Remote Sensing Ecological Index Research Articles

Analyzing the Spatiotemporal Dynamics and Driving Forces of Ecological Environment Quality in the Qinling Mountains, China

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

Spatial–temporal patterns of ecological changes in the Dongting Lake region and their responses to climate factors and human activities

ABSTRACT The Dongting Lake region is an important ecological and environmental protection zone in the Yangtze River Economic Belt of China. Long-term monitoring and evaluation of its ecological environment is of great significance. In this study, the spatio-temporal pattern of ecological condition change and its response to climate factors and human activities in the Dongting Lake region was quantitatively analyzed using RSEI (Remote Sensing Ecological Index) model. The primary conclusions are as follows: (1) The temporal pattern of ecological changes has a small fluctuation period (1990–2007) and a continuous improvement period (2007–2020) with an ecological improvement rate of 1.17% per year. (2) The spatial pattern of ecological changes is directly tied to the land use types in this area. 17.36% of the regions with an excellent ecological level are primarily woodland, whereas 4.45% of the areas with a low ecological level are mostly urban. (3) The deterioration in ecological conditions in urban areas is primarily attributable to human activities, whereas the decline in ecological conditions in surrounding mountainous regions and central agricultural regions is primarily attributable to climate factors. Our research provides valuable support for ecological environment monitoring and management strategy formulation in the Dongting Lake region.

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.

Remote Sensing Ecological Quality and Its Response to the Rocky Desertification in the World Heritage Karst Sites

Clarifying the spatial and temporal evolution characteristics of the ecological environment quality of World Heritage Karst Sites (WHKSs) and its response to different rocky desertification grades at spatial scales is crucial for the monitoring and protection of WHKSs as well as the implementation of ecological and environmental policies in karst regions. The ecological evaluation model of Remote Sensing Ecological Index (RSEI) was used to evaluate the ecological environment of Libo–Huanjiang World Heritage Karst site and Shibing World Heritage Karst site, and then the spatial autocorrelation and geo-detection model was used to further analyze the ecological environment, and final spatial overlay of RSEI and rocky desertification by year to analyze the linkage relationship between RSEI and rocky desertification. The results showed that (1) in the three-phase ecological environmental quality evaluation of the two heritage sites, the RSEI in 2010, 2016, and 2022 reached 0.60, 0.67, and 0.64 for the Libo–Huanjiang heritage site, and RSEI in 2010, 2016, and 2022 for the Shibing heritage site reached 0.60, 0.74, and 0.70, respectively; (2) the RSEI of both heritage sites show a gradually increasing positive spatial correlation, and has significant spatial aggregation characteristics, with both heritage sites dominated by the high-high and low-low spatial aggregation categories; (3) both heritage sites have the highest degree of explanation of changes in ecological quality by the NDBSI factor, indicating that this factor plays a key role in changes in ecological quality at heritage sites; (4) the response of the RSEI mean value of Libo–Huanjiang in each grade of rocky desertification area is, from high to low, no rocky desertification, non-karst, potential rocky desertification, mild rocky desertification, moderate rocky desertification, intensive rocky desertification, and extreme intensity rocky desertification, and the response of the RSEI mean value of Shibing is, from high to low, non-karst, no rocky desertification, potential rocky desertification, mild rocky desertification, and moderate rocky desertification. The spatial superposition analysis of the RSEI index and rocky desertification index can quantitatively study the changing status of the ecological environment in different rocky desertification areas, and the results of the study can provide theoretical references for the environmental monitoring and the prevention and control of rocky desertification in the karst areas and WHKSs.

Long-Term Dynamic Monitoring and Driving Force Analysis of Eco-Environmental Quality in China

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors.

Landsat series images for evaluating ecological restoration effect from multi-time scale based on an ideal reference

Multi-time scale assessment of ecological restoration effects based on objective and scientific approaches can provide crucial information for implementing environmental protection policies and ensuring sustainable regional development. This study evaluated the effect of ecological restoration based on a natural evolution as a reference frame, using yearly Landsat time series. Southern Ningxia in China was selected as the study area. The remote sensing ecological index (RSEI) was calculated. The features of natural evolution were derived from the time series of the RSEI in the natural reserve areas (NRAs). LandTrendr was employed to characterize the disturbance–recovery processes. Furthermore, we adopted the dynamic time-warping method for the entire study period, along with the relative variation ratio (during the disturbance–recovery cycle) to capture the long-term and short-term ecological restoration effects, respectively. The following conclusions were drawn: First, a time-series RSEI based on LandTrendr was used to successfully monitor disturbance–recovery processes. Second, the majority of RSEI disturbances (i.e., >60%) occurred between 2000 and 2005. It is characterized by fewer disturbance times and obvious spatial heterogeneity in disturbance duration. Notably, from 2000 to 2022, the RSEI improved. Additionally, approximately 40% of the study area portrayed a strong similarity to the RSEI of the NRAs. We conclude that quantifying the ecological restoration effect at multi-time scales is a practical operational approach for policymakers and environmental protection. Our study presents novel insights for assessing regional ecological quality, by capturing the processes of natural evolution features in NRAs.

Ecological Vulnerability Assessment and Spatiotemporal Characteristics Analysis of Urban Green-Space Systems in Beijing–Tianjin–Hebei Region

The evaluation and analysis of the ecological vulnerability of urban green-space systems are conducive to the sustainable development of urban green-space systems. Taking the urban green-space system in the Beijing–Tianjin–Hebei region in 2010, 2015, and 2020 as the research object, an ESSR model was first constructed, and a total of ten types of impact factors were integrated into the four dimensions of “Exposure, Sensitivity, State and Response”. The weight of the impact factors was objectively determined via spatial principal component analysis, and the ecological vulnerability of the urban green-space system was evaluated via superposition analysis; the evaluation’s results were graded. Moreover, the transfer matrix, center-of-gravity migration model, standard deviation ellipse, and spatial autocorrelation analysis were used to study the temporal and spatial variation characteristics of the evaluation results; then, the driving force of impact factors was analyzed based on a geographical detector. Finally, the rationality of the evaluation results was verified using the changing trend of the remote sensing ecological index (RSEI). The results show that the ecological vulnerability of the urban green-space system in the Beijing–Tianjin–Hebei region has decreased year by year for the past ten years. The distribution law of “Hebei surrounds Beijing and Tianjin” is presented in this space, and 2015 is the mutation node. In 2010, the moderately and severely vulnerable areas of Hebei surrounded the mildly vulnerability areas of Beijing and Tianjin. Moreover, in 2015 and 2020, the mildly vulnerable areas of Hebei surrounded the severely vulnerable areas of Beijing and Tianjin. Vulnerability expands slightly in the east–west direction and shrinks slightly in the north–south direction, and the center of gravity shifts towards Beijing year by year along the northeast direction. Moreover, the spatial distribution of vulnerability shows significant positive spatial autocorrelations and exhibits very obvious agglomeration. In addition, vulnerability is the result of the combined effect of various factors, and education degree, human disturbance index, and annual average precipitation are the dominant factors. The analysis results provide a reference for the effective application and sustainable development of urban green-space ecological functions.

[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.

Spatiotemporal Changes and Driving Analysis of Ecological Environmental Quality along the Qinghai–Tibet Railway Using Google Earth Engine—A Case Study Covering Xining to Jianghe Stations

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

Spatiotemporal evolution and multi-scale coupling effects of land-use carbon emissions and ecological environmental quality

The coupling relationship between land-use carbon emissions (LCE) and ecological environmental quality (EEQ) is critical for regional sustainable development. Rapid urbanization promotes a notable increase in LCE, which imparts significant stress on EEQ. This study used land use and cover change (LUCC) and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) data from the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) to evaluate LCE, applied a remote sensing ecological index (RSEI) model to calculate EEQ, and combined gravity and centroid movement trajectory models to analyze the spatiotemporal evolution characteristics of LCE and EEQ. Four-quadrant and coupling degree (CD) models were used to analyze the synergistic relationship and interaction intensity between LCE and EEQ based on three different scales of pixels, counties, and cities. The results show that: (1) LCE and EEQ exhibit clear spatial inequality distribution, and the total amount of LCE increased from 40.16 Mt. in 2000 to 131.99 Mt. in 2020; however, LCE has not yet reached peak carbon emissions. (2) From 2000 to 2020, cities with a strong correlation between LCE and EEQ showed an increasing trend, and the centroid of LCE moved sharply to Jiangxi during 2000–2005 and 2005–2010. (3) High-CD areas were primarily located in quadrant II, and low-CD areas in quadrant IV. The relationship between LCE and EEQ has improved over the past 21 years, and CD has been increasing. (4) The stability of the coupling results between LCE and EEQ was affected by different research scales; the larger the research scale is, the greater the change in the results. This study provides a scientific basis and practical scheme for LCE reduction, ecological environmental management, and regional sustainable development in the UAMRYR.

A remote sensing based index for assessing long-term ecological impact in arid mined land

Large-scale mining operations in western China would accelerate the fragility and degradation to arid ecological environments. Remotely sensed monitoring and assessing of mining effects in long term change could provide sound understanding to guide mine ecological restoration and local ecosystem sustainability. This study's primary goal is to develop an arid mined land status (AMLS) index based on remote sensing data to assess ecological effects in mining areas of arid land. Furthermore, the performance of AMLS was contrasted with that of the remote sensing ecological index (RSEI) and the land surface ecological status composition index (LSESCI) in arid mines under various landscapes conditions. Eventually, the Sen + Mann–Kendall method was used to analyze the temporal and spatial land status variations in the mining effect between 2005 and 2020 based on Landsat time-series images. The following was revealed by the results: (1) AMLS had high correlation (r > 0.65) with each factor, including bare soil flatness, dryness, land surface temperature, and slope. (2) Comparing with LSESCI and RSEI, the proposed AMLS could characterize the mining area and surrounding natural bare soil and rock with different ecological status levels in three mines, hence indicated the mining directional heterogeneity and distance attenuation of ecological status. (3) Based on time series Landsat data, the AMLS demonstrated long-term ecological variability from 2005 to 2020 due to mining and reclamation in three mines.

An Ecological Quality Evaluation of Large-Scale Farms Based on an Improved Remote Sensing Ecological Index

The ecological quality of large-scale farms is a critical determinant of crop growth. In this paper, an ecological assessment procedure suitable for agricultural regions should be developed based on an improved remote sensing ecological index (IRSEI), which introduces an integrated salinity index (ISI) tailored to the salinized soil characteristics in farming areas and incorporates ecological indices such as the greenness index (NDVI), the humidity index (WET), the dryness index (NDBSI), and the heat index (LST). The results indicate that between 2013 and 2022, the mean IRSEI increasing from 0.500 in 2013 to 0.826 in 2020 before decreasing to 0.646 in 2022. From 2013 to 2022, the area of the farm that experienced slight to significant improvements in ecological quality reached 1419.91 km2, accounting for 71.94% of the total farm area. An analysis of different land cover types revealed that the IRSEI performed more reliably than did the original RSEI method. Correlation analysis based on crop yields showed that the IRSEI method was more strongly correlated with yield than was the RSEI method. Therefore, the proposed IRSEI method offers a rapid and effective new means of monitoring ecological quality for agricultural planting areas characterized by soil salinization, and it is more effective than the traditional RSEI method.

Analysis of Changes in Ecological Environment Quality and Influencing Factors in Chongqing Based on a Remote-Sensing Ecological Index Mode

Chongqing is a large municipality in southwestern China, having the characteristics of a vast jurisdiction, complex topography, and a prominent dual urban–rural structure. It is vitally important to optimize the spatial layout of land and efficiency of natural resource allocation, achieve sustainable development, and conduct influence assessment and causation analysis in this region. Here, using the Google Earth Engine platform, we selected Landsat remote-sensing (RS) images from the period 2000–2020 and constructed a remote-sensing ecological index (RSEI) model. Considering the urban spatial pattern division in Chongqing, the Sen + Mann–Kendall analytical approach was employed to assess the fluctuating quality of the ecological environment in different sectors of Chongqing. Subsequently, single-factor and interaction detectors in the Geodetector software tool were used to conduct causation analysis on the RSEI, with the use of eight elements: elevation, slope, aspect, precipitation, temperature, population, land use, and nighttime lighting. Findings indicate that, over the course of the investigation period, the eco-quality in Chongqing displayed a pattern of degradation, succeeded by amelioration. The RSEI decreased from 0.700 in 2000 to 0.590 in 2007, and then gradually recovered to 0.716 in 2018. Overall, the eco-environment quality of Chongqing improved. Spatially, changes in the RSEI were consistent with the planning and positioning of the urban spatial pattern. The main new urban area and periphery of the central urban area showed a slight deterioration, while other regions showed marked improvement. The combined effect of any two elements enhanced the explanatory power of a single factor, with elevation, temperature, and land use being the strongest explanatory elements of eco-quality in Chongqing. The most influential factor explaining the spatial variation of the RSEI was determined to be the combined impact of elevation and land use. At the temporal scale, elements related to human activities showed the most evident trend in explanatory power.

Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China

The environmental quality of a mining city has a direct impact on regional sustainable development and has become a key indicator for assessing the effectiveness of national environmental policies. However, against the backdrop of accelerated urbanization, increased demand for resource development, and the promotion of the concept of ecological civilization, mining cities are faced with the major challenge of balancing economic development and ecological environmental protection. This study aims to deeply investigate the spatial and temporal variations of environmental quality and its driving mechanisms of mineral resource-based cities. This study utilizes the wide coverage and multitemporal capabilities of MODIS optical and thermal infrared remote sensing data. It innovatively develops the remote sensing ecological index (RSEI) algorithm on the PIE-Engine cloud platform to quickly obtain the RSEI, which reflects the quality of the ecological environment. The spatial and temporal evolution characteristics of the environmental quality in seven typical mining cities in China from 2001 to 2022 were analyzed. Combined with the vector mine surface data, the spatial and temporal variability of the impacts of mining activities on the ecological environment were quantitatively separated and explored. In particular, the characteristics of mining cities were taken into account by creating buffer zones and zoning statistics to analyze the response relationship between RSEI and these factors, including the distance to the mining area and the percentage of the mining area. In addition, the drivers and impacts of RSEI in 2019 were analyzed through Pearson correlation coefficients pixel by pixel with 10 factors, including natural, economic, and mining. Regression modeling of RSEI in 2019 was performed using the random forest (RF) model, and these drivers were ranked in order of importance through random forest factor importance assessment. The results showed that (1) the ecological quality of mining cities changed significantly during the study period, and the negative impacts of mining activities on the ecological environment were significant. (2) The areas with low RSEI values were closely related to the mining areas and cities. (3) The RSEI in the mining areas of mining cities was generally lower than the average level of the cities. The RSEI gradually increased as the distance to the mine site increased. (4) The increase in the size of the mine area initially exacerbates the impact on the ecological environment, but the impact is weakened beyond a certain threshold. (5) The distance to the mining area is the most important factor affecting the quality of the ecological environment, followed by DEM, GDP, and precipitation. This study is of great importance for advancing sustainable development in mining cities and formulating sustainable strategies.

Spatio-Temporal Heterogeneity of the Ecological Environment and Its Response to Land Use Change in the Chushandian Reservoir Basin

Conducting ecological monitoring assessments and revealing the effects of driving factors are crucial for enhancing ecological safety and promoting sustainable development. Taking the Chushandian Reservoir basin as the research object, this paper employed the Remote Sensing Ecological Index (RSEI), constructed based on remote sensing data, to monitor and assess the ecological environment of the study area from 1990 to 2021, and predicted its future development trend through the Hurst index. On this basis, we integrated land use data to elucidate the response of the ecological environment to human activities. The results show that: (1) The mutation test indicates that selecting 1990, 2004, 2008, 2013, and 2021 as the study time nodes can comprehensively reflect the spatio-temporal information regarding changes in ecological quality in the study area. Specifically, both 1990 and 2021 exhibit higher ecological quality ratings, while 2008 has the lowest ecological quality rating. The spatial distribution of ecological quality is strongly clustered, with high–high clustering and low–low clustering dominating. (2) The overall trend of ecological quality in the study area appears in a pattern of initial decline followed by subsequent improvement. From 1990 to 2004, the degraded area constituted the largest proportion, accounting for 87.82%. After 2008, the quality of the ecological environment began to rebound. Between 2008 and 2013, the proportion of regions with improved ecological conditions was 57.91%, and from 2013 to 2021, 46.74% of the regions showed improvement. (3) In the research area, 36.70% of the regions exhibit a trend of sustainable stability into the future, representing the highest proportion. Approximately 34.3% of the areas demonstrate a trend of sustainable improvement, while the regions exhibiting sustainable degradation account for only 5.72%. While the ecological environment is demonstrating a positive overall developmental trend, it is crucial to stay vigilant regarding areas of ongoing degradation and implement appropriate protective measures. (4) Land use change significantly impacts the ecological environment, with the expansion of land for urban build up causing some ecological deterioration, while the later expansion of forest improves ecological quality. The results provide theoretical approaches and a foundation for decision-making in the ecological management of the Chushandian Reservoir basin.

Space-time cube uncovers spatiotemporal patterns of basin ecological quality and their relationship with water eutrophication

Maintaining an optimal eco-environment is important for sustainable regional development. However, existing methods are inadequate for examining both spatial and temporal dimensions. Here, we propose a systematic procedure for spatiotemporal examination of the eco-environment using the space-time cube (STC) model and describe a preliminary investigation of the coupling relationships between basin ecological quality and water eutrophication in upstream of the Han River basin between 2000 and 2020. The STC model considers the temporal dimension as the third dimension in calculations. We first categorized the basin into three sub-watershed types: forest, cultivated land, and artificial surface. Subsequently, the ecological quality and driving factors were assessed and identified using the remote sensing ecological index (RSEI) and Geodetector method, respectively. The findings indicated that the forest basin and artificial surface basin had the highest and lowest ecological quality, respectively. The spatiotemporal cold spots of ecological quality during the past 20 years were mostly located in the vicinity of reservoirs, rivers, and artificial surface areas. Human activity, precipitation, and the percentage of cultivated land were other important driving factors in the artificial surface, forest, and cultivated land sub-watersheds, respectively, in addition to the dominant factors of elevation and temperature. The results also indicated that when the ecological quality degraded to a certain extent, water eutrophication was significantly coupled with the ecological quality of the catchments. The findings of this study are useful for ecological restoration and sustainable river basin development.

Highway Ecological Environmental Assessment Based on Modified Remote Sensing Index—Taking the Lhasa–Nyingchi Motorway as an Example

The Lhasa to Nyingchi Expressway in Xizang made efforts to protect the ecological environment during its construction, but it still caused varying degrees of damage to the fragile ecosystems along the route. Accurately assessing the process of change in the ecological environment quality in this region holds significant research value. This study selected the Linzhi-to-Gongbo’gyamda section of the Lhasa-to-Nyingchi Expressway as the research area. Firstly, based on the remote sensing ecological index (RSEI), this study constructed an ecological environmental quality evaluation system for the Xizang region. Subsequently, using the Google Earth Engine (GEE) platform, sub-indicators were extracted, and the combination weighting method of game theory was employed to determine indicator weights. This process resulted in the calculation of the MRSEI for the study area from 2012 to 2020. Finally, by utilizing the spatial distribution of the MRSEI, monitoring the level of MRSEI changes, and employing the transition matrix, this study analyzed the changing trend of the ecological environmental quality from 2012 to 2020. The results indicate that the MRSEI are 0.5885, 0.5951, 0.5296, 0.6202, 0.59, 0.5777, 0.5898, 0.5703, and 0.5987, showing a gradual increasing trend with an initial decrease followed by an ascent. This trend is mainly attributed to concentrated road construction and subsequent ecological restoration, leading to an improvement in the restoration effect. Simultaneously, the ecological environmental quality remains relatively stable, with 69.5% of the region showing no change, and the remaining 30.5% experiencing improvement exceeding degradation. Specifically, there were significant improvements in the land with ecological quality levels categorized as poor, fair, moderate, and good. The types of degradation primarily involved lands originally classified as excellent and good degrading to good and moderate levels, respectively. The above results serve as a theoretical reference for the ecological restoration project of the Lhasa-to-Nyingchi Expressway.

Assessment of optimal seasonal selection for RSEI construction: a case study of ecological environment quality assessment in the Beijing-Tianjin-Hebei region from 2001 to 2020

Timely and objective assessment of the optimal season for the construction of remote sensing ecological index (RSEI) is of great significance for accurate and effective assessment of ecological environment quality. We manipulated RSEI in to monitor seasonal variations in ecological environment quality (EEQ) in the Beijing-Tianjin-Hebei (JJJ) region from 2001 to 2020. First, we evaluated image quality across all four seasons and filled in missing observations through liner interpolation. Second, Seasonal RSEI was constructed using MODIS and compared across different years. Third, temporal and spatial variations within the same seasons in EEQ. Additionally, Moran’s I was utilized to evaluate spatial autocorrelation of EEQ, and the stability of the correlation between RSEI and four indicators seasonal indicators was compared. The results showed that: 1) the PC1 component concentrates most of the characteristics of the four indicators, especially in summer (over 71%); 2) the Moran’ I in the summer of 2001, 2006, 2011, 2016 and 2020 are 0.909, 0.898, 0.917, 0.921 and 0.892, respectively, which indicated that the EEQ has a strong positive spatial correlation. 3) the correlation between the four indicators and summer RSEI showed high correlation in different years, and the standard deviation of the correlation between the four indicators and RSEI fluctuated most slightly in summer, which the std of NDVI, WET, LST and, NDBSI were 0.005, 0.052, 0.026 and 0.017, respectively. This study theoretically demonstrates that summer is the optimal season for constructing RSEI, filling the research gap in previous studies regarding the rationale for selecting images from periods of vigorous vegetation growth for RSEI construction, which can provide a reference for selecting the optimum season for the ecological quality monitoring of urban in the future.

Spatial-temporal variation, driving mechanism and management zoning of ecological resilience based on RSEI in a coastal metropolitan area

Urbanization results in drastic land use/cover change (LUCC) and ecological resilience (ER) issues, which has become an important and urgent question of regional sustainability. This study applied the remote sensing ecological index (RSEI) to represent the ER, and further combined ordinary least squares (OLS), and the Google Earth Engine (GEE) platform to analyze the spatiotemporal variations of ER and LUCC at the regional and city scales from 1990 to 2020 in Hangzhou Bay Metropolitan Area (HBMA), explore the relationship between ER and LUCC, and apply for urban resilience management zoning. The results demonstrated that the ER in HBMA showed a characteristic of “slightly worse overall and improved locally”, the mean value of which decreased from 0.508 in 1990 to 0.502 in 2020. Specifically, the ER in Shanghai, Shaoxing, and Ningbo increased, and others decreased. ER exhibited a very obvious spatial aggregation feature of “high value in the southwest and low value in the northeast”. The spatiotemporal dynamic feature of LUCC from 1990 to 2020 depicted that the expansion of construction land was at the expense of occupying cropland. The LUCC degree and population have significant negative effects on the ER. The ER management zoning of HBMA could be divided into five zones, namely the ecological core protection area, ecological monitoring area, ecological optimization area, ecological restoration area, and ecological potential governance area. Results of this study could provide a more comprehensive understanding of ER change in metropolitan areas and the implication of resilience management was provided to improve the adaptation and sustainable development of the coastal urban area.