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The ecological quality of a region is significantly influenced by its geographical conditions, which can yield different effects on ecosystems. Nevertheless, the lack of adequate technology has impeded quantitative investigations into these differences. Consequently, there is an increasing demand for effective techniques to quantitatively measure differences in ecological quality resulting from variations in geographical conditions. This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. By linking geographical factors with their corresponding ecological responses, we conducted a comprehensive analysis to determine whether the contrasting geographical conditions between the two regions had caused significant disparities in their ecological status. The results indicate that the contrasting geographical conditions have indeed led to marked ecological differences, with Fujian exhibiting excellent ecological status, while Ningxia lags behind due to unfavorable geographical conditions. In terms of RSEI scores, Fujian consistently achieved higher RSEI values (>0.8) in the study years, reaching an excellent ecological level, whereas Ningxia recorded scores lower than 0.45 during the comparable years, corresponding to a poor to moderate ecological level. Regarding the impact of geographical factors on ecological conditions, the positive contributions of greenness and wetness indicators to the ecology in Fujian were significantly greater than those in Ningxia (58% vs. 39%), whereas the contributions of negative indicators, dryness and hotness, were notably higher in Ningxia compared to Fujian (|-61|% vs. |-42|%). The successful concurrent application of RSEI to these two geographically distant regions also demonstrates the robustness of the RSEI technique.

Ecosystem health assessments are crucial to protect the ecological environment and ensure the sustainable ecological functions of alpine ecoregions. At present, few studies evaluating the ecosystem health of the Gannan alpine grassland, China, an ecologically fragile area, based on a remote sensing theoretical framework exist. As such, this study assessed the ecosystem health of the Gannan alpine grassland based on the Remote Sensing-based Ecological Index (RSEI) and provided a comparative analysis of the RSEI and Gross Primary Productivity (GPP), extending the study of their spatiotemporal patterns and influencing factors. The results suggested that RSEI and GPP showed strong comparability in an ecological sense, with the RSEI better reflecting changes in ecosystem health of the Gannan alpine grassland than the GPP. Overall, the health of the Gannan alpine grassland ecosystem was good (RSEI of 0.61-0.76) and a slow, fluctuating upward trend was seen from 2000 (RSEI = 0.66) to 2020 (RSEI = 0.72). Notably, the RSEI was high in the south and low in the north of the region. Over the past 21 years, 43.92% of the ecologically healthy grassland in the southwest of Gannan has been degrading, while the poor ecological health of 39.04% of the grasslands in the southeast and northeast improved. The model test results show that RSEI could reasonably evaluate the ecosystem health of Gannan alpine grassland. Our assessment results provide important scientific data and information on health monitoring and targeted ecological restoration efforts in the Gannan region.

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.

The dramatic land use changes that occur in rapidly urbanized areas are important inducement to changes in the eco-environmental quality. Investigating urban land use changes and their eco-environmental quality responses can provide theoretical support and a decision-making basis for sustainable and high-quality development in rapidly urbanizing areas. Taking Wuhan, China, as the study area, this paper extracts land use information using Landsat satellite remote sensing images and a support vector machine classification. Based on this, a remote sensing-based ecological index evaluation model including humidity, greenness, dryness and heat is constructed to explore the changes in land use and their eco-environmental quality responses from 2000 to 2018. The results show that (1) the structure, extent and spatial layout of land use in Wuhan from 2000 to 2018 have undergone tremendous changes under rapid urbanization, and the change of construction land is the greatest among all land use types; (2) the overall quality of eco-environment in Wuhan continues to improve as the scale of the improved eco-environment areas is greater than that of the deteriorated areas. The direction and magnitude of the impact of each indicator on the eco-environmental quality are different; (3) the improvement or deterioration of eco-environmental quality is closely related to the changes of different land use types within the study area. The eco-environmental quality shows significant spatial heterogeneity, especially between the main urban areas and the suburban areas. This paper argues that reasonably adjusting the land use structure can serve to maintain or even improve the quality of the regional eco-environment. Finally, this study puts forward suggestions for the coordinated development of land use and the eco-environment in rapidly urbanizing areas.

As a region susceptible to the impacts of climate change, evaluating the temporal and spatial variations in ecological environment quality (EEQ) and potential influencing factors is crucial for ensuring the ecological security of the Tibetan Plateau. This study utilized the Google Earth Engine (GEE) platform to construct a Remote Sensing-based Ecological Index (RSEI) and examined the temporal and spatial dynamics of the Tibetan Plateau’s EEQ from 2000 to 2022. The findings revealed that the RSEI of the Tibetan Plateau predominantly exhibited a slight degradation trend from 2000 to 2022, with a multi-year average of 0.404. Utilizing SHAP (Shapley Additive Explanation) to interpret XGBoost (eXtreme Gradient Boosting), the study identified that natural factors as the primary influencers on the RSEI of the Tibetan Plateau, with temperature, soil moisture, and precipitation variables exhibiting higher SHAP values, indicating their substantial contributions. The interaction between temperature and precipitation showed a positive effect on RSEI, with the SHAP interaction value increasing with rising precipitation. The methodology and results of this study could provide insights for a comprehensive understanding and monitoring of the dynamic evolution of EEQ on the Tibetan Plateau amidst the context of climate change.

Establishing a method for characterizing spatiotemporal changes in the quality of the ecological environment in a timely and accurate manner is of great significance for the protection and sustainable development of the ecological environment in the Yellow River Basin (YRB). In this study, the Google Earth Engine (GEE) platform was used as a basis for constructing the remote-sensing-based ecological index (RSEI), and the RSEI was used to evaluate the quality of the ecological environment in the YRB. The results indicated that the mean of the RSEI values showed two stages of rapid improvement and slow improvement during 1990–2020. From 1990 to 2000, the average growth trend was 0.005/a with a growth rate of 21.15%, with the main contributions of bad to poor (101,800 km2), poor to medium (56,900 km2), and medium to good (70,800 km2) ecological environmental quality levels. From 2000 to 2020, the average growth trend was 0.002/a with a growth rate of 2.13%, with main contributions of poor to bad (65,100 km2) and good to medium (35,200 km2) ecological environmental quality levels. From 1990 to 2020, there was a 76.38% improvement in the ecological environmental quality of the entire YRB, in which significant improvement accounted for 26.14%. The reductions in the ecological environmental quality accounted for 23.62%, of which significant reductions accounted for just 1.46%. The improvement in the ecological environmental quality of the YRB showed a trend of increasing sustainability, which is expected to continue. The distribution of the ecological environmental quality in the YRB showed obvious regional aggregation, whereby cold spots were concentrated in the northern and central regions of the YRB, which are the sandy and hilly ravine areas of the Loess Plateau. However, the areas corresponding to hot spot clusters decreased with time, and their significance also decreased. Thus, our study demonstrates that the GEE platform can be used to determine the spatiotemporal changes in the ecological environmental quality of the YRB in a timely and accurate manner.

Panzhihua City is a typical agricultural-forestry-pastoral and ecologically sensitive city in China. It is also an important ecological defense in the upper Yangtze River. It has abundant mineral resources, including vanadium, titanium, and water supplies. However, ecological and environmental problems emerge due to the excessive development of mining, agriculture, animal husbandry, and other non-natural urban economies. Therefore, a scientific understanding of the spatio-temporal changes of the eco-environment of Panzhihua is critical for environmental protection, city planning, and construction. To objectively evaluate the eco-environmental status of Panzhihua, the remote sensing-based ecological index (RSEI) was first applied to Panzhihua, a typical resource-based city, and its ecological environmental quality (EEQ) was quantitatively assessed from 1990 to 2020. This study explored the effects of mining activities and policies on EEQ and used change detection to reveal the spatial-temporal changes of EEQ in Panzhihua City over the past three decades. In addition, this study also verified the suitability of RSEI for evaluating EEQ in resource-based city using spatial autocorrelation, revealed the spatial heterogeneity of EEQ in Panzhihua City using optimized hot spot analysis, and showed different ecological clustering by hot spot analysis at two scales of urban and mining areas. According to the results: (1) From 1990 to 2020, the general eco-environmental condition of Panzhihua is improving, but there are still regional differences. (2) The Moran’s I value ranges from 0.436 (1990) to 0.700 (2020), indicating that there is autocorrelation in the distribution of eco-environmental quality. (3) At the mine, the mean value of RSEI dropped by 20–40%, and the EEQ decreased significantly due to mining activities. (4) A series of ecological restoration policies can buffer the negative impact of mining activities on the ecosystem, resulting in a slight improvement in the quality of the ecological environment. This study evaluates the EEQ of resource-based city and its spatial-temporal changes using RSEI constructed by the Google Earth Engine (GEE) platform, which can provide theoretical support for ecological and environmental conditions monitoring, development planning, and environmental protection policy-making of a resource-based city.

Human activities have been stressing the ecological environment since we stepped into the Anthropocene Age. It is urgent to formulate a sustainable plan for balancing socioeconomic development and ecological conservation based on a thorough understanding of ecological environment changes. The ecological environment can be evaluated when multiple remote sensing indices are integrated, such as the use of the recently prevalent Remote Sensing-based Ecological Index (RSEI). Currently, most of the RSEI-related studies have focused on the ecological quality evolution in small areas. Less attention was paid to the spatio-temporal heterogeneity of ecological quality in large-scale urban agglomerations and the potential links with Land Use and Cover Change (LUCC). In this study, we monitored the dynamics of the ecological quality in the Hangzhou Greater Bay Area (HGBA) during 1995–2020, using the RSEI as an indicator. During the construction of the RSEI, a percentile de-noising normalization method was proposed to overcome the problem of widespread noises from large-scale regions and make the RSEI-based ecological quality assessment for multiple periods comparable. Combined with the land use data, the quantitative relationship between the ecological quality and the LUCC was revealed. The results demonstrated that: (1) The ecological quality of the HGBA degraded after first improving but was still good (averaged RSEI of 0.638). It was divergent for the prefecture-level cities of the HGBA, presenting degraded, improved, and fluctuant trends for the cities from north to south. (2) For ecological quality, the improved regions have larger area (57.5% vs. 42.5%) but less increment (0.141 vs. −0.195) than the degraded regions. Mountains, downtowns, and coastal wetlands were the hot spots for the improvement and urbanization, and reclamation processes were responsible for the degradation. (3) The ecological quality was improved for forests and urban areas (△RSEI > 0.07) but degraded for farmland (∆RSEI = −0.03). As a result, the ecological cost was reduced among human-dominant environments (e.g., farmland, urban areas) while enlarged for the conversion from nature-(e.g., forests) to human-dominant environments.

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries.

The Upper Yellow River Region plays an irreplaceable role in water conservation and ecological protection in China. Due to both natural and human-induced factors, this area has experienced significant grassland deterioration, land desertification, and salinization. Consequently, evaluating the region’s environmental status plays a vital role in promoting ecological conservation and sustainable growth in the Upper Yellow River Basin. This study constructed an ecological index based on remote-sensing data and examined its spatiotemporal changes from 1990 to 2020. Future ecological dynamics were predicted using the Hurst index, while key influencing factors were examined through an optimal-parameter-based GeoDetector and geographically weighted regression. The findings revealed the following: (1) RSEI values were generally lower in the north and increased progressively toward the south, indicating a notable spatial disparity. (2) Ecological conditions remained largely stable, with notable improvements observed in 65.47% of the study area. (3) It was anticipated that 52.76% of the region would continue to improve, whereas 24% is expected to experience further degradation. (4) Precipitation, temperature, elevation, and land cover were major factors contributing to ecological variation. Their impact on ecological quality varies across different geographic locations. These research findings provided references for the sustainable development and ecological civilization construction of the Upper Yellow River Region.