Shanxi Province holds an important strategic position in the overall ecological pattern of the Yellow River Basin. To investigate the changes of the ecological environment in the Shanxi section of the Yellow River Basin from 2000 to 2020, we selected MODIS remote sensing image data to determine the remote sensing ecological index (RSEI) based on the principal component analysis of greenness, humidity, dryness, and heat. Then, we analyzed the spatial and temporal variations of ecological quality in this region to explore the influencing factors. We further used the CA-Markov model to simulate and predict the ecological environment under different development scenarios in the Shanxi section of the Yellow River Basin in 2030. The results showed that RSEI had good applicability in the Shanxi section of the Yellow River Basin which could be used to monitor and evaluate the spatiotemporal variations in its ecological environment. From 2000 to 2020, the Shanxi section of the Yellow River Basin was dominated by low quality habitat areas, in which the ecological environment quality continued to improve from 2000 to 2010 and decreased from 2010 to 2020. The high quality habitat areas mainly located on the mountainous areas with superior natural conditions and rich biodiversity, while the low ecological quality areas were mainly in the Taiyuan Basin and the northern part of the study area, where the mining industry developed well. Climate factors were negatively correlated with ecological environment quality in the northern and central parts of the study area, and positively correlated with that in the mountainous area. Under all three development scenarios, the area of cultivated land, forest, water and construction land increased in 2030 compared to that in 2020. Compared to the natural development scenario and the cultivated land protection scenario, the ecological constraint scenario with RSEI as the limiting factor had the highest area of new forest and the lowest expansion rate of cultivated land and construction land. The results would provide a reference for land space planning and ecological environment protection in the Shanxi section of the Yellow River Basin.
山西省在黄河流域总体生态格局中具有重要的战略地位。为深入研究2000—2020年黄河流域山西段生态环境的变化,选用MODIS遥感影像数据,基于绿度、湿度、干度和热度的主成分分析确定遥感生态指数(RSEI),对该区域生态环境质量的时空变化进行分析并探讨影响因素;同时,利用CA-Markov模型对2030年黄河流域山西段不同发展情景下生态环境进行模拟和预测。结果表明: RSEI在黄河流域山西段具有较好的适用性,可用于监测和评估其生态环境的时空变化特征。2000—2020年,黄河流域山西段以低生境质量区为主,其中,2000—2010年生态环境质量持续改善,而2010—2020年则有所退化;高生境质量区集中于山区,其自然条件优越、生物多样性丰富,低生态质量区主要分布在城市群集中的太原盆地及研究区北部采矿业发达的地区;在研究区的北部和中部,气候因子与生态环境质量呈负相关关系,而在高山区域二者呈正相关关系。3种发展情景下,2030年研究区的耕地、林地、水体和建设用地面积均较2020年有所增加;相较于自然发展情景和耕地保护情景,在以RSEI为限制因子的生态约束情景中,新增林地面积最多,而耕地和建设用地的扩张速率最低。研究结果可为黄河流域山西段的国土空间规划及生态环境保护提供参考。.

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.

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.

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.

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.

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.
构建生态安全格局、识别生态重要区域是目前区域生态安全研究的重点。本研究以宁波市为例,利用遥感生态指数识别生态源地,利用电路理论模型识别生态廊道和生态夹点,利用图论算法识别最小生成树和割边。结果表明: 宁波市共有生态源地203处,土地覆被类型以林地为主,也包括少量的水田和水淹植被;生态廊道共368条,总长度为573.42 km,整体呈西南密、东北疏的趋势;生态夹点91处,夹点主要分布在海涂区域和联系紧密的生态源地之间。根据现状提出优化策略,确定一级廊道187条,二级廊道181条,生态修复优先区50处,远期生态修复区 59处。结合图论与电路理论模型的优化策略为生态安全格局的构建提供了科学依据。.

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.

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.

Urban development in developing regions increases ecological and environmental pressures. Few annual ecological studies have been conducted on tourist-oriented cities. Guilin is famous as an international tourist destination in Chine. Analyzing its coupling coordination between urbanization and ecology is vital for subsequent sustainable development. This paper constructed a night-time light index (NTLI) based on DMSP/OLS, NPP/VIIRS night-time light data in response to these problems. The remote sensing ecological index (RSEI) model was established in this study by using four indexes: greenness, wetness, dryness and heat. The coupling coordination degree model (CCDM) was built. From the dynamic time-series changes of CCDM, the urban development and ecological environment of the urban area of Guilin, from 2000 to 2020, were analyzed. The results showed that the urban area of Guilin\'s urbanization had developed rapidly over the past 20 years. NTLI in 2020 was 7.72 times higher than in 2000. The overall ecological quality of the main urban area of Guilin has improved significantly, while local ecological pressure in Lingui District has increased. CCDM has shifted from low to high coupling coordination, and the relationship between urban development and the ecological environment has improved. The method of annual spatial-temporal analysis of urban ecology in this paper can be applied in similar studies on other cities, and the results obtained for Guilin have reference value for future urban planning and environmental protection work.

Landsat remote sensing images obtained from 2000, 2005, 2010, 2015, and 2020 were analyzed. The normalized vegetation index (NDVI), moisture index (WET), land surface temperature (LST), and normalized building-soil index (NDBSI) were extracted based on the four aspects of greenness, humidity, heat, and dryness. The Remote Sensing Ecological Index (RSEI) was calculated using principal component analysis to quantitatively analyze and dynamically monitor and evaluate the ecological environment changes in the Kuye River Basin over the past 20 years. From the perspective of spatial and temporal distribution, the ecological and environmental quality of Kuye River Basin had a downward trend from 2000 to 2020. The overall RSEI grade was medium or poor, and the average RSEI decreased. The proportion of excellent and good grade watershed areas decreased, whereas that of medium, low, and poor grade watershed areas increased over the study period. Spatially, RSEI decreased gradually from southeast to northwest. The degraded areas were mainly distributed in urban areas with frequent human activities. Conversely, the superior eco-environmental quality areas were mainly distributed in eastern sections of the watershed. Compared with 2000, the eco-environmental quality of the Yulin urban area and Shenmu County in the southern section of the watershed are worsening.