Eco-environmental Quality Research Articles

Spatio-Temporal Dynamic Impacts of Land Use/Cover Change on Eco-Environment Quality in Li River Basin, China

Clarifying the spatio-temporal evolution characteristics of eco-environment quality (EEQ) under land use/cover change (LUCC) and its coordinated relationship is of great importance for formulating reliable environmental protection strategies and measures to promote regional sustainable development. Most studies have emphasized the importance of LUCC for regional ecological quality. However, deeply unraveling the complex interrelationships between them remains a significant challenge, particularly in ecologically fragile regions like the Li River Basin. Therefore, based on the historical land use data and the remote sensing ecological index (RSEI) of the Li River Basin from 1990 to 2020, we analyzed the spatio-temporal evolution characteristics of EEQ and LUCC, and explored the influences and non-linear effects between them by using the bivariate spatial autocorrelation and XGBoost model. The key findings are as follows: (1) Land use/cover (LUC) in the Li River Basin was predominantly characterized by forestland and cropland, which together accounted for approximately 97% of the region. The interconversion between forestland and cropland represented the primary form of regional LUCC, while built-up land demonstrated a growth trend by encroaching on cropland. (2) The EEQ exhibited a volatile upward trend within the research period, with an average RSEI value of 0.5891, indicating a generally favorable ecological condition. (3) A significant negative spatial correlation was observed between land use intensity (LUI) and the RSEI, characterized by H–L, L–H, and non-significant clusters. (4) There was a distinct non-linear relationship that existed between LUCC and the RSEI, underscoring that appropriately regulating regional land use scale can help maintain ecological balance. These findings provide a scientific basis for optimizing land spatial management models and formulating policies to improve ecological environment quality, while also offering a new framework and reference for further ecological research on EEQ influencing factors and driving mechanisms.

Study on the Eco-Environmental Index and Its Application: A Case Study of the Surablak Coal Fire Area, Xinjiang, China

Coal fires are disasters that occur when underground coal seams are subjected to combustion conditions induced by natural or human factors. This study attempts to investigate the impact of coal fires on the surrounding environment by assessing the eco-environmental quality and its dynamic changes in the Surablak coal fire area. To achieve this, an improved remote sensing ecological index (termed RSEIds) is introduced to assess and track the quality and dynamics of eco-environmental conditions in the Surablak coal fire area from 1990 to 2022. Subsequently, this index is combined with a geographic detector (GeoDetector) model to identify potential factors influencing eco-environmental quality. The findings indicate that (1) compared with the established Remote Sensing Ecological Index (RSEI), the RSEIds provides a high degree of precision in reflecting the eco-environmental conditions within the regions affected by coal fires, (2) the eco-environmental quality within the Surablak coal fire area underwent a continuous deterioration from 1990 to 2022, with the area of ecological degradation constituting 53.41% of the study region, (3) regions with excellent and good RSEIds values are mainly found in the forested mountainous regions located in the northern section of the coal fire area, whereas regions with poor and fair RSEIds values largely coincide with the coal fire locations, and (4) since 2006, the distance to the coal fire has become the key factor influencing eco-environmental quality in the Surablak area, while temperature and precipitation remained important factors. The outcomes of this study will provide essential references for guiding ecological restoration and promoting sustainable development in coal fire areas.

The Combined Effects of Topography and Climate Factors Dominate the Spatiotemporal Evolution of the Ecological Environment in the Yangtze River Economic Belt

ABSTRACTThe Yangtze River Economic Belt (YREB) is a key eco‐environmental protection barrier and economic engine in China. Understanding the characteristics of its eco‐environmental spatiotemporal evolution and mechanisms driving these changes is important for revealing shifts in ecosystem structure and function. However, the governing elements of the ecological environment and how these components interact remain unclear due to the strong coupling impact of urbanization and climate change. This paper constructed a long‐term series remote sensing based ecological index (RSEI) to evaluate the characteristics of eco‐environmental variation of YREB during 2000–2024. Subsequently, the fundamental reasons of the eco‐environmental evolution were quantitatively analyzed using optimal parameters‐based geographical detector (OPGD). Results indicated: (1) The average RSEI of YREB was 0.68, indicating a rather high overall eco‐environmental quality (EEQ), with 73% of the region having RSEI values more than 0.60. The regions with excellent conditions were concentrated in Zhejiang, Jiangxi, Guizhou, and Hunan, while the poorer areas were in northwestern Yunnan, southwestern Sichuan, and western Hubei. (2) The EEQ of YREB mainly showed a degradation trend, with degraded areas exceeding 80% over the past 25 years. In the future, most areas might face higher risks of ecological degradation. (3) Digital elevation model (DEM), temperature (Tem), and population (POP) were key driving factors of the ecological environment. There were significant interactions between certain factors, with the interaction between DEM and potential evapotranspiration (Pet) explaining 62.2% of the ecological quality distribution. The findings assisted the creation of an ecological civilization by offering policy proposals for managing and conserving ecological environments.

An Evaluation of Eco-Environmental Quality Based on the Fused Remote Sensing Ecological Index (FRSEI) in the Jinjie Coal Mine Area, Northwest China

This study focused on the Jinjie mining area by addressing the severe resource and environmental issues arising from excessive coal mining in Shenmu County, Northwest China. Multi-source remote sensing data from GF-1 and Landsat 8 OLI were utilized in this study. Specifically, the fused remote sensing ecological index (FRSEI) was constructed to conduct a detailed evaluation and analysis of the eco-environmental quality in the mining area from 2013 to 2023. The results indicated that the overall eco-environmental quality of the Jinjie mining area exhibited a trend of initial improvement, followed by degradation, and then improvement again over the decade. The eco-environmental quality of the mine pits and their surrounding areas was significantly lower than the overall level, confirming the destructive impact of coal mining on the eco-environment. Meanwhile, as the study area, Shenmu County is actively utilizing coal mining subsidence areas to develop the photovoltaic power generation industry, aiming to achieve green and low-carbon transformation. Although the construction of photovoltaic power plants initially led to the degradation of the condition of vegetation and the FRSEI, both gradually improved after the plants were operational. Furthermore, by comparing the FRSEI with the remote sensing ecological index (RSEI) calculated solely using Landsat 8 OLI data, we found a high degree of similarity between the two, thereby validating the stability and accuracy of the FRSEI. This study not only provides high-precision data support for eco-environmental monitoring in mining areas but also highlights the potential of multi-source remote sensing data fusion technology in improving monitoring accuracy, further providing a scientific basis for formulating sustainable development strategies specifically for the eco-environment in mining areas.

An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality

ABSTRACT Remote sensing-based ecological index (RSEI) is a widely used index for monitoring eco-environmental quality (EEQ). However, the traditional estimations usually face poor stability in tracking the continuous EEQ changes. To overcome this issue, this study introduces an improved framework for RSEI estimation to enhance the temporal comparability of EEQ assessments. It incorporates annual mean values and temporal normalization of key ecological indicators to address limitations, including susceptibility to seasonal variability and reliance on single-phase data. Through applying to Liangshan Prefecture, a mountainous region in Southwest China, the improved RSEI captures both long-term trends and short-term ecological dynamics from 2001 to 2020, revealing relatively stable EEQ, with mean values ranging from 0.465–0.470. Regions experiencing significant improvement or degradation accounted for less than 10% of the total area. Environmental improvements were largely attributed to human disturbance reduction and ecological restoration projects in high-altitude areas, while degraded regions were concentrated in the Anning River Valley, driven by population growth and urban expansion, affecting 51% of the area. The assessments demonstrate that the improved method shows greater stability and spatial coherence than the traditional ones, providing a robust metric for tracking EEQ trends for environmental monitoring and sustainable development planning.

Spatial–Temporal Changes and Driving Mechanisms of Ecological Environmental Quality in the Qinghai–Tibet Plateau, China

This study examines the evolution of eco-environmental quality and its driving forces in the Qinghai-Tibet Plateau, with a particular focus on the Qinghai Lake region (QLR). By employing principal component analysis (PCA) on nearly 20 years of remote sensing data, we reveal the dynamic characteristics of ecological quality in this sensitive area. The results indicate that the ecological quality of the QLR has exhibited significant fluctuations over the past two decades, influenced by multiple factors such as climate change, human activities, and policy adjustments. Specifically, the fluctuations in ecological quality are closely associated with key ecological indicators, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Wetness Index (WET), and Normalized Differential Bare Soil Index (NDBSI). Vegetation cover and moderate humidity have substantial positive effects on ecological quality, while high temperatures and dry soil conditions exert negative impacts.

Effects of Land Use Transition on Regional Ecological Environment—A Case Study of Zhaosu County, Xinjiang

As a focal point in contemporary land system science research, land use transitions significantly impact the ecological environment. Zhaosu County, a typical ecological county in the arid region of Northwest China, offers significant insights into the processes of land use transition and their effects on the ecological environment. Studying these dynamics is crucial for the county’s rational spatial allocation and sustainable development. Based on the remote sensing monitoring data of land use in Zhaosu County in 2000, 2010, and 2018, this article classified land use according to three primary functions: “production, living, and ecological”. By comprehensively applying the research methods of land use transfer matrix model, land use center of gravity shift model, eco-environmental quality index, and regional ecosystem contributions, this paper quantitatively analyzed the process of land use functional transition and spatial transition in Zhaosu County from 2000 to 2018 and scientifically investigated the spatiotemporal distribution characteristics of land use transitions as well as their varying impacts on the ecological environment. This research indicates that: (1) From 2000 to 2018, the ecological land in Zhaosu County initially experienced an increase followed by a decrease; in contrast, production land underwent a decline before rebounding, while living land has shown a continuous upward trajectory. (2) The spatial distribution of the three primary functional land uses in Zhaosu is unbalanced, and the center of gravity of all land uses has shifted during the study period. Among them, the center of gravity of water area and other ecological land underwent the most pronounced displacements, and the spatial migration intensified initially before gradually diminishing, while the degree of deviation of urban living land was the least pronounced. (3) The comprehensive eco-environmental quality index of Zhaosu County continued to decline from 0.584 in 2000 to 0.549 in 2018, indicating a persistent degradation trend of the ecological environment quality. (4) The negative effects of the ecological environment in Zhaosu County outweighed the positive effects, and the main factors contributing to the decline in ecological environment quality were grassland degradation and the decline of the snowline.

Investigating the urban eco-environmental quality utilizing remote sensing based approach: evidence from an industrial city of Eastern India

Urbanization, coupled with industrialization, leads to both economic growth and exponential urban growth, resulting in deteriorating environmental quality in urban areas, which poses a significant threat to the sustainability of cities. Hence, to restore biodiversity and ensure regional sustainability, it is necessary to immediately evaluate the eco-environmental quality of urban areas. The present research investigates the spatio-temporal changes in urban eco-environmental quality of the Asansol industrial city using an integrated ‘Urban Eco-Environmental Index’ (UEEQI) developed utilizing the Google Earth Engine platform and a remote sensing-based approach. The study used four spectral indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), along with Land Surface Temperature (LST) (as a thermal index), derived from the remote sensing data to measure environmental quality. Global Moran’s I and LISA were used to quantify spatial autocorrelation, showing the clustering of similar values or outliers of UEEQ within geographic space. The findings showed that high mean values of NDBI and NDBaI contributed to a lower mean UEEQI value of 0.38 in 2021 compared to previous decades. The spatio-temporal distribution of UEEQI showed that the ‘Very Poor’ category had grown from 0.06% in 1991 to 2% in 2021, while the ‘Poor’, ‘Good’, and ‘Excellent’ categories had declined. Over the 30 years, the UEEQI showed a rising trend of ‘Highly Degraded’ and ‘Degraded’ areas while decreasing the trend of ‘Improved’ or ‘Highly Improved’ environmental quality in the city. Moran’s scatter plot illustrated a highly positive clustered pattern of UEEQ across the city. Hotspots were mainly found in urbanized areas that had an “Average” urban eco-environment quality. Conversely, areas covered with bare surfaces, fallow lands, and brickfields were recognized as Coldspots. This study is crucial for determining specific regions of declining environmental quality and encourages local authorities and decision-makers to integrate eco-environmental conservation zones into city planning to foster healthier, more resilient, and sustainable cities.

Land-Use Transfer and Its Ecological Effects in Rapidly Urbanizing Areas: A Case Study of Nanjing, China

Rapid transformations in land use and land cover (LULC) serve as critical drivers influencing the eco-environmental quality in swiftly urbanizing areas. This study aims to assess and analyze the land-use transfer in Nanjing, China from 2003 to 2023 and its effects on ecological environment quality, utilizing the land expansion intensity (En), the land use composite index (LUCI), the remote sensing ecological index (RSEI), and other models. The results show that (1) farmland and forestland decreased significantly, with forestland showing the largest decrease (−20.65%), while construction land showed the largest increase (98.70%), mainly from farmland; (2) the overall RSEI level in Nanjing was relatively high, with a slight decline in fluctuation in the past 20 years. The RSEI values of forest land (0.8786) and farmland (0.8318) were higher, while the construction land (0.3790) and unused land (0.3701) were lower; (3) there was significant correlation (p < 0.05) and spatial autocorrelation between land-use changes and RSEI changes. The RSEI of rural areas was better than that of urban centers. There was a significant negative linear correlation between RSEI and LUCI (R2 = 0.711), a significant positive correlation with the area proportion of farmland, forest land and water, and a significant negative correlation with the area proportion of construction land. (4) Predictions indicate continued declines in farmland and forestland, accompanied by further expansion of construction areas, and the RSEI will continue to decline. It is suggested that forestland and farmland should be protected in the future, the expansion of construction land should be restrained, urban construction and ecological protection should be balanced, and the sustainable and high-quality development of rapid urbanization areas should be maintained by accurate land-use planning.

Detection of spatiotemporal changes in eco-environmental quality based on RSEI and SG filtering and its driving force analysis: a case study in Sichuan Province, China.

Landsat images were extracted using Google Earth Engine (GEE) platform and optimized by Savitzky-Golay (SG) filtering. The Remote Sensing Ecological Index (RSEI) method was used to analyze the eco-environmental quality in Sichuan Province in recent 20years. In addition, Theil-Sen median method and Mann-Kendall (MK) test were used to test the change trend of eco-environmental quality. Furthermore, drivers were evaluated by partial correlation analysis, 2D scatter plots, and t tests. The results showed that (1) in the past 20years, the eco-environmental quality of Sichuan Province was on the rise, and the eco-environmental quality in the western region was better than that in the eastern region. The eco-environmental quality was positively correlated with forest and grassland types, and negatively correlated with cultivated land and urban and rural construction land types. (2) The eco-environmental quality of Sichuan Province is linearly correlated with the digital elevation model, but poorly correlated with slope and slope direction. In the range of slope 0° ~ 9° and southeast direction, the eco-environmental quality is the worst. (3) The eco-environmental quality of Sichuan Province was most significantly affected by soil moisture and sunshine hours. The study can help us to understand and assess the health of ecosystems in Sichuan Province, provide a scientific basis for protecting and improving the environment, and guide the formulation and implementation of environmental protection policies.

Identifying the Interactive Coercive Relationships Between Urbanization and Eco-Environmental Quality in the Yangtze and Yellow River Basins, China

Urbanization, as an important engine of modernization, plays an important role in promoting regional economy and improving living standards. Nevertheless, unchecked urban expansion over recent decades has strained natural resources and the environment, leading to crises, especially in densely populated urban areas that act as ecological barriers within river basins. The investigation of the interactive coercive relationship between the urbanization level (UL) and eco-environmental quality (EEQ) can facilitate the identification of sustainable pathways towards regional sustainability. Therefore, this study employed a set of multidisciplinary approaches, integrating simple linear regression, bivariate spatial autocorrelation, and coupling coordination degree (CCD) models, alongside multi-source remote sensing data to analyze the interactive coercive relationship between UL and EEQ in the Yangtze and Yellow River basins (YYRBs) in China. Key findings included a 6.97% improvement in EEQ in the Yellow River basin (YLRB) from 2001 to 2020, with higher values in the southeastern and southwestern regions and lower values in the central region, while the Yangtze River basin (YTRB) saw only a 1.28% increase, characterized by a lower EEQ in the west and higher levels in the middle and east, although the Yangtze River Delta showed a decline and significant variation among tributaries. UL rose steadily in both basins, especially in the middle reaches of the YLRB. Spatial autocorrelation analysis revealed a positive correlation between UL and EEQ in the YLRB, whereas a negative correlation was found in the YTRB. The CCD between UL and EEQ in the YYRBs improved, particularly in the middle and lower reaches, indicating the need for integrated urban development strategies that consider regional ecological capacities. These findings provided a scientific basis for ecological protection and sustainable urban development at a large river basin scale.

Exploring symbiotic pathways: Unveiling the evolution and key drivers of China's human-environment relationship

Rapid urbanization increased human demand for natural resources, disrupting the balance between human and environment. This disruption led to regional environmental degradation, frequent natural disasters, and severe challenges in achieving sustainable development goals. In this context, fostering human and environment harmony is essential. However, research on quantifying their relationship and exploring the driving factors and mechanisms at a national fine spatial scale remains insufficient. This study endeavours to address this gap by constructing China's eco-environmental quality index (EQI) and human activity intensity (HAI) evaluation index systems with a 1 km resolution. Furthermore, the study utilizes the four-quadrant model and trend analysis to reveal the human-environment relationship, and employs the Bayesian network model to explore key drivers and underlying mechanisms influencing their relationship, aiming to foster harmonious human-environment interaction. Our results show that: (1) Both EQI and HAI increased over time, with their high values concentrated on the southeast side of the Hu Line but low on the northwest side. (2) The proportions of coordination and running-in areas increased while those of degradation and conflict areas declined, with 36.77% of the regions showing positive synergy during 2000–2020, indicating that China's human-environment relationship tended toward coordination. (3) Bayesian network analysis identified land use intensity, GDP, fraction vegetation coverage, and population density as critical factors affecting human-environment relationship. From a quadrant-based analysis, natural and eco-environmental factors including precipitation, water yield, and net primary productivity mainly influenced the spatial distribution of coordination and running-in intensity, while socioeconomic factors like population density and GDP primarily drove the spatial distribution of degradation and conflict intensity. This study finally proposed tailored management recommendations which would provide scientific support to facilitate the coordination and harmony of regional human-environment relationship.

Spatial Relationship between Eco-environment Quality and Land Use Change in Guangdong Province-Hong Kong-Macao, China Based on Remote Sensing-based Ecological Index

The relationship between eco-environment quality and land use change is an important guarantee for regional sustainable development. Guangdong Province-Hong Kong-Macao have always been closely linked and are now entering a period of accelerated development, but there is a lack of research on the spatial relationship between eco-environment quality and land use change integrated among the three places. Based on historical land use data and Google earth engine （GEE）, MODIS images from the summer of 2000 to 2020 （from June 1 to September 1） were selected （with a time span of five years）. The remote sensing-based ecological index （RSEI） was calculated to characterize the status of eco-environment quality and explore the spatial relationship between eco-environment quality and land use change. The results showed that： ① The land use type in the study area was mainly forest land, which was the base of the northern ecological development area （ecological area） and the northwestern landscape of the Guangdong-Hong Kong-Macao Greater Bay Area （the Greater Bay Area）. The land use degree presented a spatial pattern of "high in the middle, low in the periphery," "high in the south and low in the north," and "high in the southwest and northeast." The intensity of land use change was the least in the ecological area. ② According to RSEI, the regions with an eco-environment quality from low to high were the Greater Bay Area, the eastern wing of the coastal economic belt （east wing）, the western wing of the coastal economic belt （west wing）, and the ecological area. ③ There was a strong negative correlation between eco-environment quality and land use degree. The distribution range of high to low （H-L） cluster was relatively stable, mainly concentrated in the center of the Greater Bay Area, while the distribution of low to high （L-H） cluster was relatively unstable, mainly distributed in the ecological region, and there was no significant correlation between the eastern and western counties, Hong Kong and Macao. ④ The low-value patches of ecological environment quality in Guangdong-Hong Kong-Macao centers were consistent with artificial surface expansion. The center of Foshan in Guangzhou, the eastern part of Shantou, and the southwestern part of Zhanjiang were mainly coupled, while the ecological area, the central part of the east wing, the eastern part of the west wing, and the northwestern part of the Greater Bay Area were mainly uncoupled. Land use change and eco-environment quality were closely related in space. The eco-environment quality of the study area mainly presented a spatial pattern of "high in the middle and low in the north" and "a certain low value in the east." The Greater Bay Area had the lowest eco-environment quality, followed by that of the eastern wing. In the past 20 years, the expansion of low-value eco-environment quality in the center of the Greater Bay Area had formed a continuous landscape base of low-value eco-environment quality. The ecological area had most of the high value areas of eco-environment quality, which provided a large area of ecological environment of good patches for the study area. From 2000 to 2020, there was a significant negative correlation between land use degree and eco-environment quality in the study area, with regional heterogeneity. Land use change is an important factor in the change in eco-environment quality, so it is necessary to focus on the whole planning and layout of land use structure to improve the eco-environment quality.

Carbon Emission Trajectory and Eco-environment Effects in the Aksu River Basin Based on Agriculture-town-ecological Space Changes

Due to the expansion of artificial oases in these areas, scientific assessment of the carbon emission and eco-environment effects caused by changes in agriculture, towns, and ecological space is of great significance in grasping the carbon emission characteristics of various land use types, formulating land use emission reduction strategies, promoting the construction of ecological civilization, and optimizing the spatial pattern of the national territory. Based on land use data and socio-economic data, taking the inland river basin of the arid zone of the Xinjiang-Aksu River Basin as the research object, the carbon emission coefficient method was used to measure land-use carbon emissions, and spatial autocorrelation analysis and standard deviation ellipse were used to analyze the spatial evolution characteristics of carbon emissions from agriculture-town-ecological space in the Aksu River Basin. Based on the eco-environment quality index and ecological contribution rate, the impact of the agriculture-town-ecological space transformation on the eco-environment effects in the Aksu River Basin was explored. The results showed that： ① The scale of agricultural space and town space continued to rise, while the overall scale of ecological space was shrinking. The carbon source provided by agricultural space and town space showed a continuous upward trend, while the carbon sink provided by ecological space showed a trend of first increasing and then decreasing, with an overall downward trend. ② There was a positive spatial correlation between the net carbon emissions and the carbon emissions provided by the agriculture-town-ecological space. The center of gravity of carbon emissions in the basin generally moved to the southwest except for the ecological space, whereas the other space and net carbon emissions moved to the southeast. ③ The eco-environment quality index in the basin increased first and then decreased and generally showed a downward trend. From 1990 to 2000, the ecological environment quality improved overall； from 2000 to 2010, it deteriorated severely； and from 2010 to 2020, it deteriorated slowly.

Eco-Environmental Quality Assessment of Two Seasons Based on Pressure-State-Response Framework by Using Remote Sensing and GIS Techniques

Determining the urbanization and sustainable development rate in Kirkuk City and Taza District (a town located in a suburb of Kirkuk) requires an evaluation of its ecological environment quality (EEQ). This study aims to determine the ecological index (El) and assess it using the popular pressure-state-response (PSR) method based on a set of statistical and remote sensing indicators. The indicators developed by remote sensing data were obtained using Sentinel-2 satellite images to measure the quality of the environment. As new and modern technical assistance for long-term, comprehensive mapping, monitoring through GIS techniques, and the comparison of the Ecological Index in 2023 of the spring and autumn seasons, these indicators provide a basis for decision-making related to sustainable development. It demonstrates the primary relationship between human activity and health under various weather circumstances and contrasts them. It also reveals that the ecological situation is healthier in spring than in autumn (these two seasons are considered transmission periods) due to meteorological conditions, social activities, and economic activity.

Assessment of eco-environment quality using multi-source remote sensing data

ABSTRACT Accurate analysis of regional eco-environment quality (EEQ) changes is essential for urban sustainability. Most of the widely used EEQ indicators ignore the impact of air pollution caused by urbanization and economic growth. This study proposed a remote sensing-based approach using a time-series comprehensive evaluation index (CEI), which was developed by PM2.5 concentration, Land surface temperature (LST), and vegetation coverage (VC) to assess the EEQ in the Demonstration Zone of Green and Integrated Ecological Development of The Yangtze River Delta (demonstration zone) from 2000 to 2020. The EEQ changes of all districts and townships were classified into five levels. The results suggested that from 2000 to 2020, the CEI was significantly decreased (p < 0.01) in the demonstration zone, indicating an overall improvement of the EEQ. Differences in the mean annual CEI values and EEQ changes were found among districts and to wnships. Higher CEI values, which indicated a lower degree of EEQ were found in areas where build-up land comprised a higher percentage of the total area. Townships with degraded EEQ change exhibited a higher percentage of increase in build-up land (32.3%) than others (8.67% to 16.68%) from 2000 to 2020. Moreover, the change of annual CEI at the administrative district scale was found to be strongly correlated with the proportion of the secondary sector in GDP in three districts. The results of our study indicated the importance of land use optimization, air pollution reduction, and vegetation coverage enhancement to improve the regional EEQ in the urban wetland ecosystems with rapid urbanization.

Land Use Change in a Typical Transect in Northern China and Its Impact on the Ecological Environment

This paper uses seven periods of land use/cover data from 1990 to 2020 to investigate the spatio-temporal features of land use change in a typical transect in northern China. By using the land use transfer matrix, transect analysis, and ecological contribution rate, image interpretation data obtained from the Chinese Academy of Sciences Resource and Environmental Science Data Platform were analyzed using a spatial overlay to quantitatively examine the transect’s land use changes and their impact on the eco-environment. The results indicate that (1) the transect’s land use is dominated by cropland and grassland. (2) Cropland, forest land, and waters experienced significant transitions in 1995 and 2000, which was linked to socio-economic development and policy factors. (3) The total dynamic percentage of land use change is 4.52%, with built-up land and cropland showing the highest change. (4) The transect’s eco-environmental quality (EQ) has significantly declined, with the quality index dropping from 0.3839 to 0.3773. The transformation of cropland to forest land improves the eco-environment, while the transformation to built-up land has negative impacts. Human activities adversely affect the EQ. The findings are promising for leading the development of conserving the eco-environment and supporting the formation of regionally differentiated paths of ecological civilization construction in the transect.

Spatiotemporal evolution and driving factors of ecosystem service value in the Upper Minjiang River of China.

The stability of ecosystems in high mountain canyon areas is poor, and the interaction between humans and the land is complex, making these ecosystems more vulnerable to destruction. Quantitatively assessing the ecosystem service value (ESV) in high mountain canyon areas and revealing its spatiotemporal evolution patterns and driving factors play a crucial role in the construction of regional ecological barriers and the assurance of ecological security. This study focuses on the Upper Minjiang River as the research area, using the InVEST model and the Equivalent Factor Method to estimate ESV. This combination aims to address the inadequacy of the Equivalent Factor Method in reflecting the variability of ESV across different regions, and the sensitivity of the InVEST model to data changes that results in insufficient accuracy of ESV assessments. By harnessing spatial au-tocorrelation and the geodetector method, we unravel the spatiotemporal evolution characteristics and driving factors of ESV. The results show that: (1) From 2000 to 2020, the ESVs estimated by the two estimations increased by 31.28% and 22.47%, respectively, both indicating that the eco-environment quality of the upper Minjiang River has been continuously improved. (2) When Moran's I was greater than 0.5 (p < 0.05), the spatial clustering of "High-High" and "Low-Low" ESV was obvious. It is clear that the ESV varies geographically. High values are primarily found in the study area's center and southern regions, as well as on both banks of the Minjiang River, whereas low values are more common in the region's northern region. (3) Slope and human activity intensity (HAI) are the principal contributors to the spatial differentiation of the ESV, more than 60% of the interaction types between the two factors were classified as dual-factor enhancement. The synergistic reinforcing effects of HAI, slope, elevation, and temperature collectively shape the shifts in ESV spatial distribution. This study offers a novel evaluative lens on the ESV of the Upper Minjiang River area, supplying a sturdy data support for crafting specific ecological preservation and rejuvenation strategies in the coming years.

Assessment and simulation of eco-environmental quality changes in rapid rural urbanization: Xiong’an New Area, China

Xiong’an New Area was established as a state-level new area in 2017 and serves as a typical representative area for studying the ecological evolution of rural areas under rapid urbanization in China. Remote sensing-based ecological index (RSEI) is a regional eco-environmental quality (EEQ) assessment index. Many studies have employed RSEI to achieve rapid, objective, and effective quantitative assessment of the spatio-temporal changes of regional EEQ. However, research that combines RSEI with machine learning algorithms to conduct multi-scenario simulation of EEQ is still relatively scarce. Therefore, this study assessed and simulated EEQ changes in Xiong’an and revealed that: (1) The large-scale construction has led to an overall decline in EEQ, with the RSEI decreasing from 0.648 in 2014 to 0.599 in 2021. (2) Through the multi-scenario simulation, the non-unidirectional evolution of RSEI during the process of urban-rural construction has been revealed, specifically characterized by a significant decline followed by a slight recovery. (3) The marginal effects of urban-rural construction features for simulated RSEI demonstrate an inverted “U-shaped” curve in the relationship between urbanization and EEQ. This indicates that urbanization and EEQ may not be absolute zero-sum. These findings can provide scientific insights for maintaining and improving the regional EEQ in urban-rural construction.

Spatial–Temporal Assessment of Eco-Environment Quality with a New Comprehensive Remote Sensing Ecological Index (CRSEI) Based on Quaternion Copula Function

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.