生态工程对植被的成效

Achieving sustainable resource management is essential to address the rising demand for ecosystem services. The absence of targeted vegetation restoration based on ecological function positioning has, nevertheless, made it challenging to effectively combat the ecological decline. This study attempted to classify four dominant ecological function areas based on the assessment of water conservation, soil retention, habitat quality, and food supply and determined the vegetation coverage threshold by exploring the trade‐offs among ecosystem services and constraint effects between ecosystem services and vegetation coverage. The results highlighted the impacts of ecosystem services on vegetation coverage across the years 1990, 2000, 2010, and 2020 and established differentiated ecological restoration targets. The optimal vegetation coverage in the water conservation area was found to be 58%–63%, in the soil retention area was 52%–56%, in the food supply area was 34%–40%, and in the habitat quality area was 65%–70%. Finally, the study identified the subwatersheds with reasonable vegetation coverage, excessive restoration, and those that failed to reach the optimal vegetation coverage to develop targeted restoration strategies for each subwatershed according to its unique vegetation conditions. This study provides valuable insights into the specification of differentiated vegetation coverage targets and serves as a useful tool for more effective ecosystem planning and management.

This study evaluates the effectiveness of soil reconstruction and restoration in the Jiangcang coal mining area on the Qinghai-Tibet Plateau, where harsh environmental conditions pose significant challenges to ecological restoration.Two phases of ecological restoration were implemented, with outcomes assessed based on vegetation coverage, species diversity, biomass, soil properties, and community similarity.The results demonstrate that soil reconstruction significantly improved soil fertility, vegetation coverage, and community stability without noticeable degradation over time. The use of sheep manure increased species diversity by introducing native seeds, addressing the shortage of suitable grass species in alpine areas. Comparatively, the second phase of restoration, which included soil reconstruction, has elevated the vegetation coverage to 80%, matching natural background levels, and has also demonstrated superior outcomes in terms of soil stability, nutrient content, and other aspects compared to traditional methods. While aboveground biomass showed rapid recovery, belowground biomass lagged, indicating a need for longer-term restoration. Restored slopes exhibited higher similarity to natural alpine meadows compared to platforms, suggesting the dominance of the artificially seeded species on the platform areas hinders the reproduction of other species, which is unfavorable for the evolution of vegetation diversity.This study emphasizes the effectiveness of soil reconstruction, organic amendment, and other restoration measures, providing important experience and reference for mine ecological restoration in similar high-altitude mining areas.

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In recent years, China has invested substantial funds in ecological restoration, achieving significant accomplishments. The forest coverage rate in the Chengde Bashang area, located in the transitional zone between the monsoon and non-monsoon regions, has now reached 82%. However, the area has also encountered a series of environmental issues, including lake shrinkage, soil salinization, and large-scale die-offs of planted forests. Whether the forests in this region can achieve sustainable development in the future, and whether ecological restoration should prioritize tree planting or grass cultivation, are critical questions that require attention. By studying the historical vegetation dynamics in afforested areas, we can better understand the relationship between climatic environmental changes and vegetation, providing baseline data for future ecological restoration. This study utilized AMS 14C dates to establish a chronological framework for the core and employed pollen to investigate vegetation dynamics over the past 5000 years in the artificial Larix Mill. forest area. The vegetation and environmental history of this core can be divided into three zones: Zone 1 (5100–4100 a B.P.): vegetation was dominated by pine and spores, with low herbaceous pollen content. Zone 2 (4100–1400 a B.P.): vegetation was primarily herbaceous. Zone 3 (1400 a B.P.–present): arboreal pollen content increased slightly, but herbaceous plants remained dominant. This period included the warm–dry Medieval Warm Period (1400–900 a B.P.), the cold–humid Little Ice Age (900–300 a B.P.), and the recent 300 years of anthropogenic disturbance. Notably, the large-scale afforestation efforts in recent decades are clearly reflected in the profile. A comparative analysis of records from the monsoon–non-monsoon transition zone reveals that, except for Angulinao Lake, other records were dominated by herbaceous vegetation over the past 2000 years. Additionally, the Mu Us Sandy Land, Hunshandake Sandy Land, Hulunbuir Sandy Land, and Horqin Sandy Land in China have experienced aeolian sand accumulation over the same period. Given the anticipated warming–desiccation trend, phytoremediation strategies should favor xerophytic shrubs and herbaceous over monospecific forest plantations.

Tropical coral island vegetation poses formidable challenges, particularly in elucidating the determinants of vegetation species richness. In response, our study compared the differences in plant species biodiversity and soil physicochemical properties on seven adjacent coral islands at different stages of vegetation succession, from bare land to 100% vegetation coverage in the South China Sea, all of which were less than 0.3 km2. Contrary to the established island ecological theories, our results indicated that soil nutrients significantly govern the species diversity of tropical coral islands. However, the timing of soil development, island area, distance from larger islands, and island altitude were not significantly correlated. Cluster analysis showed that the diverse islands of Qilianyu Island (Seven Sisters) represent distinct stages of tropical coral island succession: pioneer vegetation, shrub and grass communities, and coral island forest vegetation. As island vegetation underwent succession, plant species increased from 6 to 57, and organic carbon, total nitrogen, and available phosphorus content significantly increased, accompanied by increasing salinity and decreasing pH. Our findings revealed a nested structure in the vegetation of tropical coral islands, primarily dominated by environmental filtering on a small scale, at least on Qilianyu Island. This indicates that the restoration of damaged island vegetation can begin with soil rehabilitation. We contend that improving soil nutrient conditions and development status contribute to the establishment of island vegetation, with careful consideration of interspecific combinations that expedite the restoration process on tropical coral islands. This study addresses the lack of clarity surrounding the determinants of vegetation species richness on tropical coral islands, thus providing a novel perspective grounded in soil nutrient‐driven succession.

Coal mining will change the land nutrient conditions and affect the growth of surface vegetation. In view of the lack of analysis and research on the spatio-temporal changes of vegetation coverage in Yungang District, Shanxi Province, in the hinterland of Datong coalfield, deeply explored the vegetation index information from remote sensing data and conducted statistical analysis of vegetation time series. Based on landsat8 oli images from 2019 to 2022, the normalized difference vegetation index (NDVI) , vegetation coverage and greenness change rate were extracted, and the long-term vegetation coverage types, vegetation coverage and vegetation coverage changes in Yungang district were mined from the vegetation index, so as to study the distribution characteristics and changing trend of vegetation in Yungang district. The results show that: (1) the surface vegetation in the mining area is mainly cultivated land, shrubs and trees, and the overall vegetation coverage is high. The vegetation change trends in the Northwest Mountainous Area and the southeast plain area divided by the Kouquan fault zone are quite different. From 2019 to 2022, the dominant surface vegetation types in the Northwest Mountainous area gradually change from shrubs and trees to shrubs and grassland, and the vegetation coverage changes from high to medium-high to medium, and the surface vegetation coverage types in the southeast plain area change little, and the vegetation coverage degrades slightly. (2) From 2019 to 2022, the degraded area of vegetation coverage in Yungang district is 40%, the basically unchanged area is 37%, and the improved area is 23%. Mining and farmland harvesting have a significant negative impact on vegetation, while mining area greening and farmland planting have a significant positive impact on vegetation. In the next step, it is necessary to continuously monitor the dynamic changes of vegetation in Yungang District, and study the relationship between vegetation changes and mining and ecological restoration, to provide data support for guiding the ecological construction of the whole region.

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This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring.

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The Three-River Headwaters Region (TRHR) is an important part of the ecological security barrier of the Qinghai–Tibet Plateau in China. Twenty years after the implementation of the TRHR ecological protection and construction project, the restoration degree and restoration potential of its major ecosystem services still lack clear quantification. In this paper, taking the core area of the nature reserve as the climax background of the TRHR zonal ecosystem, based on the multiple regression analysis (MLR) and model parameter control method based on the eco-geographical area, ecosystem types, and climate factors; the climax background, restoration degree, and restoration potential of TRHR’s water retention (WR), soil retention (SR), and windbreak and sand fixation (WD) services were quantitatively researched. The main conclusions were as follows: (1) The evaluation method of climax background, restoration degree, and restoration potential based on fractional vegetation cover (FVC) can accurately quantify the regional differences of the restoration degree and restoration potential of TRHR’s key ecosystem-regulating services. The restoration degree and restoration potential of WR and SR services showed a spatial pattern of high in the southeast and low in the northwest, and the restoration degree and restoration potential of WD services showed a spatial pattern of high in the west and low in the east, which was closely related to natural conditions such as precipitation and wind speed. (2) The proportion of restoration potential to climax background for WR, SR, and WD services were 48.38%, 62.15%, and 56.37%, respectively. (3) The implementation of the TRHR ecological project in the future should focus on the vicinity of the 400 mm dry and wet zone dividing line, as well as in the southeastern mountains, hills, and river valleys, to carry out degraded vegetation restoration and soil and water conservation measures to improve ecosystem services. Near-natural restoration measures should be considered in Zhiduo and Geermu in the western part of the TRHR, where wind erosion is high, and the restoration goals of ecological projects should be formulated in combination with local climatic conditions and restoration potential.

Abstract: In the coal base of Ningdong, there are many ecological problems associated with the existing local technologies, such as the imperfect technical system, a poor engineering effect, limited generalization value, and the lack of monitoring and evaluation. Based on the screening and integration of the existing technologies in the coal base of Ningdong, we have designed and constructed 14 ecological restoration plots in this study. The 14 plots were composed of two replicates for each of six technical modes and CK treatment (nothing treatment). These technical modes include ecological bag, ecological rod, wire gabion, gravel sand barrier, living sand barrier and wheat straw sand barrier modes. The 14 plots were all constructed in the slope of Yangchangwan waste dump of Ningdong. Several monitoring indicators were selected for vegetation growth observation and data collection, including erosion amount, runoff amount, runoff depth, richness, coverage, herbal biomass, bush biomass and total biomass. Furthermore, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was utilized to evaluate the effects of the six ecological restoration modes. The results showed that the wheat straw sand barrier mode area had the best vegetation restoration effect, with coverage of 45%, richness of 1.23 and an aboveground biomass of 0.60 kg m–2. Its monitoring results were 45.16%, 43.02%, and 71.43% higher than in the CK, respectively. The gravel sand barrier model presented the least runoff and erosion yield, and its total erosion was 133.46 g m–2 which was only 26.80% of the CK. The runoff amount was 863.32 cm3 m–2, even 50.00% less than CK. The TOPSIS results show that the living sand barrier, gravel sand barrier, and wire gabion modes are the three best ecological restoration modes overall.

Grazing rest during the spring regreening period is the most economical and feasible measure for the ecological restoration of degraded alpine meadows and has been widely popularized and applied in China. The aim of the present study was to undertake a comparative analysis of the effects of grazing rest on the ecological restoration of degraded alpine meadows by plant photosynthesis and respiration. Coverage, height, ground biomass, belowground biomass of degraded alpine meadow vegetation, net photosynthetic rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, chlorophyll fluorescence parameters, relative chlorophyll content, respiration rate, metabolite content, leaf relative water content, and related mineral element content of the dominant grass Elymus nutans Griseb. were measured in degraded alpine grassland with different grazing rest years. The results show that grazing rest during the spring regreening period promoted the ecological restoration of degraded alpine meadows by enhancing the photosynthesis and respiration of the dominant grass E. nutans Griseb. Grazing rest enhanced photosynthesis in dominant grass by increasing metabolites related to the Calvin cycle, chlorophyll content, leaf relative water content, and related mineral element content. Grazing at rest enhanced the respiration of dominant grass by increasing metabolites related to the TCA cycle, leaf relative water content, and related mineral element content. This positive effect gradually became stable with increasing years of grazing rest. Our results provide a fundamental basis for the popularization and application of grazing rest during the spring regreening period on degraded Tibetan Plateau grasslands.

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As a significant ecological barrier, the source region of the Yellow River serves as a crucial water source in China, and its vegetation dynamics play a pivotal role in water conservation. Monitoring vegetation dynamics is essential for ecological protection and the achievement of sustainable development goals, as it facilitates systematic assessment of vegetation restoration, supports the advancement of ecological civilization, and promotes coordinated economic and environmental development. Based on the newly released AVHRR GIMMS NDVI3g data from 1982 to 2020 provided by the NASA Goddard Space Flight Center, this study aims to identify the driving mechanisms influencing vegetation dynamics in the source region of the Yellow River over the past 40 years (1982–2020) by utilizing ordered cluster analysis, Pearson correlation analysis, and the Geodetector method. The influence of each driving factor on NDVI was systematically examined, and the spatial and temporal characteristics of vegetation as well as the effects of key drivers were clarified to inform ecological protection and sustainable development strategies. The results indicate that: (1) the overall NDVI in the source region of the Yellow River exhibited a significant upward trend from 1982 to 2020, with a noticeable shift occurring in 2009. Prior to 2009, NDVI demonstrated a slight declining trend, whereas a significant increase was observed afterward; (2) NDVI distribution displayed a spatial gradient, increasing from northwest to southeast, with higher values in the southeast and lower values in the northwest; (3) the interaction between any two driving factors had a more substantial influence on NDVI than individual factors, demonstrating a two-factor enhancement effect. Notably, the interaction between precipitation and temperature with other variables exhibited the strongest explanatory power, with q-values exceeding 0.5. Overall, natural factors such as temperature and precipitation played a crucial role in NDVI variation, and the abrupt change in 2009 may be attributed to regional warming and the implementation of ecological protection measures.

Manganese ore, as an important strategic metal resource for the country, was subject to unreasonable mining practices and outdated smelting technologies in early China, leading to severe ecological damage in mining areas. This study examines the trends in vegetation cover change in the historical manganese mining areas of Yongzhou under the influence of policy, providing technical references for mitigating the ecological impact of these legacy mining areas and offering a basis for adjusting mine restoration policies. This paper takes the manganese mining area in Yongzhou City, Hunan Province as a case study and selects multiple periods of Landsat satellite images from 2000 to 2023. By calculating the Normalized Difference Vegetation Index (NDVI) and the Fractional Vegetation Coverage (FVC), the spatiotemporal changes and driving factors of vegetation coverage in the Yongzhou manganese mining area from 2000 to 2023 were analyzed. The analysis results show that, in terms of time, from 2000 to 2012, the vegetation coverage in the manganese mining area decreased from 0.58 to 0.21, while from 2013 to 2023, it gradually recovered from 0.21 to 0.40. From a spatial perspective, in areas where artificial reclamation was conducted, the vegetation was mainly mildly and moderately degraded, while in areas where no artificial restoration was carried out, significant vegetation degradation was observed. Mining activities were the primary anthropogenic driving force behind the decrease in vegetation coverage, while effective ecological protection projects and proactive policy guidance were the main anthropogenic driving forces behind the increase in vegetation coverage in the mining area.

In agro-pastoral transitional zones, monitoring vegetation fraction coverage (FVC) and understanding its relationship with land use and climate change are crucial for comprehending how complex land-use/land-cover change (LUCC) improves ecological restoration and land management. This study focuses on the agro-pastoral transitional zone of Inner Mongolia, aiming to analyze vegetation cover changes from 2000 to 2020 using the Mann–Kendall (MK) significance test, Theil–Sen median trend analysis, and coefficient of variation (CV) analysis. Additionally, the study explores the impacts of LUCC, precipitation, and temperature on vegetation cover using methods such as geo-detector, pixel-based statistical analysis, and univariate linear regression. Based on the PLUS land-use prediction model and linear regression results, vegetation cover was simulated under different land-use scenarios for the future. The main findings are as follows: first, from 2000 to 2020, the spatial distribution of vegetation cover in the study area showed a distinct pattern of higher vegetation cover in the east compared to the west, with significant spatiotemporal heterogeneity. Although the overall vegetation cover slightly increased, there were notable differences in the trend across regions, with some areas experiencing a decrease in FVC. Second, LUCC is the most significant explanatory factor for vegetation cover changes, and the interactions between LUCC and other factors have a particularly notable impact on vegetation cover. Third, scenario simulations based on the PLUS model indicate that, by 2040, vegetation cover will perform optimally under the farmland protection and sustainable development scenarios. Particularly under the farmland protection scenario, the conversion of cropland, forestland, and grassland is notably suppressed. In contrast, the unmanaged natural development scenario will lead to a decline in vegetation cover. The results of this study show that vegetation cover in the agro-pastoral transitional zone of Inner Mongolia exhibits substantial fluctuations due to land-use change. Future ecological restoration policies should incorporate land-use optimization to promote vegetation recovery and address ecological degradation.

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: [Objective] Vegetation is an important component of terrestrial ecosystems and a key regulator of the climate system. The Yangtze River Basin is an important economic center and key ecological zone in China. Its vegetation status is crucial to maintaining China’ s ecological security and supporting regional coordinated development across the country. There is still a lack of comprehensive and consistent understanding of the spatial differences, driving factors, and relative contributions of vegetation coverage changes in the Yangtze River Basin. Researching the impact of climate change and human activities on the spatiotemporal changes in vegetation coverage in the Yangtze River Basin has important practical significance for developing ecological protection and restoration strategies for the Yangtze River Basin in accordance with local and temporal conditions, and for scientifically coordinating the synergistic relationship between natural restoration and artificial restoration. [Methods] Based on long-term time series data on meteorology, normalized difference vegetation index (NDVI), and land use/cover, the spatiotemporal dynamics of vegetation coverage and influencing factors of NDVI in the Yangtze River Basin from 2000 to 2022 are studied using methods such as Sen’s Slope, Mann-Kendall significance test, partial correlation analysis, and the relative contributions of climate change and human activities to vegetation coverage changes are quantified using residual analysis. [Results] From 2000 to 2022, the overall vegetation coverage in the Yangtze River Basin has been good, with an average NDVI value of 0.741. The NDVI in most regions is above 0.5, roughly showing a spatial distribution pattern of “high in the middle and low in the east and west”. Affected by factors such as climate change

: Floating/emergent aquatic vegetation is an important aquatic vegetation group in lakes, and its area/coverage is an important parameter for lake ecological health assessment and carbon sequestration potential accounting. Accurately obtaining the area/coverage of floating/emergent aquatic vegetation over large lake areas and understanding their changes is crucial for lake ecological restoration and carbon sink accounting. Satellite remote sensing is the most effective means to obtain the area/cover of floating/emergent aquatic vegetation in lakes. However, traditional satellite monitoring methods can only obtain the presence or absence of aquatic vegetation within satellite pixels, and cannot quantitatively estimate the coverage of aquatic vegetation in the pixels Consequently, it is impossible to quantitatively and accurately obtain the area/coverage of floating/emergent aquatic vegetation in lakes. To address this issue, we utilized UAV, Sentinel-2 MSI, and Landsat 8 OLI remote sensing data. Using the XGBoost modeling method, we developed quantitative estimation models for floating and emergent aquatic vegetation coverage at the Sentinel-2 MSI and Landsat 8 OLI pixel scales through a stepwise upscaling approach, and successfully applied it to China’s four largest freshwater lakes. The results showed that the test sets of the two estimation models based on Sentinel and Landsat images had R 2 of 0.95 and 0.97, RMSE of 7.85% and 4.80%, and MAE of 5.35% and 3.35%, respectively. From 1990 to 2022, Lake Dongting and Lake Poyang showed highly significant increasing trends ( p ＜ 0.01), Lake Taihu showed an increasing and then decreasing trend ( p ＜ 0.01), and Lake Hongze had a non-significant increasing trend ( p = 0.59). The long-term application of the model in the four largest freshwater lakes proved the robustness and application potential of the model, which is expected to provide methodological and data support for the accounting of carbon sinks in lake ecosystems and the assessment of carbon sequestration potential

This paper explores the ecological protection and vegetation restoration model applicable to typical ecologically fragile areas in western China, and verifies its effect through experimental simulation. First, this paper designs a comprehensive ecological protection and vegetation restoration model, combines different ecological restoration technologies, such as soil and water conservation, vegetation replanting, soil improvement, etc., and proposes a restoration plan that adapts to the natural environment of western China. Then, through the environmental impact assessment model based on remote sensing technology and GIS platform, combined with multiple restoration modes, simulation Xijun is carried out to evaluate the ecological restoration effect under different modes. The simulation results show that the mode of "combining original ecological protection with artificial restoration" has achieved remarkable results in soil stability, vegetation coverage and biodiversity. According to the data analysis, the vegetation cover of the restored area is increased 30%, the soil erosion rate is reduced 18%, and the biodiversity index is increased 15%. By analysing the simulation results, the feasibility and validity of the proposed ecological restoration model is proved.

Improvements in vegetation coverage are driven by both resource endowment conditions and policy behaviors. To accurately reflect the vegetation restoration effect after ecological policies, this study used the potential realization model to calculate the potential realization degree of vegetation restoration on the Loess Plateau and to assess the vegetation restoration effect after the Grain for Green Program from 2000 to 2020. Then, the influencing factors were explored using the spatial Dubin model. The results reveal that (1) the EVI value of the Loess Plateau in northern Shaanxi increased from below 0.25 at the beginning of the study to approximately 0.35 by the end, indicating that the green territory of the Loess Plateau gradually expanded to the northwest over the study period, and that the east and west of the Loess Plateau are key areas of vegetation cover for further improvement; (2) compared to the traditional EVI indicator, the potential realization degree can more accurately evaluate the vegetation restoration effect driven by ecological policies; (3) policy intensity is positively correlated with the growth rate of the vegetation restoration potential realization degree by 0.183 and significant at 1% level, making it the primary factor influencing the effect of vegetation restoration. Additionally, annual average precipitation and annual sunshine percentage have significant spatial positive contributions to the improvement of vegetation restoration on the Loess Plateau. The study’s findings are expected to contribute to the development of a scientific basis for adjusting the vegetation restoration policy on the Loess Plateau and enhancing ecological restoration efforts.

Vegetation serves as a crucial indicator for monitoring ecosystems and plays a vital role. This paper employs remote sensing techniques to monitor vegetation in Taojiang County, aiming to explore the effects of ecological restoration projects on vegetation in mining areas. The study uses the Theil–Sen median slope and Mann–Kendall tests to analyze the trend of fractional vegetation coverage (FVC) changes in mining areas, the CASA model to estimate net primary productivity (NPP) in mining areas, and random forest models to assess the importance of influencing factors. Overall, FVC in the study area has slightly increased from 0.729 to 0.847. The FVC in mining areas reached its lowest point at 0.423 in 2011 and recovered to 0.718 in 2023 due to artificial restoration. From 2004 to 2011, FVC in mining areas showed an overall downward trend, while from 2013 to 2023, it showed an overall upward trend. The trend of NPP in mining areas is similar to that of FVC, with NPP being 939.8 g/m2 y in 2004, 2011, and 2020, 788.3 g/m2 y in 2011, and 855.7 g/m2 y in 2020. Results from the random forest simulation indicate that the primary factor affecting FVC in mining areas is distance from roads, followed by elevation. This study finds that ecological restoration projects play a significant role in achieving ecological recovery and sustainable development in mining areas.

Vegetation restoration is an important way to improve the sustainability of the ecosystem in the hilly Loess Plateau. The variation of vegetation coverage, caused by the combined effects of meteorological factors and human activities, reflects the succession trend of regional ecosystems. Given the complexity and the diversity of landform in the hilly Loess Plateau, vegetation restoration is more affected by topographic factors. Nevertheless, few studies have considered the characteristics and trends of vegetation restoration under different micro-topographic types in the long-time series. From the perspective of ecological resilience based on the fractional vegetation cover (FVC), the trend, the hurst exponent, and the geographical spatial research were used to analyze the variation and future sustainability of vegetation restoration on different micro-topographic types for 20 years. Besides, the spatial autocorrelation, principal component analysis (PCA) and geographically weighted regression (GWR) were applied to identify the driving factors of vegetation restoration. The results showed: (1) the average of the overall regional vegetation coverage was 61.32%, and only 0.95% of the regional vegetation was degraded in the past 20 years. However, in the future, 69.87% of the area would be degraded from improvement, and 0.52% would be significantly decreased; (2) the vegetation coverage in descending order was as follows: ridge area with shady and steep slope, gully area with shady and steep slope, ridge area with sunny and steep slope, gully area with sunny and steep slope, gully area with shady and gentle slope, ridge area with shady and gentle slope, ridge area with sunny and gentle slope, gully area with sunny and gentle slope, valley area; (3) the difference of vegetation degradation among micro-topography was remarkable, and the valley area and gully area with sunny and steep slope have the greatest decrease; (4) the primary factors affecting vegetation restoration in the hilly Loess Plateau were temperature, moisture, soil quality, and social economical condition, and the dominant factors were various under different micro-topographic types and villages. Therefore, it is necessary to adjust ecological engineering measures by comprehensively considering the regional differences among dominant factors of vegetation restoration.

The karst region in southwestern China is particularly prominent and has become a core issue constraining ecological environment restoration and sustainable development in this area. This study utilized long-term remote sensing data to reveal the spatial pattern evolution characteristics of rocky desertification in the region in 2000, 2010, and 2020. Meanwhile, it analyzed the dynamic trend of vegetation coverage recovery in the area from 2000 to 2020, as well as the analysis of related factors. The results showed that the spatial distribution of the Normalized Difference Vegetation Index (NDVI) remained highly clustered, though the clustering gradually weakened over time. When NDVI exceeded 0.6, the probability of rocky desertification reversal increased. Currently, a core contradiction of “quantity increases but quality stagnates” exists in regional vegetation cover, characterized by a continuous rise in NDVI mean values coexisting with reduced spatial clustering. This phenomenon reflects the evolution of vegetation patterns under the combined effects of ecological engineering interventions, adjustments in human-land relationships, and constraints of karst landforms. Through factor analysis, slope and humidity were identified as key factors influencing vegetation restoration. The findings provide an important theoretical foundation and practical reference for targeted rocky desertification management, optimization of ecological restoration projects, and coordinated human-land development in karst regions.

The Beijing-Tianjin Sandstorm Source Region (BTSSR), a region with significant vegetation degradation in China, has been subjected to ecological engineering intended to curb vegetation browning. Nevertheless, few studies have used multisource data to quantitatively evaluate the vegetation restoration effectiveness in the BTSSR, and the relationship between ecological engineering and vegetation restoration effectiveness in this region from statistical evidence has received little attention so far. Here, we employed the comprehensive vegetation parameters to describe the vegetation restoration effectiveness, and examined the driving mechanism of natural and human factors in different sub regions. First, we evaluated the vegetation restoration effectiveness in the BTSSR using an index that combined Fractional Vegetation Coverage (FVC) and Net Primary Productivity (NPP). Our results showed that the vegetation restoration effectiveness has significantly increased over time. From 2000 to 2020, 60.9% of the area achieved significant vegetation restoration, and the area with higher vegetation restoration effectiveness was concentrated in the southern part of the study area. Then, we used the Geodetector Model to explore the main factors and their interactions affecting vegetation restoration effectiveness. We found that the vegetation restoration effectiveness in the entire area was dominated by annual precipitation, in the northern part of the study area was led by climate, and in the southern part of the study area was dominated by ecological engineering. We further demonstrated that the interaction between ecological engineering and climate, soil conditions, geographical background and socioeconomic had the synergistic effect on vegetation restoration effectiveness, and the interaction between ecological engineering and annual precipitation had the greatest impact. We recommend that the northern region of the BTSSR continue to build low-density wind and sand control forests, while the southern region needs to be strengthened to prevent soil erosion problems caused by the expansion of human activities.

The desertified ecological restoration vegetation of Wuzhumuqin grassland plays an important role in the ecological restoration and protection of the region. However, there are few studies on the monitoring of the changes in ecological restoration vegetation in grassland sandy land in the past. In order to improve the low efficiency of ecological restoration vegetation monitoring, this study used Gaofen-6 (GF-6) remote sensing data to calculate the kernel Normalized Difference Vegetation Index (kNDVI) and vegetation coverage of ecological restoration vegetation and analyze their spatial and temporal trends. At the same time, a transform three-branch network structure based on deep learning is proposed to extract visual features. The kernel Normalized Difference Vegetation Index-position-temporal awareness transformer (kNDVI-PT-Former) model monitoring method based on two-phase remote sensing image features combined with kNDVI for spatio-temporal feature extraction can accurately obtain the vegetation changes in desertification ecological restoration in Wuzhumuqin grassland. The results show that the kNDVI of the study area shows an increasing trend from 2019 to 2024. The kNDVI value is 0.4086 in 2019 and 0.4927 in 2024. From the perspective of the change trend of vegetation coverage, the overall vegetation coverage of the Wuzhumuqin desertification restoration study area showed a gradual increase trend from 2019 to 2024, and the vegetation coverage increased by 19% in 2024 compared with 2019. The transformation of vegetation coverage from low level to high level in the study area is more prominent. Based on the self-built monitoring dataset of more than 5.2 million pairs of grassland vegetation changes, through model comparison and analysis, the kNDVI-PT-Former model obtains that the Class Pixel Accuracy (CPA) is 0.7295, the Intersection over Union (IoU) is 0.7228, and the overall monitoring accuracy of the model is improved by 11%. Furthermore, the stability of the model’s performance was confirmed through evaluation with five-fold cross-validation.

The Qilian Mountains (QLMs) serve as a vital ecological security barrier in the northwest China, determining the optimal fractional vegetation cover (FVC) of different land-use types is crucial for ecological restoration and protection. Based on the normalized vegetation index, meteorological data, soil data and land-use data, the InVEST model, bivariate spatial autocorrelation and elastic analysis, we calculated the water conservation, soil conservation and carbon storage, analyzed the spatio-temporal distribution characteristics of FVC and ecosystem services (ES) and the correlation between them. This study aims to evaluate the optimal FVC of the different land-use types and vegetation restoration’s effect on it in the QLMs. The results showed that the FVC and ES showed an increasing trend from 2000 to 2020, with a spatial distribution pattern of high values in the southeast and low values in the northwest. There was a significant and positive correlation between FVC and ecosystem service functions (p < 0.05), FVC had the most obvious effect on soil conservation, followed by water conservation. The optimal FVC varied by land-use type, with its value between 0.4 and 0.48 for forest land, 0.24 and 0.30 for grassland and 0.19 to 0.20 for unused land. In the eastern of the QLMs, nearly 70% of the forests and 50% of the grasslands exceeded the optimal vegetation coverage, while 36% of the grasslands and 62% of the unused lands were under-covered in the western of the QLMs. The findings suggest that differentiated ecological restoration and management measures should be formulated according to the status of vegetation restoration, to enhance the comprehensive capacity of ecosystem services in the QLMs.

No abstract available

The subsidiary of Yunnan Phosphate Group Co., Ltd., Kunyang Phosphate Mine’s mining area is the subject of study. The mining of open-pit phosphate mines has caused significant damage to the ecological environment. Therefore, carrying out ecological restoration and green reclamation of the ecosystem in the mining area has become the top priority for current development. This article establishes an evaluation system for ecological restoration indicators, selecting four indicators including vegetation coverage, soil and water conservation, restoration of native plants, and Plant Species Diversity Index to assess the effects of ecological restoration and green reclamation of phosphate mines. The techniques of reconstructing soil ecological structure using phosphate tailings substrate and improving acidic soil with soil conditioner and calcium-magnesium phosphate compound fertilizer were applied. A series of other measures were taken, including: drafting scientific ecological restoration plans; employing physical, chemical, and biological methods for ecological restoration and green reclamation; selecting suitable plant species for planting; enhancing planting techniques; and strengthening post-restoration ecological monitoring and regulation. After ecological restoration and green reclamation, the Ecological Remediation Effect Index (EREI) for the years 2020, 2021, and 2022 were 48.40, 87.38, and 93.23 respectively, indicating significant improvement in the ecological environment. Furthermore, the difficulties encountered during the ecological system restoration process of the mine and the future development directions were summarized, providing practical and guiding significance for ecological restoration and green reclamation of abandoned mining areas both domestically and internationally.

To protect and restore this downstream ecosystem, the Tarim River Basin Administration Bureau (TBAB) initiated the Ecological Water Compensation (EWC) project from 2000 to 2018. Revealing the mechanism of vegetation-hydroecological response processes in the lower reaches of the Tarim River before and after EWC work is conducive to water resource planning, utilization and protection. In this paper, the spatiotemporal responses of vegetation and groundwater to EWC were examined at the points, lines, and area (PLA) scale by coupling remote sensing techniques and field station observation data collected between 2000 and 2017. The findings indicated that (1) In general, the regional fractional vegetation coverage (FVC) increased significantly, and the average FVC growth rate was 3.5%/year from 2000 to 2017 (R2 > 0.84, p < 0.01, 2-tailed). (2) The regional vegetation restoration process showed obvious fluctuations and stage characteristics, but the spatial scope of the significantly increased vegetation area was limited. Plants grew rapidly within 10 km of the river, while 10 km away from the water channel, no obvious change was observed. (3) Strong coupling relationships were identified among the FVC growth, EWC volume and groundwater depth variations (p < 0.05, 2-tailed). The response times of the regional vegetation and groundwater depth to EWC indicated one-year lags. The above results imply that the regional ecological environment was significantly improved over the study period, thus confirming that the EWC project made remarkable accomplishments. However, the effect of ecological restoration is not sufficiently stable at present. Vegetation restoration has mainly been centralized around the river channel and is greatly dependent on the annual EWC volume. In addition, the local conditions begin to degrade soon after an EWC project is terminated, and vice versa; when EWC commences, the FVC immediately begins to improve. Therefore, the current EWC achievements need to be further consolidated and strengthened in the future.

In many arid shrubland lands around the world, such as the Monte Desert of Argentina, ecological restoration efforts often prioritize direct seeding or outplantings. Another restorative intervention with lower costs, such as assisted natural regeneration (ANR) through soil tillage, are poorly studied and implemented. In this context, we selected 16 abandoned petroleum drilling platforms that were scarified with furrows to evaluate the natural establishment of vegetation. An equal number of neighboring sites were chosen as ecological reference sites. Five years after scarification, an analysis was conducted on the similarity, richness, diversity of species, plant coverage, density, and soil in soil‐scarified petroleum drilling platforms and ecological reference sites. Similarity, diversity, plant coverage, and density were low between degraded and reference sites. However, 40% of the total species found in the ecological reference colonized one or more of the degraded sites studied. The species found in degraded sites belonged to various life forms (shrubs, perennial, and annual herbs). Additionally, the colonization exhibited notable differences with the typical succession sequence of pioneer, intermediate, and mature species. Many of them were considered typical of climax states in previous studies, such as the case of Larrea divaricata and Larrea cuneifolia. The discussion addresses the succession process in arid lands and highlights the importance of considering ANR with more emphasis on restoration efforts.

In the current ecological environment restoration assessment process, there are problems such as imperfect models and difficulty in quantifying and standardizing assessment results. In this paper, a multidimensional assessment model based on fuzzy comprehensive evaluation method and analytic hierarchy process (AHP) is proposed, combining the actual needs of ecological environment restoration and the shortcomings of existing assessment methods. First, the indicator weights are constructed by AHP method, and then the fuzzy comprehensive evaluation method is used to comprehensively evaluate each restoration project, and the results are visualized using Citespace. Finally, typical soil, vegetation and water quality ecological restoration cases are selected, and the model is applied to actual assessment. The vegetation coverage increased from 25% in 1 month to 60% in 6 months, and the species diversity index increased; the vegetation biomass increased from 4t/ha in 1 month to 10t/ha in 6 months, an increase of 6t/ha. This paper provides strong theoretical support and methodological guidance for the field of ecological environment restoration.

No abstract available

Over the past decades, ecological restoration initiatives in China have made great progress in restoring degraded forests and increasing vegetation coverage, yet the carbon sequestration effects of these initiatives in the context of climate change are not clear. In this study, we assessed the effects of vegetation restoration on gross primary production (GPP) in China’s forestry engineering areas, where large-scale vegetation restoration programmes were launched, during 2001–2020 by disentangling the respective roles of land cover change (LCC), CO2 fertilization, and climate changes using a two-leaf light use efficiency model. We found that LCC attributed by the vegetation restoration dominantly accelerated the increase of GPP in seven out of the eight areas, and CO2 fertilization played a near-equivalent role in all areas. By contrast, the changes in different climate factors contributed to GPP variations diversely. The solar radiation variation greatly inhibited the vegetation GPP over time in seven out of these areas, and the changes in air temperature and vapor pressure deficit regulated GPP inter-annual variations without clear trends in all areas. This study advances our understanding of the contribution of China’s afforestation on its forest GPP in a changing climate, which may help to better manage forests to tackle the challenge of the climate crisis in the future.

Background Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features. Results In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes. Conclusions In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China.

China has implemented a series of ecological protection and restoration projects in Tianshan Mountain and Yili Valley in Xinjiang, which have significantly improved regional vegetation coverage. Vegetation improves soil structure through roots, especially increasing non-capillary porosity, which enhances the precipitation infiltration performance, thus reducing surface runoff, increasing the interception and infiltration of groundwater resources, and enhancing regional water retention capacity of soil. In order to quantitatively study the impact of ecological conservation and restoration (represented by fraction of natural vegetation coverage, FVC) on groundwater storage (GWS), we investigated GWS changes in this region, identified the main factors, and quantified their relative impacts. Here, we combined data from the Gravity Recovery and Climate Experiment (GRACE) satellite, GRACE Follow-On (GRACE-FO), and Global Land Data Assimilation System (GLDAS) hydrological model from January 2003 to December 2020 and evaluated GWS changes. We used the variable importance in projection and partial least squares regression methods to determine the main influencing factors. We found that (1) before and after 2012, GWS decreased at a rate of 0.80 cm/yr and 0.75 cm/yr (with statistical significance p < 0.01), respectively. (2) Before 2012, the main factors affecting the decrease in GWS were agricultural planting areas, and after 2012, they were temperature, evaporation, and FVC, with relative contributions of 54.72%, 34.59%, and 10.69%, respectively. FVC has a positive regulating effect on the increase in regional GWS.

Coastal green infrastructure has increasingly been touted as a sustainable alternative to traditional gray infrastructure such as bulkheads and seawalls. In particular, aquatic vegetation (e.g., salt marshes, seagrasses) has been cited for its capacity to buffer storms and protect coastlines. One of the key remaining challenges with utilizing these (eco)systems is engineers’ ability to gauge their efficacy in a variety of environmental conditions. In order for green infrastructure to be a viable option for coastal communities, engineers need the tools to rigorously evaluate the expected performance of a proposed project, whether it be a marsh restoration or hybrid levee system. For the evaluation of nearshore coastal green infrastructure, engineers turn to hydrodynamic models.

The excessive exploitation of mineral resources will lead to environmental pollution, resource depletion, environmental disaster, and other problems. The contradiction between the environment and development, and the management of the ecological environment in mining areas are urgent p-problems to be solved. An ecological environment assessment is an important part of the ecological environment in a mining area. The accurate evaluation of the ecological environment is the premise behind environmental governance in a mining area. However, current ecological assessment indicators were not developed specifically for mine environment monitoring and, thus, cannot provide an effective and comprehensive assessment of the mineral environment. To this end, in order to improve the environmental monitoring performance in mining areas, a novel Mine-Specific Eco-Environment Index (MSEEI) was proposed, integrating factors from five main aspects associated with minerals, including temperature, vegetation, soil moisture, atmospheric environment, and mining scale. Meanwhile, a widely concerned mine—Luanchuan mine—was used as the case area to test the performance of our MSEEI. The results showed a significant correlation between RSEI and MSEEI (p < 0.01). The mean correlation achieved between RSEI and MSEEI was 0.91, which was much higher than the correlations between RSEI and enhanced vegetation index (EVI), soil moisture monitoring index (SMMI), normalized difference built-up and soil index (NDBSI), PM2.5 concentration (DI), and heat (LST). In addition, based on our long-term MSEEI results of Luanchuan mine from 1997 to 2021, the ecological status of Luanchuan mine showed a trend of first declining and then rising. Specifically, the MSEEI first declined from 0.85 to 0.77 between 1997 and 2012, and then rebounded to about 0.8 in recent years. The MSEEI exhibited a good applicability in the ecological assessment of mining areas. Our MSEEI can provide useful guidance for mine environment monitoring. MSEEI can directly reflect the ecological damage after mining, provide scientific guidance for the exploitation and utilization of mineral resources, and promote the protection and sustainable development of Earth’s resources and mine ecological environments.

Propelled by rapid economic growth, the southwestern Shandong urban agglomeration (SSUA) in China has become a crucial industrial hub, but this process has somewhat hindered vegetation growth and environmental quality. Leveraging the functionalities of the Google Earth Engine (GEE) platform, we derived the fractional vegetation coverage (FVC) through the Normalized Difference Vegetation Index (NDVI) and assessed the eco-environmental quality using the Remote Sensing Ecological Index (RSEI). To examine the patterns and shifts in the SSUA, we employed the Theil–Sen median slope estimation, which provided robust estimates of linear trends, the Mann–Kendall trend test to determine the statistical significance of these trends, and the Hurst exponent analysis to evaluate the long-term persistence and predict future changes in the vegetation coverage and eco-environmental quality. Furthermore, to explore the interdependencies between vegetation coverage (VC) and environmental quality, we applied an improved coupling coordination degree model (ICCDM). This model allowed us to assess the co-evolution and synergy between these two factors over the study period, providing comprehensive insights for sustainable urban and ecological planning in the region. The VC and eco-environmental quality improved consistently across most of the SSUA from 2000 to 2020. The dominance of VC had transitioned from being predominantly characterized by relatively high VC to being mainly characterized by high VC. A substantial portion of the SSUA is predicted to experience improvements in its VC and environmental quality moving forward. Furthermore, the coupling coordination relationship between VC and environmental conditions in the southwest of Shandong Province generally exhibited a state of orderly coordinated development. With the passage of time, there was a clear tendency toward expansion in the coupled uncoordinated areas distributed in a network within each regional economic center. Our research unveils the dynamics and spatial-temporal patterns of VC and ecological quality in the southwestern Shandong urban agglomeration (SSUA) and elucidates the coupling and coordination mechanism between these two aspects, which provides theoretical support for understanding the healthy development of vegetation and ecology in urban agglomerations in an industrial context.

Vegetation change and ecological quality of the Loess Plateau (LP) are directly related to ecological protection and high-quality development of the Yellow River Basin. Based on LP ecological zoning and multisource remote sensing data, we analyzed vegetation change and its relationship with climate, terrestrial water storage (TWS), and land use/cover change from 2000 to 2020, using the normalized difference vegetation index (NDVI), fraction of vegetation cover (FVC), and net primary productivity (NPP). And ecological environmental quality was evaluated based on the remote sensing ecological index (RSEI). The results showed that the spatial distribution pattern of NDVI, FVC and NPP decreased from southeast to northwest in the LP as a whole. Vegetation in the LP recovered significantly, and NDVI, FVC, and NPP showed significant increases of 35.66%, 34%, and 54.69%, respectively. The average NDVI and FVC in the earth–rocky mountainous region and river valley plain region (Area D) were the highest, but the growth rate was the slowest. The average NDVI, FVC, and growth rates in the loess hilly and gully regions (Area B) were slightly higher than those in the loess sorghum gully region (Area A). The average NDVI, FVC, and NPP in the sandy land and agricultural irrigation regions (Area C) were the lowest but showed significant increase. RSEI in most LP areas changed from poor to medium, increasing by 43.45%. Precipitation is the basic factor affecting vegetation cover pattern, with the increase (40.79 mm/10a) promoting vegetation restoration in the LP. Vegetation restoration lost much TWS (−0.6 mm/month), and Area D had the highest average NDVI, FVC, and NPP but the largest TWS loss. Anthropogenic land use/cover change (LUCC) (decrease in cultivated land and unused land; increase in forest, grassland, and construction land) is the primary factor affecting LP vegetation change. This study provides a scientific reference for further vegetation restoration in the LP.

Priority ecological reserves (PER) aim to protect areas with significant ecological value and crucial ecological functions, optimizing resource allocation to maximize the benefits of ecological conservation. However, most previous studies have considered only ecosystem services (ESs) in delineating PER, neglecting eco-environmental quality (EEQ). This study used the Remote Sensing-based Ecological Index (RSEI) to represent EEQ and combined it with ESs to delineate PER at the county scale in the Yellow River Basin (YRB). Additionally, it employed Multiscale Geographically Weighted Regression to identify the driving factors influencing the ESs and EEQ of PER. The results showed that: (1) From 2000 to 2020, both RSEI and the Comprehensive ESs (CES) in the YRB exhibited a fluctuating upward trend; (2) Three types of PER were extracted, with ESs reserve mainly distributed in the upstream region, EEQ reserve primarily in the middle and lower reaches, and integrated ecological reserve mainly in the midstream region, all dominated by vegetation land-use types; (3) Within the extracted PER, RSEI was mainly influenced by soil, aspect, population (pop), PM2.5, temperature (tmp), and potential evapotranspiration (pet), while CES was affected by soil, pop, PM2.5, slope, tmp, precipitation, and pet. To enhance the EEQ and ESs of the YRB, it was recommended to incorporate at least 105,379 km2 into the existing protected areas in the YRB. These areas should be subdivided based on their ecological status, with specific management measures for different types of PER. This study provides recommendations for environmental protection and land planning in the YRB, actively responding to current policies on high-quality development and ecological environmental protection in the YRB.

Urbanization, coupled with industrialization, leads to both economic growth and exponential urban growth, resulting in deteriorating environmental quality in urban areas, which poses a significant threat to the sustainability of cities. Hence, to restore biodiversity and ensure regional sustainability, it is necessary to immediately evaluate the eco-environmental quality of urban areas. The present research investigates the spatio-temporal changes in urban eco-environmental quality of the Asansol industrial city using an integrated ‘Urban Eco-Environmental Index’ (UEEQI) developed utilizing the Google Earth Engine platform and a remote sensing-based approach. The study used four spectral indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), along with Land Surface Temperature (LST) (as a thermal index), derived from the remote sensing data to measure environmental quality. Global Moran’s I and LISA were used to quantify spatial autocorrelation, showing the clustering of similar values or outliers of UEEQ within geographic space. The findings showed that high mean values of NDBI and NDBaI contributed to a lower mean UEEQI value of 0.38 in 2021 compared to previous decades. The spatio-temporal distribution of UEEQI showed that the ‘Very Poor’ category had grown from 0.06% in 1991 to 2% in 2021, while the ‘Poor’, ‘Good’, and ‘Excellent’ categories had declined. Over the 30 years, the UEEQI showed a rising trend of ‘Highly Degraded’ and ‘Degraded’ areas while decreasing the trend of ‘Improved’ or ‘Highly Improved’ environmental quality in the city. Moran’s scatter plot illustrated a highly positive clustered pattern of UEEQ across the city. Hotspots were mainly found in urbanized areas that had an “Average” urban eco-environment quality. Conversely, areas covered with bare surfaces, fallow lands, and brickfields were recognized as Coldspots. This study is crucial for determining specific regions of declining environmental quality and encourages local authorities and decision-makers to integrate eco-environmental conservation zones into city planning to foster healthier, more resilient, and sustainable cities. Over the past 30 years, Asansol industrial city’s environmental quality had significantly deteriorated, highlighting growing ecological challenges. Bare surfaces, fallow lands, and areas close to brickkilns and coal mines exhibited to have “Poor” and “Very Poor” environmental quality. “Highly Degraded” and “Degraded” eco-environmental quality were prominent in industrial, commercial, and densely built-up regions. Over the past 30 years, Asansol industrial city’s environmental quality had significantly deteriorated, highlighting growing ecological challenges. Bare surfaces, fallow lands, and areas close to brickkilns and coal mines exhibited to have “Poor” and “Very Poor” environmental quality. “Highly Degraded” and “Degraded” eco-environmental quality were prominent in industrial, commercial, and densely built-up regions.

In recent years, the Yangtze River estuary ecosystem has been under increasing pressure with the rapid economic development, which restricts the sustainable development of the social economy. Therefore, it is of great significance to assess the ecosystem health status of the Yangtze River estuary. Since the connotation and standards of ecosystem health have not yet formed a unified cognition, there were various methods for the ecosystem health assessment of the Yangtze River estuary in previous studies, and the results of different methods varied greatly. However, most studies believed that the ecosystem health status of the Yangtze River estuary declined since the late 1990s and reached the worst level in the mid-2000 s. After that, the trend of slow recovery appeared, but the current condition was still not optimistic. In the future, it is still necessary to further clarify the connotation and standard of estuarine ecosystem health, innovate the methods of ecosystem health assessment, and study the trend of further development of ecological status, so as to provide a basis for ecological restoration and scientific management of the Yangtze River estuary.

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 With the continuous intensification of mineral resource development in China, ecological issues in mining areas have become increasingly prominent. Traditional ecological restoration technologies exhibit significant limitations in restoration efficiency, monitoring methods, and intelligent decision-making. In recent years, the rapid development of artificial intelligence (AI) and big data technologies has introduced new technical pathways and governance models for mine ecological restoration. From the perspective of AI and big data empowerment, this paper systematically reviews the current research background, mainstream methods, and typical challenges in mine ecological restoration. It focuses on summarizing the latest application advancements of AI and big data in ecological damage identification, dynamic monitoring, restoration effect evaluation, and intelligent decision support. The study reveals that deep learning algorithms based on remote sensing imagery, damage identification systems driven by knowledge graphs, and comprehensive restoration supervision systems built on big data platforms have been successfully implemented in multiple mine restoration cases. The paper also analyzes current technical challenges in data standardization, model interpretability, and cross-regional adaptability, proposing future efforts should focus on multi-source heterogeneous data integration, construction of industry-specific intelligent models, and coordinated policy-technology advancement to achieve digital, intelligent, and refined transformation in mine ecological restoration. This research provides technical support and theoretical references for green mining development and ecological civilization construction.

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近年来水土流失的问题也逐步的被人所关注。国内外有关专家从各个层面对水土流失的原因、影响、规律和有效的控制措施进行了一系列研究。遥感数据与GIS分析技术逐渐成为研究水土流失的有效的数据来源与方法。本文采用定量遥感与GIS分析相结合的方法，以梨树县为例，利用六因子模型计算土壤侵蚀强度，利用NDVI结合二分模型计算植被盖度，并根据水利部水力侵蚀分类标准，对土壤侵蚀强度和植被覆盖度进行分级，研究了2016年梨树县的水土流失强度分布情况，以及植被盖度和水土流失分布的关系。 In recent years, with the rapid development of urban construction, soil and water loss problems also gradually are paid attention by people. A series of studies have been conducted by experts at home and abroad from all aspects of the reason of soil and water loss, effect, rule and effective control measures. Analysis of remote sensing data and GIS technique has become an effective source of data for the research of soil erosion. Based on the analysis of domestic and foreign re-search urban heat island effect, on the basis of a variety of methods, this paper adopts the method of combining the quantitative analysis of remote sensing with GIS, takes Lishu County as an example, the six factor model is used to calculate the soil erosion intensity, NDVI and binary model are used to calculate vegetation coverage, and according to the hydraulic erosion classification standard of ministry of water resources, the strength of soil erosion and vegetation coverage was graded, 2016 Lishu County soil erosion intensity distribution, and the relationship between vegetation coverage and soil erosion distribution are studied.

Re-establishment of submerged plants is widely used in restoration of eutrophic shallow lakes, with water quality varying significantly, which may be related to sediment characteristics. This research aimed to study the effects of ecological restoration on the sediment phosphorus form and the water phosphorus concentration in a shallow lake. The South Lake in Jinan University is a shallow eutrophic lake which was restored by recovering submerged plants after reducing external loading. This research selected sampling sites with different water quality and collected water and sediment samples. Sediment phosphorus was analyzed using a sequential extraction scheme as six forms of NH 4 Cl-P (loosely sorbed phosphorus), Fe-P (iron bound phosphorus), Al-P (aluminum bound phosphorus), Bio-P (bioavailable phosphorus), Ca-P (calcium bound phosphorus) and Ref-P (refractory phosphorus). The results showed that sediments density was significantly higher in Site 2 which had more abundant submerged plants than Site 1. The Fe-P, Al-P and Ref-P content in sediments and P-MSP (Phosphorus Maximum Solubilization Potential) were higher in Site 2 (4.87 mg/L) than in Site 1 (2.58 mg/L), whereas the phosphorous concentration of water was lower in Site 2 than in Site 1. The concentration of TP, PP, TDP and SRP in Site 1 was 1.29 times, 1.11 times, 1.17 times and 1.14 times higher than in Site 2, respectively. These findings indicate that submerged plants can inhibit the release of phosphorus from sediments and increase the phosphorus retention by sediments, and thus reduce phosphorus concentration in lake water.

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Protected areas (PAs) are crucial for conserving species and ecosystems but are still susceptible to deforestation and degradation from human and natural causes. The Uaymil Protected Area in Quintana Roo, Mexico, is a key ecological corridor facing deforestation risks due to its location. Due to this the objective of this study was to evaluate the conservation status and analyze the spatial temporal changes within vegetation type of the protected area of flora and fauna “Uaymil” using the Ecosystem Quality Index (EQI). MODIS Terra satellite data for Leaf Area Index (LAI), Gross Primary Productivity (GPP), and Fractional Vegetation Cover (FVC) were used to calculate the annual EQI over 23 years. The results showed a strong integration of LAI, GPP, and FVC into the EQI, improving the model's ability to capture ecosystem quality changes. Significant shifts occurred in 2005, 2011, 2015, and 2023, indicating both degradation and recovery. Lower EQI values were found in mangrove and marsh areas, while forests had higher ecological indicators. Overall, the Uaymil Protected Area maintains high vegetation cover and ecosystem quality, indicating a strong conservation status.

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Habitat quality is a key indicator of ecosystem services. However, current habitat quality assessment methods mainly depend on land-use types, which ignore the differences within the specific land-use type and have difficulty reflecting the actual situation of an ecosystem. Therefore, this study proposes an improved habitat quality assessment method that incorporates vegetation growth status by introducing the leaf area index (LAI). This method first uses the LAI to assess pixel-level habitat suitability and then incorporates threat indicators for refined habitat quality evaluation. Finally, the proposed method is used to assess habitat quality and its changes on the Qinghai‒Tibet Plateau (QTP). The results show that the proposed method can effectively distinguish habitat suitability differences among pixels with the same land use type, enabling a more reasonable and precise evaluation of habitat quality. Habitat quality assessment on the QTP revealed that most regions improved between 2000 and 2020, except for urban areas, southeastern forests, and the Qiangtang region, where significant declines occurred. In particular, the Ruoergai Wetland, Qilian Mountains, Datong Beichuan River Source, and Yellow River Source exhibited greater improvements, with net habitat quality growth exceeding 30%. Furthermore, the proposed method has great potential for habitat quality assessment in other regions with various vegetation growth conditions, which will provide further support for environmental management.

This study aims to assess the quality and health of vegetation cover in Thi-Qar Governorate using spectral drought indices (NDVI, VCI, TCI, and VHI) derived from satellite imagery for the period 1990–2023. The objective is to diagnose the dynamics of vegetation condition and to monitor the intensity of water and thermal stress in arid and semi-arid environments. Remote sensing data were analyzed using Landsat satellite images (TM, ETM+, and OLI) within a Geographic Information Systems (GIS) environment to identify the spatial and temporal changes in the spectral indices.The results showed that vegetation indices (NDVI) indicated the dominance of very severe and severe degradation levels over large areas, reaching a peak in 1990 with an area of 98.77 km² (99.64%), reflecting the region’s exposure to recurrent drought events and weak vegetation regeneration. The Vegetation Condition Index (VCI) revealed the prevalence of very severe drought classes in most years, reaching 54.66% in 1990 and 92.62% in 2018. Meanwhile, the Thermal Condition Index (TCI) showed a marked increase in thermal stress, peaking in the years 1990, 1996, and 2023, with an affected area exceeding 60 km² (>60%) of the total study area. The Vegetation Health Index (VHI) reflected clear environmental degradation, as the area classified as very severe drought reached 59.34 km² (59.86%) in 2023.These findings indicate that Thi-Qar Governorate suffers from high environmental fragility and extreme sensitivity to climatic variations. The dynamics of vegetation cover are directly affected by thermal and moisture drought factors and by fluctuations in rainfall. The study recommends the implementation of rehabilitation and integrated management programs based on increasing permanent vegetation cover, rainwater harvesting, and improving soil properties to limit vegetation degradation and enhance its sustainability.

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Ecological Environmental Quality (EEQ) is an important measure of evaluating the comprehensive characteristics of ecosystem elements, structure, and function that can reflect the strengths and weaknesses of the regional ecological environment. In the present study, an attempt has been made to assess the Eco-Environment Quality (EEQ) and its spatio-temporal changes in Darjeeling and Kalimpong districts using Remote Sensing based Ecological Index (RSEI) during the years 1999 and 2022. It is assessed by synthesizing four ecological indicators viz. Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up and Bare Soil Index (NDBSI), Land Surface Moisture (LSM), and Land Surface Temperature (LST) representing greenness, dryness, wetness, and heat. Further, Principal Component Analysis (PCA) is performed within the PressureState-Response (PSR) Framework, where NDBSI is put under the “Pressure” category, NDVI under “State” and “LSM” & “LST” under the “Response” category. The results indicate that mean RSEI values have increased from 0.49 in 1999 to 0.58 in 2022, showing an overall improvement of 15% in ecological environment quality during the study period. The spatial distribution of ecological indicators shows that the northern part of the study area (part of Darjeeling Himalaya) has relatively higher values in case of NDVI and LSM which indicates better ecological quality; while the southern region which is the plain area shows relatively higher values in case of NDBSI and LST, indicating poor ecological quality of the region.

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The continuous deterioration of terrestrial ecosystems has led to the destruction of many biological habitats in recent years. The Tumen River cross-border basin, an important biological habitat, is also affected by this changing situation. Assessing habitat quality (HQ) is crucial for restoring and protecting habitats, and vegetation plays a significant role in this process. In this study, we used geographical detector (GD) to extract fraction vegetation coverage (FVC) features and quantify the contribution of driving factors. By coupling vegetation cover and land use data, we assessed HQ. Our findings reveal a declining trend in FVC from 2000 to 2020, which mainly assumed a spatial pattern inclined from northeast and southwest to southeast. Human activities and natural factors interacted to cause these changes in FVC, with human activities having a more significant impact. Vegetation and land use changes led to a decline in the basin’s HQ index. This study highlights the crucial role of FVC in HQ and provides a relevant scientific reference for optimizing the evaluation of HQ in the Tumen River cross-border basin and promoting the sustainable development of regional ecology.

The northern Shaanxi coal mining area is an important coal production base in China and an ecologically fragile area, and it is of great significance to explore the ecological environment quality and spatial evolution trend of the mining area. Based on Sentinel-2 and MODIS image data, an improved remote sensing ecological index was constructed by principal component analysis method: normalized difference vegetation index, normalized difference water index, normalized differential buildup and bare soil index, and net primary productivity. The ecological environment quality of the northern Shaanxi coal mining area and its influencing factors, as well as the spatial autocorrelation analysis of ecological environment quality, were discussed. The results showed that: 1) The vegetation coverage in the study area showed an overall increasing trend, but it was greatly affected by the average annual temperature. The NDVI index at the mine area is higher than the NDVI index at the non-mine area. 2) The water area gradually decreases with the year, which has a certain negative correlation with the total raw coal production. 3) The NDBB index showed a decreasing trend with the year, and compared with the non-mining area. 4) The coupling of year-by-year precipitation and temperature leads to interannual fluctuation of NPP value. 5) The change of ecological environment quality in the study area is the result of the comprehensive effect of natural factors and human factors. The implementation of ecological protection projects such as geological environmental protection and land reclamation in mining areas also has a certain impact on the trend of ecological environment quality. 6) There was a significant spatial autocorrelation in the quality of the ecological environment in the study area. There are significant “High-High” gathering areas of ecological environment quality within the coal mining area.

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.

The assessment of the ecological quality of the environment in El Kala National Park plays an important role in the protection and management of its tourist potential in the face of ecological constraints that have arisen. The present study is based on the use of remote sensing data; its main objective is to analyze the ecological quality in a protected area using the remote sensing ecological index which is based on the calculation of vegetation indices based on Landsat images taken in 2013 and 2023. This observation period shows that the values of drought, temperature, and humidity in the study area increased while the greenness values decreased. The RSEI index was calculated using principal component analysis of the fourth indicators (NDVI, WET, NDBSI, and LST) which made it possible to quantitatively analyze, monitor, and dynamically evaluate changes in the ecological quality of the environment in this park over the past 10 years. The results obtained show that the spatio-temporal distribution of the ecological quality of the environment of the park experienced a downward trend from 2013 to 2023 with a regression rate of -10.16% for the classes of good and excellent quality ecological. This study is considered a reference for the formulation of measures aimed at protecting the quality of the environment in El Kala National Park, and also a database to determine monitoring indicators for sites characterized by significant tourism potential.

Land reclamation is crucial for restoring ecosystems in mining areas, improving land use efficiency, and promoting sustainable regional development. Traditional single-indicator assessments fail to capture the full complexity of reclamation, highlighting the need for a more comprehensive evaluation approach. This study combines field-measured and remote sensing data to develop multiple evaluation indices, creating a comprehensive framework to assess reclamation effectiveness. A soil quality index based on the Minimum Data Set (SQIMDS) was developed to analyze spatial variations in soil quality, efficiently capturing key soil attributes. Remote sensing data were used to calculate the Dump Reclamation Disturbance Index (DRDI) and the Enhanced Coal Dust Index (ECDI) to evaluate vegetation recovery and ecological improvements. The Comprehensive Evaluation Quality Index (CEQI) was introduced, synthesizing soil, vegetation, and ecological conditions for a holistic assessment. Key findings include significant soil quality improvement over time, with MDS effectively capturing variations; vegetation recovery increased with reclamation duration, though regional disparities were observed; ecological conditions steadily improved, as evidenced by a decline in ECDI values and reduced contamination; and the CEQI reflected overall improvements in reclamation effectiveness. This study offers a practical framework for coal mining land reclamation, providing scientific support for decision-making and guiding effective reclamation strategies for ecological restoration and sustainable land management.

Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

Green space is an integral part of ecotourism, it is also an important indicator f sustainable development. The purpose of this study is to assess the change in green space quality using the Normalized Difference Vegetation Index (NDVI) in the period 2005 - 2021 in Moc Chau district, Son La. Four satellite images including 2 Landsat 5 images and 2 Landsat 8 images taken in 2005, 2010, 2015, and 2021 are used to determine the NDVI value. The green space map is then classified into four classes based on the corresponding value ranges, namely, areas with non-green, low green, moderate green, and dense green. The results show a rapid decrease in areas with dense green while non-green areas and low green areas tend to increase rapidly in the past 16 years (2005 - 2021). This result is considered an important basis in the management and planning of sustainable development in the study area.

Water quality assessment is crucial for understanding the environmental status of wetlands, which are among the most significant ecosystems on the planet. Satajaan Beel, a small yet vital wetland located in the Lakhimpur district of Assam on the floodplains of the Ranganadi River, serves as the focus of this study. This research evaluates various water quality parameters from samples collected at ten stations within the study area. The Water Quality Index (WQI) was determined using the weighted arithmetic method. The results revealed WQI values indicating very poor water quality for most samples: Sample 1 (77.93), Sample 2 (92.60), Sample 5 (75.47), Sample 6 (78.27), and Sample 8 (98.275). Samples 3 (117.38), 4 (113.47), 7 (131.79), and 10 (119.23) were deemed unsuitable for use without proper treatment, while Sample 9 (46.02) was the only one indicating good water quality. Additionally, the study assessed the biodiversity status of the area. The Normalized Difference Vegetation Index (NDVI) calculation revealed a significant degradation of aquatic vegetation, with a calculated degradation rate of 2.84 acres or 7.84%. A survey conducted from 2018 to 2019 recorded 262 species of vascular plants within this wetland. The study also identified 42 species of fish belonging to 19 families, highlighting the ecological diversity and the need for conservation efforts in Satajaan Beel.

Abstract This study aims to assess the spatio-temporal dynamics of vegetation in the Zouagha Forest, located in northeastern Algeria, over the period 2000-2020, taking into account the degradation induced by the complex interactions between biotic, abiotic, and anthropogenic factors. The analysis of vegetation indices, notably the NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), for the years 2000, 2010, and 2020 reveals divergent trends. The NDVI shows a slight increase, rising from 0.20 in 2000 to 0.33 in 2020, suggesting a moderate regeneration of the vegetation cover. In contrast, the EVI records a notable decline, dropping from 0.38 in 2000 to 0.30 in 2020, indicating a continuous degradation in vegetation quality. Additionally, the SAVI shifts from −0.10 to 0.07, reflecting a modest improvement, yet the vegetation remains weak in terms of density and quality. This divergence between the indices suggests a spatial variation in vegetation conditions, indicating a growing heterogeneity in vegetation distribution across the forest. This phenomenon is exacerbated by unfavorable climatic conditions, such as reduced rainfall and rising temperatures. These changes, combined with anthropogenic pressures such as deforestation, greatly limit the forest’s natural regeneration capacity. Moreover, pest attacks, particularly by (Lymantria dispar) and (Tortrix viridana), contribute to uneven degradation of the forest ecosystem. These findings highlight the urgency of developing sustainable management strategies, incorporating soil restoration and water resource management, to strengthen the ecological resilience of the Zouagha Forest in the face of growing challenges posed by climate change and human pressures.

This research paper analyzes the dynamics of vegetation cover in the territory of Uralsk for the period from 1999 to 2025 using the methods of remote sensing (data from Landsat missions) and the NDVI vegetation index. Remote sensing works make it possible to objectively observe the dynamics of the spatial distribution of vegetation cover over the years. This method can quickly and economically assess changes in the quality and area of urban green areas and serve as the basis for their effective management. Based on the analysis of space surveys, three distinct stages of development of changes in the area of vegetation in the city area can be identified: active degradation (1999-2010), in which the share of the area of vegetation decreased from 18.9% to 13.9%. The stabilization period (2010-2020) and the recovery period observed on the basis of space surveys for 2020-2025. During this period, the share of green areas increased to 26.2%. This positive dynamic is associated with organized landscaping measures, such as the creation of the "Directorate of Parks and squares in Uralsk" of the city hall. The study notes that the high rate of green area per inhabitant (536.5 M2) is mainly due to the natural floodplain forests and steppe areas included in the administrative boundaries of the city, while recreational areas are still limited. The results are useful for planning and managing urban green infrastructure.

Yuxi, located in China’s central plateau of Yunnan, is grappling with ecological and environmental challenges as it continues to develop its economy. While ecological quality assessment serves as the foundation for ecological protection, it is pivotal to have reliable and long-term methods for assessing the ecological status to support informed decision-making in ecological protection. Reliable and long-term methods for assessing ecological status in order to facilitate informed decision-making in ecological protection are applied. This study utilized Landsat data to reconstruct four indices (greenness, wetness, dryness, and heat) during the vegetation growth in Yuxi from 2000 to 2020 that employs Harmonic Analysis of Time Series (HANTS) method. Subsequently, the annual Remote Sensing Ecological Index (RSEI) was computed by using the reconstructed indices to evaluate ecological quality in Yuxi. Additionally, spatiotemporal patterns and determinants of Yuxi’s ecological quality are unveiled through Sen’s slope estimator and Mann–Kendall test (Sen + MK) trend analysis, spatial auto-correlation analysis, and geographical detectors applied to year-by-year RSEI data. The findings in the paper indicate that the accuracy of the RSEI is significantly influenced by the vegetation season, suggesting that constructing the RSEI model with data from the vegetation growth season is crucial. Moreover, the HANTS optimization method effectively enhances the ecological indices used in the RSEI model, leading to smoother and more continuous filling of missing data. The difference between the reconstructed RSEI and the original RSEI falls within the range of − 0.15 to 0.15. Yuxi has an average RSEI of 0.54 to emphasis a moderate level of comprehensive ecological quality. Compared with river valley plains, the ecological quality of mountainous areas is higher, and the ecological quality of Yuxi presents a distinct center-edge pattern. From 2000 to 2020, Yuxi’s ecological quality exhibited fluctuations, with a slight overall improvement. Land use patterns, particularly in forestry land and impervious surfaces, are identified as the main drivers of these changes. The research offers valuable insights for scientific decision-making related to sustainable development and ecological protection.

The ecological conditions in urban area are greatly changed during the process of industrialization and urbanization of China. The pressure-state-response (PSR) framework is the most popular method to evaluate the ecological quality by integrating a set of remote sensing and statistical indicators into one index through a weighting method. However, a completely remote-sensed ecological index (RSEI), integrating normalized difference vegetation index (NDVI), Wet, land surface temperature (LST), and the normalized differential build-up and bare soil index (NDBSI) through principal components analysis (PCA) method, has been proposed to assess the regional ecological quality. The publications about urban ecological evaluation by RSEI often focus on only one city or a certain area and there are few types of research on the ecological quality assessment by RSEI of 35 major cities in China. In this paper, we employed RSEI to monitor the changes in the ecological quality in China’ 35 major cities. The results of RSEI were compared to that of PSR and stepwise regression method was applied to establish the quantitative relationship among RSEI, NDVI, Wet, NDBSI, and LST. The results show that there are 18 cities with ecological quality deteriorated, mainly located in the east and southwest of China (Shanghai, Guangzhou, Hongkong, Macao, Nanjing, Haikou, Shijiazhuang, and Xi’an), and 17 cities with better ecological quality, mainly located in the north and central area of China (Beijing, Tianjin, Shenzhen, Taipei, Fuzhou, Chongqing, and Jinan), from 1990 to 2015. The 3D-scatter plots of RSEI, NDVI, Wet, NDBSI, and LST demonstrate that the levels of very bad and bad mainly situate in where with a high density of built-up and low vegetation cover and soil water content. The PSR map, acquired from integrating 17 indicators, is quite similar to that of RSEI generated by merging only four remote-sensed indicators. This indicates that RSEI can be adopted to characterize regional ecological quality. Take the quantitative equation of Shanghai in 2015 as an example, every 1.46 decrement in NDBSI or each 3.72 increments in NDVI value can result in one increment in RSEI value and the ecological quality can be improved. Specifically, the expansion of the built-up area can lead to ecological degradation, and vegetation construction can promote eco-environmental quality.

Rapid global urbanization and its progress have profoundly affected urban vegetation. The ecological quality of urban vegetation is a vital indicator of regional ecological stability and health. A comprehensive assessment of the coupling coordination and coercive relationship between urbanization and the vegetation ecological quality is essential for promoting sustainable regional green development. Using the rapidly urbanizing Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration in China as an example, this study evaluates the vegetation quality condition and the level of urbanization and explores the dynamic relationship between vegetation ecological quality and urbanization processes. This study introduces the vegetation ecological quality index (VEQI) based on net primary productivity (NPP) and fractional vegetation cover (FVC), as well as the comprehensive urbanization index (CUI) derived from gross domestic production (GDP), population density, and nighttime lighting data. The coupling coordination and Tapio decoupling models are employed to assess the degree of coupling coordination and the decoupling relationship between the VEQI and CUI across different periods. The results showed that (1) from 2000 to 2020, the VEQI in the GBA showed a significant increase, accompanied by continuous urbanization, particularly evident with the high CUI values in central areas; (2) the coupling coordination degree (CCD) exhibits high values and significant change slopes in the central GBA, indicating dynamic interactions between urbanization and vegetation ecological quality; (3) the decoupling states between the VEQI and CUI are dominated by weak decoupling (WD), strong decoupling (SD), expansive negative decoupling (END), and expansive coupling (EC), suggesting improvements in the relationship between urbanization and vegetation ecological quality; (4) the coordinated development level of the VEQI and CUI in the study area shows improvement, and their decoupling relationship displays a positive trend. Nevertheless, it remains crucial to address the impact of urbanization pressure on vegetation ecological quality and to implement proactive measures in response. The results of this study provide theoretical support for mesoscale development planning, monitoring vegetation ecological conditions, and formulating environmental policies.

The mining of ilmenite has irreversible negative environmental impacts on the ecosystem of the area where mining companies operate. First of all, it leads to disturbance of the soil and vegetation layer, changes in the natural landscape, formation of depression sinkholes, which causes changes in water flow and water distribution in the mining area, lowering of groundwater levels, pollution of the atmosphere, soil and water bodies, and loss of species diversity of flora and fauna. In general, the mining process lasts for decades, during which time the territory is subject to irreversible changes and disturbances and requires high-quality restoration after the completion of ilmenite mining. The article suggests a methodology for assessing the forest vegetation potential of soils in areas disturbed by ilmenite mining using remote earth sensing (RES). Based on satellite images and spectral characteristics, we determined the parameters of soil type and moisture, as well as the vegetation and moisture index of the forest vegetation layer The results of the remote earth sensing were compared with the results of laboratory analyzes of soil samples from the territory operated by the branch of the Irshansk Mining and Processing Plant of PJSC UMCC. Normalized Difference Vegetation Index, Normalized Difference Moisture Index, soil type and moisture were calculated and identified using QGIS software from data obtained from free-access satellite images. The results showed that a combination of laboratory and remote sensing methods can be quite effective for studying areas disturbed by mining activities and the state of their recovery after reclamation.

No abstract available

This study applied the improved remote sensing ecological index (IRSEI) to evaluate the spatiotemporal dynamics of urban ecological quality in Ho Chi Minh city from 2016 to 2024. The IRSEI framework integrated four key ecological indicators: the enhanced vegetation index (EVI), fractional vegetation cover (FVC), impervious surface area (ISA), and land surface temperature (LST), all derived from Landsat 8 and Landsat 9 imagery. These indicators were synthesised into a composite ecological index using principal component analysis (PCA), providing an integrated measure of urban ecological conditions. Temporal comparisons indicate notable improvements, with a marked reduction in areas classified as “very low” quality, while zones of “acceptable” and “good” quality expanded considerably. Despite these positive trends, central districts continue to show degraded ecological conditions due to intensive urbanisation and the increasing influence of climate change. Results from the Kolmogorov–Smirnov test confirmed significant temporal shifts in IRSEI distributions between periods (p < 0.001). The findings demonstrate that IRSEI offers a robust tool for long-term ecological monitoring, supporting urban planning and guiding sustainable development strategies in rapidly urbanising regions.

The Liaohe River Estuary Wetland, located in Panjin City, plays a critical role in reducing pollution loads, maintaining biodiversity, and ensuring ecological security in China’s coastal regions, contributing significantly to the implementation of the land–sea coordination strategy. As key components of ecological restoration projects, monitoring and evaluating restoration effectiveness provide a reliable basis for decision-making and ecosystem management. This study established an innovative three-dimensional integrated monitoring and evaluation system combining satellite imagery, UAV aerial photography, and field sampling surveys, addressing the technical gaps in multi-scale and multi-dimensional dynamic ecological monitoring. Through systematic monitoring and the assessment of key indicators, including water environment, soil environment, biodiversity, water conservation capacity, and carbon sequestration capacity, we comprehensively evaluated the enhancement effects of ecological restoration projects on regional ecosystem structure, quality, and service functions. The findings demonstrated that the satellite–airborne–ground integrated monitoring technology significantly improved water quality and soil properties, enhanced soil–water conservation capabilities, and increased biodiversity indices and carbon sequestration potential. These results validate the scientific validity of ecological protection measures and the comprehensive benefits of restoration outcomes. The primary contributions of this research lie in the following: developing a novel monitoring framework that provides critical data support for decision-making, project acceptance, effectiveness evaluation, and adaptive management in ecological restoration; establishing transferable methodologies applicable not only to the Liaohe River Estuary wetlands, but also to similar ecosystems globally, showcasing broad applicability in ecological governance.

Generally, the importance of monitoring tools in watershed management, particularly ecosystem restoration strategies, has been widely recognized. However, fewer studies have been conducted in areas with limited data. This study aimed to describe the effectiveness of restoration-associated projects linked to monitoring plan-based. First, we used a literature review to analyze various restoration projects developed in some countries. In most cases, the key to successfully implementing restoration strategies involves continued pre- and post-monitoring data. Next, we discuss a case study of developing restoration approaches in a floodplain area of the Batanghari watershed, Sumatra, Indonesia, with more data availability necessary. Our results highlighted that spatiotemporal analysis based on long-term hydrological data is one of the essential baselines required for sustainability management. Several improvements related to monitoring approaches need to be conducted in the Batanghari watershed, including an increase in the number of representative monitoring, time frames of monitoring (continuous and seasonal basis), automatization of monitoring methods, and strengthening community participation in monitoring through citizen science. To conserve or restore floodplain ecosystems, monitoring should be an essential component of the restoration strategies plan that needs to be integrated with the decision-making process in the context of watershed scale.

In the context of wetland restoration, the reconstruction of an ecosystem’s structure typically manifests within a relatively short timeframe, while the restoration of its function often necessitates an extended period of time following the implementation of restoration measures. Consequently, it becomes imperative to engage in the comprehensive, long-term dynamic monitoring of restored wetlands to capture timely information regarding the ecological health status of wetland restoration. In this paper, we aimed to precisely assess the ecosystem health of a typical wetland that had been converted from farmland to wetland in Fujin National Wetland Park in 2022. We selected 18 ecological, social, and economic indicators to establish a wetland ecological health evaluation model, and then used the method of an analytic hierarchy process (AHP) to calculate the weights for each indicator and acquire the ecological health index (EHI) score. The results of our study revealed that the ecosystem health index was 3.68, indicating that the FNWP wetland ecosystem was in “good” condition; this result was mainly affected by wetland water quality (0.382). The ecological health assessment of restored wetlands can monitor wetland ecological resources and provide a scientific basis for the management and protection of restored wetlands.

Ecological restoration is an important strategy for mitigating environmental degradation, and the effectiveness evaluation of ecological restoration is of profound significance for the scientific implementation of restoration projects. This study improved the Patch-generating Land Use Simulation (PLUS) model. It was used to simulate the land use patterns under multi-scenarios such as natural development (ND), economic priority (EP), and ecological restoration (ER) in 2030. An evaluation framework covering ecological “Restoration–Monitoring–Effectiveness” (RME) was proposed. Based on 30 m high-resolution remote-sensing data from 2000 to 2020, the land use distribution, landscape pattern changes, and ecosystem services under different scenarios were evaluated and predicted in the Yellow River Basin of Sichuan to verify the effectiveness of the evaluation framework. The results showed the following: (1) Under the ER scenario, the transfer of land use types in 2020–2030 was mainly characterized by an increase in the area of wetlands and a decrease in the area of built-up land. (2) There were obvious differences in land use and landscape patterns under different scenarios. Compared with the ND and EP scenarios, the growth of the construction rate was suppressed in the ER scenario, and the coverage of grassland and wetlands increased significantly. (3) The mean values of ecosystem services in the ER scenario were higher than those in the ND and EP scenarios. These findings clearly indicate that the RME evaluation system can accurately evaluate the ecological restoration effects under multi-scenarios in the future, providing a new perspective for ecological restoration evaluation in other regions.

Ecosystem restoration is crucial for halting biodiversity loss and reversing environmental degradation, playing a key role in addressing the climate crisis and ensuring global human well-being and security. The long-term success and cost-effectiveness of restoration efforts depend on continuous monitoring and management. Here, we explore the main challenges in ecological restoration project implementation, scientific monitoring, and present preliminary findings on changes in plant community composition and diversity. These changes are assessed in response to both short- and long-term climatic conditions, as well as the effects of passive and assisted restoration techniques in Portugal. The projects focus on three distinct contexts: the restoration of a coastal dune system, the rehabilitation of a limestone quarry, and the recovery of agroforestry systems in dryland regions. The ecological restoration of the dunes in S. João da Caparica began in 2014. Scientific monitoring since then has demonstrated the successful establishment of vegetation and faunal communities, alongside positive geomorphological evolution. These results confirm that dune restoration is an effective strategy for protecting coastal ecosystems. The restoration of a quarry site in Arrábida Natural Park started in 1983 and has been under continuous scientific monitoring. After 30 years, the restored vegetation has low similarity to the natural reference and shows a stabilization trend in some recovery indicators, primarily influenced by soil characteristics and the type of restoration intervention (plantations or hydroseeding).Our findings have helped evaluate recovery progress, identify limiting factors, and propose adaptive management strategies to enhance restoration outcomes. Agroforestry systems of oak woodlands (montado) dominating in Portuguese drylands are in decline due to complex environmental pressures, including climate change and unsustainable land use. Over the past decades, several restoration projects have been implemented to enhance their resilience and adaptability to climate change conditions. Its scientific monitoring over the years has provided valuable insights into climate change impacts, guiding land management strategies and informing decision-making to combat desertification and improve the sustainability of these vital dryland agroforestry systems.

Saltmarshes provide important ecosystem services, including coastline protection, but face decline due to human activities and climate change. There are increasing efforts to conserve and restore saltmarshes worldwide. Our study evaluated the effectiveness of Sentinel-2 satellite imagery to monitor landcover changes using a saltmarsh restoration project undergoing its 9th to 12th year of recovery in the megatidal Bay of Fundy in Maritime Canada. Specifically, in 2019–2022, five satellite images per growing season were acquired. Random Forests classification for 13 landcover classes (ranging from bare mud to various plant communities) achieved a high overall classification accuracy, peaking at 96.43% in 2021. Field validation points confirmed this, with high validation accuracies reaching 93.02%. The classification results successfully distinguished ecologically significant classes, such as Spartina alterniflora–S. patens mix. Our results reveal the appearance of high marsh species in restoration sites and elevational-based zonation patterns, indicating progression. They demonstrate the potential of Sentinel-2 imagery for monitoring saltmarsh restoration projects in north temperate latitudes, aiding management efforts.

Xylella fastidiosa subsp. pauca ( Xfp ), has attacked the olive trees in Southern Italy with severe impacts on the olive agro-ecosystem. To reduce both the Xfp cell concentration and the disease symptom, a bio-fertilizer restoration technique has been used. Our study applied multi-resolution satellite data to evaluate the effectiveness of such technique at both field and tree scale. For field scale, a time series of High Resolution (HR) Sentinel-2 images, acquired in the months of July and August from 2015 to 2020, was employed. First, four spectral indices from treated and untreated fields were compared. Then, their trends were correlated to meteo-events. For tree-scale, Very High Resolution (VHR) Pléiades images were selected at the closest dates of the Sentinel-2 data to investigate the response to treatments of each different cultivar. All indices from HR and VHR images were higher in treated fields than in those untreated. The analysis of VHR indices revealed that Oliarola Salentina can respond better to treatments than Leccino and Cellina cultivars. All findings were in agreement with in-field PCR results. Hence, HR data could be used to evaluate plant conditions at field level after treatments, while VHR imagery could be used to optimize treatment doses per cultivar.

This study aims to develop a workflow methodology for collecting substantial amounts of Earth Observation data to investigate the effectiveness of landscape restoration actions and support the implementation of the Above Ground Carbon Capture indicator of the Ecosystem Restoration Camps (ERC) Soil Framework. To achieve this objective, the study will utilize the Google Earth Engine API within R (rGEE) to monitor the Normalized Difference Vegetation Index (NDVI). The results of this study will provide a common scalable reference for ERC camps globally, with a specific focus on Camp Altiplano, the first European ERC located in Murcia, Southern Spain. The coding workflow has effectively acquired almost 12 TB of data for analyzing MODIS/006/MOD13Q1 NDVI over a 20-year span. Additionally, the average retrieval of image collections has yielded 120 GB of data for the COPERNICUS/S2_SR 2017 vegetation growing season and 350 GB of data for the COPERNICUS/S2_SR 2022 vegetation winter season. Based on these results, it is reasonable to asseverate that cloud computing platforms like GEE will enable the monitoring and documentation of regenerative techniques to achieve unprecedented levels. The findings will be shared on a predictive platform called Restor, which will contribute to the development of a global ecosystem restoration model.

No abstract available

The restoration of former peat extraction areas offers an opportunity to integrate nature-based solutions (NbS) into post-extraction land use, enhancing carbon sequestration, biodiversity, and water retention. By rewetting degraded peatlands and implementing NbS-based land-use strategies such as wetland creation and Sphagnum moss re-establishment, these areas can regain essential ecosystem functions while contributing to the EU Green Deal’s climate objectives. However, obtaining sciece-based evidence for the long-term success of these measures requires continuous monitoring to assess their effectiveness in reducing greenhouse gas emissions and improving ecosystem resilience. This study examines how different post-extraction land-use options affect water quality, greenhouse gas emissions, and biodiversity in the former Komppasuo peat extraction area. Baseline measurements were conducted before restoration, and ongoing monitoring tracks the site's recovery into a carbon sink. Key methods include hydrological monitoring, greenhouse gas flux measurements, and standardized vegetation and bird surveys. Active and passive vegetation reintroduction and optimized water level management have been tested to reduce peat decomposition. Drone imagery has been used to monitor spatial changes, providing valuable insights into vegetation development and wetland formation dynamics. Results show that vegetation recovery has progressed rapidly, especially in ash-treated areas, where the pre-treatment has enhanced plant establishment. Wetland habitats have developed diverse ecological conditions, supporting increased species diversity and altering bird community composition. The introduction of water level management structures has facilitated hydrological stabilization, but further adjustments may be needed to optimize conditions for peat-forming vegetation. Initial greenhouse gas data indicate that CO₂ emissions have decreased, but methane fluxes remain variable and require further long-term monitoring to determine net climate effects. Water quality results show that restoration has increased nutrient loading to downstream waters, and after removing peat extraction-related water protection structures, runoff is more nutrient-rich than during peat extraction. These findings underline the importance of site-specific management strategies to minimize unintended environmental impacts while maximizing restoration benefits. Peatland restoration requires balancing multiple, sometimes conflicting, objectives. Hydrological restoration can improve carbon sequestration potential but may temporarily increase methane emissions and nutrient leaching. Long-term monitoring is essential to determine whether degraded peatlands can become carbon sinks and how different land-use strategies influence this process. A critical zone approach is needed in the monitoring framework to fully understand complex interactions between hydrology, soil processes, vegetation dynamics, and greenhouse gas fluxes. Restoring hydrological connectivity is particularly important for ensuring long-term ecosystem recovery and stability.

Channel incision degrades ecosystems by lowering water tables and disconnecting floodplains. Stream restoration often aims to reverse these impacts. However, projects typically receive minimal monitoring, and treatment effectiveness has not been validated. We used trait‐based analysis to evaluate whether two stream restoration techniques—beaver dam analogs (BDAs) and plug‐and‐ponds—raised water tables and increased overbank flooding, whether these altered environmental filters facilitated recovery of riparian plant communities, and how reassembly impacted the representation of traits that influence ecosystem function. We report on a before‐after‐control‐impact study and Bayesian analysis that estimated the probability that treatments affected riparian plant functional diversity and composition. We found a high probability (0.99 and 0.97, respectively) that BDAs decreased functional dispersion by ≥50% and plug‐and‐ponds decreased dispersion by ≥30%. Both treatments increased the relative abundance of high moisture use plants, wetland plants, and plants with high anaerobic tolerance. For example, BDAs increased the relative abundance of obligate wetland plants by 100%, and plug‐and‐ponds increased the relative abundance of facultative wetland plants by 105%, on average. These results suggest treatments modified environmental filters and recovered riparian plant communities. Ecosystem function was likely altered as the streamside plant community reassembled. Small increases in functional divergence suggest both treatments increased resource use efficiency, and we found a high probability of small treatment effect sizes (<20%) related to changes in community‐level C:N and nitrogen fixation. Our results demonstrate trait‐based analysis can detect a rapid response to restoration and offer a cost‐effective monitoring approach to compare treatments across space and time.

Although large‐scale land reclamation (LR) has been implemented in open‐pit metal mining areas, long‐term ecological restoration effects remain unsystematically revealed due to insufficient continuous monitoring, hindering the accurate achievement of mining area ecosystem resilience and carbon neutrality goals. This study proposed an Iron Mine Eco‐Quality Index (IM‐EQI) to better reflect the Malan Iron Mine's ecological quality (1990–2024), with multiple methods exploring IM‐EQI's long‐term temporal evolution, spatial pattern changes, and nonlinear driving mechanisms. The results illustrated that: (1) IM‐EQI had high consistency with the Remote Sensing Ecological Index (RSEI) and the Mine‐Specific Eco‐Environment Index (MSEEI) ( R 2  = 0.90, p  < 0.01) and better characterized the information richness of the iron mining ecosystem; (2) After 2010 reclamation, most areas' ecological environment quality (EEQ) improved sustainably (61.45% mild/significant improvement) with continuous H–H clustering; (3) XGBoost‐SHAP revealed nonlinear relationships/threshold effects between driving factors and IM‐EQI. Single‐factor importance and inter‐factor interaction analyses consistently showed land use dominated IM‐EQI spatial distribution—mining land exacerbated ecological risks and reduced land sustainability, while land use's synergies with precipitation/temperature amplified open‐pit mining's negative ecological impacts. This study's findings provide quantitative support for targeted metal mine reclamation optimization and long‐term ecological management and offer practical paradigms references for “ecology first, green development” in resource‐based regions.

With the continuous advancement of the Hainan Free Trade Port, marine ecological protection faces unprecedented challenges and opportunities. This study focuses on improving the management efficiency of MPAs in Hainan Province by exploring the integrated application of digital technologies such as the IoT, marine big data, remote sensing, and GIS. Based on a comparative analysis of ecological monitoring data from Hainan's coastal waters, the study reveals significant positive outcomes, including a steady increase in live coral cover, enhanced biodiversity indices, and improved water quality—demonstrating the effectiveness of digital tools in ecological restoration and management. The paper further proposes strategies such as developing a unified information management platform, establishing real-time monitoring networks, enhancing early warning and response capabilities, and promoting public participation to achieve the dual goals of conservation and development. The findings indicate that the integration of digital technologies significantly enhances monitoring accuracy, management efficiency, and scientific decision-making, providing both theoretical and practical pathways for sustainable marine ecological governance.

Mining activities have caused extensive vegetation degradation, compromising ecosystem stability and carbon sequestration. This study evaluated the spatiotemporal dynamics and restoration effectiveness of vegetation in the Hengshanli mining area using Sentinel-2A imagery (2019-2024) and DJI Mavic 3M and 3E UAV multispectral data (2024-2025). NDVI exhibited clear seasonal variation with annual peaks in May; 53.30 % of the area showed low to moderate variability, while 19.44 % displayed high variability mainly on steep slopes. From 2019 to 2024, 85.30 % of the region experienced slight degradation, 0.36 % significant improvement, and 4.89 % severe degradation. R/S analysis revealed persistent degradation in 84.60 % of the area, with only 7.50 % showing sustained improvement. UAV-based monitoring recorded substantial recovery from November 2024 to May 2025: GNDVI, NDVI, and FVC increased from 0.28, 0.18, and 18.10 % to 0.35, 0.34, and 44.00 %, respectively, yielding a 143.09 % greening rate. A strong positive correlation between NDVI and GNDVI was observed across all monitoring periods (p < 0.001), confirming their reliability and complementarity for vegetation assessment. Slope-based analysis further indicated that NDVI generally increased with slope; low-slope areas (0-15°) showed lower NDVI (Q6 = 0.31), while mid-to-steep slopes (30-60°) achieved higher values (Q6 = 0.40-0.43) due to engineering-ecological hybrid restoration that improved soil-water conditions. Overall, vegetation stability was greater in low-variability zones, whereas high-variability areas were more responsive to environmental stressors. These findings establish a robust, multi-scale framework for dynamic vegetation monitoring and quantitative evaluation of ecological restoration in post-mining landscapes.

This paper discusses the application of cloud computing and big data analysis technology in ecological restoration on the Qinghai-Xizang Plateau to improve the efficiency and effectiveness of ecological protection and restoration work. The Qinghai-Xizang Plateau, known as the "Roof of the World", is an important ecological security barrier for China, and its ecosystem faces many challenges. The paper analyzes the current situation of ecological protection and restoration, and points out the main problems, including grassland degradation, poor forest ecological quality and wetland degradation. Through big data analysis, research can identify and prioritize key ecological issues and develop scientific restoration plans. Cloud computing technology provides strong support for data storage and processing, making ecological monitoring, assessment and management more efficient. The paper emphasizes the importance of multi-sectoral coordination to ensure the smooth implementation of various ecological restoration projects. At the same time, it proposes future research directions and recommends combining advanced technologies with ecological restoration practices to achieve sustainable ecological environment construction.

Restoration of forest ecosystems is a common objective of land managers throughout the western United States. Unfortunately, limited federal funding and a lack of specific enforcement of existing regulations has resulted in a lack of effectiveness monitoring (monitoring that provides information on the successes and impacts of the activity or project) after forest restoration activities on federal lands, thus inhibiting learning about, and improving the success of, restoration efforts. Monitoring could potentially be conducted on limited federal budgets through use of (1) multiparty teams composed of volunteers on a portion of restoration sites, (2) a statistical sampling strategy on a limited set of sites for intensive monitoring by federal monitoring teams, and (3) remote sensing to monitor a select set of variables across a broad portion of the affected landscape.

Ecological restorations of contaminated sites balance the human and ecological risks of residual contamination with the benefits of ecological recovery and the return of lost ecological function and ecosystem services. Risk and recovery are interrelated dynamic conditions, changing as remediation and restoration activities progress through implementation into long‐term management and ecosystem maturation. Monitoring restoration progress provides data critical to minimizing residual contaminant risk and uncertainty, while measuring ecological advancement toward recovery goals. Effective monitoring plans are designed concurrently with restoration plan development and implementation and are focused on assessing the effectiveness of activities performed in support of restoration goals for the site. Physical, chemical, and biotic measures characterize progress toward desired structural and functional ecosystem components of the goals. Structural metrics, linked to ecosystem functions and services, inform restoration practitioners of work plan modifications or more substantial adaptive management actions necessary to maintain desired recovery. Monitoring frequency, duration, and scale depend on specific attributes and goals of the restoration project. Often tied to restoration milestones, critical assessment of monitoring metrics ensures attainment of risk minimization and ecosystem recovery. Finally, interpretation and communication of monitoring findings inform and engage regulators, other stakeholders, the scientific community, and the public. Because restoration activities will likely cease before full ecosystem recovery, monitoring endpoints should demonstrate risk reduction and a successional trajectory toward the condition established in the restoration goals. A detailed assessment of the completed project's achievements, as well as unrealized objectives, attained through project monitoring, will determine if contaminant risk has been minimized, if injured resources have recovered, and if ecosystem services have been returned. Such retrospective analysis will allow better planning for future restoration goals and strengthen the evidence base for quantifying injuries and damages at other sites in the future. Integr Environ Assess Manag 2016;12:284–295. © 2015 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of SETAC.

The California Prescribed Fire Monitoring Program dataset (2019–2024) provides comprehensive ecological monitoring data from prescribed fire treatments across California’s diverse forest ecosystems. This dataset encompasses forest structure and cover, fuel loads, and post-fire recovery metrics, collected using a standardized protocol, from over 36 disparate sites (114 burn units, 972 plots, and 1,838 total surveys). Data collected during pre-fire, immediate, and multi-year post-fire sampling episodes allow for robust analysis of prescribed fire effects across variable environmental conditions. The monitoring framework captures key ecological indicators, including tree mortality, fuel consumption, understory vegetation response, species composition, and regeneration. This dataset can address critical knowledge gaps regarding prescribed fire effectiveness for ecological restoration, hazardous fuel reduction, and ecosystem resilience objectives. These data can support evidence-based fire management decisions, validate fire effects models, and establish baseline reference conditions for future prescribed fire implementation throughout California’s fire-prone landscapes.

Nature-based Coastal Defense is increasingly used to reduce climate risk, because considered effective, inexpensive and cost-effective, easy to implement, and no-regrets. This article discusses this positive framing through the analysis of 23 projects implemented in French overseas territories, using an ex-post expert judgment method considering enabling conditions (context, governance, funding, social acceptability), risk reduction (technical effectiveness; studies, monitoring and evaluation) and externalities (co-benefits and disbenefits; contribution to adaptation). 80% of projects aimed at reducing coastal erosion; 47.8% were implemented in natural or rural areas; 87.1% included restoration; 82.6% targeted one single ecosystem and 51.7% beach/dune systems; 47.8% were led by public actors; all relied on multiple funding sources. Performance indices range from 39.4 to 77.2%. The highest scores concern governance and social acceptability, and the lowest scores risk reduction. No project included an evaluation of risk reduction and was calibrated for future risk. Internal (i.e. project related) and external (more general) levers and barriers to effectiveness were identified. Internal barriers include the lack of political support to nature-based options, the difficulty to secure the required long-term funding and to upscale action in the face of strong land tenure constraints.

This study monitored of flora and fauna from 2022 to 2024 to investigate changes in biodiversity in relation to the wetland restoration project. A total of 220 plant species from 72 families and 141 animal species from 75 families were identified. Endangered species included five level II endangered plant species, Cicuta virosa , Epilobium hirsutum , Lychnis kiusiana , Lychnis wilfordii , and Euryale ferox , along with one endangered mammal species, Prionailurus bengalensis. Additionally, three plant species ( Solanum carolinense , Hypochaeris radicata , and Humulus japonicus ) and one amphibian species ( Lithobates catesbeianus ) were identified as ecosystem-disturbing species designated under the Act on the Conservation and Use of Biological Diversity in Korea. This study not only evaluates the effectiveness of wetland ecosystem restoration and management but also provides foundational data for future restoration projects and conservation policies, emphasizing the importance of long-term monitoring.

Mangroves play a crucial role in climate change mitigation and coastal protection, yet they face pressures from environmental degradation and limited policy implementation. This study employs a systematic literature review to examine the role of artificial intelligence (AI) in mangrove management and its policy implications in Indonesia. Findings indicate that AI, through machine learning, deep learning, and integration with remote sensing data, is effective in monitoring, mapping, restoration assessment, and predicting ecosystem changes. These technologies have the potential to support evidence-based policymaking, including prioritizing restoration areas, optimizing resource allocation, and enhancing decision-making systems. However, AI adoption faces challenges related to data quality and availability, institutional capacity, and ethical and governance concerns. The study highlights opportunities for developing inclusive and interdisciplinary AI governance frameworks, which could strengthen policy effectiveness and promote sustainable mangrove management in Indonesia.

Mangrove rehabilitation is vital for maintaining coastal ecosystem integrity, mitigating shoreline erosion, and supporting community resilience, particularly in urban and peri-urban areas. This study assessed the survival, growth, and leaf development of mangrove seedlings in a rehabilitated coastal area in Lantebung, Makassar City, aiming to evaluate the effectiveness of long-term restoration interventions. Six plots with diffrent planting densities and environmental conditions were established, and systematic monitoring was conducted monthly from December 2024 to February 2025. Seedling survival rates were calculated, while height and leaf production were measured on 10–15% of randomly selected seedlings per plot. Protective measures, including bamboo-framed beds (Guludan) and