[Ecological Environment Dynamical Evaluation of Hutuo River Basin Using Remote Sensing].

Hutuo River Basin straddles Shanxi and Hebei provinces, and Hutuo River was once cut off due to economic development and urban expansion after 2000; however, with the national emphasis on ecological civilization and the implementation of the South-North Water Diversion Project, the ecological protection of Hutuo River Basin has been significantly improved. MODIS data, Landsat data, and night light remote sensing data were selected based on the google earth engine (GEE) platform, and a new evaluation index system was generated by combining the biological richness index, vegetation cover index, land stress index, and pollution load index in the ecological environment index (EI) and the humidity index in the remote sensing ecological index (RSEI), using the variation coefficient method and entropy weighting method to assign weights to these indices. An ecological environment evaluation model was constructed to evaluate and classify the ecological environment quality of Hutuo River Basin from 2000 to 2020, and the driving factors were interpreted by using geographic probes. The results showed that:① on a time scale, the ecological environment of Hutuo River Basin was in a decline period from 2000 to 2015 and a recovery period from 2015 to 2020. From a grid scale, the ecological environment quality in the central part of the basin showed a state of improvement year by year, and in the western and eastern parts of the basin, the ecological environment quality in the decline period decreased year by year, whereas the ecological environment quality in the recovery period improved. ② Hot spot analysis showed that the spatial distribution of the ecological environment quality in Hutuo River Basin was high in the middle and low on both sides. Cold spot regions were mainly located in major cities and towns in the eastern and southern parts and scattered in the river valley area on the west side. ③ Geodetection analysis showed that the single factor detection drivers were mainly population density, vegetation net primary productivity (NPP), fractional vegetation cover (FVC), and geomorphological type. The dominant factor of cross-detection was "geomorphological type + FVC." With the deepening of ecological civilization construction and the implementation of Hutuo River Protection Regulations, in combination with different factors such as the natural environment and social characteristics in this basin, the research on ecological environment evaluation in Hutuo River Basin can provide data support for proposing localized policies to improve the ecological environment.

Application of a novel remote sensing ecological index (RSEI) based on geographically weighted principal component analysis for assessing the land surface ecological quality.

In evaluating the integrated remote sensing-based ecological index (RSEIPCA), principal component analysis (PCA) has been extensively utilized. However, the conventional PCA-based RSEI (RSEIPCA) cannot accurately evaluate component indicators' spatially shifting relative significance. This study presented a novel RSEI evaluation strategy based on geographically weighted principal component analysis (RSEIGWPCA) to address this deficiency. Second, compared to the classic RSEIPCA, RSEIGWPCA was tested at English Bazar and surrounding areas using two-fold validation. In this regard, the Jaccard test from a different setting and correlation analysis were utilized to examine the geographical distribution of RSEI derived by PCA and GWPCA. The validation output revealed better effectiveness of GWPCA over PCA in assessing the RSEI. The findings revealed that (i) in RSEI assessment, the spatial heterogeneity of the dataset helped to formulate individual weights by GWPCA that was not performed by PCA; and (ii) the areas having higher RSEI were primarily located around the Chatra wetland of this study area, and the areas with lower RSEI were located mainly in the industrial part. It has been concluded that RSEIGWPCA is a helpful approach in the RSEI evaluating for the regional and local scale like English bazaar city and its neighbourhood.

Comprehensive comparison of two models evaluating eco-environmental quality in Fangshan.

It is crucial to employ scientifically sound models for assessing the quality of the ecological environment and revealing the strengths and weaknesses of ecosystems. This process is vital for identifying regional ecological and environmental issues and devising relevant protective measures. Among the widely acknowledged models for evaluating ecological quality, the ecological index (EI) and remote sensing ecological index (RSEI) stand out; however, there is a notable gap in the literature discussing their differences, characteristics, and reasons for selecting either model. In this study, we focused on Fangshan District, Beijing, China, to examine the differences between the two models from 2017 to 2021. We summarized the variations in evaluation indices, importance, quantitative methods, and data acquisition times, proposing application scenarios for both models. The results indicate that the ecological environment quality in Fangshan District, Beijing, remained favorable from 2017 to 2021. There was a discernible trend of initially declining quality followed by subsequent improvement. The variation in the calculation results is evident in the overall correlation between the RSEI and EI. Particularly noteworthy is the significantly smaller correlation between EI and the RSEI in 2021 than in the other two years. This discrepancy is attributed to shifts in the contribution of the evaluation indices within the RSEI model. The use of diverse quantitative methods for evaluating indicators has resulted in several variations. Notably, the evaluation outcomes of the EI model exhibit a stronger correlation with land cover types. This correlation contributes to a more pronounced fluctuation in RSEI levels from 2017 to 2021, with the EI model's evaluation results in 2019 notably surpassing those of the RSEI model. Ultimately, the most prominent disparities lie in the calculation results for water areas and construction land. The substantial difference in water areas is attributed to the distinct importance assigned to evaluation indicators between the two models. Moreover, the notable difference in construction land arises from the use of different quantification methods for evaluation indicators. In general, the EI model has suggested to be more comprehensive and effectively captures the annual comprehensive status of the ecological environment and the multiyear change characteristics of the administrative region. On the other hand, RSEI models exhibit greater flexibility and ease of implementation, independent of spatial and temporal scales. These findings contribute to a clearer understanding of the models' advantages and limitations, offering guidance for decision makers and valuable insights for the improvement and development of ecological environmental quality evaluation models.

Eco-environmental changes due to human activities in the Erhai Lake Basin from 1990 to 2020.

Human activities have increased with urbanisation in the Erhai Lake Basin, considerably impacting its eco-environmental quality (EEQ). This study aims to reveal the evolution and driving forces of the EEQ using water benefit-based ecological index (WBEI) in response to human activities and policy variations in the Erhai Lake Basin from 1990 to 2020. Results show that (1) the EEQ exhibited a pattern of initial degradation, subsequent improvement, further degradation and a rebound from 1990 to 2020, and the areas with poor and fair EEQ levels mainly concentrated around the Erhai Lake Basin with a high level of urbanisation and relatively flat terrain; (2) the EEQ levels were not optimistic in 1990, 1995 and 2015, and areas with poor and fair EEQ levels accounted for 43.41%, 47.01% and 40.05% of the total area, respectively; and (3) an overall improvement in the EEQ was observed in 1995-2000, 2000-2005, 2005-2009 and 2015-2020, and the improvement was most significant in 1995-2000, covering an area of 823.95 km2 and accounting for 31.79% of the total area. Results also confirmed that the EEQ changes in the Erhai Lake Basin were primarily influenced by human activities and policy variations. Moreover, these results can provide a scientific basis for the formulation and planning of sustainable development policy in the Erhai Lake Basin.

[Ecological Environment Assessment and Driving Mechanism Analysis of Nagqu and Amdo Sections of Qinghai-Xizang Highway Based on Improved Remote Sensing Ecological Index].

The ecological environment along the Qinghai-Xizang highway is an important part of the construction of the ecological civilization in the Xizang region, and current research generally suffers from difficulties in data acquisition, low timeliness, and failure to consider the unique "alpine saline" environmental conditions in the study area due to the unique geographical environment of the Qinghai-Xizang plateau. Based on the GEE platform and the unique geographical environment of the study area, the remote sensing ecological index (RSEI) was improved, and a new saline remote sensing ecological index (SRSEI) applicable to the alpine saline region was constructed by using principal component analysis as an ecological environment quality evaluation index. The spatial distribution pattern and temporal variation trend of ecological environment quality along the Qinghai-Xizang Highway Nagqu-Amdo section were analyzed at multiple spatial and temporal scales using the ArcGIS 10.3 platform and geographic probes, and the driving mechanisms of eight control factors, including natural and human-made, on the spatial and temporal changes in SRSEI were investigated. The results showed that:① compared with RSEI, SRSEI was more sensitive to vegetation and had a stronger discriminatory ability in areas with sparse vegetation and severe salinization, which is suitable for ecological quality evaluation in alpine saline areas. ② The spatial scale of ecological environment quality in the study area had obvious geographical differentiation, and the areas with poor ecological quality were mainly concentrated in the northern Amdo County, whereas the areas with excellent and good quality grades were mainly distributed in the central-western and southeastern Nagqu areas. On the temporal scale, the ecological environment of the study area as a whole showed an improvement trend over 32 years, and the vegetation cover in the central-western and southeastern areas increased significantly, which had a strong improvement effect on the ecological environment. The improvement area was 1 425.98 km2, accounting for 99.82%. The mean value of SRSEI was 0.49, with an overall fluctuating upward trend and an average increase of 0.015 7 a-1. ③ The land use pattern was the most driving influence factor in the change of ecological environment quality in the study area, with an average q value of 0.157 6 over multiple years, and the influence of environmental factors was low. The multi-factor interaction results showed that the ecological environment in the study area was the result of multiple factors acting together, all factors had synergistic enhancement under the interaction, the influence of human factors was gradually increasing, and the interaction of the net primary productivity (NPP) of vegetation and land use pattern was the main interactive control factor of ecological environment quality in the study area. This study can provide a theoretical basis for ecological environmental protection and sustainable development along the Nagqu to Amdo section.

[Spatial-temporal Evolution and Driving Factors Analysis of Ecological Environment Quality in Daihai Basin based on AWRSEI].

Watersheds are an important ecological security barrier and social and economic development area. In order to evaluate the ecological environment quality of arid and semi-arid watersheds more objectively and accurately, based on the remote sensing ecological index (RSEI), the salinity index was introduced to construct a remote sensing ecological index (AWRSEI) suitable for arid and semi-arid watersheds, and the Daihai watershed was taken as an example to analyze its applicability. Based on the AWRSEI model, four phases of Landsat TM/OLI composite images were used to quantitatively evaluate the ecological and environmental quality of the Daihai Basin from 2001 to 2020. The spatio-temporal evolution of the ecological and environmental quality of the Daihai Basin was analyzed using the coefficient of variation and spatial autocorrelation, and the cause analysis and driving factor explanation power analysis were carried out using geographic detectors. The results showed that:① the average correlation coefficient between AWRSEI and various ecological factors was 0.860, which was more representative than that of a single index component. The load positive and negative values and ranking of the first principal component were consistent with those of RSEI, the contribution rate of eigenvalues was 3.69% higher than those of RSEI, and the evaluation results were closer to the real surface conditions, which is suitable for the ecological environment quality assessment of arid watersheds. ② The annual average of the AWRSEI index in the Daihai Basin from 2001 to 2020 was 0.427, which indicated a poor ecological environment quality basis. During the study period, the average of AWRSEI showed a fluctuating trend and reached the highest value of 0.502 in 2020. The overall ecological environment quality improved significantly, the deterioration area decreased by 20.51%, and the improvement area increased by 12.71%. In terms of spatial distribution, the ecological environment quality of forest land in the southern and northwestern high-altitude areas of the Daihai Basin was good, whereas that in the northern and southern mid-altitude areas was poor, and that in the northern area was better than that in the southern area. ③ The average variation coefficient of AWRSEI in the Daihai Basin was 0.280, the ecological environment quality was stable, and the overall change fluctuation was small; the high fluctuation was mainly concentrated in the southern part of the lake and the residential area. There was a significant spatial autocorrelation in the ecological environment quality of the Daihai Basin, and the high-high agglomeration area was mainly distributed in the forest area at high altitude and the cultivated land area at low altitude. Low-low concentration areas were scattered in the middle altitude area. ④ The improvement of the ecological environment in Daihai Basin from 2001 to 2020 was mainly due to the increase in NDVI and the decrease in NDBSI and NDSI. NDVI and NDBSI were the combination with the strongest interaction and the strongest interpretation of the ecological environment. Land use was the dominant factor of AWRSEI and had the strongest explanatory power. The combination of land use and meteorological factors was the strongest interaction, and the relationship between each driving factor was enhanced.

Grazing alters the soil nematode communities in grasslands: A meta-analysis.

Grazing causes great disturbances in grassland ecosystems and may change the abundance, diversity, and ecological function of soil biota. Because of their important role in nutrient cycling and as good environmental indicators, nematodes are very representative soil organisms. However, the mechanisms by which grazing intensity, livestock type, duration, and environmental factors (e.g., climate and edaphic factors) affect soil nematodes remain poorly understood. In this study, we collected 1964 paired observations all over the world from 53 studies to clarify the grazing response patterns of soil nematodes and their potential mechanisms. Overall, grazing significantly decreased the abundance of bacterial-feeding (BF) nematodes (-16.54%) and omnivorous-predatory (OP) nematodes (-36.81%), and decreased nematode community diversity indices (Shannon-Weiner index: -4.33%, evenness index: -9.22%, species richness: -5.35%), but had no effect on ecological indices under a global regional scale. The response of soil nematodes to grazing varied by grazing intensity, animals, and duration. Heavy grazing decreased OP nematode abundance, but had no effect on the abundance of other trophic groups, or on diversity or ecological indices. Grazing by small animals had stronger effects than that by large animals and mixed-size animals on BF, fungal-feeding (FF), plant-feeding (PF) and OP nematodes, the Shannon-Wiener index, and the species richness index. The abundance of FF and OP nematodes influenced significantly under short-term grazing. The evenness index decreased significantly under long-term grazing (>10 years). Climate and edaphic factors impacted the effects of grazing on nematode abundance, diversity, and ecological indices. When resources (i.e., rain, heat, and soil nutrients) were abundant, the negative effects of grazing on nematodes were reduced; under sufficiently abundant resources, grazing even had positive effects on soil nematode communities. Thus, the influence of grazing on soil nematode communities is resource-dependent. Our study provides decision makers with grazing strategies based on the resource abundance. Resource-poor areas should have less grazing, while resource-rich areas should have more grazing to conserve soil biodiversity and maintain soil health.

Spatiotemporal patterns and evolutionary trends of eco-environmental quality in arid regions of Northwest China.

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.

Effects of large-scale land consolidation projects on ecological environment quality: A case study of a land creation project in Yan'an, China.

Large land consolidation projects modify the structures and functions of regional ecosystems through the reshaping of the territorial spatial pattern, thereby affecting the ecological environmental quality (EEQ). To investigate the effects of large-scale land consolidation projects on EEQ, this study takes the major land consolidation project of "bulldoze mountains to create land" (BMCL) in Yan'an City as a research object and evaluates the change of EEQ based on Remote Sensing Ecological Index (RSEI). The consolidated area and the control area were set up to comparatively analyze the EEQ change processes and spatial distribution characteristics of these two areas in the full life cycle of BMCL. According to the results, the mean RSEI of the consolidated area was 0.128 lower than that of the control area, and the EEQ of the consolidated area was always lower than that of the control area. BMCL had a strong negative impact on the EEQ grade of the consolidated area, especially in the early stage. However, the positive effect of BMCL on EEQ gradually emerged in the late stage of the large land consolidation project. The overall EEQ grade of the consolidated area has also improved. The results of the stepwise regression analysis indicated that the wetness component and the normalized differential vegetation index played key roles in improving the EEQ of the BMCL. Overall, the local BMCL strongly affected the EEQ of the consolidated area but would not cause the EEQ of the whole region to experience any dramatic, abrupt change in the short term. This study provided references for the evaluation and analysis of the ecological effects of land consolidation at the regional scale, offering a feasible way to evaluate the spatio-temporal change of EEQ in BMCL.

Spatio-temporal evolutionary analysis of surface ecological quality in Pingshuo open-cast mine area, China.

The open-pit mining area is highly affected by human activities, which aggravate soil erosion and disturb surface ecology, bringing many problems and challenges to its environmental management and restoration, which has received widespread attention. The establishment of an objective, timely and quantitative remote sensing monitoring, and evaluation system for the spatio-temporal evolution of the surface ecological environment in the open-pit mining area is of great significance for its environmental protection, management decisions, and sustainable social development. Based on the Google Earth Engine (GEE) platform, this paper uses Landsat images to construct and calculate the remote sensing ecological index (RSEI) of the Pingshuo open-cast mine area (POMA) from 1990 to 2020 and monitor and evaluate its surface ecological environment. Combined with the Theil-Sen median, Mann-Kendall test, and Hurst index, the spatio-temporal process was analyzed. The results showed that the ecological environmental quality of the mining area first decreased and then increased from 1990 to 2020. 1990-2000 was a period of serious ecological degradation, followed by improvement. The overall improvement area reached 87.03%, and the degradation was concentrated in the coal mining area. Between 1990 and 2020, the Hurst index of the mining area was 0.452, indicating that the region has a fragile ecological environment and has difficult maintaining its stability. The global Moran's I mean value of the RSEI of the study area is 0.92, which combined with Moran's scatter plot to indicate that there is a strong positive spatial correlation rather than a random distribution of its ecological environment. During the study period, the impact on the climate of the ecological environmental change of POMA was weak, and human factors such as coal mining, land reclamation, and social construction were the main driving forces for the change in ecological quality. The results of this study reveal the changing trend of surface ecology in the mining area over the past 30 years, which is helpful for understanding its impact mechanism on ecological quality and provides support for the management of the region.

Exploring evolution characteristics of eco-environment quality in the Yangtze River Basin based on remote sensing ecological index.

As an important ecological-economic development area in China, scientific understanding of the spatial and temporal changes in eco-environment quality (EEQ) and its drivers in the Yangtze River Basin (YRB) is crucial for the effective implementation of ecological protection projects in the YRB. To address the lack of large-scale EEQ assessment in the YRB, this paper uses the Google Earth Engine (GEE) platform and the Remote Sensing Ecological Index (RSEI) to investigate the spatial and temporal characteristics of EEQ in the YRB from 2000 to 2020, and to analyze the impact of various factors on the EEQ of the YRB. This study showed that: (1) The overall EEQ of YRB was at the 'good' grade over the past 20 years, showing an increasing trend, with the value changing from 0.70 to 0.77. (2) The YRB's EEQ has positive spatial aggregation characteristics, with the northern part of the Jialing River basin and the Han River basin exhibiting a high-high aggregation type and the upper reaches exhibiting a low-low aggregation type. (3) In the past 20 years, the human activities had a greater impact on the EEQ of the YRB; moreover, all factors had a greater impact on the EEQ than a single factor. The interaction between the biological abundance index and population density had the most effect, with a q-value of 0.737 in 2020.

Construction of ecological security pattern in Ningbo based on remote sensing ecological index and graph theory knowledge.

Constructing ecological security pattern and identifying ecological important areas are the focus of current research on regional ecological security. With Ningbo City as a case study area, we identified ecological sources by remote sensing ecological index, the ecological corridors and pinch point by circuit theory model, and the minimum spanning tree and cuts by graph theory algorithm. The results showed that there were 203 ecological sources in Ningbo, and that the main type of land cover was forest, including a small amount of paddy fields and flooded vegetation. There were 368 ecological corridors with a total length of 573.42 km, being dense in the southwest and sparse in the northeast. There were 91 ecological pinch points, which mainly distributed between coastal areas and closely related ecological sources. According to current situation, we put forward the optimization strategy with 187 primary corridors, 181 secondary corridors, 50 ecological restoration priority areas and 59 long-term ecological restoration areas. The optimization strategy combined with graph theory and circuit theory model would provide a refe-rence for the constructing of ecological security pattern.

An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer.

BACKGROUND AND OBJECTIVE: In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME. MATERIALS AND METHODS: In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS). RESULTS: The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001). CONCLUSION: This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.

Monitoring the ecological restoration effect of land reclamation in open-pit coal mining areas: An exploration of a fusion method based on ZhuHai-1 and Landsat 8 data.

Land reclamation is a long-term, dynamic process; postreclamation monitoring and management are particularly important, and the use of remote sensing technology is a good way to conduct ecological quality monitoring and evaluations. In this study, we fused ZhuHai-1 and Landsat 8 data; selected the best band combinations to calculate ecological quality indicators such as the inverted red-edge chlorophyll index, modified soil moisture monitoring index, normalized difference built-up and soil index and land surface temperature; and constructed the fusion remote sensing ecological index to monitor the ecological restoration effect of the reclaimed area in Pingshuo, China. The results showed that the inverted red-edge chlorophyll index and modified soil moisture monitoring index had positive contributions, the normalized difference built-up and soil index had a low impact on the ecological quality of the study area, and the land surface temperature had a negative effect on ecological quality. The environment of the reclaimed area was better than that of the surrounding areas where these areas were affected by mining. The mean value of the fusion remote sensing ecological index showed a trend of "rising first, then falling" with increasing reclamation time. The ecological quality of the reclaimed area was best in areas with 20-22 years of reclamation time. The ecological condition of the area has been declining for 25 years or more of reclamation, so it is suitable to apply artificial intervention to ensure good ecological quality. The use of remote sensing technology for monitoring the effects of ecological restoration can provide a reference basis for the targeted and accurate implementation of land reclamation management measures.

[Spatiotemporal Pattern and Driving Force Analysis of Ecological Environmental Quality in Typical Ecological Areas of the Yellow River Basin from 1990 to 2020].

Scientific evaluation of ecological environmental quality is the premise of realizing regional ecological sustainable development. Taking Landsat series satellite images from 1990 to 2020 as the data source, on the basis of the entropy remote sensing ecological index (E-RSEI), combining the Mann-Kendall significance test, Theil-Sen Median analysis, Hurst exponent, and stability analysis, the spatial-temporal variation characteristics of ecological environmental quality in typical ecological areas of the Yellow River Basin were analyzed in the context of multi-spatiotemporal scales. In addition, the effects of eight environmental and human factors on the change in E-RSEI were quantified using a geodetector. The results showed that:① in the past 31 years, the average value of E-RSEI was 67.5%, which showed an increasing trend on the time scale, with an average increase of 0.066·(10 a)-1. On the spatial scale, E-RSEI was higher in the west and the south lower in the east and the north. ② The ecological environmental quality will continue to improve in the future, but 9.33% of the areas have potential risks of degradation. ③ Precipitation was the dominant environmental factor that affected the spatial distribution of E-RSEI in this area, and the influence of human factors was low. Compared with that of single factors, the interaction of factors had a stronger impact on ecological environmental quality, and the interaction between precipitation and other factors played a leading role. The results of this study can provide a scientific reference for the sustainable development of ecological environmental quality in the ecological zone of the Yellow River Basin.

Analysis of the implementation effects of ecological restoration projects based on carbon storage and eco-environmental quality: A case study of the Yellow River Delta, China.

As an important means to address global climate change and land-use/land-cover (LULC) change, ecological restoration projects (ERPs) have a large effect on carbon storage functions and eco-environmental quality. However, the various ERPs carried out in the Yellow River Delta region have important implications for ecological security strategies in China. Therefore, based on land-use data and remote sensing image data, with the help of ArcGIS and Google Earth Engine (GEE) platforms, this study uses the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, an improved remote sensing ecological index (RSEI) model and other methods to deeply examine the evolutionary trends of eco-environmental quality and carbon storage during the implementation of ERPs in the Yellow River Delta and selects key implementation areas for in-depth analysis to determine the implementation effects of ERPs. Our findings suggested that the RSEI and carbon storage levels in the study area had opposite evolutionary trends from 2001 to 2020. Among them, the RSEI showed a fluctuating upwards trend (0.4461 (2001) and 0.5185 (2020)), while the total carbon stock showed a fluctuating downwards trend (30.67 Tg (2001) and 26.40 Tg (2020)). However, from 2015 to 2020, the RSEI and carbon storage were at a relatively stable level, which indirectly indicated that the ERPs carried out during the period from 2015 to 2020 had achieved a good comprehensive implementation effect. In addition, the areas with better improvement effects from 2015 to 2020 were primarily located in the mouth of the Yellow River Delta (Areas C and D), and their RSEI and the total carbon stock showed a certain upwards trend. This research can promote the formulation of the management strategy of ERPs in the Yellow River Delta, which is of tremendous importance to the ecological environmental preservation and high-quality development of the Yellow River Basin.

[Dynamic Monitoring and Driving Factors Analysis of Ecological Environment Quality in Poyang Lake Basin].

The ecological environment of Poyang Lake basin is an important part of the construction of ecological civilizations in the south of China. Based on the Landsat satellite remote sensing images, using the principal component analysis (PCA) method to construct the remote sensing ecological index (RSEI) as an evaluation index of ecological environment quality, introducing the Geodetector model to quantitatively detect the explanatory power of different influencing factors on the spatial divergence of the ecological environment, and exploring the changes in ecological environment quality in the Poyang Lake basin from 1990 to 2020 and the impact of different driving factors. The results of the study showed that there were obvious regional differences in the ecological environment quality in the basin. The areas with bad and poor ecological quality were mainly distributed in the central and northern plains; the areas with high and good quality grades were mainly distributed in the hilly and mountainous region of the southwestern part of the basin; the overall ecological environment of the Poyang Lake basin has been improving over the past 30 years; and the improved areas were mainly distributed in low-altitude areas. Geodetector results showed that population density was the factor with the highest explanatory power for the spatial divergence of ecological environment quality in the Poyang Lake basin. Among different natural factors, topographic factors (slope, aspect) had a higher driving force than meteorological factors (temperature, precipitation). The night light index factor showed an increasing yearly trend, indicating that the ecological environment quality of the Poyang Lake basin was gradually increased due to the influence of urbanization development. The construction of the RSEI model based on Google Earth Engine could not only effectively ensure the accuracy of ecological environment quality evaluation in different years but could also quickly realize image preprocessing and index calculations, which greatly improved the efficiency of ecological environment evaluation. These research results can provide a theoretical basis and scientific data support for the ecological environment protection work in the Poyang Lake basin.

Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains' Northern Slopes, Xinjiang, China.

Accurately capturing the changing patterns of ecological quality in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) and researching its significant impacts responds to the requirements of high-quality sustainable urban development. In this study, the spatial and temporal distribution patterns of remote sensing ecological index (RSEI) were obtained by normalization and PCA transformation of four basic indicators based on Landsat images. It then employed geographic detectors to analyze the factors that influence ecological change. The result demonstrates that: (1) In the distribution of land use conversions and degrees of human disturbance, built-up land, principally urban land, and agricultural land, represented by dry land, are rising, while the shrinkage of grassland is the most substantial. The degree of human disturbance is increasing overall for glaciers. (2) The overall ecological environment of the northern slopes of Tianshan is relatively poor. Temporally, the ecological quality changes and fluctuates, with an overall rising trend. Spatially, ecological quality is low in the north and south and high in the center, with high values concentrated in the mountains and agriculture and low values in the Gobi and desert. However, on a large scale, the ecological quality of the Urumqi-Changji-Shihezi metropolitan area has worsened dramatically compared to other regions. (3) Driving factor detection showed that LST and NDVI were the most critical influencing factors, with an upward trend in the influence of WET. Typically, LST has the biggest influence on RSEI when interacting with NDVI. In terms of the broader region, the influence of social factors is smaller, but the role of human interference in the built-up area of the oasis city can be found to be more significant at large scales. The study shows that it is necessary to strengthen ecological conservation efforts in the UANSTM region, focusing on the impact of urban and agricultural land expansion on surface temperature and vegetation.

A temporospatial assessment of environmental quality in urbanizing Ethiopia.

Global environmental quality has been negatively affected by urbanization, particularly vulnerable in the Sub-Saharan Africa. Therefore, understanding the underlying mechanism and driving forces for the change of environmental quality with urbanization process is essential to improve the environmental sustainability. In this study, the compounded night light index (CNLI) and remote sensing ecological index (RSEI) were used respectively to evaluate the urbanization level and environmental quality in Ethiopia from 2010 to 2020. On this basis, a temporospatial assessment framework was proposed, followed by methods of coupling coordination degree, spatial autocorrelation, elasticity, and decomposition. The results showed that 63 out of 690 woredas experienced environmental deterioration. Socioeconomic effect, carbon intensity, and climate change were decomposed as drivers to environmental quality, with socioeconomic effects contributing >68% of environmental improvement, while carbon intensity and climate change were responsible for >51% and >58% of environmental deterioration from 2010 values. Continuous increase in impervious surfaces resulted in a six-fold increase in surface runoff, which raised the flooding risk in sub areas and rural landscapes. This demands reforms of climate strategies and proper livestock management.

Study on the Correlation between Life Expectancy and the Ecological Environment around the Cities along the Belt and Road.

The impact of building the Belt and Road on the ecological environment and the health of the related cities along this belt deserves more attention. Currently, there are few relevant pieces of research in this area, and the problem of a time lag between the ecological environment and health (e.g., life expectancy, LE) has not been explored. This paper investigates the aforementioned problem based on five ecological indicators, i.e., normalized difference vegetation index, leaf area index, gross primary production (GPP), land surface temperature (LST), and wet, which were obtained from MODIS satellite remote-sensing products in 2010, 2015, and 2020. The research steps are as follows: firstly, a comprehensive ecological index (CEI) of the areas along the Belt and Road was calculated based on the principle of component analysis; secondly, the changes in the trends of the five ecological indicators and the CEI in the research area in the past 11 years were calculated by using the trend degree analysis method; then, the distributions of the cold and hot spots of each index in the research area were calculated via cold and hot spot analysis; finally, the time lag relationship between LE and the ecological environment was explored by using the proposed spatiotemporal lag spatial crosscorrelation analysis. The experimental results show that ① there is a positive correlation between LE and ecological environment quality in the study area; ② the ecological environment has a lagging impact on LE, and the impact of ecological indicators in 2010 on LE in 2020 is greater than that in 2015; ③ among the ecological indicators, GPP has the highest impact on LE, while LST and Wet have a negative correlation with LE.