is expected to continue improving in the future is 33.74%, whereas 21.21% of the area is forecasted to transition from improvement to degradation. The results showed that the high-quality framework for MODIS time-series data reconstruction enables the effective continuous monitoring of EEQ over long periods and large areas, providing robust scientific support for long time-series data reconstruction research. Full article

Mining activities in coal mining areas have exacerbated ecological and geological environmental risks. To explore the impact of mineral resources on the ecological and geological environment risk (EGER) in coal mining areas, we developed a novel ecological and geological risk assessment framework. This framework first quantifies the impact of mining activities on the surface of coal mining areas using remote sensing interpretation and Differential Interferometric Synthetic Aperture Radar (DInSAR) technology. Then, this framework selected six indicators, including subsidence, surface occupation and damage, FVC, RSEI, precipitation, and temperatures. The weights of the evaluation indicators were calculated using a coupled weighting model combining the Analytic Hierarchy Process (AHP) and the Entropy Method (EM). This approach was applied to the Wangwa mining area to assess its ecological and geological risks. The results show that the surface subsidence increase year by year. The EGER in the study area was medium and the change rate of the EGER index in Wangwa mining area from 2017 to 2022 was −0.460 to 0.598. The EGER index increased southwest of the study area but reduced in the pre-investigation area and north of the investigation area. This study can support decision-making to reduce the adverse environmental impact of coal mining activities. Full article

Understanding the response of ecological quality (EQ) to forest landscape connectivity is essential to global biodiversity conservation and national ecological security. However, quantitatively measuring the properties and intensities within these relationships from a spatial heterogeneity perspective remains challenging. This study takes the Fujian Delta region as its case study. The Google Earth Engine platform was employed to compute the remote sensing ecological index (RSEI), the landscape metrics were applied to represent the structural connectivity of the forest landscape, and the minimum cumulative resistance model was adopted to measure the cost distance index representing the functional connectivity of the forest landscape. Then, the spatial correlation and heterogeneity between the EQ and forest landscape connectivity were analyzed based on spatial autocorrelation and geographical weighted regression at three scales (3, 4, and 5 km). The results showed the following: (1) from 2000 to 2020, the overall EQ increased, improving in 37.5% of the region and deteriorating in 13.8% of the region; (2) the forest landscape structural and functional connectivity showed a small decreasing trend from 2000 to 2020, decreasing by 1.3% and 0.9%, respectively; (3) eight forest landscape structural and functional connectivity change modes were detected under the conditions of an improving or degrading EQ based on the change in RSEI and forest landscape structural and functional connectivity; (4) the geographical weighted regression results showed that compared with the forest landscape structural connectivity index, the cost distance index had the highest explanatory power to RSEI in different scales. The effect of forest landscape functional connectivity on EQ is greater than that of structural connectivity. It provides a scientific reference for ecological environmental monitoring and the ecological conservation decision-making of managers. Full article

The interactions between economic development, environmental sustainability, population growth, and urbanization are vital in assessing the ecological dynamics of urban agglomerations. This study explores the relationship between economic development, environmental sustainability, population growth, and urbanization within the Xia-Zhang-Quan urban agglomeration in Fujian Province from 1989 to 2022. Utilizing Landsat remote sensing images, we calculated the Remote Sensing Ecological Index (RSEI) to evaluate changes in ecological quality. The results show that the average RSEI values for 1989, 2000, 2010, and 2022 were 0.5829, 0.5607, 0.5827, and 0.6195, respectively, indicating an initial decline followed by a significant increase, culminating in an overall upward trend. The spatial distribution of RSEI classification shows that the study area has the largest proportion of mainly “good” ecological quality. The proportion of areas with “excellent” ecological environmental quality has increased (13.41% in 1989 and 25.12% in 2022), while those with “general” quality has decreased (28.03% in 1989 and 21.21% in 2022). Over the past three decades, Xiamen experienced substantial ecological degradation (RSEI change of −0.0897), Zhangzhou showed marked improvement (RSEI change of 0.0519), and Quanzhou exhibited slight deterioration (RSEI change of −0.0396). Central urban areas typically had poorer ecological conditions but showed signs of improvement, whereas non-central urban regions demonstrated significant environmental enhancement. The factor detector analysis identified land use as the dominant factor influencing ecological environmental quality, with precipitation having a relatively minor impact. Interaction analysis revealed that all other factors demonstrated bi-variable enhancement or nonlinear enhancement, suggesting that the interactive effects of these factors are greater than the effects of individual factors alone. Land use consistently showed solid explanatory power. Temperature also exhibited significant influence in 2022 when interacting with other factors. Due to urban planning that can plan for land use, these findings suggest that effective urban planning can harmonize economic development with ecological protection within the Xia-Zhang-Quan urban agglomeration. Full article