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Researchers and managers of natural resource conservation have increasingly emphasized the importance of maintaining a connected network of important ecological patches to mitigate landscape fragmentation, reduce the decline of biodiversity, and sustain ecological services. This research aimed to guide landscape management and decision-making by developing an evaluation framework to construct ecological security patterns. Taking the Jianghan Plain as the study area, we identified key ecological sources by overlaying the spatial patterns of ecological quality (biodiversity, carbon storage, and water yield) and ecological sensitivity (habitat sensitivity, soil erosion sensitivity, and water sensitivity) using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model and the Chinese Soil Loss Equation Function. Ecological corridors were obtained by the least-cost path analysis method and circuit theory. A total of 48 ecological sources (3812.95 km2), primarily consisting of water area, forestland, and cropland, were identified. Ninety-one ecological corridors were derived, with a total length of 2036.28 km. Forty barriers and 40 pinch points with the highest improvement coefficient scores or priority scores were selected. There were 11 priority corridors with very high levels of connectivity improvement potential and conservation priority, occupying 16.15% of the total length of corridors. The overall potential for ecological connectivity is high on the Jianghan Plain. Our framework offers a valuable reference for constructing ecological security patterns and identifying sites for ecological restoration at the regional scale.

The importance of local hydraulic conditions on the structuring of freshwater biotic communities is widely recognized by the scientific community. In spite of this, most current methods based upon invertebrates do not take this factor into account in their assessment of ecological quality. The aim of this paper is to investigate the influence of local hydraulic conditions on invertebrate community metrics and to estimate their potential weight in the evaluation of river water quality. The dataset used consisted of 130 stream sites located in four broad European geographical contexts: Alps, Central mountains, Mediterranean mountains and Lowland streams. Using River Habitat Survey data, the river hydromorphology was evaluated by means of the Lentic-lotic River Descriptor and the Habitat Modification Score. To quantify the level of water pollution, a synoptic Organic Pollution Descriptor was calculated. For their established, wide applicability, STAR Intercalibration Common Metrics and index were selected as biological quality indices. Significant relationships between selected environmental variables and biological metrics devoted to the evaluation of ecological quality were obtained by means of Partial Least Squares regression analysis. The lentic-lotic character was the most significant factor affecting invertebrate communities in the Mediterranean mountains, even if it is a relevant factor for most quality metrics also in the Alpine and Central mountain rivers. Therefore, this character should be taken into account when assessing ecological quality of rivers because it can greatly affect the assignment of ecological status.

The ecological environment is important for the survival and development of human beings, and objective and accurate monitoring of changes in the ecological environment has received extensive attention. Based on the normalized difference vegetation index (NDVI), wetness (WET), normalized differential build-up and bare soil index (NDBSI), and land surface temperature (LST), the principal component analysis method is used to construct a comprehensive index to evaluate the ecological environment’s quality. The R package “Relainpo” is used to estimate the relative importance and contribution rate of NDVI, WET, NDBSI, and LST to the remote sensing ecological index (RSEI). The optimal parameter geographic detector (OPGD) model is used to quantitatively analyze the influencing factors, degree of influence, and interaction of the RSEI. The results show that from 2001 to 2020, the area with a poor grade quality of the RSEI in Guangzhou decreased from 719.2413 km2 to 660.4146 km2, while the area with an excellent quality grade of the RSEI increased from 1778.8311 km2 to 1978.9390 km2. The NDVI (40%) and WET (35%) contributed significantly to the RSEI, while LST and NDBSI contributed less to the RSEI. The results of single factor analysis revealed that soil type have the greatest impact on the RSEI with a coefficient (Q) of 0.1360, followed by a temperature with a coefficient (Q) of 0.1341. The interaction effect of two factors is greater than that of a single factor on the RSEI, and the interaction effect of different factors on the RSEI is significant, but the degree of influence is not consistent. This research may provide new clues for the stabilization and improvement of ecological environmental quality.

It is of great significance for regional ecological protection and sustainable development to quickly and effectively assess and monitor regional ecological quality and identify the factors that affect ecological quality. This paper constructs the Remote Sensing Ecological Index (RSEI) based on the Google Earth Engine (GEE) platform to analyze the spatial and temporal evolution of ecological quality in the Dongjiangyuan region from 2000 to 2020. An ecological quality trend analysis was conducted through the Theil-Sen median and Mann-Kendall tests, and the influencing factors were analyzed by using a geographically weighted regression (GWR) model. The results show that (1) the RSEI distribution can be divided into the spatiotemporal characteristics of “three highs and two lows,” and the proportion of good and excellent RSEIs reached 70.78% in 2020. (2) The area with improved ecological quality covered 17.26% of the study area, while the area of degradation spanned 6.81%. The area with improved ecological quality was larger than that with degraded ecological quality because of the implementation of ecological restoration measures. (3) The global Moran’s I index gradually decreased from 0.638 in 2000 to 0.478 in 2020, showing that the spatial aggregation of the RSEI became fragmented in the central and northern regions. (4) Both slope and distance from roads had positive effects on the RSEI, while population density and night-time light had negative effects on the RSEI. Precipitation and temperature had negative effects in most areas, especially in the southeastern study area. The long-term spatiotemporal assessment of ecological quality can not only help the construction and sustainable development of the region but also have reference significance for regional ecological management in China.

Coal mining area is a special man–land system, where the ecological environment is affected by coal mining and other human activities. While many studies have focused on landscape pattern change and its ecological effects, little attention has been paid to the comprehensive ecological quality and its dynamic change in coal mining area during long time periods, especially lacking an ecological effect assessment pre and post the land reclamation. This paper, taking the coal mining area of Peixian in Xuzhou, China, as a case area, presents a method of monitoring and assessing landscape ecological quality using a landscape ecological assessment model based on landscape ecological theories, remote sensing and geographic information systems technology. The results show that landscape ecological quality changed substantially, and the overall trend became worse gradually during 1990–2014. The landscape ecological quality gradually improved in the reclamation area but greatly deteriorated in the urbanized areas of Peixian between 2004 and 2014, indicating satisfactory reclamation activity. Coal mining, land reclamation and urban expansion were major factors that affected the ecological environment of Peixian. The present analyses result in a close range between the appraisal and actual situation, suggesting that the model is generally applicable. This work provides a quantitative method for assessing landscape ecological quality, and will contribute to future resource development, landscape planning and land reclamation in coal mining areas.

As a major coal-producing province, understanding the spatiotemporal evolution of ecological quality and its driving factors in Shanxi is essential for promoting environmental protection and sustainable development. This study employs MODIS data to calculate the Remote Sensing Ecological Index (RSEI) for Shanxi Province and its designated mining areas from 2000 to 2023, aiming to investigate the spatial and temporal dynamics of ecological quality. The CatBoost model and Geographically Weighted Regression (GWR) are applied to identify and analyze the underlying driving factors. The results show that ecological quality in both Shanxi Province and its planned mining regions exhibited an overall upward trend between 2000 and 2020, with varying levels of improvement observed across different mining zones. Trend analysis indicates a general enhancement in ecological conditions over the past two decades. RSEI displays significant spatial autocorrelation, characterized by high-value clustering in the southern regions and low-value clustering in the northern and western mining zones and areas with intensive human activity. Key influencing factors include elevation, net primary productivity (NPP), precipitation, and population density. The CatBoost model, supplemented with SHAP (SHapley Additive exPlanations) values, quantifies the relative importance and predictive contribution of each factor to RSEI outcomes. The GWR model further reveals spatial heterogeneity in these relationships, uncovering localized effects, spatial gradient patterns, and clustering phenomena. Additionally, the Hurst index analysis indicates that most areas within Shanxi Province and its designated mining zones are likely to maintain an upward trend in ecological quality in the future. As a comprehensive large-scale and long-term assessment, this study provides valuable theoretical and empirical support for regional planning, ecological monitoring, and the management of mining areas, thereby contributing to sustainable development and ecological conservation efforts.

Based on a total of 519 images, the composite images with the lowest possible cloud cover were generated at pixel level with image synthesis method on Google Earth Engine (GEE) platform. The Remote Sensing Ecological Index (RSEI) was adopted, and calculated in an efficient way with the assistance of parallel cloud computing of the GEE platform. The RSEI was used in this paper to evaluate and monitor the eco-environmental quality of the Lhasa Metropolitan Area. Results show that: (1) The ecological quality is better in the west than in the east of Lhasa Metropolitan Area, with Lhasa as an approximate dividing point. The ecological quality improved and then deteriorated dramatically before 2000, with the mean RSEI value dropping from 0.51 to 0.46; the trend was followed by a gradual increase up until 2017, with the mean RSEI value increased from 0.46 to 0.55. (2) The RSEI is weakly and positively correlated with socioeconomic indicators. This indicates that the population growth and economic development did not negatively influence the ecological quality, but actually boosted it. (3) The GEE can serve as an efficient computing platform for the assessment and monitoring of eco-environmental quality in vast regions.

Ecological changes affected by increasing human activities have highlighted the importance of ecological quality assessments. An appropriate and efficient selection of ecological parameters is fundamental for ecological quality assessments. On the basis of remote sensing data and methods, this study developed an enhanced ecological evaluation index (EEEI) with five integrated ecological parameters by containing pixel and sub-pixel information: normalized difference vegetation index, impervious surface coverage, soil coverage, land surface temperature, and wetness component of tasseled cap transformation. Significantly, the EEEI simultaneously considered the five aspects of land surface ecological conditions (i.e., greenness, human activities, dryness, heat, and moisture), which provided an effective guide for the systematic selection of ecological parameters. The EEEI has a clear theoretical framework, and all the parameters can be obtained quickly on the basis of the remote sensing datasets and methods, which is suitable for the promotion and application of ecological quality assessments to various areas and scales. Furthermore, the EEEI was applied to assess and detect the ecological quality of the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China. Assessment results indicated that the ecological quality of the GBA is currently facing great challenges with a degradation trend from 2000 to 2020, which emphasizes the significance and urgency for eco-environmental protection of the GBA. This provided evidence that the EEEI can be used as an effective index for scientific, objective, quantitative, and comprehensive ecological quality assessment, which can also aid regional environmental management and ecological protection.

The present study used the recently developed Remote Sensing-Based Ecological Index (RSEI) to assess the temporal-spatial variation of ecological quality in the metropolitan city of Isfahan (Iran) as a member of the UNESCO Creative Cities Network. This study was conducted from the Landsat TM/OLI satellite images of 2004, 2009, 2014 and 2019. The RSEI was synthesized by principal component analysis for four indices of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Land Surface Moisture (LSM) and Normalized Differential Build-up, and Bare Soil Index (NDBSI) based on the framework of the Pressure-State-Response (PSR) in the aforementioned years. The ecological quality of the city was assessed by RSEI over a 15-year period. The index has a value range of 0 (completely poor ecological quality) to 1 (completely desirable). In addition, the spatial heterogeneity of RSEIs at different intervals was assessed by the Moran index. The results showed that the RSEI value was always less than 0.4, which indicated the unfavourable ecological quality of the city. This index was 0.34, 0.37, 0.26 and 0.30 in 2004, 2009, 2014 and 2019, respectively. Therefore, the ecological quality of the city did not have a constant trend during the studied period and had several fluctuations, which could be attributed to the natural and anthropogenic changes in the studied period. Additionally, the results of the Moran index showed a steady decline, which indicated a declining homogeneity during this period. Matching the calculated RSEIs with the realities of the region at each time interval suggested that the index could be a useful tool for assessing urban ecological quality.

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.