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Accurately monitoring and evaluating changes in ecological environment quality under earthquake disturbances is of great significance for the restoration and protection of regional ecological environment. In view of the "8 • 8" earthquake in Jiuzhaigou County in 2017, we used high-precision remote sensing image to analyze the vegetation damage caused by the earthquake, and calculated remote sensing ecological index (RSEI) for the pre-earth-quake period, post-earthquake period and 3-year recovery period based on GEE platform to analyze the spatio-temporal variation of ecological environment in Jiuzhaigou County, Sichuan Province. Then, we used geodetector to reveal the influencing factors of spatio-temporal variations in ecological restoration. The results showed that the fractional vegetation cover of Jiuzhaigou County decreased from 0.71 before the earthquake to 0.69 after the earthquake. The area of higher coverage zone decreased by 310.78 km2, while the area of others increased. The mean RESI decreased from 0.50 in the pre-earthquake period to 0.42 in the post-earthquake period, and increased to 0.50 after the 3-year recovery period. The ecological environment quality in the three period was mainly at the good and average levels, and it was distributed in the central and southern mountains and the eastern river valley. Annual precipitation, elevation, wet and greenness were the main factors controlling ecological quality restoration in Jiuzhaigou County, and the increases in the interaction among these factors would affect the spatial variations of regional ecological environment quality restoration. [ABSTRACT FROM AUTHOR]

This study used Google Earth Engine (GEE) to get and process Landsat images of Xuzhou from 2008 to 2020, and built remote sensing ecological indices (RSEI). This study analyzed the spatiotemporal features and drivers of ecological environment quality after Xuzhou’s transformation using landscape pattern index, spatial autocorrelation, and geographic detector methods. The results showed that: (1) The ecological environment quality of Xuzhou improved steadily but unevenly from 2008 to 2020; the west and central urban areas had poor but improving quality, while the east had the opposite trend; (2) The landscape pattern indices of ecological environment quality in different regions of Xuzhou did not change consistently from 2008 to 2020; (3) The spatial distribution of ecological environment quality had significant autocorrelation, with high-high clusters in the east and low-low clusters in the center and the urban areas of each district and county; (4) NDVI is the component index that has the greatest impact on the changes in the RSEI. Among the single factors, land use conditions have the strongest influence on ecological environmental quality. After the interaction of dual factors, the explanatory power is stronger than that of any single factor. The research results can support urban planning and design, ecological environment protection, etc. in Xuzhou, and offer insights for monitoring and evaluation of ecological environment quality after the transformation of resource-exhausted cities.

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. [ABSTRACT FROM AUTHOR]

Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection. [ABSTRACT FROM AUTHOR]

Real-time assessment of ecological environment quality in arid and semi-arid regions is crucial for the sustainable development of ecological environments in China. In this study, we constructed a topsoil remote sensing ecological index (TRSEI) by coupling five indicators, greenness, wetness, dryness, topsoil grain size, and heat, with the Google Earth Engine (GEE). With the index, we evaluated the ecological environment quality of Wuchuan County from 1990 to 2020, and examined the spatio-temporal variations of ecological environment quality and its driving factors by using univariate linear regression, multiple regression residual analysis, and Hurst index. Results showed that the first principal component of the TRSEI in the study area contributed over 70%, with a mean eigenvalue of 0.148, indicating the effective integration of various ecological indicators by TRSEI. The topsoil grain size index was essential for the assessment of ecological environment quality in arid and semi-arid regions. From 1990 to 2020, the fluctuation range of TRSEI in the study area was between 0.289 and 0.458, showing an overall slight deterioration trend. The ecological environment quality of cropland and de-farming region had improved, with the improved area accounting for 47.9% of the total area. The grassland, barren land, and construction land areas had deteriorated, with the deteriorated area accounting for 52.1% of the total area. In the future, 36.9% of the regions would experience continuous improvement in ecological environment quality, while 41.4% might continue to deteriorate. Human activities were the primary driving factor for the changes in ecological environment quality in arid and semi-arid regions, accounting for 88.6% of the total area. Climate change also had a significant impact, accounting for 11.4% of the total area. The TRSEI could effectively assess the ecological environment quality of arid and semi-arid regions, providing a scientific basis for ecological conservation and construction in these areas. [ABSTRACT FROM AUTHOR]

Propelled by rapid economic growth, the southwestern Shandong urban agglomeration (SSUA) in China has become a crucial industrial hub, but this process has somewhat hindered vegetation growth and environmental quality. Leveraging the functionalities of the Google Earth Engine (GEE) platform, we derived the fractional vegetation coverage (FVC) through the Normalized Difference Vegetation Index (NDVI) and assessed the eco-environmental quality using the Remote Sensing Ecological Index (RSEI). To examine the patterns and shifts in the SSUA, we employed the Theil–Sen median slope estimation, which provided robust estimates of linear trends, the Mann–Kendall trend test to determine the statistical significance of these trends, and the Hurst exponent analysis to evaluate the long-term persistence and predict future changes in the vegetation coverage and eco-environmental quality. Furthermore, to explore the interdependencies between vegetation coverage (VC) and environmental quality, we applied an improved coupling coordination degree model (ICCDM). This model allowed us to assess the co-evolution and synergy between these two factors over the study period, providing comprehensive insights for sustainable urban and ecological planning in the region. The VC and eco-environmental quality improved consistently across most of the SSUA from 2000 to 2020. The dominance of VC had transitioned from being predominantly characterized by relatively high VC to being mainly characterized by high VC. A substantial portion of the SSUA is predicted to experience improvements in its VC and environmental quality moving forward. Furthermore, the coupling coordination relationship between VC and environmental conditions in the southwest of Shandong Province generally exhibited a state of orderly coordinated development. With the passage of time, there was a clear tendency toward expansion in the coupled uncoordinated areas distributed in a network within each regional economic center. Our research unveils the dynamics and spatial-temporal patterns of VC and ecological quality in the southwestern Shandong urban agglomeration (SSUA) and elucidates the coupling and coordination mechanism between these two aspects, which provides theoretical support for understanding the healthy development of vegetation and ecology in urban agglomerations in an industrial context. [ABSTRACT FROM AUTHOR]

The natural ecological background of the Yellow River Basin is fragile. Long-term human development and construction activities have led to habitat fragmentation and biodiversity reduction. The Landsat remote sensing image data of 2000, 2010 and 2020 were selected to calculate four indicators of greenness, humidity, dryness and heat to build remote sensing ecological index (RSEI). The RSEI was used to evaluate spatial-temporal evolution characteristics and trends of ecological environment quality in 8 cities and counties (districts) along the main stream of the Yellow River in Henan Province, and geographic detector was used to analyze its driving factors. The results indicate that a) the average RSEI in the study area from 2000 to 2020 is 0.7311, and the overall ecological environment quality is above the middle level. Lingbao City and Zhengzhou City have poor ecological environment quality, while Xin' an County and Mengjin District have good ecological environment quality. b) Over the past 20 years, the ecological environment quality has first been deteriorated and then improved, and most of the areas show an overall improvement trend. The improvement of ecological environment quality in the lower reaches is better than that in the middle reaches. c) The single factor detection results indicate that the driving factors that characterize human activities, such as land use types and GDP, gradually enhance their impact on the quality of the ecological environment. The results of multi factor interaction detection indicate that there is a strong interaction between GDP, land use type and population density with other factors. [ABSTRACT FROM AUTHOR]

[Objective]The aims of this study are to investigate the spatial and temporal evolution characteristics of land use and ecological environment quality in the Three Gorges Reservoir area, to reveal the ecological environment effects caused by land use changes, and to provide a basis for land resource integration and ecological environment protection in the study area.[Methods]Remote sensing monitoring data of land use in 2000, 2010 and 2020 were selected to predict the spatial pattern of land use in 2030 under the Natural Change Scenario(NCS)and the Ecological Protection Scenario(EPS)using the CA_Markov model, respectively, and to quantitatively analyze the long time series of land use changes and ecological and environmental effects of the Three Gorges Reservoir Area by transfer matrix, ecological environment quality index, geostatistical analysis, and ecological contribution rate.[Results](1)From 2000 to 2020, the Three Gorges Reservoir Area had obvious transformation between land use types, in which cropland shifted to construction land, grassland shifted to forest land and cropland, and the growth of construction land area amounted to 1 321.56 km2, with a growth rate of 275.02％.(2)The simulation results show that the proportions of forestland and grassland area under the ecological protection scenario in 2030 will be higher than those under the natural change scenario, while the expansion of construction land significantly reduces compared with the natural change scenario.(3)The ecological quality of the Three Gorges Reservoir Area shows a stepped distribution pattern of gradual increase from west to east, and its ecological quality slightly decreases from 2000 to 2030, from 0.547 9 in 2000 to 0.533 6 in 2030.(4)From 2000 to 2030, the two dominant factors reducing the ecological quality of the Three Gorges Reservoir Area are the conversion of forests and grasslands to croplands, and the main favorable factor improving its ecological quality is the conversion of croplands to forests, with a proportion of ecological contribution exceeding 70％.[Conclusion]The return of farmland to forests and grasslands and rapid urban development have led to both ecological improvement and ecological deterioration in the Three Gorges Reservoir Area, but in general, the ecological environment quality in the study area shows a trend of improvement slightly less than deterioration, and the transformation between construction land, forestland, cropland and grassland is the main reason for this. [ABSTRACT FROM AUTHOR]

As an important economic development area in China, the ecological environment quality of the Yellow River Basin not only supports its sustainable development, but also is related to the realization of the major national strategy of "ecological protection and high-quality development in the Yellow River Basin". Based on the spatial analysis function of geographic information systems, this study constructs an ecological environment quality evaluation index system and uses the spatiotemporal geographic weighted regression model, analyses time and space evolution of ecological environment quality index and its driving force at the city scale of the Yellow River from 2010 to 2020. The research results show that the ecological environment quality index (EI) of the Yellow River Basin increased from 54.41 to 55.48. EI in the middle reaches was higher than that in the upper and lower reaches, and the ecological environment of Gansu and Ningxia located in the central regions significantly improved. The distribution of ecological environment grades in the Yellow River Basin has significant spatial differences, and the quality index presents spatial agglomeration, with low and low agglomerations in the north and west, and high and high agglomerations in the south. Economy, population, industry, and policy factors jointly affect the ecological environment quality index of the Yellow River Basin. The urbanization rate of permanent residents, the density of permanent residents, the proportion of the primary industry and the proportion of the secondary industry are the main driving forces of the ecological environment quality index, but its driving force presents show a trend that the pressure of urbanization and environmental governance policies are gradually increasing, and the pressure of economic development and industrial structure transformation is gradually weakening. [ABSTRACT FROM AUTHOR]

Based on panel data from 285 prefecture-level cities in China from 2019 to 2023, the synergistic effects of the digital economy, green technology innovation, and ecological environment quality were analyzed. First, using the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Second, employing a neural network model with these measurements as input variables, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was explored. Finally, based on the calculation results of the neural network model, the importance and impact of each input parameter on ecological environment quality were determined using weight analysis methods. The research findings indicate: (1) Utilizing the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Analysis of each indicator measurement reveals that environmental pressure has a significant impact on ecological environment quality, with significant differences in environmental pressure among different regions. Industrial digitization emerges as the core factor influencing the digital economy, being the most significant driving effect, followed by digital industrialization. Green technology innovation is crucial for promoting environmental protection and achieving high-quality green economic development. (2) Based on the neural network model, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was revealed. The results indicate that the digital economy has a direct impact on improving ecological environment quality. The relationship between the digital economy and the ecological environment exhibits nonlinear effects, with the rate of change in environmental pressure and environmental status measurements initially increasing significantly and then gradually slowing down as the measurement levels of digital industrialization and industrial digitization increase. Improvement in digital governance and data value measurement levels will contribute to enhancing environmental status and environmental governance levels. (3) Through weight analysis, it was found that in terms of direct effects, industrial digitization, and digital industrialization have the most significant impact on environmental pressure, with importance coefficients of 0.45 and 0.3, respectively, while data valorization has the least impact. Regarding intermediary effects, industrial digitization and green technology innovation have the most significant impact on environmental pressure, while digital governance and green technology innovation have a relatively clear impact on environmental status and environmental governance. These results lay the foundation for promoting the coordinated cooperation between the digital economy and green technology innovation and for advancing the establishment of a win–win situation between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

The use of territories in border areas is sensitive, unique, and ecologically fragile. A scientific understanding of the transformation of the national territorial space and its ecological and environmental responses is crucial for optimizing spatial patterns and promoting sustainable utilization. This study focused on 45 cities in the land border areas of China and employed techniques such as the land transfer matrix, Theil index, and ecological environment index to explore the spatiotemporal evolution process and eco-environmental effects of territorial space from three dimensions: spatial pattern, structural transformation, and ecological response. The results show that: (1) During the study period, there was an increasing trend in living and production space, along with a decrease in ecological space, and a significant pattern of "one belt, three districts, and multipoints" emerged. (2) In the urbanization process, population growth and industrial agglomeration have led to the transformation and conflict of territorial spaces, with the conversion of ecological spaces into production spaces being the primary form of land-use transformation. Rapid development has resulted in spatial differentiation of the territorial space between regions. (3) During this period, the ecological quality in China's border areas deteriorated, yet the distribution pattern of ecological space remained stable, exhibiting a "high value in the southeast, low value in the northwest" pattern. (4) Improvements and degradation of the ecology coexist in different border areas; transforming agricultural production space into green and potential ecological spaces has positively contributed to enhancing ecological quality. In contrast, converting green ecological space into potential ecological space, agricultural production space, and aquatic ecological space has become a key factor in ecological degradation. Therefore, the border areas of China should utilize national preferential policies and strategies, recognize the vast and varied expanse of China's border areas, and adopt differentiated planning and management measures in different regions to achieve the coordinated development of the PLES, thus promoting a positive trend in eco-environmental quality. [ABSTRACT FROM AUTHOR]

In this study, using remote sensing methods, we analyzed the evolution of ecological environment quality from 2000 to 2020 in Yanshan County, a typical rocky desertification area in southeastern Yunnan Province. Based on this analysis, we examined the primary driving factors affecting changes in the local ecological environment, thereby aiming to develop a method for the quantitative evaluation of ecological environments in rocky desertification areas. Using Landsat data obtained from the Google Earth Engine (GEE) platform, a remote sensing ecological index (RSEI) of the study area from 2000 to 2020 was constructed. The influence of the main driving factors on the quality of the ecological environment were quantified using a geographical detector. The results revealed that the mean values of RSEI in Yanshan in 2000, 2010, and 2020 were 0.46, 0.44, and 0.57, respectively. Furthermore, from 2000 to 2020, there was a 57.28% improvement in the quality of the ecological environment, characterized by an initial decline followed by a subsequent rise. Additionally, we detected an overall decline in rocky desertification in Yanshan in 2000, 2010, and 2020, with a reduction of 1 181.77 km² in the area of rocky desertification. Moreover, rocky desertification and land-use change were identified as the main driving factors of the ecological environment from 2000 to 2020, with rocky desertification being found to have the strongest explanatory power for determining the evolution of ecological environment quality. Overall, we established that the quality of the ecological environment in Yanshan County gradually improved from 2000 to 2020. From the perspective of ecological environment management, we should pay greater attention to the influence of karst rocky desertification and land cover type stability. [ABSTRACT FROM AUTHOR]

In order to reduce the health damage caused by ecological environment pollution and alleviate the burden of residents' health expenditure, it is of great significance to study the impact of ecological environment quality on residents' health expenditure. In this study, we use the Ecological Environment Index (EI) in China from 2009-2019 and the SYS-GMM model. The conclusions are as follows: (1) The decline of ecological environment quality significantly increased residents' health expenditures. (2) The ecological environment mainly affects the residents' health expenditure through the health effect and driving effect. (3) Compared with economically developed regions and lower environmental emphasis, residents in economically undeveloped regions and high environmental emphasis are more sensitive to health expenditures. (4) Through a series of robustness test and use thermal inversion as an instrumental variable for endogeneity analysis, the regression results remain robust. Therefore, this study provides important evidence for further improving the ecological environment and reducing the residents' health expenditures. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to explore the spatiotemporal evolution of the ecological environment quality in Xuzhou urban area during the transition period, and to provide a basis for ecological protection and related policy formulation. [Method] Using five-period Landsat data from 2000 to 2020, the Remote Sensing Ecological Index (RSEI) was constructed, and the spatiotemporal characteristics of the ecological environment quality in Xuzhou urban area were analyzed based on Pearson correlation coefficient, spatial autocorrelation and other methods. The geodetector was used to detect the main factors affecting the ecological environment quality. [Result] (i) The RSEI value of the study area fluctuated between 0.5 and 0.6 from 2000 to 2020, indicating that the ecological environment quality was unstable; the low-grade ecological environment quality was mainly distributed in Yunlong district, Quanshan district and Gulou district, while the high-grade ecological environment quality was mainly distributed in Tongshan district and Jiawang district; (ii) The area proportion of the improved ecological environment quality from 2000 to 2020 was 35.70%, while the area proportion of the deteriorated ecological environment quality was 23.23%. There was a significant negative correlation between the ecological environment quality and its change, and the higher the RSEI grade, the easier it was to deteriorate, and the lower the RSEI grade, the easier it was to improve; (iii) The ecological environment quality had obvious spatial clustering characteristics, and the high-high clustering area was distributed in Tongshan district, while the low-low clustering area was mainly distributed in Yunlong district, Quanshan district, Gulou district and Jiawang district. In the past 20 years, the standard deviation ellipse of the high-high clustering area showed obvious directionality, and always presented a 'northwest-southeast' distribution pattern, while the standard deviation ellipse of the low-low clustering area presented a 'southwest-northeast' distribution pattern; (iv) The geodetector results showed that all the influencing factors were significant, and the dominant factor was greenness, and the interaction effect of the two factors was obvious. [Conclusion] The ecological environment quality of Xuzhou urban area improves after the transition, and the ecological governance achieves some results. [ABSTRACT FROM AUTHOR]

The urban ecological environment is crucial to the quality of life of residents and the sustainable development of the region, and the assessment and prediction of the ecological environment quality can provide a scientific guidance for ecological environment management and improvement. We proposed a novel approach to assess and simulate the urban ecological environment quality using the Geographic Information System Ecological Index (GISEI). First, we calculated the remote sensing ecological index (RSEI) for Xi'an in 2020. Second, we selected land use data, mean annual temperature, and mean annual relative humidity as ecological indicators. We regressed these indicators on the RSEI to obtain the GISEI of Xi'an in 2020. Finally, we simulated the GISEI of Xi'an in 2030 by predicting the ecological indicators and analyzed the changes in the ecological environment quality. The results of the study show that the ecological environment quality in Xi'an in 2020 is better overall. By 2030, most of the ecological environment quality in Xi'an will be worse, and the proportion of the excellent area will decrease from 42.8% to 3.8%. The more serious ecological degradation is mainly located in the regions bordering the Qinling Mountains and the Guanzhong Plain, and the ecological environment quality in most areas of the Qinling Mountains will deteriorate from excellent to good. [ABSTRACT FROM AUTHOR]

The quality of the ecological environment directly affects the generation of ecological benefits, and the remote sensing ecological index (RSEI) provides rapid monitoring of regional aboveground ecological environment quality using satellite data. However, it relies on aboveground indicators and cannot fully capture its evolutionary trend. Ecosystem services (ES) indicate the well-being and capacity of ecosystems to benefit humans, and ES directly reflect ecological benefits. They provide a qualitative or quantitative assessment of ecosystem status, but with a delay relative to aboveground environmental changes. In order to quantitatively evaluate ES, ecosystem service index (ESI) was proposed in this paper. The coupling of RSEI and ESI was achieved through the four-quadrant model and the coupling degree model, which compensated for the dependence of RSEI evaluation on aboveground indicators, and the delayed effect of ESI, and provided a more objective spatiotemporal analysis of regional ecological benefits. The example is provided from Fushun City, Liaoning Province, China. Results showed: that ESI could effectively reflect the impact of land use and land cover (LULC) changes on ecosystem quality. The RSEI and ESI showed an overall upward trend in the study area, with the exception of a notable decrease in the central Wanghua District, the southern Dongzhou District, and the northern Fushun County. The quadrant I of the study area showed a continuous decrease, while the rest of the quadrants show a continuous increase. The coupling index significantly decreased in areas impacted by building site expansion and ecological restoration measures implementation. The four-quadrant model and coupling index can reflect the spatiotemporal changes and synergy level of RSEI and ESI, and can provide a more objective spatiotemporal analysis of ecological benefits. Therefore, coupling RSEI and ESI can quickly and comprehensively analyze the ecological environment quality and ecosystem service capacity in time and space, and reflect its evolution trend, which is of great significance for the management, prevention and planning of the regional ecological environment.

Oasis ecosystems in dryland regions take on crucial significance in human survival and development since the quality of these regions that are characterized by ecological environments directly affects the well-being and socio-economic progress of their residents. Accordingly, it is of utmost importance to evaluate changes in ecological environment quality (EEQ) and gain insights into how land use transformation affects the local ecological condition, thus guiding the sustainable development of the dryland oasis. In this study, we evaluated the EEQ of the Middle Heihe River (MHR) from 1985 to 2020 by constructing the dryland oasis ecological index (DOEI) through the Google Earth Engine (GEE) platform. Moreover, we investigated how land use transformation influenced the EEQ of the MHR. Results suggested that (1) the DOEI can effectively capture the comprehensive dynamics of long-term EEQ changes in MHR, providing a more accurate representation of land type characteristics. (2) The EEQ in MHR generally exhibited a trend of initial improvement, followed by deterioration. The desert-oasis transition zone acts as the most significant area of EEQ changes in the MHR. The mutual transformation among cropland, grassland and desert is the primary factor for EEQ in the MHR. (3) The expansion of cropland in the MHR pronouncedly affected the EEQ. However, this continuous expansion may impose greater stress on water resources while potentially threatening ecological degradation in the lower reaches of the river basin. The DOEI built through the GEE platform is capable of indicating the spatiotemporal status of the EEQ in the dryland oasis, which holds significant implications for studying the interaction between human activities and EEQ in the dryland oasis.

The ecological environment is closely related to natural conditions, climate change, and human activities. However, existing research predominantly concentrates on evaluating the direct impacts of various factors on the ecological environment, yet lacks an in-depth exploration of their causal relationships and pathways. This paper introduces a new perspective to research in this field by incorporating spatial cross-sectional data analysis methods. The study utilized the 2020 China’s High-resolution Eco-Environmental Quality (CHEQ) dataset to evaluate the spatial differentiation characteristics of Ecological Environmental Quality (EEQ) within the western urban agglomeration of Guangdong Province. A framework of “terrain-urbanization-climate” influencing factors was constructed, and the Geographical convergent cross mapping (GCCM) method was employed to infer the causal relationship between EEQ and influencing factors. Additionally, the spatial distribution characteristics of influencing factors on ecological environment quality were studied using the Multiscale Geographically Weighted Regression Model (MGWR). The results indicate that seven influencing factors including Elevation, Population, Precipitation, TEMP, TMMX, TMMN, and Slope have clear causal relationships with EEQ and exhibit bidirectional coupling relationships. There exist complex causal relationships among influencing factors, revealing the interaction mechanisms and causal pathways among multiple factors in the ecological environment system. These analyses enrich our understanding of the intricate and complex relationships between the ecological environment and influencing factors, providing a valuable reference for ecological protection and sustainable development.

As an important ecological barrier in the Guangdong-Hong Kong-Macao Greater Bay Area, Shimentai Nature Reserve aims to protect the integrity of the ecosystem and biodiversity. It was established as a provincial-level nature reserve in 1998 and upgraded to a national level nature reserve in 2012. However, the changes in the quality of the ecological environment remain unclear. This study quantitatively assessed the changes in the ecological quality of the Shimentai Nature Reserve from 1997 to 2021 based on the Remote Sensing Ecological Index (RSEI), explored the changes in ecological quality before and after the establishment of the nature reserve and the upgrading of the control level, and discussed the reasons for the evolution using the Random Forest Algorithm and correlation analysis method. The results showed that the RSEI value of Shimentai Nature Reserve dynamically increased from 0.637 in 1997 to 0.714 in 2011, and the ecological environment quality steadily improved as the nature reserve was upgraded from the provincial to the national level; the RSEI reached 0.788 by 2021. The quality of the ecological environment has improved in 64.5% of the protected area, the buffer zone has improved more obviously, and the deteriorated areas are mainly concentrated in the experimental area where human activities are frequent and in the high-altitude ridge area in the core area. Meanwhile, the ecological environment quality of the buffer zone and the low-altitude area in the core area is adequate and relatively stable. The RSEI responded most clearly to elevation, with the best ecological quality between 300 and 600 m and relatively poorer quality above 900 m. On the slope upward with the south slope as the turning point, the quality of the ecological environment gradually improved from the north slope to the south slope in a clockwise direction, and then gradually deteriorated from the south slope to the north slope. At a cut-off point of 15° on the slope, the RSEI briefly increased and then decreased continuously. The RSEI was highest at a soil pH of 5.3, organic carbon mass fraction of 4.1%, and clay mass fraction of 32%. The degree of influence of residential activities on the quality of the ecological environment from the outside to the inside (i.e., the experimental area-buffer area-core area) was gradually reduced and negatively affected. The RSEI was positively correlated with precipitation, 23.6% of the area passed the significance test (P < 0.05). The local year was influenced by precipitation, and the influence of precipitation was greater in certain years. Overall, the ecological environment quality of the Shimentai Nature Reserve has shown a clear improvement trend, and the protection and management of the bare soil areas on the top of the high mountains, and the areas of frequent human activities in the lower altitudes, should be strengthened in the future.

As the largest developing nation, China is currently experiencing rapid urban growth. Conducting a thorough scientific assessment of this expansion and its ecological consequences is of paramount importance for advancing China's ecological civilization and aligning with the United Nations' Sustainable Development Goals. This study employs multi-source remote sensing data to investigate the spatiotemporal trends in Chinese urban development and explore its impact on the ecological environment. From 2013 to 2021, the findings indicate an increasing trend in China's total nocturnal light, with the southern and central regions exhibiting higher growth rates. This suggests a decade-long expansion of Chinese cities, especially in the southern and central regions. However, the impact of urban expansion on ecological quality varies. Beijing, Shenyang, and Xi'an have witnessed improved environmental quality, while Kunming and Shenzhen have experienced minimal changes, and Hefei and Wuhan have encountered a decline. The observed spatial heterogeneity underscores the intricate relationship between urban expansion and ecological shifts. This study reveals the spatiotemporal dynamics of China's urban expansion and its ecological impact, providing valuable insights and policy recommendations for fostering the harmonized development of urbanization and ecological preservation. [ABSTRACT FROM AUTHOR]

The Qinling Mountains serve as a vital ecological barrier for China, needing regular monitoring for sustainable progress. The present study analyzed the Qinling Mountains (sections in Shaanxi province) via the Google Earth Engine (GEE) and Landsat images, constructing a Remote Sensing Ecological Index (RSEI) for quantitatively analyzing the spatiotemporal evolution of the ecological environment quality from 1990 to 2020. Additionally, this study integrated geodetector and multiscale geographically weighted regression (MGWR) to discern the driving factors influencing ecological quality. The results indicated the following: (1) over the 30 years, the RSEI in the study area initially declined (1990–2005) and then began to improve (2005–2020), demonstrating an overall upward trend; (2) RSEI varied with topography: lower values corresponded to lower elevations (<1200 m) and gentle slopes (<10°), whereas higher values were linked to higher elevations (>1600 m) and steeper slopes (>20°); and (3) NDVI, DEM, land use, and the presence of protected areas emerged as the main driving forces of ecological quality. The interaction between ecological conservation policies and socioeconomic factors enhanced the explanatory strength for the differentiation in ecological environment quality. In conclusion, factors like increased vegetation cover and the establishment of protected areas have significantly promoted the improvement of ecological environment quality in the Qinling region, making this study a scientific reference for providing ecological environment protection and promoting high-quality development. [ABSTRACT FROM AUTHOR]

[Objective] Urbanization is an important factor affecting the pattern of regional ecological security. Clarifying the relationship between urbanization and ecological environment quality can provide reference for decision-making in promoting regional ecological protection and high-quality development. [Methods] Based on the analysis of the temporal and spatial variation pattern evolution of urbanization and ecological environment quality in Shaanxi Province every five years from 2000 to 2020, we analyzed the temporal and spatial variation characteristics of the coupling relationship between urbanization and ecological environment quality by constructing an evaluation system of urbanization and ecological environment quality. [Results] The urbanization level of Shaanxi Province presented a steady upward trend, the average RSEI of Shaanxi Province presented a steady upward trend, and the eco-environment presented a trend of improvement. However, the differences between regions were still significant, and the overall distribution pattern was low level in the north and high level in the south. The coupling coordination degree of the two showed a trend of fluctuation and rise. There was a significant gap between the coupling and coordinated development of eco-environment and urbanization in each county of Shaanxi Province, but with the passing of time, the number of coordinated development areas was gradually increasing. In contrast, the coupling coordination between urbanization and eco-environmental quality in northern Shaanxi Province showed a relatively backward situation. [Conclusion] There was an obvious gap in the coupling coordination relationship between urbanization and eco-environmental quality in Shaanxi Province, but the overall trend was getting better from 2000 to 2020. It is suggested that on the road of urbanization, Shaanxi Province should strengthen the ability of green ecological development, make up for the ecological weakness, adjust to local conditions, and formulate a more scientific and reasonable rural revitalization policy. [ABSTRACT FROM AUTHOR]

(Objective] As the core of China's Silk Road Economic Belt, the Tuha region (Turpan and Hami cities) has become a concern in balancing the local ecological environment and sustainable development in the process of high-quality economic development. (Method) The present study was based on Google Earth Engine cloud platform using MODIS remote sensing images to calculate heat, greenness, dryness and humidity, and construct Remote Sensing Ecological Index (RSEI) by principal component analysis. The research employed the Sen + Mann Kendall, Hurst index, coefficient of variation and geodetic detector to analyze the spatial and temporal changes and future trends of ecological environment quality in Tuha region from 2001 to 2022, and to explore its influence mechanism. [Result] (i) The ecological environment quality of the Tuha region showed an improving trend from 2001 to 2022. (ii) The trend of RSEI change was dominated by insignificant improvement, accounting for 71.79% of the total area, and the ecosystem was relatively stable, but the ecosystems in the northern mountainous areas and around the built-up areas of cities and counties were fragile; The trend of RSEI change in the future would be dominated by contin- uous improvement, with the area accounting for 54.02% of the total area. (iii) During the past 22 years, the area of grassland has increased the most, and the overall greening level has risen. (iv) Heat was the main factor in the spatial differentiation of ecological environment quali- ty, and when heat interacted with elevation and greenness, the effect on ecological environment quality was more significant. [Conclusion] Due to the improvement of greening level, the ecological environment quality of Tuha region is generally at a good level and on an upward trend; The habitat quality of the site has obvious spatial variability, with heat having the greatest impact on the ecological environment of Tuha region. The study can provide a scientific basis for ecological environmental protection in the Tuha region. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet Railway is located in the most fragile and sensitive terrestrial ecosystem of the Qinghai–Tibet Plateau in China, and once the ecological environment is damaged, it is difficult to restore. This study, based on the Google Earth Engine platform, focuses on the section of the Qinghai–Tibet Railway from Xining to Jianghe. It utilizes Landsat series satellite imagery data from 1986 to 2020 to calculate the Remote Sensing Ecological Index (RSEI). This approach enables large-scale and long-term dynamic monitoring, analysis, and assessment of the ecological changes along the Qinghai–Tibet Railway corridor. The results indicate that (1) the average RSEI of the study area increased from 0.37 in 1986 to 0.53 in 2020, showing an overall trend of improvement. The ecological environment quality is mainly categorized as medium and good. (2) The quality of the ecological environment in the areas along the railway experienced fluctuations during different periods of railway construction and operation. From 1986 to 1994, after the first phase of the railway opened, the overall ecological environment showed a relative decline in quality. From 1994 to 2002, the ecological quality of 60% of the region saw slight improvements. During the extension construction of the second phase of the railway from 2002 to 2007, the regional ecology fluctuated again. However, from 2013 to 2020, during the operational period, a stable recovery trend was observed in the ecological environment. (3) The ecological environment in the study area is influenced by multiple factors. Different railway station areas exhibit strong spatial heterogeneity. The impact of single factors is significant, with the existence of spatial stratification and enhanced interactions among multiple factors. The strongest interactive effects are observed between land use types, the intensity of human activities, and temperature. [ABSTRACT FROM AUTHOR]

As an important ecological barrier in the Guangdong-Hong Kong-Macao Greater Bay Area, Shimentai Nature Reserve aims to protect the integrity of the ecosystem and biodiversity. It was established as a provinciallevel nature reserve in 1998 and upgraded to a national level nature reserve in 2012. However, the changes in the quality of the ecological environment remain unclear. This study quantitatively assessed the changes in the ecological quality of the Shimentai Nature Reserve from 1997 to 2021 based on the Remote Sensing Ecological Index (RSEI), explored the changes in ecological quality before and after the establishment of the nature reserve and the upgrading of the control level, and discussed the reasons for the evolution using the Random Forest Algorithm and correlation analysis method. The results showed that the RSEI value of Shimentai Nature Reserve dynamically increased from 0.637 in 1997 to 0.714 in 2011, and the ecological environment quality steadily improved as the nature reserve was upgraded from the provincial to the national level; the RSEI reached 0.788 by 2021. The quality of the ecological environment has improved in 64.5% of the protected area, the buffer zone has improved more obviously, and the deteriorated areas are mainly concentrated in the experimental area where human activities are frequent and in the high-altitude ridge area in the core area. Meanwhile, the ecological environment quality of the buffer zone and the low-altitude area in the core area is adequate and relatively stable. The RSEI responded most clearly to elevation, with the best ecological quality between 300 and 600 m and relatively poorer quality above 900 m. On the slope upward with the south slope as the turning point, the quality of the ecological environment gradually improved from the north slope to the south slope in a clockwise direction, and then gradually deteriorated from the south slope to the north slope. At a cut-off point of 15° on the slope, the RSEI briefly increased and then decreased continuously. The RSEI was highest at a soil pH of 5.3, organic carbon mass fraction of 4.1%, and clay mass fraction of 32%. The degree of influence of residential activities on the quality of the ecological environment from the outside to the inside (i.e., the experimental areabuffer area-core area) was gradually reduced and negatively affected. The RSEI was positively correlated with precipitation, 23.6% of the area passed the significance test (P < 0.05). The local year was influenced by precipitation, and the influence of precipitation was greater in certain years. Overall, the ecological environment quality of the Shimentai Nature Reserve has shown a clear improvement trend, and the protection and management of the bare soil areas on the top of the high mountains, and the areas of frequent human activities in the lower altitudes, should be strengthened in the future. [ABSTRACT FROM AUTHOR]

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Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A significant portion of Zhanjiang City's ecological land areas have been reduced as a result of the city's growing urbanization, which has caused the city's ecological environment quality to decline. In order to monitor the quality of the ecological environment, the remote sensing ecological index (RSEI) is frequently utilized. In this study, the Landsat series satellite images from 2000, 2005, 2009, 2015, and 2020 were used. The Normalized Differential Vegetation Index (NDVI), Wetness (WET), Normalized Differential Build-up and bare Soil Index (NDBSI), and Land Surface Temperature (LST) were the four indicators utilized in the RSEI to quantitatively evaluate the changes in ecological environment quality in Zhanjiang City. The results are as follows. (1) The mean RSEI values for the years 2000, 2005, 2009, 2015, and 2020 are, respectively, 0.579, 0.597, 0.597, 0.607, and 0.601. In addition, the overall ecological environment of Zhanjiang is very good. In terms of spatial differences, the ecological environment quality in the central and southeastern parts of Zhanjiang is significantly higher than that in other areas, while the ecological environment quality in its coastal town areas is much worse. The lower RSEI index of developed land in coastal areas proves that the RSEI index can reflect the deterioration of the urban environment in coastal areas from 2000 to 2020. Therefore, the RSEI can be used to evaluate the ecological environment quality of Zhanjiang City. (2) The ecological environment changes in the study area are "substantially better," "better," "no change," "worse," and "much worse," respectively, according to the difference in RSEI processed between 2000 and 2020. These changes were 38.38, 6,047, 13.93, 6.65, and 34.58%. The percentage of ecological environmental quality in Zhanjiang City that has become better is higher than that has become worse. This indicates that the quality of ecological environment in Zhanjiang City has improved between 2000 and 2020. (3) The regression produced the following equation for the association, which was significant at the 0.053 level: 100*Rsei = 154.69-1.18*IS(R=0.66). The remote sensing ecological index for Zhanjiang in 2035 is 0.488 when the city's planned population and area are added together. [ABSTRACT FROM AUTHOR]

The arid regions of northwest China suffer from water shortages, low land quality, and a fragile ecological environment, while social and economic development has increased the ecological and environmental load. The spatiotemporal pattern and evolutionary trend of ecological environmental quality were investigated by constructing a remote sensing-based ecological environmental index (EQI) evaluation model incorporating four indicators: drought index (DI), soil erosion index (SEI), greenness index (GI), and carbon exchange index (CEI). The study found that between 2001 and 2020, the DI, the SEI, and the CEI in the northwest arid region exhibited a downward trend with reduction rates of − 3e−05, −0.0006, and −0.0018, respectively. However, the GI demonstrated an upward trend, with a growth rate of 0.002. The average EQI in 2020 was 0.315, indicating a fair grade, with only 11.56% falling above the medium level. A general increasing trend was observed throughout the study period in EQI, with an incremental rate of 0.0002. Areas with future improvements in EQI accounted for 57.547% and were principally located in the eastern part of Inner Mongolia, Qinghai, and the northern and southern portions of Xinjiang. Notably, land use was significantly correlated with EQI (p < 0.01), with a hierarchy of effects that ran: forest land (0.678) > cultivated land (0.422) > grassland (0.382) > wasteland (0.138). The highly robust findings presented here offer innovative methods for ecological and environmental monitoring in the arid region of the northwest, with potential implications at an international scale. [ABSTRACT FROM AUTHOR]

In 2014, the Chinese government upgraded the construction of the “Yangtze River Economic Belt” to a national strategy, and the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) has experienced a rapid urbanization process. The study adopts remote sensing ecological index, compound night light index, extraction of built-up areas, urbanization and ecological environment quality (EEQ) coordinated development coupling model to assess the EEQ, urbanization and the relationship between them in the UAMRYR during the period spanning 2013 to 2021. The results show that: 1) between 2013 and 2021, the EEQ of UAMRYR exhibited a notable decline in 2015, followed by a gradual but consistent improvement; 2) the urbanization level within UAMRYR demonstrated a steady increase throughout the same period from 2013 to 2021; 3) over the course of 2013 to 2021, a majority of the cities had transitioned from a state of low coordination to medium coordination. In addition, the urban dynamic development stage of the three provincial capitals progressed from a state of low coordination to coordinated development, with the entire coordinated development system on the verge of entering an advanced stage of extreme development. The experimental results were used to classify the cities in the UAMRYR into five types, and the reasons restricting the development of the urban agglomerations were analyzed. It also briefly touches upon the influence of extreme climate events on the EEQ. These findings hold significant importance in the endeavor to promote urbanization while simultaneously optimizing the EEQ, thus contributing to the construction of resilient cities.

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.

The Yellow River Delta is characterized by significant ecological fragility and strong disturbance by human activities, resulting in serious damage to its ecological environment. Timely and accurate evaluation of ecological environment quality in the Yellow River Delta is very important for ecological protection and policy-making. In order to solve the issue of difficult vegetation monitoring in the evaluation of wetland ecological environment quality, the improved hyperspectral image-based vegetation index（IHSVI） was proposed and applied to multi-spectral satellites. Combined with the water benefit-based ecological index（WBEI） and IHSVI, an optimized water benefit-based ecological index（OWBEI） was proposed to improve the retrieval accuracy of wetland ecological environment quality. The Landsat images of 2000, 2006, 2012, 2017 and 2020 were selected to evaluate the eco-environmental quality of the Yellow River Delta based on OWBEI. The results show that OWBEI effectively improves the evaluation results of wetland ecological environment quality and the inversion accuracy is improved to 90%. The quality of the ecological environment in the inland rural areas of the Yellow River Delta is better than that of in the coastal and coastal urban areas, with 2012 as the turning point, the overall ecological environment status shows a trend of first increasing and then decreasing. [ABSTRACT FROM AUTHOR]

Under the western development strategy of China, the urbanization process of Chengdu-Chongqing economic circle (CCEC) develops rapidly, but also brings a series of ecological and environmental problems. Understanding the coordination mechanism that links urbanization and the ecological environment (UE) is crucial for promoting environmental conservation and sustainable development. Using the comprehensive nighttime light index (CNLI) and the remote sensing ecological index (RSEI), this study objectively evaluated the urbanization level and ecological environment quality and used the modified coupling coordination degree model to quantify the coupling coordination of UE for the two indexes. The results show that (1) the urbanization level of each city in the CCEC presents an increasing trend year by year, showing a circle distribution pattern with the twin cities as the core. (2) The overall eco-environmental quality presents a fluctuating upward trend, and in recent years, it has been significantly improved, showing a spatial pattern of higher in the surrounding areas and lower in the middle. (3) The overall coupling coordination of the study area is developing, and the upward trend is larger in recent years. The spatial distribution is centered on the "double nucleus," and the distribution of circles is characterized by a gradual decrease from the inside to the outside; the coupling coordination of cities in the northwest and east of the CCEC is decreasing, and that of cities in the northeast and southwest is increasing. During the study period, a total of 5 cities have reached the level of coordinated development, while most other cities are in the stage of uncoordinated development, mainly due to the lagging development of urbanization. CCEC still needs to strengthen the construction of urbanization, in order to improve the degree of coordination between economic development and ecological environment. [ABSTRACT FROM AUTHOR]

[Objective] This study arms to overcome the randomness and subjectivity of ecological environment quality(EQI) evolution drivers in the discretization process, and to optimize the traditional geographic detector. [Methods] This study discretized 22 EQI evolutionary driving factors in Chongqing by using five discretization methods, namely equal interval method, quantile method, natural breakpoint method, geometric interval method and standard deviation method. Geographic detector was combined to determine the reasonable discrete method and classification number of each factor, so as to explore the driving force size of each driving factor and the interaction model of each driving factor. [Results](1) From 2005 to 2020, the number of districts and counties in grade I, Ⅲ and Ⅳ decreased by 0.211, while the number of districts and counties in Grade Ⅱ and V increased by 0.211, showing an overall downward trend and spatial gradient distribution from northwest to southeast.(2) The order of applicability of each discrete method in this study was: natural break point method＞geometric interval method＞quantile method＞equal interval method＞standard deviation method. The discretization methods applicable to different factors were different, so the discretization methods should be selected according to the characteristics of factor data.(3) Four key driving factors(0.37～0.49), 13 main driving factors(0.14～0.33), four minor driving factors(0.05～0.13) and one other factor(0.04) were identified, and the interaction among all driving factors was doubly enhanced or nonlinear enhanced. [Conclusion] Reasonable discretization can overcome randomness and subjectivity in the process of continuous data discretization to a certain extent, so as to optimize the detection results of EQI evolution driving forces in Chongqing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to explore the characteristics of land use pattern and its impact on habitat quality in the Yalu River estuary wetland area, based on remote sensing data from 2000 to 2020, the characteristics of land use pattern were calculated by land use dynamic degree, land use degree comprehensive index and land use transfer matrix, and the impact of land use change on ecological environment quality was evaluated by measuring ecological environment quality and habitat quality level.The results showed that there were mutual conversions among different land types, and the conversions to construction land and aquaculture water surface were significant.The transferred construction land and aquaculture water surface areas were 56.54 km² and 35.80 km², accounting for 50.85 % and 32.2 % of the total transferred area, respectively.The intensity of land development and utilization increased.Compared with 2000, the change rates of the comprehensive index of land use degree in 2010 and 2020 were 1.18 % and 3.95 %, respectively.The ecological environment quality indexes in 2000, 2010 and 2020 were 0.462 9, 0.455 7 and 0.441 6, respectively. From 2000 to 2020, the proportion of low-level land use types increased from 12.15 % to 16.52 %, and that of high-level land use types decreased from 32.84 % to 29.32 %. It can be seen that the ecological degradation trend of the Yalu River estuary wetland is obvious, and the expansion of construction land and aquaculture water surface land is the main factor of land pattern change and ecological environment quality decline. [ABSTRACT FROM AUTHOR]

[Objective] The study aimed to explore the spatio-temporal evolution law and driving factors of ecological environment quality in Yinchuan city, and provide theoretical reference and scientific basis for ecological environment protection and restoration. [Method] By utilizing the data sets of Landsat-OS/TM, Landsat-07 /ETM and Landsat-08/OLI during the 2000 - 2020 vegetation growth season in the study area, firstly the indicators of greenness, humidity, dryness and heat of the region in the past 21 years were statistically analyzed based on the Google Earth Engine platform, next the remote sensing ecological index of the study area was constructed, then by combined with spatial autocorrelation the spatio-temporal evolution law of ecological environment quality was explored,finally the Pearson correlation coefficient and Geographic detector were used to analyze the influence of 12 factors such as remote sensing ecological index, nature and society on regional ecological environment quality. [ Result] In the past 21 years, the ecological environment quality in Yinchuan city has shown a trend of ' risingdeclining-slowly rising', with an upward trend from 2000 to 2007. The improved ecological environment was mainly concentrated in the HeIan Mountains and surrounding areas, as well as in the northern part of Lingwu city. From 2007 to 2013, the degraded areas were mainly distributed in the hilly areas east of Yinxi high-speed Railway and Ningdong area. From 2013 to 2020, the ecological environment improvement areas were mainly concentrated in the alluvial fans of the eastern foothills of Helan, Y ueya Lake, and the Majiatan area of Lingwu city. The spatial distribution of eco-environmental quality changes in Yinchuan was clustered. The high-high accumulation areas were mainly concentrated in Helan Mountain Nature Reserve, forest farms and farmland areas in three districts and two counties, while the low-low accumulation areas were mainly distributed in hills and Gobi desert areas of Lingwu city. NOVI and WET had a positive effect on ecological environment quality, while NDBSI and LST had a negative effect on it. [Conclusion] In the single factor detection, the remote sensing ecological index NDBSI has the greatest influence on ecological quality, and the natural social factors such as elevation, PM2_ 5 and GDP are the main influencing factors. The interactive detection shows that the interaction between factors is double factor enhancement and nonlinear cooperative enhancement. Among the ecological indicators of remote sensing, NDBSI and LST have a higher degree of impact on the ecological environment, while among the natural social factors, elevation, settlement concentration, GDP, PM2_ 5 and night light have a more significant impact on the ecological environment change in Yinchuan city. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the construction of ecological civilization has become more and more important in recent years, ecological restoration and its effect assessment have also received increasing attention. Taking the Wangping coal mine in Beijing as an example, based on Landsat TM/OLI series remote sensing data, we chose five metrics, i.e., fraction vegetation coverage, humidity, heat, dryness, and black particulates, to construct the model for the modified remote sensing ecological index (MRSEI). It was also combined with the Hurst index to conduct dynamic monitoring, spatiotemporal analysis, and prediction studies of the ecological environment quality in the study area. The results showed that: (1) Compared with the RSEI, the first principal component of the MRSEI better integrates the information of each component indicator, has a better average correlation with each indicator, and better reflects the habitat condition in the study area. (2) The mean value of the MRSEI in the study area increased from 0.433 in 1990 to 0.722 in 2021, an increase of 40.03%. (3) From 1990 to 2001, the poor and fair MRSEI-grade areas in the study area were concentrated in the northeastern and southwestern parts of the area. After the ecological restoration project was carried out, the environmental quality of the Wangping coal mine improved year by year, and a small number of poor areas were concentrated in the northeastern border area. (4) The MRSEI of the Wangping coal mine predicted that the future ecological environment of the study area would show a general trend of continuous improvement, but a certain percentage of the area in the northeast had a weak antisustainability trend. The results of the study could provide a reference for ecological restoration planning, sustainable development, and the management of mining areas. [ABSTRACT FROM AUTHOR]

Changes in the spatial pattern of land use are inextricably linked to the ecosystem environment, and the assessment of regional eco-environment quality can help provide sustainable and healthy development strategies for heritage management organizations. In this study, based on RS and GIS technologies, we quantitatively analyzed the characteristics of land use changes in the Shibing Karst World Natural Heritage site from 2000 to 2020, and introduced the regional eco-environment quality assessment index for quantitative factor analysis. The results show that: (1) The heritage site is mainly dominated by forest and shrub, with more aggregated cropland, and impervious surfaces in the buffer zone. The area of shrub has increased during the 20-year period, occupying 12.63% of the total transferred area. Cropland has been basically converted to forest, accounting for more than 60% of the total transferred area, followed by shrubs, which have been basically transferred to ecologically better land types. (2) By analyzing the attitude of motivation, forest, shrub, and water have positive values, while cropland, grassland, and impervious surfaces have negative values. Grassland has the largest absolute value of kinetic attitude, and the smallest is for water. The integrated dynamic attitude is basically maintained at 24%, showing a state of rapid and then stable land category conversion. (3) Over the past 20 years, the regional eco-environment quality index has been stable between 0.68 and 0.71, and shows a trend of rapid growth and stabilization, which is consistent with the comprehensive attitude. The conversion between cropland, forest, and shrubs is the main cause of ecological improvement and deterioration. Overall, the relevant conservation measures at the site and China's corresponding responses to global climate change have led to a stabilization and increase in the regional ecological quality of the site. The management measures of the relevant authorities have begun to bear fruit, but further promotion of the sustainable development of the site is needed to provide a scientific model for the conservation of other karst heritage sites. [ABSTRACT FROM AUTHOR]

Upgrading the industrial structure and improving the quality of the ecological environment are important strategic steps to realize the modernization of China. Based on the panel data of 11 provinces (municipalities) in China's coastal areas from 2010 to 2019, this paper uses the spatial Dubin model and the threshold effect model to study the impact of industrial structure upgrading on eco-environmental quality. The results show that the influence of industrial structure upgrading on ecological environment quality has a positive "U"-shaped distribution. Based on the spatial econometric model, it is found that the rationalization of industrial structure and the optimization of industrial structure have spatial spillover effects on the ecological environment quality, and the influence of the rationalization of industrial structure and the optimization of industrial structure on the ecological environment quality of the surrounding area is positive "U"-shaped and inverted "U"-shaped, respectively. Based on the threshold model, it is found that industrial structure rationalization has a small effect on the ecological environment's quality when the degree of scientific and technological innovation is low. When scientific and technological innovation reaches a certain threshold, industrial structure rationalization has a significant effect on the quality of the ecological environment. In addition, from a regional perspective, the influence of industrial structure rationalization in the East China Sea and the South China Sea and industrial optimization in the Bohai-Yellow Seas on the eco-environmental quality of the surrounding areas has a positive "U"-shaped distribution, while the influence of the optimization of industrial structure in the South China Sea on the eco-environmental quality of the surrounding areas has an inverted "U"-shaped curve on the left side. [ABSTRACT FROM AUTHOR]

The northern Shaanxi coal mining area is an important coal production base in China and an ecologically fragile area, and it is of great significance to explore the ecological environment quality and spatial evolution trend of the mining area. Based on Sentinel-2 and MODIS image data, an improved remote sensing ecological index was constructed by principal component analysis method: normalized difference vegetation index, normalized difference water index, normalized differential buildup and bare soil index, and net primary productivity. The ecological environment quality of the northern Shaanxi coal mining area and its influencing factors, as well as the spatial autocorrelation analysis of ecological environment quality, were discussed. The results showed that: 1) The vegetation coverage in the study area showed an overall increasing trend, but it was greatly affected by the average annual temperature. The NDVI index at the mine area is higher than the NDVI index at the non-mine area. 2) The water area gradually decreases with the year, which has a certain negative correlation with the total raw coal production. 3) The NDBB index showed a decreasing trend with the year, and compared with the non-mining area. 4) The coupling of year-by-year precipitation and temperature leads to interannual fluctuation of NPP value. 5) The change of ecological environment quality in the study area is the result of the comprehensive effect of natural factors and human factors. The implementation of ecological protection projects such as geological environmental protection and land reclamation in mining areas also has a certain impact on the trend of ecological environment quality. 6) There was a significant spatial autocorrelation in the quality of the ecological environment in the study area. There are significant “High-High” gathering areas of ecological environment quality within the coal mining area.

Propelled by rapid economic growth, the southwestern Shandong urban agglomeration (SSUA) in China has become a crucial industrial hub, but this process has somewhat hindered vegetation growth and environmental quality. Leveraging the functionalities of the Google Earth Engine (GEE) platform, we derived the fractional vegetation coverage (FVC) through the Normalized Difference Vegetation Index (NDVI) and assessed the eco-environmental quality using the Remote Sensing Ecological Index (RSEI). To examine the patterns and shifts in the SSUA, we employed the Theil–Sen median slope estimation, which provided robust estimates of linear trends, the Mann–Kendall trend test to determine the statistical significance of these trends, and the Hurst exponent analysis to evaluate the long-term persistence and predict future changes in the vegetation coverage and eco-environmental quality. Furthermore, to explore the interdependencies between vegetation coverage (VC) and environmental quality, we applied an improved coupling coordination degree model (ICCDM). This model allowed us to assess the co-evolution and synergy between these two factors over the study period, providing comprehensive insights for sustainable urban and ecological planning in the region. The VC and eco-environmental quality improved consistently across most of the SSUA from 2000 to 2020. The dominance of VC had transitioned from being predominantly characterized by relatively high VC to being mainly characterized by high VC. A substantial portion of the SSUA is predicted to experience improvements in its VC and environmental quality moving forward. Furthermore, the coupling coordination relationship between VC and environmental conditions in the southwest of Shandong Province generally exhibited a state of orderly coordinated development. With the passage of time, there was a clear tendency toward expansion in the coupled uncoordinated areas distributed in a network within each regional economic center. Our research unveils the dynamics and spatial-temporal patterns of VC and ecological quality in the southwestern Shandong urban agglomeration (SSUA) and elucidates the coupling and coordination mechanism between these two aspects, which provides theoretical support for understanding the healthy development of vegetation and ecology in urban agglomerations in an industrial context.

Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection.

The urban ecological environment is crucial to the quality of life of residents and the sustainable development of the region, and the assessment and prediction of the ecological environment quality can provide a scientific guidance for ecological environment management and improvement. We proposed a novel approach to assess and simulate the urban ecological environment quality using the Geographic Information System Ecological Index (GISEI). First, we calculated the remote sensing ecological index (RSEI) for Xi’an in 2020. Second, we selected land use data, mean annual temperature, and mean annual relative humidity as ecological indicators. We regressed these indicators on the RSEI to obtain the GISEI of Xi’an in 2020. Finally, we simulated the GISEI of Xi’an in 2030 by predicting the ecological indicators and analyzed the changes in the ecological environment quality. The results of the study show that the ecological environment quality in Xi’an in 2020 is better overall. By 2030, most of the ecological environment quality in Xi’an will be worse, and the proportion of the excellent area will decrease from 42.8% to 3.8%. The more serious ecological degradation is mainly located in the regions bordering the Qinling Mountains and the Guanzhong Plain, and the ecological environment quality in most areas of the Qinling Mountains will deteriorate from excellent to good.