Search Results (383)

Rapid land use and land cover (LULC) changes in densely populated watersheds pose serious challenges to the sustainability of ecosystem services (ES), yet their spatially explicit economic consequences remain insufficiently understood. This study analyzes the spatio-temporal dynamics of LULC and ecosystem service values (ESVs) in the Citarum Watershed, Indonesia, one of the country’s most critical and intensively transformed watersheds. Multi-temporal Landsat imagery from 2003, 2013, and 2023 was classified using a Random Forest algorithm, while future LULC conditions for 2043 were projected using a Multi-layer Perceptron–Markov Chain (MLP–MC) model under three scenarios: Business-as-Usual (BAU), Protecting Paddy Field (PPF), and Protecting Forest Area (PFA). ESVs were quantified at multiple spatial scales (county, 250 m grids, and 100 m grids) using both the Traditional Benefit Transfer (TBT) method and a Spatial Benefit Transfer (SBT) approach that integrates biophysical indicators with socio-economic variables. The contribution of LULC transitions to ESV dynamics was further assessed using the Ecosystem Service Change Intensity (ESCI) index. The results reveal substantial historical forest and shrubland losses, alongside rapid expansion of settlements and dryland agriculture, indicating intensifying anthropogenic pressure on watershed functions. Scenario analysis shows continued degradation under BAU, limited mitigation under PPF, and improved forest retention under PFA; although settlement expansion persists across all scenarios. Total ESV declined from USD 2641.33 million in 2003 to USD 1585.01 million in 2023, representing a cumulative loss of 46.13%. Projections indicate severe ESV losses under BAU and PPF by 2043, while PFA substantially reduces, but does not eliminate economic degradation. ESCI results identify forest and shrubland conversion to settlements and dryland agriculture as the dominant drivers of ESV decline. These findings demonstrate that integrating multi-scenario LULC modeling with spatially explicit ESV assessment provides a more robust basis for ecosystem-based spatial planning and supports sustainable watershed management under increasing development pressure. Full article

The response of Arctic vegetation to climate warming exhibits pronounced spatial heterogeneity, driven partly by widespread permafrost degradation. However, the role of thermokarst lake development in mediating vegetation-climate interactions remains poorly understood, particularly across heterogeneous landscapes of northeastern Siberia. This study integrated multi-source remote sensing data (2001–2021) with trend analysis, partial correlation, and a Shapley Additive Explanation (SHAP)-interpreted random forest model to examine the drivers of normalized difference vegetation index (NDVI) variability across five levels of thermokarst lake coverage (none, low, moderate, high, very high) and two vegetation types (forest, tundra). The results show that although greening dominates the region, browning is disproportionately observed in areas with high thermokarst lake coverage (>30%), highlighting the localized reversal of regional greening trends under intensified thermokarst activity. Air temperature was identified as the dominant driver of NDVI change, whereas soil temperature and soil moisture exerted secondary but critical influences, especially in tundra ecosystems with extensive thermokarst lake development. The relative importance of these factors shifted across thermokarst lake coverage gradients, underscoring the modulatory effect of thermokarst processes on vegetation-climate feedbacks. These findings emphasize the necessity of incorporating thermokarst dynamics and landscape heterogeneity into predictive models of Arctic vegetation change, with important implications for understanding cryospheric hydrology and ecosystem responses to ongoing climate warming. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions. Full article

Investigating vegetation dynamics and their drivers in ecologically vulnerable regions is essential for evaluating ecological restoration outcomes. This study examined the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its influencing factors in Fengning county, the Bashang region from 2001 to 2023 using land use transition matrix, trend analysis, and geographical detector methods. Key findings include the following: (1) Land use transition exhibited a clear phased pattern, shifting from cropland-to-grassland conversion (2001–2010) to grassland-to-forest conversion (2010–2023). (2) The annual mean NDVI increased significantly, showing a southeast–northwest spatial gradient consistent with landforms. The long-term trend followed a sequential “degradation–improvement–consolidation” trajectory. (3) Factor detection identified land use type as the primary driver of vegetation spatial heterogeneity (q = 0.297), highlighting the dominant influence of human activities. (4) Interaction detection demonstrated bivariate enhancement for all factor pairs, with the combination of land use type and precipitation yielding the highest explanatory power (q = 0.440). This underscores that vegetation dynamics are predominantly governed by nonlinear interactions between human-driven land use and climate. The research highlights the effectiveness of ecological restoration policies and offers valuable insights for guiding future ecosystem management in ecologically fragile areas under climate change. Full article

Terrestrial ecosystems in Hunan Province have undergone marked yet spatially heterogeneous vegetation changes under concurrent climate change and intensifying human activities. The aim of this study is to resolve how vegetation responses vary among land-use types by quantifying kernel Normalized Difference Vegetation Index (kNDVI) dynamics during 2000–2023 using precipitation, temperature, and solar radiation, coupled with trend analysis and a partial-derivative-based attribution. Mean kNDVI increased overall at 0.0016 yr⁻¹; vegetation improved over 76.30% of the area, whereas 5.72% of the area experienced degradation. Built-up land exhibited the largest degraded fraction (35.04%). Human activities and temperature emerged as the dominant drivers of kNDVI change, contributing 62.25% and 27.92%, respectively, while precipitation (3.08%) and solar radiation (6.77%) played comparatively minor roles. Spatially, human activities primarily controlled vegetation dynamics in plains and urban clusters (~78% of the area), whereas temperature constrained vegetation in high-elevation mountain ranges. Analysis along the human footprint (HFP) gradient reveals that driver composition remains steady in resilient ecosystems (farmland and forest), despite increasing anthropogenic pressure, whereas fragile ecosystems (grassland and bareland) exhibited pronounced volatility and heightened sensitivity to environmental constraints. These findings provide a quantitative basis for developing sustainable ecological security strategies, incorporating region-specific measures such as adaptive afforestation, sustainable agricultural management, and strict ecological protection, to enhance ecosystem resilience by prioritizing the climate resilience of mountain forests and the stability of fragile grassland systems. Full article

Habitat degradation and fragmentation pose severe threats to biodiversity in protected areas, including the Giant Panda National Park (GPNP). Effective restoration strategies are urgently needed to enhance habitat connectivity and support the recovery of giant panda (Ailuropoda melanoleuca David, 1869) populations. This study aimed to evaluate the impact of targeted artificial restoration measures on plant diversity and community structure in four typical degraded habitats within the Niba Mountain Corridor of the GPNP. Over a three-year monitoring period, vegetation surveys and infrared camera trapping were conducted across pure plantations and secondary forests, with/without bamboo, using suitable habitats as controls. The results showed that: (1) Artificial restoration significantly increased shrub layer species richness and Shannon–Wiener index in most degraded habitats, approaching control levels after two years, while herb layer diversity initially increased then declined due to shrub competition. (2) Sorensen’s similarity between degraded and suitable habitats increased over time, rising from 0.08–0.42 to 0.46–0.67 for the shrub layer and from 0.09–0.22 to 0.30–0.40 for the herb layer. (3) Key species showing high variability during restoration included Litsea pungens Hemsl., Actinidia spp., Salix spp., Rubus spp., Hydrangea macrophylla (Thunb.) Ser, Carex spp., and Elatostema involucratum Franch. et Savat. (4) Bamboo regeneration was enhanced with peak live shoots in 2024. (5) Increased activity of medium-to-large mammals, notably the tufted deer (Elaphodus cephalophus Milne-Edwards, 1872), may indicate initial stages of functional recovery for resources in the restored habitats. The results confirmed that differentiated artificial restoration can effectively promote species diversity recovery and habitat convergence, providing a scientific basis for optimizing GPNP corridor management and improving population connectivity for giant pandas. Full article

Ecological quality (EQ) in the Yangtze River Economic Belt (YREB) has been profoundly reshaped by rapid urbanization and intensive ecological restoration over the past two decades. This study aimed to reveal the long-term spatiotemporal patterns of EQ and their driving forces at the basin scale. We constructed a 1 km, 25-year (2000–2024) Remote Sensing Ecological Index (RSEI) series using MODIS data and applied Sen’s slope, the Mann–Kendall and Hurst tests, and Geographically Weighted Ridge Regression (GWRR) to quantify trends, persistence, and spatially non-stationary driver effects. Results showed a significant overall improvement: by 2024, 69.6% of the YREB is classified as Good or Excellent EQ, with 34.6% of land showing continuous improvement and 6.4% faced persistent degradation risks. Forest and grassland cover exerted stable positive effects, while built-up expansion, population density, and GDP increasingly contribute to EQ decline, and the area dominated by urbanization-related negative coefficients expanded to 84.6% of the middle and lower reaches. The GWRR model achieved high average local $R^2$ (>0.92) and revealed pronounced spatial heterogeneity and multicollinearity-robust driver estimates. This study illustrates the potential of GWRR-based EQ diagnosis to support differentiated ecological governance strategies tailored to the upper, middle, and lower reaches of the YREB. Full article

Enhancing the carbon sequestration (CS) capacity of urban green spaces is crucial for mitigating global warming, environmental degradation, and urbanisation-induced issues. This study focuses on the urban community unit to establish a system of determining factors for the CS capacity of green space, considering the built-up spatial pattern and green space morphology. An interpretable machine learning approach (Random Forest + Shapley Additive exPlanations) is employed to systematically analyse the non-linear relationship of built-up spatial pattern and green space morphology factors. Results demonstrate significant urban zonal heterogeneity in green space CS, whereas southern suburban area communities exhibited higher capacity. In terms of green space morphology factors, higher fractional vegetation cover (FVC) and cohesion were positively correlated with green space CS capacity. Leaf area index (LAI), canopy density (CD), and the evergreen-broadleaf forest ratio additionally further enhanced the positive effect of two-dimensional green space factors on CS. For built-up spatial pattern factors, communities with a high green space ratio and low development intensity exhibited higher CS capacity. And the optimal ranges of FVC, LAI and CD for effective facilitation of community green space CS were identified as 0.6–0.75, 4.85–5.5 and 0.68–0.7, respectively. Moreover, cohesion, LAI and CD bolstered the CS capacity in communities with a high building density and plot ratio. This study provides a rational basis for planning and layout of green space patterns to enhance CS efficiency at the urban community scale. Full article

The exacerbation of Aluminum (Al) toxicity is a leading cause of forest degradation. However, the effects of Al on clone bamboo are not well-characterized. This study examined the influence of Al on bamboo growth using one-year-old Phyllostachys edulis seedlings subjected to control Al treatments, which aim to provide theoretical support for improving the soil quality of bamboo forests. The results indicated that the Al content in the seedlings increased by 86.42% to 162.79% compared to the control. However, it remained within a relatively stable range, with the root being the primary site of accumulation. Among the treatments, the 0.3 mM Al group (Al³⁺) exhibited the highest values in biomass indexes (LB, RB and AGB). In contrast, the 2.0 mM Al treatment led to a significantly higher root-to-shoot ratio (RSR) than other groups. Physiological analyses revealed coordinated responses in key antioxidant enzymes (POD, SOD, CAT) and osmotic adjustment substances (Pro, SP, Bet). These findings demonstrate that P. edulis possesses considerable tolerance to Al, with a significant phenotypic inhibitory effect that was not observed with 2.0 mM Al treatment. Bamboo responds to Al stress through controlling Al absorption, optimizing resource reallocation, and enhancing adaptability physiology capacity, illustrating a comprehensive collaboration adaptive mechanism. Full article

Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index (NDVI) is a key indicator for assessing the growth status of vegetation. However, the insufficiency of existing NDVI datasets in terms of spatiotemporal continuity has limited the accuracy of long-term vegetation change studies. This study proposed a machine learning-based downscaling framework that integrates the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI and the Global Inventory Monitoring and Modeling System (GIMMS) NDVI data to reconstruct a long-term, high-resolution NDVI dataset. Unlike conventional statistical fusion approaches, the proposed framework employs machine learning-based nonlinear relationships to generate long-term, high-resolution NDVI data. Three machine learning algorithms—Random Forest (RF), LightGBM, and CatBoost—were evaluated. Their performance was validated using the MODIS NDVI as reference, with the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (R) as evaluation metrics. Based on model comparison, the CatBoost model was identified as the optimal algorithm for spatiotemporal data fusion (R² = 0.9014, RMSE = 0.0674, MAE = 0.0445), significantly outperforming RF and LightGBM models and demonstrating stronger capability for NDVI spatiotemporal reconstruction. Using this model, a long-term, 1 km monthly GIMMS-MODIS NDVI dataset from 1982 to 2014 was successfully reconstructed. On the basis of this dataset, the spatiotemporal variation characteristics of vegetation in the TRSR from 1982 to 2014 were systematically analyzed. The research results show that: (1) The constructed long-series high-resolution NDVI dataset has a high consistency with MODIS NDVI data; (2) From 1982 to 2014, the NDVI in the TRSR showed an increasing trend, with an average growth rate of 0.0020/10a (p < 0.05). NDVI showed obvious spatial heterogeneity, characterized by a decreasing gradient from southeast to northwest. (3) The Yellow River source exhibited the most evident vegetation recovery, the Yangtze River Source area showed a moderate improvement, whereas the Lancang River Source area displayed little noticeable change. (4) Broad-leaved forests experienced the most significant growth, while cultivated vegetation displayed a marked tendency toward degradation. This study provides both a high-accuracy long-term NDVI product for the TRSR and a methodological foundation for advancing vegetation dynamics research in other high-altitude regions. Full article

In Kazakhstan’s Akmola Region, rural households face heightened vulnerability from climate change, driven by reliance on weather-dependent resources and amplified risks of extreme precipitation events, prolonged dry spells, and progressive soil degradation—further intensified by limited adaptive capacity and inequities affecting women-led or ethnic minority families. This study conducted stratified household surveys across four agricultural districts, developed a tailored Livelihood Vulnerability Index (LVI) incorporating shelterbelt presence, condition, and perceived effects, alongside readiness for hydrological surface recovery (contour–strip organisation, swales/valokany, and tree–shrub planting). Results revealed an average LVI of 0.45–0.55, which was higher (+10–15%) in marginalized groups; testing pathways showed correlations (r = 0.65, p < 0.05) with water security, soil condition, income stability, and hazard reduction, with potential LVI reductions of 15–25% through integrated measures. District-specific recommendations include implementing the Potapenko–Lukin method on slopes <5% with valokany (width 80 cm, depth 1.5 m, spacing 100–500 m), endemic plantings, and biomaterial, supported by subsidies (488,028 tenge/ha/year) and GIS monitoring, to enhance resilience and equity in steppe and forest–steppe farming. Full article

The structure and function of alpine steppes are maintained largely by dominant species, which in turn determine the productivity and stability of plant communities. Nutrient acquisition and stress regulation may, to some extent, be mediated by phyllospheric microbiota at the interface of plants with the atmosphere, and phyllospheric microbes are capable of amplifying and transmitting vegetation responses to degradation. Previous research has mainly addressed climate, soil, vegetation and soil microbiota or has assessed phyllosphere communities as a whole, thereby overlooking the specific responses of phyllospheric bacteria associated with the vegetation-dominant species Stipa purpurea along gradients of vegetation degradation in alpine steppes. In this study, we characterised vegetation degradation at the community level (from non-degraded to severely degraded grasslands) and quantified associated changes in the dominant species Stipa purpurea (cover, height and aboveground biomass) and its phyllospheric bacterial communities, in order to elucidate response patterns within the coupled system of host plants, phyllosphere microbiota, climate (mean annual temperature and precipitation) and soil physicochemical properties. Compared with non-degraded (ND) grasslands, degraded sites had a 22.6% lower mean annual temperature (MAT) and reductions in total nitrogen, nitrate nitrogen, organic matter (OM) and soil quality index (SQI) of 49.4%, 55.6%, 46.8% and 47.6%, respectively. Plant community cover and the aboveground biomass of dominant species declined significantly with increasing degradation. Along the vegetation-degradation gradient from non-degraded to severely degraded alpine steppes, microbial source-tracking analysis of the phyllosphere of the dominant species Stipa purpurea revealed a sharp decline in the contribution of phyllospheric bacterial sources. Estimated contributions from non-degraded sites to lightly, moderately and severely degraded sites were 95.68%, 62.21% and 6.89%, respectively, whereas contributions from lightly to moderately degraded and from moderately to severely degraded sites were 34.89% and 16.47%, respectively. Bacterial richness increased significantly, and β diversity diverged under severe degradation (PERMANOVA, F = 5.48, p < 0.01). From light to moderate degradation, biomass and relative cover of the dominant species decreased significantly, while the phyllosphere bacterial community appeared more strongly influenced by the host than by environmental deterioration; the community microbial turnover index (CMTB) and microbial resistance potential increased slightly but non-significantly (p > 0.05). Under severe degradation, worsening soil conditions and hydrothermal regimes exerted a stronger influence than the host, and CMTB and microbial resistance potential decreased by 6.5% and 34.1%, respectively (p < 0.05). Random-forest analysis indicated that climate, soil, phyllosphere diversity and microbial resistance jointly accounted for 42.1% of the variation in constructive-species biomass (R² = 0.42, p < 0.01), with the remaining variation likely driven by unmeasured biotic and abiotic factors. Soil contributed the most (21.73%), followed by phyllosphere diversity (9.87%) and climate (8.62%), whereas microbial resistance had a minor effect (1.86%). Specifically, soil organic matter (OM) was positively correlated with biomass, whereas richness, beta diversity and MAT were negatively correlated (p < 0.05). Taken together, our results suggest that under ongoing warming on the Qinghai–Tibet Plateau, management of alpine steppes should prioritise grasslands in the early stages of degradation. In these systems, higher soil organic matter is associated with greater phyllospheric microbial resistance potential and increased biomass of Stipa purpurea, which may help stabilise this dominant species and slow further vegetation degradation. Full article

Resource-based regions play an indispensable role as strategic bases for national energy and raw material supply in the global industrialization and urbanization process. However, intensive and large-scale natural resource exploitation—particularly mineral extraction—often triggers dramatic land use/cover changes, leading to a series of problems including cultivated land degradation, ecological function deterioration, and human settlement environment degradation. However, a systematic understanding of the functional transitions within the land use system and their drivers in such regions remains limited. This study takes Shenmu City, a typical resource-based city in the ecologically vulnerable Loess Plateau, as a case study to systematically analyze the transition characteristics and driving mechanisms of land use functions from 2000 to 2020. By constructing an integrated “element–structure–function” analytical framework and employing a suite of methods, including land use transfer matrix, Spearman correlation analysis, and random forest with SHAP interpretation, we reveal the complex spatiotemporal evolution patterns of production–living–ecological functions and their interactions. The results demonstrate that Shenmu City has undergone rapid land use transformation, with the total transition area increasing from 27,394.11 ha during 2000–2010 to 43,890.21 ha during 2010–2020. Grassland served as the primary transition source, accounting for 66.5% of the total transition area, while artificial surfaces became the main transition destination, receiving 38.6% of the transferred area. The human footprint index (SHAP importance: 4.011) and precipitation (2.025) emerged as the dominant factors driving land use functional transitions. Functional interactions exhibited dynamic changes, with synergistic relationships predominating but showing signs of weakening in later periods. The findings provide scientific evidence and a transferable analytical framework for territorial space optimization and ecological restoration management not only in Shenmu but also in analogous resource-based regions facing similar development–environment conflicts. Full article

Mining is an important industry for the achievement of sustainable development goals (SDGs), but it results in a significant amount of degraded land worldwide, thereby affecting local social and ecological sustainability. Little is known about the extent to which this degraded land adheres to the current SDGs. In this study, based on public geographic information data, the status of SDG 11 (Sustainable Cities and Communities) and SDG 15 (Life on Land) for global mine sites was comprehensively assessed. The results show that (1) the global aggregation index for SDG 11 and 15 in mining areas increased from 23.94 in 2000 to 24.48 in 2020, generally exhibiting a positive trend. (2) For SDG 11, all four indicators indicate improvement, suggesting enhancement of the sustainability of cities and communities surrounding global mined land, as well as urban development, mining activities, and economic growth. In contrast, regarding SDG 15, there were noticeable improvements in the water body area and land reclamation ratio, but the forest coverage ratio and net ecosystem productivity significantly declined, indicating continued stress on ecosystems caused by mining. (3) Less than 1% of mines globally met the green grade in SDG 11, and around 97% were categorized as red grade. For SDG 15, no mines reached the green grade, and at least 99.74% were categorized as red grade mines. (4) Globally, the status has exhibited obvious spatial clustering, and the region with a better status is in the equatorial region. There has been obvious spatial heterogeneity within countries, and mine sites near urban areas have had a better status according to these SDGs. The main influencing factors on the status of mines, according to the SDGs, include the degree of mining disturbance, ecosystem recovery capacity, and urban expansion. Overall, the global status of mines according to the SDGs is far from expectation, indicating a considerable gap from achieving sustainable mining and necessitating efforts to improve human habitats and restore ecosystems in mining areas. Future endeavors should focus on strengthening site specific assessment and long-term monitoring of the global SDGs in mining areas to provide foundational data and scientific evidence for sustainable mining and the realization of SDGs. Full article