Search Results (5,176)

In complex mountainous environments, the asynchronous development between external color turning and internal sugar accumulation (often termed “false maturity”) in coffee cherries poses a severe challenge to post-harvest quality sorting and the consistency of final coffee products. To overcome the limitations of single-phenotype detection in raw material screening, this study proposed a multimodal quality discrimination framework integrating fruit hyperspectral imaging, micro-topography, and plant physiological characteristics. Taking typical mountain-grown fresh coffee cherries as the research object, and after comparing various spectral preprocessing and feature dimensionality reduction algorithms, the multimodal fusion efficacy of nine machine learning classifiers was systematically evaluated. The results demonstrated that: (1) Full-spectrum difference analysis quantitatively confirmed the limitations of visual harvesting; spectral reflectance differences between high- and low-sugar fruits were highly concentrated in the red and red-edge regions, with the maximum difference precisely located at 676 nm. (2) Compared to the single-spectrum model (mean accuracy of 75.93%), the fully fused Multilayer Perceptron (MLP) network effectively mitigated background noise induced by heterogeneous environments, improving the mean classification accuracy to 77.22% with a mean Area Under the Curve (AUC) of 0.827. (3) Correlation analysis clarified the quantitative association between topography and quality; micro-topographic slope (r = 0.346) was identified as the key environmental driver of spatial differentiation in fruit sugar content, while plant chlorophyll A content (r = 0.183) exhibited a corresponding physiological response trend. This study not only explains the root cause of visual assessment failure from a physical optics perspective but also reveals the spatial variation laws of quality driven by micro-topography, providing preliminary data support for the intelligent sorting of raw materials and ensuring post-harvest quality consistency of mountainous crops. Full article

Exploring the interaction relationship among multiple ecosystem services is vital for maintaining ecosystem function. However, traditional approaches are limited in their ability to: (i) characterize complex interactions and (ii) visualize the spatial connectivity of various ecosystem services delivered by social–ecological systems. To address these challenges, a framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by: (i) estimating the spatial distribution of multiple ecosystem services using the InVEST model, and (ii) generating network nodes and edges with geographical attributes based on the minimum cumulative resistance model and a multiresolution segmentation method. We conducted a case study in the Guangdong–Hong Kong–Macao Greater Bay Area and examined the topological features of the spatial networks using complex network indicators. For each network, winding and multiple edges connected adjacent nodes and formed continuous linkages across the entire study area, indicating that the proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. The different ecosystem service networks exhibited conspicuous spatial heterogeneity and generally maintained relatively high connectivity, as evidenced by their tree-like structure with winding pathways and the distribution of multi-edge nodes, indicating that each ES was predominantly connected with multiple other ecosystem services. Meanwhile, nodes with high values of degree centrality and clustering coefficient were mainly concentrated in coastal and mountainous regions. This study advances the representation of complex interactions among multiple ecosystem services from a spatial perspective, thereby facilitating a deeper understanding of the interaction mechanisms underlying ecosystem functioning. Full article

Land surface temperature (LST) is crucial for understanding winter landslide evolution. This study combines Unmanned Aerial Vehicle (UAV) photogrammetry and infrared thermography (IRT) to monitor winter landslides in China’s Wumeng Mountain region. Using the Yangjiazhai landslide—induced by underground coal mining—as a case study, we demonstrate significant correlations between IRT-detected LST anomalies and surface cracks: (1) cracks with elevated temperatures are likely connected to subsurface goaf zones; (2) excessively widened cracks show no thermal anomalies due to enhanced air convection. The research reveals that key landslide components have distinct LST signatures, governed by differential soil–rock moisture and crack networks. For accurate high-altitude winter LST acquisition, UAV thermal surveys should be conducted under overcast, fog-free conditions to reduce solar interference. This validates UAV visible–infrared fusion for extracting landslide boundaries, cracks, slumping zones, bedrock patterns, and moisture distribution. The methodology establishes a new pathway for investigating winter landslide deformation and instability, confirming IRT’s operational viability in high-altitude alpine regions. Full article

Enhancing nitrogen (N) use efficiency (NUE) is crucial for reconciling food security with fertilizer reduction and environmental protection in Sichuan province. This study used statistical data of rice, wheat, maize, and rapeseed in Sichuan Province from 2008 to 2022 to evaluate crop NUE within a regional N balance framework and compare spatiotemporal differences across the five major economic zones. Results showed that provincial NUE presented a distinct three-stage pattern: a gradual increase from 2008 to 2014, a significant surge in 2015, and a period of high-level but fluctuating NUE after 2016, the drivers of which require further investigation. By 2022, rice and rapeseed demonstrated the highest NUE values (42.89% and 42.90%, respectively), followed by maize (35.46%) and wheat (28.77%). Notable spatial heterogeneity was detected, with a general tendency of higher NUE in the southeastern and basin areas and lower NUE in the northwestern mountainous areas. Northeastern Sichuan, Southern Sichuan and the Chengdu Plain consistently exhibited better performance, while Northwest Sichuan remained the region with the weakest performance. These findings suggest that improving NUE in Sichuan province necessitates region- and crop-specific strategies, with priority being given to stabilizing the high NUE of rice and rapeseed, while targeting infrastructure improvement and precision fertilizer management in wheat-dominated and low-efficiency areas. Full article

Vegetation change in cryosphere-affected mountain basins reflects interacting climate and human pressures but their relative influence remains uncertain in the Upper Indus Basin. The novelty of this study is the integration of satellite vegetation, climate variables, human pressure indicators, residual attribution and diagnostic validation in a data-scarce high-mountain basin. We evaluated growing-season Normalized Difference Vegetation Index dynamics and associated drivers from 2001 to 2023 using trend analysis, correlation, Random Forest diagnostics, Sentinel 2 validation, and residual trend analysis. The results showed widespread greening across 96.59% of the basin, with stronger improvement in the lower and central areas. Significant greening covered 69.94% of the basin, while only 1.55% showed significant browning. Precipitation and temperature were predominantly positive drivers of vegetation change, whereas potential evapotranspiration and solar radiation were mostly negative. Soil moisture played a strong regulatory role along elevation gradients. Residual trend analysis provided approximate and method-dependent estimates of the possible anthropogenic influence on vegetation change at 73.09% and climatic drivers at 26.91% rather than direct causal decomposition. These values are approximate and method-dependent estimates, not direct causal decomposition. The findings highlight human-related greening in lower valleys and climate-controlled vegetation responses in high-mountain areas. Full article

Xinjiang experiences frequent dust storms, posing significant challenges to regional ecological security and public health. Based on the China High-resolution and High-quality Near-surface Air Pollutants (CHAP) dataset and ground monitoring data, this paper adopts the Potential Source Contribution Function (PSCF) to analyze the spatiotemporal characteristics of atmospheric particulate matter across Xinjiang and typical cities and to identify potential source regions and contribution intensities. The results show that (1) PM_2.5 and PM₁₀ concentrations are elevated in southern Xinjiang but reduced in the north, and particulate pollution in most areas has generally decreased. (2) Northern Xinjiang cities have high PM_2.5 in winter, while southern Xinjiang cities keep persistently high PM₁₀ levels. (3) The PM_2.5/PM₁₀ ratio is above 0.35 in northern cities, where pollution is dominated by fine particles affected mainly by human activities; southern Xinjiang is dominated by coarse particles from natural sources. (4) Particulate matter in Urumqi mainly comes from the northern Tianshan Mountains, with winter WPSCF over 0.9. Pollutants in Kashgar originate from both long-distance cross-border dust transmission and local emissions. These findings are of great significance for the sustainable development of Xinjiang and urban agglomerations along the Belt and Road. Full article

Mountain stream ecosystems are often considered among the least disturbed freshwater environments; however, increasing land-use pressures may affect their ecological integrity even under apparently high-water quality conditions. This study aimed to assess the relative influence of landscape, physicochemical, and hydromorphological factors on benthic macroinvertebrate communities in three sub-catchments (Ambroz, Jerte, and Tiétar) of the Sierra de Gredos (Central Spain). A total of 33 sampling sites were surveyed, and macroinvertebrate assemblages were analyzed in relation to environmental variables using partial Redundancy Analysis (pRDA) and variance partitioning. All sites were classified as having “Excellent” ecological status based on the Iberian Biological Monitoring Working Party (IBMWP) index. However, multivariate analyses revealed clear spatial patterns and responses to environmental gradients. Results indicated that catchment-scale landscape characteristics defined the pool of potential colonizers, while local physicochemical and hydromorphological conditions acted as secondary filters structuring macroinvertebrate assemblages. Landscape variables explained the largest fraction of variance in community structure (30.6%), followed by physicochemical parameters (29.0%) and hydromorphological indices (24.9%), with a significant shared component (16.5%) indicating interactions among drivers. Agricultural land use, particularly in the Jerte sub-catchment, was associated with shifts in community composition, favoring tolerant taxa such as Diptera, while sub-catchments dominated by natural vegetation supported higher richness of sensitive groups, including Ephemeroptera and Plecoptera. These findings highlight the importance of multi-scale processes in structuring mountain stream communities and reveal limitations of traditional biotic indices in detecting early ecological changes. The results support the integration of catchment-scale variables into ecological assessment frameworks and emphasize the need for preventive, basin-scale management strategies to maintain ecological integrity under increasing anthropogenic pressure. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

Aligning plant breeders’ objectives with farmers’ preferences is essential for improving the adoption of new crop varieties. This study evaluated the effectiveness of a Participatory Weighted Selection (PWS) index, previously developed from a survey of 869 Moroccan farmers, by comparing it with biophysical Participatory Variety Selection (PVS) trials conducted on 19 farms across four agroecological zones in Morocco. Novel CWR-derived lines of durum wheat, barley, and lentil were assessed alongside commercial checks, and both male and female farmers were interviewed to gather PVS preferences. The results showed no significant gender differences for the top-ranked varieties across crops, with minor variations in some zones. Jabal emerged as the preferred durum wheat variety overall, while Zagharin2 was favored in favorable zones. Furat-3 was generally preferred for barley, except in certain mountain and favorable zones, and Bakria was the top lentil variety across most sites. Farmers’ PWS responses clustered into three groups, emphasizing consistent prioritization of high yield potential, abiotic stress tolerance, and good nutritional quality. Comparison of biophysical performance with PVS and PWS revealed strong alignment for durum wheat; however, the highest yielding genotypes for barley and lentil were not always the most preferred. Overall, these results demonstrate that the PWS approach effectively captures farmers’ preferences and provides a reliable tool for guiding breeding decisions. These findings reveal that integrating PWS with on-farm biophysical and PVS evaluations provides a robust, farmer-informed framework for prioritizing genotypes and improving the relevance of breeding decisions across diverse agroecological contexts. Full article

Anthropogenic disturbances and climate warming threaten the rare paleoendemic species Tetracentron sinense. To identify the divers of its latitudinal adaptation, we integrated functional trait differentiation, environmental filtering, and phylogenetic conservatism. We measured 35 functional traits (leaf morphology, nutrient stoichiometry, stomatal traits, whole-plant architecture) across four natural populations spanning the species’ latitudinal range: BMXS (Baima Snow Mountain), DFD (Dafengding), FP (Foping), LGS (Leigong Mountain). Using correlation analysis, principal component analysis, and phylogenetic community metrics, we found that T. sinense dominated all communities. Populations exhibited divergent strategies: DFD expanded leaf area for light capture under high rainfall and shaded conditions; FP increased height and crown width to compete for light; LGS enhanced nutrient-use efficiency under phosphorus limitation; BMXS promoted phosphorus uptake under nitrogen limitation (N/P < 14). Trait variation correlated significantly with elevation, solar radiation, and temperature. PCA explained 90.44% of total variance, and standardized effect size (SES) values for phylogenetic signals range from −2.031 to 1.973; Phylogenetic signals were stronger in co-occurring taxa than in T. sinense. T. sinense populations in BMXS and FP are structured by competitive exclusion, while those in LGS and DFD by habitat filtering. We conclude that T. sinense achieves latitudinal adaptation by overcoming phylogenetic niche conservatism through phenotypic plasticity. While leaf economic traits remain evolutionarily conserved and niches in glacial refugium are relatively stable, populations adjust trait syndromes via metabolic shifts and structural trade-offs in response to heterogeneous environmental filters. Identifying these adaptive strategies can guide seed sourcing for restoration efforts under climate change. Full article

The Catolesia clade (Asteraceae, Eupatorieae, Gyptidinae) comprises four genera (Bahianthus, Catolesia, Lapidia, and Morithamnus), mostly confined to the Espinhaço mountain range of Eastern Brazil. Although this lineage is statistically well supported in molecular phylogenetic studies, recent findings point to Disynaphia praeficta being currently placed in the Catolesia clade, making Disynaphia paraphyletic. We analysed, scored, and mapped 102 leaf anatomical characters from all species of the Catolesia clade and selected outgroups to test the placement of D. praeficta into this clade, proposing a new monotypic genus and a taxonomic synopsis for the Catolesia clade, besides standardising descriptive anatomical terminology. We recovered several homoplasies and synapomorphies circumscribing all lineages sampled in our study, including Disynaphia s.s. and the remaining sampled outgroups. Our results also corroborated the placement of D. praeficta within the Catolesia clade with high statistical support. The cuneate to truncate lamina base was recovered as a synapomorphy supporting the Catolesia clade, whereas a petiole with three vascular bundles, ducts distributed throughout the lamina, and collenchyma sheath cell extensions were recovered as synapomorphies supporting Nadia praeficta (B.L. Rob.) A.V.F. Guterres and R.F. Almeida as a new monospecific genus. We demonstrated how highly informative leaf morphoanatomical characters are for the systematics of Eupatorieae and Asteraceae, besides demonstrating that leaf morphoanatomical characters provide a robust phylogenetic signal for generic delimitation within Eupatorieae, even if characterised as homoplasies. Full article

Mountain ecosystems are highly sensitive to climate change and require spatially explicit monitoring tools to support adaptive management. Within the framework of the Interreg-ALCOTRA “ACLIMO” project, this study investigates land cover dynamics in the Gesso Valley (Maritime Alps, Italy) over the period 2010–2021 using deep learning–based classification of high-resolution aerial orthophotos integrated with climate data analysis. Multi-temporal RGB and NIR imagery (2010, 2018, 2021) was classified using convolutional neural networks (U-Net and MMSegmentation) in ArcGIS Pro, with CORINE Land Cover datasets used for training. The best-performing model, based on CLC + Backbone 2018, achieved an overall accuracy of 82%, increasing to 87% after fine-tuning. Change detection revealed a general shift towards increased vegetation cover, while climate analysis based on regional weather stations (1990–2021) identified a warming trend of +0.4 °C/decade and recent drier conditions. Logistic regression highlighted significant associations between land cover transitions and climate anomalies, with temperature positively influencing change probability (OR = 1.40). The study demonstrates the potential of operational GIS-integrated deep learning workflows for climate change monitoring in complex alpine environments under real-world data constraints. Full article

Apis cerana (A. cerana) is a native and widely managed honey bee species in China. Body size and body weight are crucial breeding traits, as colonies possessing individuals with large body weight tend to be healthier and exhibit high productivity. This study aimed to clarify the relationships between body size and body weight in A. cerana and to evaluate their associations with geographic, climatic, and colony productive traits for selective breeding. Body size and body weight were measured in virgin queens, drones, and workers from Jinfo Mountain, Chongqing, and additional measurements of queens and drones were implemented in five other regions across China. Linear mixed-effects models confirmed that body size had a significant positive effect on body weight in virgin queens, drones, and workers. However, correlations of body-size and body-weight traits among different bee groups were weak and non-significant after FDR correction, indicating that drones or workers cannot be used as direct substitutes for queen body-size traits in the present dataset. Standardized model estimates showed that queen and drone body-size and body-weight traits were consistently negatively associated with annual minimum and annual mean temperatures, but positively associated with latitude after FDR adjustment. Annual precipitation was also negatively associated with queens’ body size, queens’ body weight, and drones’ body size, whereas annual maximum temperature, longitude, and elevation showed no significant associations after FDR adjustment. Moreover, queens’ body size and body weight were significantly positively associated with honey yield, honey yield during the main nectar flow, and colony gentleness after FDR correction, whereas their associations with the number of effective eggs laid by queens, colony strength, and robbery were not significant after FDR correction. These findings suggest that queen body-type traits may serve as useful auxiliary indicators for selecting colonies with higher honey production and gentler behavior, but their relationships with other colony traits should be interpreted cautiously. This research is beneficial for initiating a body size-weight selective breeding program for A. cerana, as it can help optimize breeding objectives and accelerate genetic progress. Full article

Infrared small-UAV detection remains difficult because the target often appears as a weak thermal point rather than a clear object. This problem is clear in the SIDD dataset, where most test targets are smaller than 32 × 32 pixels. To address this case, this paper proposes S-Drone-YOLO, a compact YOLO-based detector that maintains a high-resolution P2 prediction path and leverages it carefully during classification. The model starts from a lightweight YOLOv5-style detector. It adds a stride-4 P2 path and replaces the C3 neck blocks with C2fAttn to improve feature reuse before prediction. Two components are then added to the Architecture II design. The Coordinate-Aware Residual C2f Block, CAR-C2f, strengthens the P2 branch using coordinate attention and residual scaling. The P2-Guided Quality-Aware Detection Head (P2-QADH) combines local P2 details with nearby P3 context. It produces a quality map that adjusts the classification logits. The regression branch, output tensor format, and training loss interface remain unchanged. On the SIDD infrared drone dataset, S-Drone-YOLO reaches 0.988 precision, 0.939 recall, 0.699 mAP50-95, and 0.962 F1-score. It uses 6.45 M parameters and 31.3 GFLOPs. Compared with the Architecture I model, recall increases by 0.8 percentage points and mAP50-95 increases by 0.4 percentage points. At the same time, the parameter count decreases by 20.3%, and GFLOPs decrease by 43.7%. Fine-tuning on five RGB UAV datasets and a second thermal dataset (ThermalUAV2UAV) yields F1 scores ranging from 0.941 to 0.999, with an mAP50-95 of 0.843 on the thermal dataset. The background analysis also shows stable F1-scores across sky, sea, city, and mountain scenes. These results suggest that controlled P2 guidance can improve infrared small-UAV detection while keeping the model size practical. Full article

Ecologically fragile hilly areas are key regions for safeguarding national ecological security and advancing ecological civilization construction. Accurate assessment of ecosystem service value (ESV) and future scenario simulations in these regions is crucial for improving regional land use and attaining sustainable development. Based on high-resolution remote sensing data of the Longji Mountain area in Guangxi, China, from 2013 to 2023, this study systematically assesses the spatiotemporal evolution characteristics of ESV using the equivalent factor method with localized corrections. This study adopts spatial autocorrelation analysis, geographic modeling, and scenario simulation. It predicts the spatial patterns of ESV for 2028 and 2033 under three scenarios: ecological protection, natural development, and tourism development. The results reveal that: (1) from 2013 to 2023, the total ESV in the Longji Mountain area showed an overall fluctuating trend. It increased first, then declined and recovered slightly, with an average annual growth rate of −0.15%. Spatially, the ESV presented a heterogeneous pattern, characterized by “high-value agglomeration in forest land, medium-value transition in terraced fields, and low-value interpolation in constructed areas”, with distinct clustering features; (2) regional ecological functions are mainly dominated by regulating and supporting services. Climate regulation contributes the highest value. Water supply is the only service with negative value, indicating a persistent water ecological deficit that remains unaddressed; (3) scenario simulations reveal that the total ESV is highest and spatial connectivity is strongest under the ecological protection scenario. Furthermore, a consistent trend is observed across all three scenarios: high-value ESV areas tend to become dominant, while spatial connectivity shows progressive enhancement. The human–land system coupling framework for the ecologically fragile hilly region suggests that ecologically oriented decision-making is the core pathway to sustainably improve ecosystem services and realize regional sustainable development. This study offers scientific support for regional ecological conservation and sustainable advancement. Full article