Search Results (12,184)

As a major economic corridor and ecological barrier in China, the Yangtze River Economic Belt (YREB) has experienced substantial land-use changes that have significantly influenced regional landscape patterns and sustainability. However, the long-term evolution of landscape patterns and their driving mechanisms remain insufficiently understood, particularly considering the spatial heterogeneity among different regions. Based on land-use datasets from 1993 to 2023, this study integrated land-use transfer analysis, grid-based landscape metrics, and the Geodetector model to examine the spatiotemporal evolution of landscape patterns and their driving factors across the YREB. The results showed that cultivated land decreased from 34.63% to 32.78%, forest land increased from 51.51% to 52.36%, and built-up land expanded from 1.36% to 3.61%. Rapid urban expansion was the primary driver of landscape pattern change, leading to increased fragmentation and landscape complexity. Significant regional differences were observed, with relatively stable landscape structures in the upper reaches, transitional changes in the middle reaches, and intensive landscape reorganization in the lower reaches. Both natural and socioeconomic factors influenced landscape evolution, while their interactions generally exerted stronger explanatory power than individual factors. These findings provide valuable support for ecological conservation, land-use optimization, and territorial spatial planning in the YREB. Full article

Background/Objectives: The whole plant of Tetrastigma hemsleyanum is used medicinally, but the tuberous root is by far the most commonly used part, and the way in which its metabolites are partitioned among organs and among geographic origins underpins both the rational choice of the medicinal part and the evaluation of herb quality. Methods: Here, untargeted metabolomics based on ultra-high-performance liquid chromatography coupled to high-resolution Orbitrap mass spectrometry was used to compare roots, stems and leaves collected from nine regions of southern China (81 samples) with tuberous roots collected from 17 sites in seven provinces (51 samples). Annotations were graded according to the Metabolomics Standards Initiative (MSI), curated with explicit plausibility rules, and every conclusion was re-tested across five nested annotation subsets. Results: Organ identity was the dominant source of metabolic variation: the three organs were completely separable (random forest out-of-bag accuracy 100%), the organ effect was about four times larger than that of sampling region (PERMANOVA pseudo-F 16.7 versus 4.1), and this contrast was essentially unchanged from the complete set of 615 annotations down to the most stringent subset of 19 flavonoids and phenolic acids. The organ-level pattern was chemically coherent: amino acids and lipids were relatively enriched in the tuberous root, soluble sugars and phenolic acids in the stem, and flavonoids and alkaloids in the leaf, matching the contrasting roles of a storage, a transport and a photosynthetic organ. Geographic differences among tuberous roots were, by contrast, weak and largely local: although provinces could be separated with 88.9% out-of-bag accuracy, accuracy fell to 42.2% when an entire, previously unseen collection site was held out (chance level 20%), and to 52.9% for a coarse macro-geographic zone (chance level 25%), whereas holding out an entire sampling region left organ classification unaffected (100%). Conclusions: The present data therefore document a strong, generalisable and chemically interpretable organ division of labour, but do not support the use of this metabolome for origin authentication. Because most annotations remain at MSI Level 3, individual compounds are reported as putative throughout, and all conclusions rest on multivariate and class-level evidence. Full article

Soil organic carbon (SOC) in mountain forest ecosystems exerts critical controls over regional carbon balance and climate feedback loops. This study collected 209 stratified topsoil (0–30 cm) samples across temperate mountain forests of Northeast China, conducted a boosted regression tree (BRT) modeling framework integrated with space-for-time substitution, and established 15 combined thermopluviometric sensitivity scenarios to simulate SOC shifts under diversified climate disturbances. Tenfold cross-validation yielded a model mean R² of 0.62, revealing mean annual temperature (MAT, RI = 35.31%) as the most influential predictor of SOC spatial variation, followed by elevation (ELE, RI = 19.11%), while single-season NDVI and soil particle fractions showed weak predictive capacity. Multi-scenario spatial simulation outputs demonstrated that simultaneous warming and aridification drastically reduce the coverage of high SOC zones, whereas increased precipitation can partially offset temperature-induced carbon mineralization losses. Terrain-mediated SOC spatial stratification remained stable across all climate backgrounds. This study quantifies the layered environmental association hierarchy of mountain SOC and generates spatially explicit modeled carbon sink projections under climate change. The terrain-dependent SOC response patterns provide operable differentiated carbon regulation guidance: humid low-lying convergence zones require long-term soil moisture conservation, while arid steep ridges need native mixed forest restoration to lift baseline carbon storage capacity, supporting precise watershed climate adaptation and targeted forest carbon sink management for temperate mountain regions. Full article

Greenhouse agriculture in semi-arid regions faces persistent challenges from unpredictable thermal variability, frost events, and seasonal drought stress that are difficult to manage without anticipatory climate information. This paper presents the design, implementation, and validation of an end-to-end intelligent greenhouse monitoring and temperature forecasting system deployed in Donovan, San Luis Province, Argentina. The proposed platform integrates a three-layer IoT architecture with cloud-based statistical forecasting to support real-time decision making under semi-arid climatic conditions. The system integrates sensor nodes, a push-MQTT gateway co-located at the Universidad Nacional de San Luis to bypass regional API geo-restrictions, and a cloud application layer. Three forecasting strategies with a six-hour prediction horizon were evaluated: a univariate SARIMA baseline (Model 1), a SARIMAX model using four neighboring meteorological stations as individual exogenous regressors (Model 2), and a SARIMAX model employing a single correlation-weighted synthetic exogenous index (Model 3). The models were assessed using MAE, RMSE, and Diebold–Mariano statistical significance tests. The results show that directly incorporating multiple correlated exogenous variables does not improve forecast accuracy because of multicollinearity, whereas the proposed correlation-weighted synthetic index preserves the spatial predictive signal while reducing model complexity and achieving performance comparable to the baseline overall, with statistically significant improvement during the overnight block. Additional benchmarking against Random Forest, Support Vector Regression, Temporal Convolutional Networks, and Long Short-Term Memory models demonstrates that increasing model complexity does not necessarily translate into improved predictive performance for short-horizon greenhouse temperature forecasting. Together, the proposed IoT architecture and forecasting framework provide an operationally validated solution for intelligent greenhouse monitoring and predictive decision support in resource-constrained semi-arid environments. Full article

Accurate quantification of aboveground biomass (AGB) for sparse desert vegetation is fundamental for assessing regional carbon sink potential, yet large-scale data retrieval remains constrained by high spatial heterogeneity and severe background noise. Focusing on Haloxylon ammodendron communities in the Gurbantunggut Desert, this study integrated plot-level ground truth derived from Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) with multi-source satellite imagery (Sentinel-2 and Jilin-1) to evaluate AGB estimation accuracy and spatial distribution patterns across various feature combinations and employed four machine learning algorithms at a 10 m pixel scale. The Difference Vegetation Index (DVI) exhibited the strongest explanatory power for AGB spatial variance, whereas downsampled high-resolution textures induced feature redundancy. Among the evaluated algorithms, the Random Forest (RF) model driven solely by multispectral parameters achieved the optimal cross-scale mapping accuracy (R² = 0.72, RMSE = 1.32 t ha⁻¹). The total regional AGB storage was estimated to be approximately 6.99 × 10⁴ t, with low-density habitats (0.2–2.0 t ha⁻¹) occupying 90.39% of the area. This study confirms the feasibility of integrating UAV point clouds with multi-source satellite imagery for the large-scale retrieval of sparse shrub biomass, providing a quantitative basis for desert carbon management. Full article

Urban forest policies are increasingly used as nature-based solutions, but their large-scale effects on ambient air quality remain uncertain. This study evaluates China’s National Forest City Construction Policy (NFCC) using a panel of 2076 counties from 2001 to 2021 and a staggered difference-in-differences (DID) design. Annual mean PM_2.5 concentration is used as the main outcome. In the preferred specification with county and year fixed effects, NFCC exposure is associated with a reduction of approximately 1.39 μg/m³ in annual mean fine particulate matter (PM_2.5). The estimated effect remains negative across most robustness checks, although its magnitude and statistical precision weaken under more restrictive specifications. Both direct county-level designation and prefecture-level exposure are associated with lower PM_2.5, with a larger point estimate for directly designated counties. The effect is stronger in counties with higher pre-policy pollution and population density. Spatial analyses further indicate that neighboring NFCC exposure is associated with lower PM_2.5, although the estimated separation between own-county and neighboring effects depends on the definition of spatial proximity. Supplementary normalized difference vegetation index (NDVI) results are consistent with increased vegetation greenness following NFCC exposure. These findings suggest that forest-city construction can complement source-oriented pollution control, while the estimated magnitude remains sensitive to regional shocks and assumptions about treatment dynamics. Full article

Understanding the spatial distribution and associated factors of sustainable environmental development (SED) is crucial for effectively formulating action plans aimed at achieving the environment-related Sustainable Development Goals (SDGs). Because of spatial heterogeneity characterized by non-uniform distributions of SED within a region, the use of a single or aggregated value at the national or provincial scale in existing research obscures internal variabilities and fails to adequately reveal the spatial disparities in the progress of environment-related SDGs. Consequently, this study aims to investigate the spatially heterogeneous patterns of SED in prefecture-level cities within the Yangtze River Economic Belt (YREB), China—a critical economic zone characterized by stark intra-regional disparities and pressing environmental challenges. To achieve this objective, the SED index is first constructed using principal component weighted aggregation to quantify the SED status, local Moran’s I is then employed to identify heterogenous patters of the spatial distribution patterns of SED, and geographical random forest is utilized to explore spatially varying association patterns between SED and influences factors. In the YREB, the findings indicate the following key insights: (1) SED has generally shown a positive trend across most cities from 2013 to 2021, with approximately 30% experiencing a downward trend, particularly in Jiangxi, Hubei, and Hunan Provinces; (2) the number of high- or low-value aggregation clusters has decreased, concurrent with an increase in spatial variability; (3) human activity disturbance and population density have been identified as significant factors influencing SED, with a notable impact in cities across Sichuan, Chongqing, Guizhou, and Hubei Provinces. This research contributes technical support and a scientific foundation for evaluating and enhancing SED. Full article

Oil palm plantations play an important role in agricultural production and carbon storage in tropical regions. The accurate estimation of aboveground carbon (AGC) is essential for sustainable plantation management, climate change mitigation, and carbon monitoring. Although field measurements provide reliable estimates, they are often time-consuming, labor-intensive, and costly, particularly over large plantation areas. Recent advances in remote sensing and machine learning offer efficient alternatives for AGC estimation using satellite imagery. In this study, we developed a machine learning framework for AGC estimation in oil palm plantations using Sentinel-2 multispectral imagery and Sentinel-1 synthetic aperture radar (SAR) data. Field measurements were integrated with spectral variables, vegetation indices, and SAR-derived parameters extracted from satellite data. A hybrid feature selection approach combining Pearson correlation, mutual information and mRMR was used to identify the most relevant variables. Six machine learning algorithms were evaluated, including Linear Regression, Random Forest, XGBoost, Gradient Boosting, LightGBM, and Extra Trees. Because the 160 observations comprise sixteen 10 m × 10 m grid cells nested within ten 40 m × 40 m field plots, model performance was assessed with leave-one-plot-out cross-validation: all sixteen cells of a plot were held out together, and the hybrid feature selection was repeated inside every fold using only that fold’s training plots. Performance was measured on pooled out-of-fold predictions using R², root mean squared error (RMSE), and average absolute relative error (AARE%). Under this spatially independent design the combined Sentinel-1 + Sentinel-2 dataset gave the highest accuracy (R² = 0.7950, RMSE = 4.14 t C ha⁻¹, AARE = 33.53%), followed by Sentinel-1 alone (R² = 0.7631, RMSE = 4.46 t C ha⁻¹) and Sentinel-2 alone (R² = 0.6108, RMSE = 5.71 t C ha⁻¹). Linear Regression and Extra Trees were the most robust models, whereas the boosted ensembles did not generalize to unseen plots. Repeating the evaluation with an ungrouped random split of the same data inflated R² by up to 0.70, showing that a large part of the accuracy obtainable under that design reflects within-plot spatial autocorrelation rather than predictive skill. These findings indicate that optical-SAR imagery combined with machine learning can provide useful AGC estimates in oil palm plantations, and that spatially independent validation is essential for reporting them honestly. The proposed framework can be used to support plantation-scale carbon mapping, monitoring, and carbon stock assessment, subject to further calibration and independent validation across additional plantations. Full article

Loss of soil organic resources after forest conversion is often assumed to constrain microbial functioning; however, resource status and functional potential may respond differently to land-use legacies. We compared soils under natural secondary forest, six-year poultry-integrated agroforestry, and 60-year paddy cultivation derived from the same regional forest type. Soil properties, microbial biomass, hydrolyzable organic N fractions, extracellular enzyme activities, nitrogen (N)-cycling genes, and potential N₂O emissions were measured using three independent field replicates per land use. To quantify the alignment between resources and microbial functioning, we separately standardized indicators of retained organic resources (soil organic carbon (SOC), total N, microbial biomass C, and four hydrolyzable organic N fractions) and microbial functional potential (four extracellular enzymes and five N-cycling genes). We then calculated their difference as a study-specific organic resource–microbial activation imbalance index (ORMAI), for which positive values indicate high functional potential relative to retained resources. Relative to the forest, SOC declined by 29.5% under agroforestry and 61.1% under paddy cultivation, while total N declined by 16.6% and 59.7%, respectively. Paddy soil also contained the lowest concentrations of all hydrolyzable organic N fractions, with reductions of 28.3–81.5% relative to the forest. Despite this depleted resource status, paddy soil exhibited the highest activities of all measured C-, N-, and phosphorus-acquiring enzymes and the greatest abundances of AOA-amoA, AOB-amoA, nirK, nirS, and nosZ. It also had a higher (nirK + nirS)/nosZ ratio than the other soils. Paddy soil produced 3.91 and 4.69 times as much cumulative N₂O as forest and agroforestry soils, respectively. The resulting ORMAI was strongly positive in paddy soil (2.34) but negative in the forest and agroforestry soils (approximately −1.17). The three systems therefore exhibited distinct configurations: high resource status with intermediate functional potential in the forest, partial resource retention with restrained functional activation under agroforestry, and depleted resource status coupled with high functional and emission potentials in paddy soil. These findings identify resource–function imbalance as an informative dimension of land-use legacy, revealing that soil organic-resource depletion can coincide with enhanced microbial functional and N₂O emission potentials rather than constraining them. These results highlight the importance of considering microbial functional potential alongside soil organic-resource status when assessing potential N₂O emission risks and developing land-management strategies following tropical forest conversion. Full article

Long-term soil-moisture trends on the Chinese Loess Plateau are inferred from gridded products, but product choice can alter whether change is read as drying or wetting. We audited trends from the Global Land Data Assimilation System (GLDAS), ECMWF Reanalysis v5 Land (ERA5-Land), Soil Moisture of China by in situ data (SMCI), and Global Land Evaporation Amsterdam Model root-zone soil moisture (GLEAM SMrz) for 2001–2022, with an endpoint-extension test to 2025. We compared product-specific trends, spatial agreement, core-product medians, product-set bridge and leave-one-out sensitivities, and associations with precipitation, vapor pressure deficit, forest–shrub–grass cover change, and a potential-storage proxy. The core products shared substantial detrended interannual variability, yet their regional trend point estimates did not converge in sign. GLDAS gave a positive regional Sen-slope estimate, whereas ERA5-Land, SMCI, and GLEAM SMrz gave negative estimates. The core-product median was negative over 72.7% of the study area, but unanimous decline occurred over only 24.9%, and 63.5% showed mixed product signs. Only 36.9% of the area retained direction across all five robustness checks, and no environmental variable achieved repeatable same-direction support across all core products. Hydrologically, these results indicate that apparent Loess Plateau wetting or drying should be interpreted as product-dependent evidence of soil-water availability rather than as a universally robust soil-moisture trend or direct environmental response. Full article

Afforestation can contribute to climate-change mitigation when tree establishment is matched to ecological context and sustained by long-term management, but its net climatic effect is not uniformly cooling. Existing syntheses often emphasize carbon sequestration while treating biophysical, hydrological, disturbance, and socioeconomic evidence separately. Here, we provide a structured integrative review. We distinguish afforestation from reforestation, natural regeneration, forest restoration, and improved management. We explicitly assess evidence from global modeling, remote sensing, meta-analyses, long-term observations, and regional case studies. Global forests cover about 4.14 billion ha in 2025, while annual net forest loss remained about 4.12 million ha yr⁻¹ during 2015–2025. Global forests were a sink of about 3.5 ± 0.4 Pg C yr⁻¹ in the 2010s, but this existing-forest sink should not be interpreted as an afforestation-specific removal rate. Humid tropical and subtropical regions generally have the greatest potential for net climatic cooling. In contrast, afforestation at snow-covered high latitudes may cause substantial albedo-driven warming, while water-limited regions require careful species selection and conservative planting densities. Soil carbon gains are most consistent on former croplands and other low-carbon degraded lands, but responses on carbon-rich grasslands are highly variable. Long-term benefits further depend on disturbance resilience, permanence, land competition, financing, and credible monitoring. Additionally, we identify five priorities for the future: climate-smart adaptive silviculture, digital forestry with field-calibrated uncertainty, permanence and disturbance-risk accounting, sustainable forest bioeconomy, and integrated international governance and finance. Full article

Traditional evaluations of ecological restoration in mining lands have long been dominated by above ground vegetation metrics, such as coverage and community diversity. Yet in arid and semi-arid mining regions, soil hydrology recovery is key, as soil organic carbon (SOC) and bulk density (BD) are critical factors affecting soil hydrology. However, their thresholds for water holding capacity and infiltration remain unclear. Therefore, this study determined the SOC and BD thresholds and evaluated the hydrological trends across them. The study was in a Loess Plateau coal reclamation area. Five restoration types and one reference forest were selected, and soil samples were collected from the 0–10 cm layer. Redundancy analysis (RDA), partial least squares structural equation modeling (PLS–SEM), and the threshold regression model were used for analysis. Results show the following: (1) Vegetation indirectly controls soil hydrology via soil structure and chemical properties (RDA: 76.04%). BD limited water holding capacity (path coefficient: −0.908), while chemical properties promoted infiltration (path coefficient: 0.397). (2) Soil hydrological recovery exhibits non-linear threshold responses. The SOC thresholds for water holding capacity and infiltration were 9.52 g/kg and 5.93 g/kg, respectively, while the BD thresholds were 0.93 g/cm³ and 1.03 g/cm³. The reclamation of soil hydrological functions in mining lands follows a two-stage mechanism. First, control by carbon accumulation, then follow by structural dominance. During the early stage, soil organic carbon (SOC) buildup quickly boosts hydrological performance by promoting aggregate formation. However, once the system nears its functional threshold, simply adding more carbon gives limited returns. At this stage, breaking through physical constraints becomes vital to overcoming the bottleneck. Above-ground metrics alone cannot capture below-ground ecosystem functional trajectories. Consequently, a dynamic strategy centered on SOC and BD is proposed. Full article

Arid mountain–oasis–desert regions exhibit strong terrain and surface-cover heterogeneity that is difficult to represent using conventional land-cover classification schemes. This study developed an ecosystem-oriented hierarchical framework for the Kashi region by combining a terrain-constrained Mountain extraction method with Automatic Deep Forest Shrinkage model (ADeFS). Nine ecosystem elements were defined: Mountain, Water, Forest, Cropland, Lake, Grassland, Desert, Ice, and Human. Mountain was first delineated as an independent physiographic element using a locally derived baseline surface, relative relief, slope, and topographic position, thereby reducing semantic overlap between terrain units and spectrally similar surface-cover classes. ADeFS was then adapted to classify the seven non-mountain classes, and Lake was subsequently separated from the unified Water class through visual interpretation. The results show that ADeFS achieved the highest accuracy, with an overall accuracy of 88.7% and a Kappa coefficient of 0.868. Independent field validation of the 2026 map yielded an overall accuracy of 86.2% and a Kappa coefficient of 0.825. From 2015 to 2026, the mountain-oasis-desert structure remained broadly stable, while Desert and Ice decreased and Forest, Grassland, and Cropland expanded. Ecosystem-element transitions were concentrated before 2021 and weakened thereafter. Landscape metrics showed that Desert remained the dominant matrix, Grassland had the highest patch density and edge density, and Cropland became increasingly aggregated within oasis agricultural areas. The framework provides an ecologically interpretable approach for ecosystem-element mapping and long-term monitoring in arid heterogeneous regions. Full article

Frugivorous fruit flies include several major agricultural pests, and understanding their species composition and seasonal abundance in natural environments is essential for improving management strategies. In Argentina, research has focused mainly on two economically important species, Ceratitis capitata and Anastrepha fraterculus, while native fruit flies in dryland ecosystems remain poorly studied. To address this gap, we conducted a year-long trapping survey in three dryland environments of northwestern Argentina: the dry Chaco, Chaco Montane Forest, and Monte, in order to characterize and compare community composition, diversity, species abundance, and temporal dynamics. We collected 1006 individuals representing seven native species and one exotic species. Community composition differed significantly among environments, showing strong spatial segregation. The native genus Rhagoletotrypeta dominated the overall catch and exhibited clear habitat preferences, with R. xanthogastra prevailing in the Dry Chaco and R. pastranai and R. parallela more abundant in the Monte and Chaco Montane Forest, respectively. The invasive A. fraterculus was the second most abundant species and the dominant species in Monte and Chaco Montane Forest, whereas C. capitata represented only a small fraction of the total catch. Fruit fly abundance peaked in late summer and autumn. These findings reveal strong habitat-driven structuring of fruit fly communities in drylands and provide ecological information that improves our understanding of tephritid assemblages in the region. Full article

In advanced manufacturing, designing multi-pulse composite driving waveforms for piezoelectric inkjet (PIJ) printing presents a constrained, high-dimensional, physical black-box optimization challenge. The feasible jetting region within the 12-dimensional parameter space is highly sparse; furthermore, traditional unconstrained optimization algorithms are prone to triggering nozzle flooding or actuator fatigue damage. To overcome this bottleneck, this paper proposes CA-TuRBO-m, a closed-loop collaborative architecture based on classifier-assisted multi-trust region Bayesian optimization. This architecture reconstructs the deposition morphology features on the substrate into a composite visual feedback source that implicitly incorporates fluid dynamics. Furthermore, it repurposes a Random Forest classifier into a dynamically iterating physical safety topological gating mechanism to actively intercept high-risk parameter combinations. Simultaneously, a multi-trust-region parallel exploration mechanism is introduced to balance global exploration and local exploitation. Experimental results demonstrate that over 200 online physical printing iterations, the proposed architecture reduces the number of invalid prints leading to system failures to an average of 3.8, achieving a high effective sampling rate of 98.1%. Without relying on complex fluid dynamic models, this approach enables precise morphological control over droplets of varying sizes and mitigates printing defects, successfully achieving multi-target adaptive regulation within a limited budget on a single physical platform. Full article