Earth and Environmental Sciences

Ground-based optical observation is a key sensing modality for space-object monitoring, but reliable association becomes challenging when orbital priors are degraded, site-position information is imprecise, and each frame contains multiple point-source candidates. This paper proposes a robust framework for single-target association among multiple image candidates and recursive estimation under such conditions. The method first converts image-domain candidates into unit line-of-sight (LOS) directions and represents their local deviations in a prediction-aligned tangent plane. Angular measurement noise and site-position uncertainty are then propagated into the local covariance model. Based on this representation, a kinematic-photometric five-dimensional (5D) normalized innovation squared (NIS) gating statistic is constructed by jointly evaluating local position, pseudo-velocity, and photometric consistency. After association, a three-dimensional decoupled Kalman update is performed using only the single-frame position and photometric measurements. Experiments on four real ground-based optical satellite observation sequences, including two static scenarios and two dynamic scenarios, show correct association rates of 100.00%, 100.00%, 100.00%, and 85.86%, respectively. These results demonstrate that the proposed framework improves association reliability under degraded orbital priors and imprecise site-position information while maintaining stable recursive estimation.

Shadows in high-resolution urban remote sensing imagery significantly degrade radiometric and structural information, thereby limiting the performance of downstream tasks such as classification and object extraction. Therefore, effective shadow removal is essential for improving the reliability of urban remote sensing applications. Existing methods still exhibit limitations in accurately detecting complex shadows, especially small-scale shadows and ambiguous boundaries, and shadow compensation in umbra regions often suffers from under-correction due to inadequate illumination modeling. To address these challenges, a physics-guided shadow removal framework that integrates lightweight shadow detection with illumination-aware compensation is proposed. A lightweight U-Net (LSDU) is designed to efficiently capture multi-scale shadow features, while a modified illumination intensity ratio method (MIIRM) is developed to explicitly model illumination differences between umbra and penumbra. Furthermore, a dynamic penumbra compensation method (MDPCM) is introduced to alleviate over-compensation effects in transition regions and improve radiometric consistency. Experiments on the Aerial Imagery Shadow Dataset (AISD) demonstrate that the proposed method achieves over 96% overall accuracy in shadow detection and the lowest RMSE in shadow compensation among existing state-of-the-art methods, while maintaining strong robustness across diverse urban scenes.

Infrared UAV detection plays a crucial role in both security surveillance and military applications. However, under fast UAV movement or dynamic zooming scenarios, the rapid scale variation of targets poses severe challenges to existing detection models, especially on resource-constrained edge devices. To address this, a lightweight scale-adaptive multi-scale feature fusion model, termed LMF-IR, is proposed for efficient and accurate detection under sudden target size changes. The model integrates three key components: a Multi-Dilation Residual Block (MDRB) for enhanced multi-scale feature representation, an improved Channel Attention Model–Feature Fusion Pyramid Network (CAM-FPN) to boost adaptive feature fusion, and a modified P-WIoU loss function designed for precise bounding box regression under varying target sizes. The MDRB module effectively captures fine-grained features across multiple scales and reliably identifies targets of varying sizes. The CAM-FPN incorporates a channel attention mechanism, which can dynamically adjust the weights of features, enabling the model to focus on informative feature channels. The redesigned P-WIoU loss function is designed to account for the shape characteristics of UAV target bounding boxes. It includes centroid distance, overlap ratio, and aspect ratio, thereby improving localization accuracy under rapid scale changes. The experimental results on our self-built UAV–infrared dataset show that LMF-IR reduces 1.4 G in floating-point operations compared to the baseline model, and the parameter count is reduced to 62% of the baseline. At the same time, mAP@0.5:0.95 increases by 2.4%. Moreover, on the public ANTI-UAV dataset, our method increases mAP@0.5:0.95 by 4.8%, indicating that our method has excellent performance in real-time infrared UAV detection under rapid target scale changes.

Abstract. This study updates an article published in NHESS journal in 2015 and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3006 rainfall events associated with landslides (RELs) was reconstructed using 9530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years.

In Nigeria and mainly in Imo State, flood disasters continue to be one of the most devastating environmental threats to arable crops and to the livelihoods of farmers. However, there is still limited empirical data on the factors influencing arable crop farmers’ strategies to mitigate flood disasters in Imo State. Therefore, the study examined the strategies that arable crop farmers use for mitigating flood disasters, identified the factors that influence the implementation of flood mitigation strategies, and identified the barriers that hamper their effective application. A structured questionnaire was used to collect the primary data from three-hundred and twenty (320) farmers cultivating arable crops, selected specifically from the critical flood-prone farming communities of Imo State, Nigeria. The data were analyzed using multinomial logistic regression and descriptive statistics. The results revealed that arable crop farmers practiced various strategies to mitigate flood disasters, including the construction of floodways; dams; land filling; raising beds and ridges; and crop diversification. The multinomial logit result indicated that socio-economic factors significantly influenced farmers’ choice of flood mitigation strategies. Furthermore, arable crop farmers face major barriers in effectively implementing and practicing flood mitigation strategies in the area, which include the high cost of flood disaster strategies (95.00%) and inadequate knowledge of mitigation strategies (84.00%). The study concludes that strengthening farmers’ access to resources, extension services, and cooperative organizations would enhance the use of effective flood mitigation strategies and improve resilience to flood disasters among arable crop farmers in the study area.

The soil drilosphere is a critical biogeochemical hotspot, yet its role as a key interface for microplastic (MP) accumulation and impact remains poorly characterized. We investigated how polyethylene microplastics (<150 μm) affect the drilosphere compartments (gut, burrows, and casts) of two distinct earthworm ecotypes: epigeic Eisenia fetida and endogeic Pheretima guillelmi . Results showed that MPs significantly enrich in the drilosphere compared to bulk soil, with the endogeic species exhibiting greater accumulation. While earthworm activity typically stimulated nutrient characteristics, MP exposure disrupted these functions, significantly reducing total nitrogen (5.0–25.0%) and ammonium (28.5–62.1%). Ecotype-specific host damage emerged: E. fetida exhibited pronounced immune and oxidative stress responses, whereas P. guillelmi suffered severe digestive and metabolic impairments. These impacts were mediated by distinct microbiome reprogramming. MP-induced dysbiosis intensified progressively along the soil–drilosphere–gut continuum. Multivariate and transcriptomic analyses revealed that external-drilosphere microbiota shifts drove carbon–nitrogen characteristic alterations, while internal dysbiosis triggered host physiological stress. This study highlights that ecotype-specific restructuring of drilosphere microbiomes underpins the ecosystem-scale impacts of MP pollution, demonstrating that earthworm functional diversity is essential for comprehensive soil health risk assessments.

Abstract UAV-based RGB imaging is increasingly used to track crop diseases and assess fungicide performance. However, standard disease severity measures may miss an important factor: fungicide-induced changes in canopy structure that modulate disease observability. Here we analyse a multi-site dataset of wheat yellow and leaf rust, integrating UAV-derived canopy cover and disease severity, and rater-based disease severity assessments. Results indicate that between visual and UAV imagery-based disease severity was high but fundamentally non-stationary, governed by canopy cover and epidemic intensity. Measurement error followed structured, canopy-dependent regimes. The canopy-normalized disease area, a metric that harmonizes severity with structural and spatial context, revealed ranking shifts in fungicide efficacy that are not captured through traditional metrics. This suggests that severity-based comparisons may combine true disease suppression with treatment-related changes in how visible symptoms are. Framing plant disease quantification as a measurement challenge, rather than only a detection, may support a more accurate assessment of treatment effects. Modelling the influence of canopy structure on symptom expression could provide a scalable and robust basis for disease monitoring and decision-making in precision agriculture.

Abstract Techniques for identifying stable, high-quality in-stream habitat are increasingly needed to support fish conservation and river restoration under changing flow and sediment regimes. This study presents a multi-criteria mapping framework that integrates hydraulic modelling, geomorphic form and unit classification, and riparian canopy cover assessment to identify suitable redd conditions for egg incubation planning. The framework was tested in the Folly River, Nova Scotia, using an inner Bay of Fundy Atlantic salmon egg incubation project as a case study. High flow and low flow hydraulic conditions were simulated in HEC-RAS to derive flow velocity and water depth layers, while the Geomorphic Form Variation approach and Geomorphic Unit Tool were used to map geomorphic complexity and unit assemblages. Riparian canopy cover derived from LiDAR-based canopy height models provided an additional habitat condition layer. These outputs were spatially overlaid using criteria associated with a stable incubation site to map alternative locations with suitable incubation conditions and wetted extents under low flow were then used to assess longitudinal connectivity. Extreme fluctuations in weather patterns pose additional challenges for selecting suitable egg incubation sites. Therefore, we stress-tested our suitable sites in BASEMENT software to evaluate morphodynamic stability under high flow. The framework identified targeted redd sites and provides a transferable approach for habitat assessment, restoration planning, and climate-resilient river management.

Abstract Near‐seafloor marine sediments on active margins have been shown to be stronger than those on passive margins. However, the reasons for this are not fully understood. To investigate the role of intrinsic properties, we performed resedimentation and oedometer tests and measured undrained shear strength, grain size, mineralogy, and plasticity on sediments from three active margins (Nankai, Cascadia, Surveyor Fan) and three passive margins (Amazon Fan, Carolina, and New Jersey) and compared them to their in situ field measurements. We find that all shear strengths on resedimented samples fall within the expected range for normal consolidation. However, Nankai and Cascadia exhibit anomalously high undrained shear strength and low porosity in situ, which cannot be reproduced with 1‐D consolidation experiments. These results suggest that other factors occurring on active margins contribute to strengthening near‐seafloor sediments such as the repeated exposure to earthquakes and lateral shear strain induced by non‐uniaxial stress paths.

Nearly half of the world's population relies on nonsewered sanitation, and in urban areas, intermittent onsite storage of wastewater prior to road-based transport to treatment is common. Anerobic biological activity during storage generates methane, contributing to greenhouse gas emissions and posing challenges for climate mitigation. Current estimates of methane emissions rely on methane correction factors (MCFs) in the Intergovernmental Panel on Climate Change (IPCC) guidelines that are based on theoretical assumptions and increasingly viewed as not valid. In this Perspective, we use 1349 data points from onsite storage of wastewater in 11 cities to show that obtaining accurate city-wide estimates is complicated by highly variable properties of in situ wastewater and storage conditions and hence biological production of methane. When it comes to greenhouse gas emissions from nonsewered sanitation, the sector has so far focused on storage, but as evidence suggests these estimates are too high, we propose that emphasis should in fact be placed on the entire service chain.

Abstract In many peri-urban areas of the Global South, formal water supply systems are insufficient. This gap is largely filled by a diverse informal water market and widespread informal water use, both of which depend heavily on groundwater. Peri-urban low-income communities are particularly affected. Research in such contexts requires participatory and interdisciplinary approaches. This paper aims to equip hydrogeologists with the knowledge needed to implement participatory management, thereby fostering deeper engagement with participatory methodologies and strengthening interdisciplinary collaboration. The findings are also intended to support NGOs in planning more comprehensive water projects by integrating hydrogeological considerations. Two low-income communities in peri-urban Jaipur, India, representing different hydrogeological, infrastructural, and socio-cultural conditions, were selected as study areas in cooperation with a local NGO. The study comprised four components: initial socio-hydrogeological assessments to gain baseline information; water workshops to raise awareness and exchange knowledge on groundwater; the recruitment and training of women assistants to teach a small group more advanced groundwater-related skills; and finally, the production of films to disseminate the results. The research demonstrated that the applied methods generated relevant insights and enabled the implementation of a participatory approach. However, future studies should engage more thoroughly with the literature on participatory methodologies, ideally in collaboration with social scientists experienced in such approaches. Despite these limitations, the study contributes to the growing field of participatory and socio-hydrogeological research and shows that these approaches can enhance groundwater awareness among local communities and NGOs while sensitizing hydrogeologists to the importance of inter- and transdisciplinary work.

Superpoint Transformers use superpoints as the basic processing units, thereby significantly reducing the number of tokens processed by Transformers. However, they have been seldom employed in point cloud roof plane instance segmentation, and existing superpoint Transformers suffer from limited performance due to the use of low-quality superpoints. To address this challenge, we establish a set of criteria that high-quality superpoints for Transformers should satisfy and introduce a corresponding two-stage superpoint generation process. The superpoints generated by our method not only have accurate boundaries, but also exhibit consistent geometric sizes and shapes, which greatly benefit the feature learning of superpoint Transformers. To compensate for the limitations of deep learning features when the training set size is limited, we incorporate multidimensional handcrafted features into the model. Additionally, we design a decoder that combines a Kolmogorov–Arnold Network with a Transformer module to improve instance prediction and mask extraction. Finally, our network’s predictions are refined using traditional algorithm-based post-processing. For evaluation, we annotated a real-world dataset and corrected annotation errors in the existing RoofN3D dataset. Experimental results show that our method achieves state-of-the-art performance on our dataset, as well as both the original and corrected RoofN3D datasets. Our model also shows significant advantages over existing methods when handling data with low point density, large density variations, or low 3D point precision. Moreover, it is not sensitive to plane boundary annotations during training, significantly reducing the annotation burden. We will release our code, trained models, and datasets.

Research on the seaward shoreface boundary (SSB) over broad spatial scales is limited, restricting the understanding of its macroscale characteristics. In this study, we define a theoretical wave-base-derived seaward shoreface boundary (TWSSB) based on the intersection of wave base (half the wavelength) with seabed topography. Using high-resolution wave analysis data from the Copernicus Marine Environment Monitoring Service (CMEMS) for 2022-2023, annual and seasonal wave bases were calculated in China’s coastal waters. By combining these data with nearshore bathymetry, the annual and seasonal TWSSBs were determined. The spatial distribution of annual TWSSBs and the migration of seasonal TWSSBs along China’s coast were detailed, providing a comprehensive view of TWSSB characteristics. Results show a strong correlation between TWSSBs and local isobaths. The annual TWSSBs approximately coincide with the 10 m isobath in the Bohai Sea and Beibu Gulf, align with the 20 m isobath in the central and southern Yellow Sea, and are close to the 30 m isobath east of Hainan Island. Compared to the annual averages, seasonal TWSSBs show different horizontal shifts, typically within several kilometers (median ~3 km). The maximum seasonal range (difference between seaward and landward shifts) reaches approximately 41 km in the South China Sea. Together, these findings present a large-scale synoptic mapping of the TWSSB along the entire coast of China and demonstrate that TWSSB location is influenced by regional bathymetry and seasonal wave conditions. This theoretical, wave-based approach is expected to offer a reference for future studies on SSB and coastal morphodynamic processes.

To address the challenge of vehicle-type recognition in complex traffic scenes within wide-area satellite imagery, where vehicles are easily confused with the surrounding background, this paper exploits the distinct spectral-response differences among various ground objects in multispectral data to guide the model to focus on vehicle targets. Based on satellite imagery comprising seven visible (VIS) bands and one near-infrared (NIR) band, this research is structured into a two-stage framework. In the first stage, a dedicated Preprocessing Framework for Vehicle Type Recognition (PFVTR) is developed for the satellite imagery, aiming to identify the sensitive bands within the visible-light spectrum for this specific task. Consequently, the Visible and Near-Infrared Multispectral Dataset (VNMD) is established based on the sensitive VIS bands and NIR imagery. In the second stage, to exploit the environmental robustness of the near-infrared band, the Multispectral Information Fusion-based Multimodal Vehicle Type Recognition model (MIF-MVTR) is proposed. By adaptively fusing the sensitive visible bands and the near-infrared spectrum, the model counters environmental interference to bolster recognition precision. Experimental results on the VNMD and public VEDAI datasets, as well as under complex illumination conditions, demonstrate that MIF-MVTR effectively exploits complementary spectral information to improve vehicle-type recognition performance in complex scenarios. These results further validate the robustness and generalization capability of the proposed model. Overall, spectral information plays an important role in alleviating vehicle-background confusion in wide-area traffic scenes.

Abstract Safeguarding groundwater, a vital resource in adaptation to global change, yet increasingly under pressure, requires robust knowledge and capacity. While strong foundations in hydrogeology remain indispensable, there is a clear need for joint, interdisciplinary and applied groundwater education at the postgraduate level to address the complex and multifaceted nature of groundwater science. Building on these arguments, the Joint Master’s Programme Groundwater and Global Change was successfully launched a decade ago. This essay presents the rationale behind this approach.

Abstract Seagrass beds are highly efficient blue carbon ecosystems. Despite covering only 0.2 % of the global ocean area, they play a critical role in carbon stocks. However, baseline data on carbon stocks in temperate seagrass beds remain scarce, constraining the assessment of regional blue carbon potential. This study, focusing on the Changshan Archipelago in the North Yellow Sea (covering 8 islands, with a total survey area of 324.06 ha), aims to quantify the carbon stock of temperate seagrass beds in this region and analyze their driving factors. Through in-situ sampling and laboratory analysis of 39 stations, this study revealed that the temperate seagrass meadows in the North Yellow Sea exhibit a distinct mono-dominant community structure, with Zostera marina being the absolutely dominant species.

CoPc achieved >80% defluorination at 30 °C and >30% at 10 °C, highlighting the potential of molecular template engineering as a biomimetic strategy for groundwater remediation and for overcoming C-F activation and metal-center deactivation.

exhibiting a resilience strategy. This is critical knowledge to understand the biological responses of these species and to inform future conservation strategies.

This study investigates the age and growth of the blackspot seabream in the north-western Ionian Sea (Central Mediterranean). An accurate study on determining age is necessary to provide useful information for understanding population dynamics and to perform stock assessments which are essential for the scientific and prudent management of fisheries. Using data sets from two habitats (flat muddy bottoms and heterogeneous cold-water coral and canyon habitats) and comparing three different methodologies: one direct (otolith reading) and two indirect methods (back calculation and Modal Progression Analysis), was possible to obtain a more robust age estimate. A total of 1023 specimens of blackspot seabream were collected from muddy bottoms at depths of 13-695 m, and 60 individuals were caught in the cold-water coral and canyon habitats between 183 and 612 m depth. The length-frequency distributions of P. bogaraveo showed a polymodal pattern in both habitats explored, with small to medium-sized individuals caught on flat muddy bottoms and larger ones in cold-water coral and canyon habitats. The maximum age estimated by reading otoliths was 9 years for an individual of 347 mm TL and no significant difference was observed comparing the three age-length keys. Consequently, the three methods provided consistent results confirming the same growth pattern. The growth curves were comparable among methods, with no significant difference observed: otolith readings: L ∞ 459 mm, k=0.12 year -1 , t 0 =-1.68 years; back calculation method L ∞ 412 mm, k=0.14 year -1 , t 0 =-1.20 years; MPA L ∞ 339 mm, k=0.20 years -1 , t 0 =-1.20 years. These results suggest slow growth in accordance with what has already been observed in other areas of the Mediterranean Sea. These insights provide key elements to support the development of more effective and sustainable fisheries management strategies, especially in this case for which data for stock assessments are insufficient or absent.

Persistent organic pollutants (POPs), prevalent across diverse environmental matrices, are highly hazardous and recalcitrant compounds that can be transformed into low-toxicity compounds by diverse microorganisms. Many transformation processes of POPs could intricately interface with elemental biogeochemical cycles, which are fundamental drivers of ecosystem function. While microbial pathways of POPs transformation have been extensively studied, their integration into broader element turnover in the environment remains fragmented. Here, we review the relationship between POPs metabolism and biogeochemical cycles, spanning from single-species enzymatic coupling to multispecies syntrophic interactions. We contend that POPs transformation is not an isolated microbial event but is deeply embedded within elemental metabolism through direct mechanisms of electron transfer and cross-feeding, or indirect modulation of quorum sensing and mineral-interface interactions. Across levels from gene expression to community level-energy and material exchange, microorganisms in the environment mediate POPs transformation while maintaining elemental balance through dynamic metabolic regulation. Furthermore, we propose a strategic framework that leverages functional compensation and integrative strategies of native and engineered microbiomes to reinforce POPs degradation and coordinate element cycling. Future research should focus on integrating microbiome-based approaches with omics analyses, systems modeling, and ecological engineering. These efforts facilitate the predictable regulation of pollutant-element interactions, ultimately restoring ecosystem multifunctionality within POPs-contaminated sites.

Small-scale fisheries in the Western Indian Ocean are vital to food security, livelihoods, and coastal economies, supporting over 65 million people. Yet, SSFs face mounting threats from various factors, including overexploitation, climate change, and weakly integrated governance systems. Despite growing evidence of climate impacts, critical gaps remain in understanding how these changes affect fisheries and in translating this knowledge into effective policy and practice. This policy brief highlights the urgent need to strengthen the science–policy–practice nexus, enhance cross-sector coordination, and embed climate resilience into fisheries governance to secure sustainable and equitable fisheries management outcomes for the region.

Abstract Background: Long-term residence in mining regions exposes populations to environmental contaminants such as heavy metals and natural geochemical factors, both of which may trigger genotoxic stress and disturb cellular homeostasis. The molecular mechanisms underlying these effects in human populations remain incompletely understood. This study aimed to investigate genotoxic and oxidative stress responses and their links to gene expression changes and plasma metal concentrations.Methods: Forty-three residents from mining regions (MRR) and 39 from non-mining areas were examined. Genotoxicity was evaluated using micronucleus assays in lymphocytes and buccal cells. Mitochondrial DNA copy number (mtDNAcn) was quantified by qPCR, oxidative stress was analyzed in erythrocytes and DNA (8-OHdG). Transcriptomic data were integrated with plasma metal levels to identify associations.Results: MRR exhibited higher micronuclei frequency in lymphocytes and reduced mtDNAcn. Micronuclei frequency correlated with plasma manganese and altered CDK10 and JUN expression. mtDNAcn was negatively associated with Mn, positively with Ni, and linked to HSPA1A, IL1A, and TNF expression. Gene-level analyses revealed downregulation of DNA repair genes and upregulation of stress-responsive genes, some of which were associated with both genotoxicity biomarkers and specific metals.Conclusions:Mining-region residency is associated with genomic instability and mitochondrial compromise through dysregulated DNA repair and stress-response genes.