Earth and Environmental Sciences

In aerial image small-object detection, complex imaging perspectives, arbitrary object orientations, and long-tailed category distributions jointly exacerbate sample imbalance, which significantly degrades detection stability and leads to frequent misclassification of minority categories. To address these challenges, this paper proposes a novel training framework termed SCUD. Specifically, in the label noise suppression strategy (LNSS), a contrastive learning mechanism based on semantic consistency is introduced to constrain the aggregation of similar samples in the feature space, thereby reducing the adverse impact of noisy samples on model optimization. In addition, a scale-aware resampling strategy (SARS) is designed to alleviate noise amplification and overfitting caused by excessive repetition of small objects during training. Furthermore, an adaptive instance selection mechanism (AISM) is developed by jointly modeling prediction uncertainty and global statistical priors, enabling the model to dynamically emphasize learning from informative samples. Extensive experiments are conducted on two publicly available unmanned aerial vehicle (UAV) aerial image datasets to validate the effectiveness of the proposed approach. The proposed method achieves an mAP50 of 70.7% on the DOTA-v1.0 dataset and 88.1% on the DIOR dataset. Notably, the detection accuracy of several rare categories is significantly improved, further demonstrating the effectiveness of the proposed method in addressing sample imbalance in aerial image small-object detection.

Transmission towers are fundamental components of electric power networks. However, their structure, scale and background textures vary substantially across remote sensing images acquired from different geographic regions. These discrepancies often reduce the detection accuracy of a model trained in one region when it is applied to another region. This paper proposes an enhanced DINO-based framework for cross-domain transmission tower detection that incorporates three lightweight optimisation modules. First, a Query-level Objectness Gating (QOG) module adaptively reweights decoder queries by estimating per-query objectness scores, thereby suppressing background-dominated queries. Second, MPDIoU regression is used to improve the localisation accuracy of elongated transmission tower targets. Third, a Quality-aware Scoring Module (QSM) calibrates classification confidence using predicted localisation-quality logits, thereby reducing high-confidence false detections caused by poor box alignment. Experiments are conducted on two remote sensing image datasets from different geographic regions. Under the 10% target-domain annotation setting, the proposed method achieves a precision of 0.8947, a recall of 0.8199, an F1-score of 0.8556 and an mAP@0.5 of 0.8684, outperforming the original DINO baseline and mainstream detectors including YOLOv8, YOLOv9 and YOLOv11. The results demonstrate that the proposed framework provides an effective solution for robust cross-domain detection of slender transmission tower targets in remote sensing images.

The accurate detection of unmanned aerial vehicles (UAVs) in various sizes played an important role in the practical applications. Yet the preceding works suffered from the missing inference, the false alarms, and the poor accuracy due to the the adverse scene conditions, as well as the mutable scales. To solve the problems, a hierarchical attention promoted cross-scale learning framework was proposed in this paper. First, the hierarchical attention mechanism was introduced in the backbone to generate the multi-scale features of targets, so they can be discerned and located at different scales. The resulting features were further delivered to the neck, in which two branches of features were built, respectively. The former was obtained by the target-specific feature operator, while the latter was generated by the upsampling operation. The dual branches were further connected in the quasi-residual structure. So the content of targets can be protected well, and the detail information can be reconstructed. Finally, the dynamic focusing loss measurement was presented to regress the bounding box of the target, so the learning effectiveness of presented the architecture can be promoted. To verify the proposed method, multiple rounds of experiments were performed. The results demonstrated that small and weak drones can be detected accurately, especially in adverse lighting and weather conditions. The evaluation metric of mean average precision rate (mAP) can be improved by 18.5% (YOLO6) on the collected dataset.

Measured radar point-target detection under ground clutter is difficult because weak target echoes often compete with clutter peaks, and image-space detections do not directly represent physical range-Doppler coordinates. This paper proposes a waveform- and physics-guided center detector (WPG-Center) for measured radar maps. The method reformulates detection as continuous physical-coordinate center localization, constructs a four-channel clutter-aware range-Doppler (RD) representation, uses an anisotropic backbone with range-clutter response modulation, and supervises the center heatmap with waveform-resolution-aware Gaussian targets. Conditional physical-prior decoding is used as a targeted candidate reranking step for low-bandwidth, near-zero-Doppler cases. Experiments are conducted on the measured LSS-Ku-1.0 dataset using a strict blocked 5-fold linear frequency-modulated (LFM) protocol with physical localization tolerances and decoded-center detection metrics. WPG-Center achieves a probability of detection (Pd) of 0.850±0.101 and a range root-mean-square error (RMSE) of 18.16±12.19 m, giving the best average decoded-center detection probability and range accuracy among the compared learned and constant false-alarm rate (CFAR)-style baselines, including an independent radar feature pyramid network (Radar-FPN) heatmap detector. The fixed-sigma ablation is substantially weaker, supporting the need for resolution-aware supervision. Fold-wise, qualitative, and stepped-frequency waveform (SFW) analyses are reported as auxiliary evidence to define the scope of the measured LFM conclusion.

LiDAR point clouds are widely used in remote sensing perception scenarios, such as autonomous driving. However, LiDAR-based perception models remain vulnerable to adversarial perturbations, which may compromise the reliability of safety-critical 3D perception systems. Among different attack paradigms, transfer-based attacks are particularly practical because they generate adversarial examples on accessible surrogate models and apply the generated examples directly to unknown target models. Nevertheless, existing transferable attacks on point clouds often perturb regions that are discriminative for the surrogate model but insufficiently stable across different architectures, leading to limited transferability and noticeable geometric distortion. To address this problem, we propose SAGE, a Saliency And Geometry Enhanced transferable attack framework for LiDAR point cloud perception in remote sensing. Specifically, SAGE unifies point-coordinate priors with source-model gradient signals to generate a saliency map, which serves as a transferable indicator of vulnerable local structures. SAGE further leverages this map through saliency-guided perturbation allocation and explicit geometric constraints to enhance transferability while preserving point-cloud geometry. To demonstrate the effectiveness of SAGE, we evaluate SAGE on point-cloud classification benchmarks and further validate it on LiDAR-based 3D object detection using KITTI and nuScenes. Experimental results show that SAGE consistently outperforms existing transferable attack methods in attack success rate while preserving favorable geometric quality of adversarial point clouds. These findings demonstrate that SAGE offers an effective and practical framework for assessing the transfer robustness of LiDAR-based remote sensing perception systems.

Since the 1990s, critical urban scholarship has examined how revitalization strategies mobilize consumption to build consensus around policy agendas, yet much of this work has largely focused on Northern urban contexts. This paper broadens the debate by analyzing Downtown Rio de Janeiro, where an ambitious revitalization campaign leverages curated commercial and cultural activities to reshape urban perceptions and attract/fix investments. We seek to show how this strategy reinforces hierarchies of spaces, practices, and subjects of consumption. Through an analysis of relevant documents and statements by local state representatives and stakeholders, we trace how consumption-led revitalization in the South American context is premised on dynamics of territorial destigmatization, revealing emerging moral geographies that delineate “proper” and “improper” consumption circuits, thus legitimizing the marginalization of social groups from the revitalizing effort. In particular, local street vendors become targets of heightened regulation and surveillance, as official discourse links their presence to crime and disorder. This has theoretical implications for debates on citizenship and belonging, as consumption-led development strategies redefine social presences and activities in the city.

ABSTRACT Understanding urban residential spatial patterns is vital for resource allocation and planning. Current approaches often overlook demographic shifts, reducing projection accuracy. To address this, we propose a novel framework that integrates an improved cohort‐component model with the PLUS model for multi‐scenario population simulation. This integration allows the dynamic coupling of demographic forecasts with land‐use change. Our method incorporates policy‐sensitive parameters, simulates land‐use transitions via random forest under three development scenarios—natural growth, cropland protection, and urban expansion—and downscales population using multi‐source POI and geospatial data. Applied to Zhengzhou City at a 30‐m resolution, our model demonstrates higher predictive accuracy, evidenced by a lower mean squared error compared to the WorldPop and LandScan datasets, and achieves a substantially better fit ( R 2 = 0.76) with the Seventh National Population Census of China data. These results validate the robustness of this policy‐sensitive approach for fine‐scale spatial population modeling.

At the midpoint of the Sustainable Development Goals (SDGs) implementation, current progress underscores the need for a more community-driven approach to co-producing and implementing SDG-focused initiatives. Beyond their traditional roles in teaching and research, Higher Education Institutions (HEIs) have the potential to significantly impact local SDG efforts through outreach and engagement activities. This study examines the existing literature on HEIs’ contributions to SDG implementation via community engagement programs. Drawing from a thematic analysis, we propose an analytical framework to analyze and compare key characteristics, including modes, actors, SDG intensity, motivations, roles, and implementation strategies of HEIs’ community engagement initiatives. This framework is supported by comparative case studies from Taiwan/China, Brazil, and the United Kingdom (UK). The study reveals that HEI-initiated SDG implementation at the local level is comprehensive, addressing multiple SDGs with diverse actors and motivations within a polycentric ecosystem. By emphasizing HEI-led local SDG initiatives, this article provides the foundation for transformative SDG implementation by universities.

Abstract Growth in blue industries require heightened attention to justice in coastal governance. However, our understanding of different societal groups’ perceptions of justice in the context of the blue economy and coastal governance is limited. To address this gap, we developed a survey instrument to assess justice perceptions for local fisheries and subsequently used this instrument to elicit justice perceptions among coastal planners and three groups of fishers (Indigenous Sami, the Kven national minority and other non-Indigenous). Specifically, we examined perceptions about coastal zone use and decision-making with respect to three key dimensions of justice: distribution (how costs and benefits are distributed), procedure (how decisions are made), and recognition (whether sociocultural diversity is recognised and respected). Based on data from 705 fishers from the three groups and 41 planners across 69 coastal municipalities in Northern Norway, we found significant differences in perceptions of justice between planners and fishers, while the groups of fishers had relatively similar perceptions. Overall, planners tended to have more favorable justice evaluations than fishers. Moreover, planners report knowledge gaps pertaining to how changes in the use of the coastal zone have impacted local fisheries. Our quantitative analysis shows that fishers’ perceptions of injustice are widespread and extend well beyond what is often dismissed as anecdotal evidence – such as injustice claims by individual fishers. Our results highlight the importance of examining local actors’ perceptions of justice, particularly in the case of local fishers, who are facing increasing pressures with the rise of the blue economy.

Species absences can provide meaningful insights for ecological restoration. This ‘dark diversity’ can reveal species pools, the number and characteristics of suitable yet absent species, and the proportion of potential diversity realised (community completeness). Here, patterns in species richness, dark diversity, species pools and community completeness were assessed along a 25-year chronosequence of jarrah ( Eucalyptus marginata ) forest under restoration in southwestern Australia. Species were classified into plant functional types (PFTs) using a multi-trait approach to assess the functional analogues of the same metrics. A decline in species and functional richness over time corresponded with increased dark diversity and lower community completeness, and the composition of species and functional pools changed over time; however, whether these short-term trends are consistent with long-term succession patterns in the jarrah forest warrants further investigation. Variability detected among plots of the same age indicated that site and year effects influence the diversity and composition of species and functional pools. Restoration practices driving dark diversity affinity—the likelihood of species and PFTs being absent from suitable sites—were then assessed. Topsoil handling, fertilisation, the species richness of the applied seed mix and the timing of seeding emerged as influential. Species and PFTs absent from restoration plots remained members of the species and functional pools as dark diversity, suggesting that those plots maintain the potential to recruit missing species. This research exemplifies how dark diversity coupled with functional approaches can advance the monitoring and management of restoration.

The arrival of humans to the Americas at the end of the Pleistocene has been one of the foundations of the hypothesis that hunter-gatherer groups had a direct role in the extinction of megafauna. However, empirical evidence of the exploitation of megafauna by humans and associated chronological context are still scarce, especially in South America. Using evidence of modifications on osteological remains and the association of some lithic artifact we provide a reassessment of the megafauna that was exploited at the classic Taima-Taima site (Venezuela), with an updated chronology and paleoenvironmental inferences. Our preliminary results show that, at least, five species of extinct megaherbivores were probably exploited at the site, expanding the previous records, restricted to the proboscidean Notiomastodon platensis and the giant armadillo Glyptotherium cf. G. cylindricum. The new data of the other likely exploited taxa include the terrestrial sloth Glossotherium, the macrauchenid cf. Xenorhinotherium bahiense and a toxodontid. Both killing and slaughtering activities were likely practised. Seven new enamel radiocarbon dates show an age of 17.48–15.6 cal kyr BP for the fossiliferous strata, although this is likely to be a minimum age given the material's propensity for modern carbon uptake. The study of phytoliths and starch grains from the dental calculus of N. platensis identified five plant taxa from the phytoliths: arboreal dicotyledons, Poaceae, Marantaceae, Asteraceae and Arecaceae. Herbivore dental ecometrics and dietary inferences suggest that humans apparently exploited a diversity of grazing and browsing megaherbivores within heterogeneous landscapes of northern South America.

The decarbonization of maritime and inland waterway transport requires implementation pathways that go beyond fuel substitution and address energy, water, safety, infrastructure, and lifecycle constraints. This study proposes a sustainable technical pathway for hydrogen implementation in small-scale maritime and inland waterway vessels, using Colombia as a territorial case study. The methodology integrates technological surveillance, national energy-transition assessment, sectoral and territorial analysis, hydrogen pathway selection, water-resource management, safety and regulatory review, lifecycle criteria, and progressive validation under Technology Readiness Level principles. The results identify compressed gaseous hydrogen combined with Proton Exchange Membrane fuel cells and hybrid battery support as the most feasible short-term configuration for small vessels due to its modularity, operational flexibility, and compatibility with decentralized applications. The framework also shows that hydrogen production must be designed as a coupled water–energy–hydrogen system, prioritizing treated wastewater, rainwater, desalinated water, or other non-potable sources to avoid pressure on community and agricultural water demand. Laboratory and prototype validation demonstrated a progressive route from didactic hydrogen systems to small-vessel maquettes and scaled prototypes. The proposed pathway provides an implementation-oriented framework for safe, sustainable, and territorially adapted hydrogen deployment in small maritime systems.

Flood risk is increasingly shaped by the combined effects of climate change and the vulnerability of built environments, while building-level flood damage severity analysis is often constrained by limited data availability. This study develops a transparent and reproducible framework for analyzing building-level flood damage severity under climate-adjusted hazard conditions in data-constrained environments. The framework integrates administrative post-event damage records, GIS-based terrain information, a terrain-based proxy flood-depth reconstruction procedure, and a standardized Rhine Atlas/ICPR depth–damage relationship. Representative terrain-based proxy flood depths are reconstructed using building locations, terrain elevation, and settlement-level exposure assumptions. Observed damage categories are not used to assign proxy flood depths directly, but serve exclusively as empirical ordinal reference information for ordinal consistency assessment of model-derived damage severity. Climate effects are incorporated through a simplified hazard adjustment based on projected changes in extreme precipitation intensity. The framework is applied to 413 residential buildings affected by flood events in Serbia during the period 2016–2021. Results show a consistent nonlinear relationship between terrain-based proxy flood depth and ICPR-derived structural damage severity, as well as a strong influence of terrain elevation on relative hazard intensity. Climate-adjusted sensitivity scenarios indicate that even moderate increases in extreme precipitation lead to measurable increases in structural damage severity and an upward shift in model-derived damage levels. The proposed framework provides a practical approach for flood damage severity analysis in data-constrained environments, supporting improved decision-making in sustainable flood risk management and climate adaptation planning.

The Sustainable Development Goals (SDGs) are widely presented as an integrated framework for social, economic, and environmental progress, yet recent assessments indicate substantial implementation shortfalls. This scoping review maps post-2015 scholarship on one of the framework’s most contested fault lines: the relationship between Goal 8 (economic growth) and the ecologically oriented goals, especially Goals 6, 12, 13, 14, and 15. Following established scoping review guidance, 32 sources published between 2015 and 2026 were identified from Scopus, Web of Science, Google Scholar, citation searching, and selected grey literature. The synthesis indicates four main patterns in the included corpus. First, a substantial share of the reviewed literature characterizes continued growth-centred development and ecological sustainability as difficult to reconcile under current technological and institutional conditions, particularly given evidence on material throughput, emissions, and planetary boundaries. Second, the corpus recurrently describes three mechanisms through which this tension is muted within the SDG architecture: the rhetorical absorption of ecological limits into “green growth” discourse, strategic vagueness in targets and indicators, and the marginalization of alternative development ontologies. Third, the review synthesizes these mechanisms under the interpretive concept of paradigmatic stacking. Fourth, the corpus identifies alternative resources for a successor framework, including relational and plural conceptions of well-being associated in the reviewed literature with Ubuntu, Buen Vivir, and Gross National Happiness. Taken together, the findings suggest that debates about SDG underperformance cannot be reduced to implementation alone but also involve questions of conceptual design. The article concludes by outlining ontological pluralism as a possible direction for post-2030 framework design.

Maintaining regional landscape ecological stability and enhancing rural landscape ecosystem services are critical research priorities. This study selected Xiangxi Autonomous Prefecture (XXAP), a representative mountainous region in Southwestern (SW) China, as the case study area. This study aims to construct a rural landscape ecological risk (RLER) evaluation index system based on five-period remote sensing data (2000–2020), analyze the spatio-temporal characteristics of RLER, and provide a scientific basis for landscape ecological management and rural spatial governance. The results show that the RLERI exhibited a balanced multi-ring development trend, decreasing slightly from 0.295 in 2000 to 0.282 in 2020, suggesting a slight alleviation of overall risk. Medium-risk areas of the RLERI consistently accounted for the largest proportion (over 37%). Notably, the share of high-risk areas remained relatively stable, fluctuating narrowly between 7.98% and 8.73%. Meanwhile, high-risk areas of the landscape disturbance degree (LDD) expanded markedly from 1.87% to 10.28%. Correspondingly, high-risk areas of the landscape fragility degree (LFD) also increased significantly, rising from 0.93% to 2.8%. Spatially, RLER displayed significantly positive spatial autocorrelation, with high–high (H-H) clusters concentrated in the central-southern part and low–low (L-L) clusters distributed in the northern and southern margins, indicating pronounced spatial differentiation. In conclusion, this study provides a transferable framework for ecological risk assessment in mountainous regions. Furthermore, it underscores the importance of optimizing landscape patterns in ecologically fragile areas, strengthening ecological risk management, and mitigating ecological risks in rural settings.

Abstract Lakes and reservoirs are estimated to be globally important sources of nitrous oxide (N 2 O) to the atmosphere but recent evidence of N 2 O uptake across a broad range of lakes have called the accuracy of emission estimates into question. Here, we use a new national-scale dataset of dissolved N 2 O concentration and a Bayesian hierarchical model to predict summertime N 2 O concentration and emission rates in 465,896 waterbodies in the conterminous U.S. (CONUS). We found that N 2 O undersaturation was pervasive throughout the CONUS during the summer of 2017, with an estimated 72.9% (95% credible interval: 68.9–76.6%) of lakes functioning as N 2 O sinks. The model predicts dissolved N 2 O concentrations reasonably well based partly on interactions between nitrate concentration, waterbody surface area, and water temperature. Despite working with the largest aquatic N 2 O dataset to date, our national-scale estimate of summertime N 2 O emissions from CONUS lakes is poorly constrained, with a 95% credible interval ranging from net uptake to net emission (−282 − 482 metric tons N 2 O summer −1 ). Pervasive N 2 O undersaturation in CONUS waterbodies during the summer highlights the need to revisit N 2 O models which presume surface waters are a N 2 O source.

Abstract Observational data sparsity in Antarctica and the Southern Ocean results in lower forecast skill for atmospheric circulation over the Southern Hemisphere (SH) than the Northern Hemisphere. Additional Antarctic radiosonde observations at existing observation stations can enhance accurate forecasting of severe events over the SH; however, the number of daily radiosonde observations is limited, owing to financial and operational limitations. Using a data assimilation system, this study investigates the impact of hourly wind profiles from the PANSY radar at the Japanese Antarctic Syowa Station. Assimilation of PANSY data leads to differences in the initial atmospheric conditions for weather forecasts, even including extra radiosonde observations from the Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) campaign in 2022. These differences in initial atmospheric condition persist in Antarctica and the Southern Ocean. We show that Antarctic radar data with relatively high temporal resolution has an impact on the reproducibility of atmospheric basic state (wind speed, temperature and geopotential height) over the high latitudes and the prediction of atmospheric circulation and integrated water vapor associated with atmospheric rivers over the mid latitude in SH.

Abstract Bacteria restrict viral replication not only through dedicated defense systems but also by entering physiological states that limit cellular resources, yet how phages overcome such host-imposed barriers remains unclear. The stringent response, driven by the alarmone nucleotides ppGpp and pppGpp, can impose a growth-restrictive state that hinders phage infection in specific phage–host contexts. Here, we show that alarmone signaling constrains bacteriophage T7 infection and that the portal protein Gp8 counteracts this barrier by engaging RelA and SpoT, inhibiting their synthetase activities and suppressing alarmone accumulation. Portal mutations that disrupt this interaction sustain alarmone elevation, delay lysis and impair replication in a manner relieved in alarmone-deficient hosts. Portal proteins from representative coliphages share related stringent-response-linked features, indicating that essential virion components can moonlight as antagonists of host stress physiology and that this mechanism is not unique to T7 and may extend to additional coliphages.

Abstract Canopy structural diversity varies systematically with climate and species diversity across boreal, temperate, and tropical biomes. Yet, how this latitudinal variation affects forest productivity—particularly the role of structural diversity in mediating effects of climate on productivity across biomes—remains unresolved. By synthesizing airborne laser scanning data and ground-based forest inventories spanning boreal to tropical forests, we show that beyond its direct effects on forest productivity, climate influences productivity indirectly by regulating canopy structural diversity—a significant, yet previously underappreciated mechanism whose precise magnitude is challenging to isolate. Notably, we observe a pronounced latitudinal congruence between hotspots of structural diversity and productivity. This positions structural diversity not only as a complementary indicator of productivity but also as a critical mediator of the influence of climate and species diversity, essential for improved forecasting.