Search Results (9)

The Dabie Mountain area in Anhui Province is an essential ecological security barrier and a critical protected area in East China. It is very important to assess its ecological environment quality and identify its key driving forces. Five indicators, including Greenness, Wetness, Dryness, Heat, and Biological Richness, were used to construct an improved remote sensing ecological Index (IRSEI) to assess ecological environment quality. The weights of the five indicators were determined by coupling the analytic hierarchy process (AHP) and the entropy weight method (EWM). The optimal parameters-based geographical detector (OPGD) was used to recognize driving factors. The main conclusions were as follows: (1) the overall rank of ecological environment quality was mainly good and excellent. The ecological quality of forest land was excellent, that of farmland was good, and that of built-up areas was poor. (2) The change in ecological environment quality was mainly stable from 2000 to 2020. The ecological quality of some forests and farmlands improved, with a deteriorating trend in the built-up areas. (3) The Moran’s Index of ecological quality ranged from 0.77 to 0.85, indicating high spatial agglomeration. (4) The OPGD indicated that the DEM had the most explanatory power for ecological quality, and the interactive relationship between the DEM and population density had the most significant impact. (5) In comparison to the conventional remote sensing ecological Index (RSEI), the IRSEI exhibited higher congruence with observed circumstances and improved ecological interpretability. Full article

In light of global climate change and accelerated urbanization, preserving and restoring island ecosystems has become critically important. This study focuses on Changxing Island in Dalian, China, evaluating the quality of its ecological environment. The research aims to quantify ecological changes since 2000, with an emphasis on land use transformations, coastline evolution, and the driving factors behind these changes. Using the Google Earth Engine (GEE) platform and remote sensing technology, an island remote sensing ecological index (IRSEI) was developed. The development of the IRSEI was grounded in several key ecological parameters, including the normalized difference vegetation index (NDVI), wetness index (WET), land surface temperature index (LST), multiband drought stress index (M-NDBSI), and land use intensity index (LUI). The research results show that, since 2002, land use types on Changxing Island have undergone significant changes, with a notable decrease in arable land and a significant increase in built-up areas, reflecting the ongoing urbanization process. With respect to coastline changes, the total coastline length of Changxing Island steadily increased from 2002 to 2022, with an average annual growth rate of 2.15 km. This change was driven mainly by reclamation and infrastructure construction. The IRSEI analysis further revealed a clear deterioration in the quality of the ecological environment of Changxing Island during the study period. The proportion of excellent ecological area decreased from 39.3% in 2002 to 8.89% in 2022, whereas the areas classified as poor and very poor increased to 56.23 km² and 129.84 km², both of which set new historical records. These findings suggest that, as urbanization and coastline development intensify, the ecosystem of Changxing Island is at significant risk of degradation. The optimized IRSEI effectively captured the ecological environment quality of the island, improved the long-term stability of the index, and adequately met the requirements for large-scale and long-term ecological environment quality monitoring. Full article

This study aims to assess the spatiotemporal changes in ecological environment quality (EEQ) in arid regions, using Xinjiang as a case study, from 2000 to 2023, with an improved remote sensing ecological index (IRSEI). Due to the complex ecology of arid regions, the traditional remote sensing ecological index (RSEI) has limitations in capturing ecological dynamics. To address this, we propose an enhanced IRSEI model that replaces normalization with standardization, improving robustness against outliers. Additionally, the kernel normalized difference vegetation index (kNDVI) and normalized difference salinity index (NDSI) are integrated to assess saline areas more effectively. The methodology includes time series analysis, spatial distribution analysis, and statistical evaluations using the difference method, coefficient of variation, and the Hurst index. Results show that the IRSEI more accurately reflects ecological dynamics than the RSEI. Temporal analysis reveals stable overall EEQ, with some areas improving. Spatially, the environment is generally better in the north and in mountainous regions than in the south and plains. Statistical evaluations suggest a positive trend in ecological changes, with improved areas surpassing degraded ones. This study contributes to the monitoring, protection, and management of arid region ecosystems, emphasizing the need for high-resolution data and further analysis. Full article

The Remote Sensing Ecological Index (RSEI) model is widely used for large-scale, rapid Ecological Environment Quality (EEQ) assessment. However, both the RSEI and its improved models have limitations in explaining the EEQ with only two-dimensional (2D) factors, resulting in inaccurate evaluation results. Incorporating more comprehensive, three-dimensional (3D) ecological information poses challenges for maintaining stability in large-scale monitoring, using traditional weighting methods like the Principal Component Analysis (PCA). This study introduces an Improved Remote Sensing Ecological Index (IRSEI) model that integrates 2D (normalized difference vegetation factor, normalized difference built-up and soil factor, heat factor, wetness, difference factor for air quality) and 3D (comprehensive vegetation factor) ecological factors for enhanced EEQ monitoring. The model employs a combined subjective–objective weighting approach, utilizing principal components and hierarchical analysis under minimum entropy theory. A comparative analysis of IRSEI and RSEI in Miyun, a representative study area, reveals a strong correlation and consistent monitoring trends. By incorporating air quality and 3D ecological factors, IRSEI provides a more accurate and detailed EEQ assessment, better aligning with ground truth observations from Google Earth satellite imagery. Full article

The integrity and resilience of our environment are confronted with unprecedented challenges, stemming from the escalating pressures of urban expansion and the need for ecological preservation. This study proposes an Improved Remote Sensing Ecological Index (IRSEI), which employs humidity (WET), the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), a standardized Building–Bare Soil Index (NDBSI), aerosol optical depth (AOD), and the comprehensive salinity index (CSI). The IRSEI model was utilized to assess the ecological quality of Hangzhou over the period from 2003 to 2023. Additionally, the random forest model was employed to analyze the factors driving ecological quality. Furthermore, the gradient effect in the horizontal direction away from the urban center was examined using the buffer zone method. Our analysis reveals the following: (1) approximately 95% of the alterations in ecological quality observed from 2003 to 2023 exhibited marginal improvements, declines, or were negligible; (2) the transformations in IRSEI during this period, including variations in surface temperature and transportation networks, exhibited strong correlations (0.85) with human activities. Moreover, the influence of AOD and the comprehensive salinity index on IRSEI demonstrated distinct spatial disparities; (3) the IRSEI remained generally stable up to 30 km outside the city center, indicating a trend of agglomeration in the center and significant areas in the surroundings. The IRSEI serves as a robust framework for bolstering the assessment of regional ecological health, facilitating ecological preservation and rejuvenation efforts, and fostering coordinated sustainable regional development. Full article

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. Full article

Monitoring the quality of the urban ecological environment has become one of the important elements of promoting a sustainable urban development. The remote sensing ecological index (RSEI) provides a new research direction in urban ecological environment monitoring, combined with remote sensing. However, by using the principal component analysis method in RSEI, the calculation results are complicated and the workload is huge. To effectively assess the urban ecological environment, an improved remote sensing ecological index (IRSEI) was created to improve the ease of data use by using the entropy weighting method with spatiotemporal characteristics and seasonal variations. Furthermore, a geographically weighted regression model was used to quantify the impact of human activities on the urban ecological environment quality. The results showed that the IRSEI could provide a new method for monitoring the urban ecological environment quality, which makes the work easier while ensuring the validity of the results. It was concluded that (1) seasonal differences in the ecological quality of the study area were evident in the IRSEI model and the overall ecological environment quality of Jining City had been on an upward trend in the past 20 years; (2) the ecological quality in the study area was unevenly distributed spatially, with the southwestern part being better than the northeastern part, and the ecological grade being predominantly between moderate and good; and (3) the spatial aggregation effect of the IRSEI was increasing with time. The geographically weighted regression (GWR) revealed the influence of human activities on the ecological environment quality, among which economic level was positively related to ecological improvement, but the population density and night light index were negatively related to improvements in the ecological environment; road network density only showed a negative correlation in 2020. As Jining urbanizes, attention should be paid to protecting the built environment and population distribution. Full article

Rapid urbanization significantly affects the productivity of the terrestrial ecosystem and the foundation of regional ecosystem services, thereby detrimentally influencing the ecological environment and urban ecological security. The United Nations’ Sustainable Development Goals (SDGs) also require accurate and timely assessments of where people live in order to develop, implement and monitor sustainable development policies. Sustainable development also emphasizes the process of protecting the ecological environment for future generations while maintaining the current needs of mankind. We propose a comprehensive evaluation method for urban ecological quality (UEQ) using Landsat TM/ETM+/OLI/TIRS images to extract remote sensing information representing four ecological elements, namely humidity, greenness, heat and dryness. An improved comprehensive remote sensing ecological index (IRSEI) evaluation model is constructed by combining the entropy weight method and principal component analysis. This modeling is applied to the city of Wuhan, China, from 1995 to 2020. Spatial autocorrelation analysis was conducted on the geographic clusters of the IRSEI. The results show that (1) from 1995 to 2015, the mean IRSEI of Wuhan city decreased from 0.60 to 0.47, indicating that environmental deterioration overwhelmed improvements; (2) the global Moran’s I for IRSEI ranged from 0.535 to 0.592 from 1995 to 2020, indicating significant heterogeneity in its spatial distribution, highlighting that high and low clusters gradually developed at the edge of the city and at the city center, respectively; (3) the high clusters are mainly distributed in the Huangpi and Jiangxia districts, and the low clusters at the city center, which exhibits a dense population and intense human activity. This paper uses remote sensing index methods to evaluate UEQ as a scientific theoretical basis for the improvement of UEQ, the control of UEQ and the formulation of urban sustainable development strategies in the future. Our results show that the UEQ method is a low-cost, feasible and simple technique that can be used for territorial spatial control and spatiotemporal urban sustainable development. Full article

Healthcare has long been marked by the authoritative-physician–passive-patient interaction, with patients seeking help and physicians seeking to restore patients back to health. However, globalisation, social movements, and technological advancements are transforming the nature of this relationship. We aim to identify core values that influence the power dynamic between patients and healthcare professionals, and determine how to steer these interactions towards partnership, a more suitable approach to current healthcare needs. Patients with chronic diseases (10 men, 18 women) and healthcare professionals (11 men, 12 women) were interviewed, sessions transcribed, and the framework method used to thematically analyse the data. Validation was done through analyst triangulation and member check recheck. Core values identified as influencing the patient-healthcare professional power dynamic include: (A) values that empower patients (acceptance of diagnosis and autonomy); (B) values unique to healthcare professionals (HCPs) (acknowledging patients experiential knowledge and including patients in the therapeutic process); and (C) shared capitals related to their interactions (communication, information sharing and exchange, collaboration, and mutual commitment). These interdependent core values can be considered prerequisites to the implementation of the patient-as-partner approach in healthcare. Partnership would imply a paradigm shift such that stakeholders systematically examine each other’s perspective, motivations, capabilities, and goals, and then adapt their interactions in this accord, for optimal outcome. Full article