Search Results (145)

Mapping forest growth potential across varying environments is challenging, especially when field measurements are limited. In this study, we integrated Airborne Laser Scanning (ALS) terrain derivatives and Sentinel-2 spectral indices to model Site Index (SI), using forest plantations, at 10-m spatial resolution across three ecologically distinct regions in British Columbia (Aleza Lake, Deception, and Eagle Hills). Random Forest regression models were calibrated using field-measured SI and a multistep variable-selection procedure that included Variance Inflation Factor (VIF) screening followed by model-based variable importance assessment. Model performance was evaluated using repeated 10-fold cross-validation. The combined ALS–Sentinel-2 models substantially outperformed single-source models, yielding cross-validated R² values of 0.63, 0.44, and 0.56 for Aleza Lake, Deception, and Eagle Hills, respectively, compared with R² values of 0.40, 0.40, and 0.46 for ALS-only models. Key predictors consistently included terrain metrics, such as the Topographic Position Index (TPI) and the Topographic Wetness Index (TWI), along with satellite-derived chlorophyll-sensitive indices including S2REP (Sentinel-2 red-edge position), MTCI (MERIS terrestrial chlorophyll), and GNDVI (Greenness Normalized Difference Vegetation Index). A general model using predictors common to all regions performed comparably (R² = 0.63, 0.41, 0.52), demonstrating the transferability and operational potential of the approach. These findings demonstrate that integrating ALS-derived terrain metrics with Sentinel-2 spectral indices provides a robust, age-independent framework for capturing spatial variability in forest productivity across landscapes. This multi-sensor fusion approach enhances traditional SI methods and single-sensor models, providing a scalable and operational tool for forest management and long-term planning in changing environmental conditions. Full article

The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images from sources (MERIS, AVHRR, SPOT, PROBA, and Sentinel-3). In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions under the Common Agricultural Policy (CAP) 2023–2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protecting biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes. Our study highlights that the CAP for mountain farms should be targeted, long-term, and compensatory, so as to compensate for the naturally unfavorable farming conditions and support their multifunctional role. The most important assumptions of CAP for mountain farms are a fair system of compensatory payments (LFA/ANCs), support for local and high-quality production, income diversification, and investments adapted to mountain conditions. Full article

Recent advances in hyperspectral imaging (HSI) and multimodal deep learning have opened new opportunities for crop health analysis; however, most existing models remain limited by dataset scope, lack of interpretability, and weak cross-domain generalization. To overcome these limitations, this study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches. The proposed architecture enables robust and interpretable feature learning across heterogeneous data sources, facilitating the estimation of spectral proxies of chlorophyll content, plant vigor, and disease stress indicators rather than direct physiological measurements. Experiments were performed on seven publicly available benchmark datasets encompassing diverse spectral and visual domains: three hyperspectral datasets (Indian Pines with 16 classes and 10,366 labeled samples; Pavia University with 9 classes and 42,776 samples; and Kennedy Space Center with 13 classes and 5211 samples), two plant disease datasets (PlantVillage with 54,000 labeled leaf images covering 38 diseases across 14 crop species, and the New Plant Diseases dataset with over 30,000 field images captured under natural conditions), and two chlorophyll content datasets (the Global Leaf Chlorophyll Content Dataset (GLCC), derived from MERIS and OLCI satellite data between 2003–2020, and the Leaf Chlorophyll Content Dataset for Crops, which includes paired spectrophotometric and multispectral measurements collected from multiple crop species). To ensure statistical rigor and spatial independence, a block-based spatial cross-validation scheme was employed across five independent runs with fixed random seeds. Model performance was evaluated using $R^2$, RMSE, F1-score, AUC-ROC, and AUC-PR, each reported as mean ± standard deviation with 95% confidence intervals. Results show that Agri-DSSA consistently outperforms baseline models (PLSR, RF, 3D-CNN, and HybridSN), achieving up to $R^2=0.86$ for chlorophyll content estimation and F1-scores above 0.95 for plant disease detection. The attention distributions highlight physiologically meaningful spectral regions (550–710 nm) associated with chlorophyll absorption, confirming the interpretability of the model’s learned representations. This study serves as a methodological foundation for UAV-based and field-deployable crop monitoring systems. By unifying hyperspectral, chlorophyll, and visual disease datasets, Agri-DSSA provides an interpretable and generalizable framework for proxy-based vegetation stress estimation. Future work will extend the model to real UAV campaigns and in-field spectrophotometric validation to achieve full agronomic reliability. Full article

The ongoing mpox clade Ib outbreak was first detected in the eastern Democratic Republic of Congo (DRC) and was associated with sexual transmission. It emerged in Kamituga, a mining city and spread rapidly in surrounding health zones and reached cities like Bukavu and Goma. Here, we describe the clinical, epidemiological, and virological characteristics of the first case of clade Ib in Kinshasa, the capital city in the western DRC. The case involved a young adult woman from Kinshasa who reported unprotected sexual contact with an occasional partner, a former friend, and subsequently developed genital lesions, including vesicles and pustules. These lesions evolved and spread to the entire body, including the limbs, eyes, and soles. The diagnosis was confirmed by PCR and sequencing allowed us to assign clade Ib. We show that infection with mpox clade Ib through sexual transmission can lead to limbal nodular keratoconjunctivitis and focal conjunctivitis as complications. Importantly, these results suggest that clade Ib may have been circulating silently in Kinshasa prior to the official declaration by the Ministry of Health. This also raises concerns about the potential risk of global spread, as is currently being observed. Further studies are needed to investigate whether subsequent outbreaks of clade Ib in Kinshasa may have emerged independently of introductions from Kivu, pointing to a more complex pattern of co-circulation that could define the mpox epidemic in the capital. Full article

Background: Poliomyelitis is a vaccine-preventable disease, with oral poliomyelitis vaccines (OPVs) and injectable poliomyelitis vaccines. In the Democratic Republic of the Congo (DRC), circulating vaccine-derived polioviruses (VDPVs) persist due to intrinsic and extrinsic factors, including the quality of the cold chain, which may make the vaccines less effective. This study’s objective was to evaluate the cold chain’s quality of OPVs and its effect on the vaccine’s viability and potency at different levels in health systems in Kinshasa. Methods: A cross-sectional study was conducted in Kinshasa, collecting OPVs at different levels of the health pyramid. Vaccine viability was assessed by cell culture using a modified World Health Organization (WHO) protocol, and the viral titer was determined using the Karber formula. The vaccine titer was classified as “very good”, “good”, or “poor” according to the WHO standard’s viral titer. Results: A total of 53 vaccines were collected and analyzed, compressing 38 bivalent oral poliomyelitis (bOPV) vaccines and 15 novel oral poliomyelitis vaccines, type 2 (nOPV2). The viral titer ranged from log10^5.8 to log 10^7.3 and from log10^5.4 to log10^8.9 for the nOPV2 and the bOPV, respectively. Of these 53 vaccine samples, 10% of the bOPVs showed viral titers below the recommended WHO threshold (>10⁶ CCID₅₀/dose), 100% of the nOPV2 had viral titers within the WHO standards (>10⁵ CCID₅₀/dose), and a significant decline in the viral titer was observed for both types of vaccines (nOPV2 and bOPV) as the distribution progressed along the level of the health pyramid. Conclusions: This study demonstrated that the viral titer of OPV declined from central to peripheral areas in routine and campaign strategies in Kinshasa. Full article

Infections with zoonotic pathogens have received increasing attention in recent years, as reflected in the literature of both veterinary and human medicine. Cryptosporidium and Giardia are recognised as the principal causes of waterborne outbreaks worldwide, but there is still limited data on the role of wild carnivores, such as red foxes and wolves, as reservoir hosts and in disseminating these pathogens in the environment. The aim of the current project was to analyse the prevalence and abundance of Cryptosporidium and Giardia infections in foxes from seven voivodeships and in wolves from the Warmia-Masuria Voivodeship in Poland and to conduct a phylogenetic analysis of the detected parasites. For the detection of both parasites, we used the commercial immunofluorescent assay MeriFluor Cryptosporidium/Giardia. For Cryptosporidium detection we also applied modified Ziehl–Neelsen (ZN) staining of faecal smears and, following PCR amplification, sequenced the 18S rDNA locus. For Giardia detection, we sequenced the glutamate dehydrogenase (gdh) gene. In total, 117 and 69 faecal samples obtained from red foxes and wolves, respectively, were screened for the presence of Cryptoporidium/Giardia. In red foxes, prevalence was 38.5% and 15.4% for Cryptosporidium spp. and G. intestinalis, respectively. In wolves, the prevalence of Cryptosporidium spp. was 14.5%, and only one sample was Giardia-positive. Cryptosporidium canis, Cryptosporidium sp. vole genotype, C. baileyi and Cryptosporidium sp. were identified in red foxes, while C. canis and Cryptosporidium sp. were detected in wolves. Our results indicate that red foxes and grey wolves act as reservoir hosts of Cryptosporidium spp. and G. intestinalis in natural areas in Poland. Full article

Remote sensing (RS) has been widely used to monitor cyanobacterial blooms in inland water bodies. However, the accuracy of RS-based monitoring varies significantly depending on factors such as waterbody type, sensor characteristics, and analytical methods. This study comprehensively evaluates the current capabilities and challenges of RS for cyanobacterial bloom monitoring, with a focus on achievable accuracy. We find that chlorophyll-a (Chl-a) and phycocyanin (PC) are the primary indicators used, with PC demonstrating greater accuracy and stability than Chl-a. Sentinel and Landsat satellites are the most frequently used RS data sources, while hyperspectral images, particularly from unmanned aerial vehicles (UAVs), have shown high accuracy in recent years. In contrast, the Medium-Resolution Imaging Spectrometer (MERIS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) have exhibited lower performance. The choice of analytical methods is also essential for monitoring accuracy, with regression and machine learning models generally outperforming other approaches. Temporal analysis indicates a notable improvement in monitoring accuracy from 2021 to 2023, reflecting advances in RS technology and analytical techniques. Additionally, the findings suggest that a combined approach using Chl-a for large-scale preliminary screening, followed by PC for more precise detection, can enhance monitoring effectiveness. This integrated strategy, along with the careful selection of RS data sources and analytical models, is crucial for improving the accuracy and reliability of cyanobacterial bloom monitoring, ultimately contributing to better water management and public health protection. Full article

The accurate and efficient detection of steel surface defects remains challenging due to complex backgrounds, diverse defect types, and varying defect scales. The existing CNN-based methods often struggle with capturing long-range dependencies and handling complex background noise, resulting in suboptimal performance. Meanwhile, although Transformer-based approaches are effective in modeling global context, they typically require large-scale datasets and are computationally expensive, limiting their practicality for industrial applications. To address these challenges, we introduce a novel attention-based salient object detector, called the ASOD, to enhance the effectiveness of detectors for strip steel surface defects. In particular, we first design a novel channel-attention-based block including global max/average pooling to focus on the relevant channel-wise features while suppressing irrelevant channel responses, where maximizing pooling extracts the main features of local regions, while removing irrelevant features and average pooling obtain the overall features while removing local details. Then, a new block based on spatial attention is designed to emphasize the area with strip steel surface defects while suppressing irrelevant background areas. In addition, a new cross-spatial-attention-based block is designed to fuse the feature maps with multiple scales filtered through the proposed channel and spatial attention to produce features with better semantic and spatial information such that the detector adapts to strip steel defects of multiple sizes. The experiments show that the ASOD achieves superior performance across multiple evaluation metrics, with a weighted F-measure of 0.9559, an structure measure of 0.9230, a Pratt’s figure of meri of 0.0113, and an mean absolute error of 0.0144. In addition, the ASOD demonstrates strong robustness to noise interference, maintaining consistently high performance even with 10–20% dataset noise, which confirms its stability and reliability. Full article

Eucalyptus plantations with fast growth and short rotation play an important role in improving economic conditions for local farmers and governments. It is necessary to map and update eucalyptus distribution in a timely manner, but to date, there is a lack of suitable approaches for quickly mapping its spatial distribution in a large area. This research aims to develop a uniform procedure to map eucalyptus distribution at a regional scale using the Sentinel-2 imagery on the Google Earth Engine (GEE) platform. Different seasonal Senstinel-2 images were first examined, and key vegetation indices from the selected seasonal images were identified using random forest and Pearson correlation analysis. The selected key vegetation indices were then normalized and summed to produce new indices for mapping eucalyptus distribution based on the calculated best cutoff values using the ROC (Receiver Operating Characteristic) curve. The uniform procedure was tested in both experimental and test sites and then applied to the entire Fujian Province. The results indicated that the best season to distinguish eucalyptus forests from other forest types was winter. The composite indices for eucalyptus–coniferous forest separation (${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{C}}$) and for eucalyptus–broadleaf forest separation (${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{B}}$), which were synthesized from the enhanced vegetation index (EVI), plant senescing reflectance index (PSRI), shortwave infrared water stress index (SIWSI), and MERIS terrestrial chlorophyll index (MTCI), can effectively differentiate eucalyptus from other forest types. The proposed procedure with the best cutoff values (0.58 for ${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{C}}$ and 1.29 for ${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{B}}$) achieved accuracies of above 90% in all study sites. The eucalyptus classification accuracies in Fujian Province, with a producer’s accuracy of 91%, user’s accuracy of 97%, and overall accuracy of 94%, demonstrate the strong robustness and transferability of this proposed procedure. This research provided a new insight into quickly mapping eucalyptus distribution in subtropical regions. However, more research is still needed to explore the robustness and transferability of this proposed method in tropical regions or in other subtropical regions with different environmental conditions. Full article

Polio-associated paralysis is one of the diseases under national surveillance in the Democratic Republic of the Congo (DRC). Although it has become relatively rare due to control measures, non-polio paralysis cases are still reported and constitute a real problem, especially for etiological diagnosis, which is necessary for better management and response. From September 2022 to April 2023, we investigated acute flaccid paralysis (AFP) cases in Kinshasa following an alert from the Provincial Division of Health. All suspected cases and their close contacts were investigated and sampled. Among the 57 sampled patients, 21 (36.8%) were suspects, and 36 (63.2%) were contacts. We performed several etiological tests available in the laboratory, targeting viruses, including Poliovirus, Influenza virus, SARS-CoV-2, Enterovirus, and arboviruses. No virus material was detected, but the serological test (ELISA) detected antibodies against Chikungunya Virus, i.e., 47.4% (27/57) for IgM and 22.8% (13/57) for IgG. Among suspected cases, we detected 33.3% (7/21) with anti-Chikungunya IgM and 14.3% (3/21) of anti-Chikungunya IgG. These results highlight the importance of enhancing the epidemiological surveillance of Chikungunya. Full article

The use of Earth Observation (EO) for water quality monitoring has substantially raised in the recent decade; however, harmonisation of EO-based indicators across the seas to support environmental policies is in great demand. EO-based Cyanobacterial Bloom Index (CyaBI) originally developed for open waters, was tested for transitional and coastal waters of the Lithuanian Baltic Sea and the Ukrainian Black Sea during 2006–2019. Among three tested neural network-based processors (FUB-CSIRO, C2RCC, standard Level-2 data), the FUB-CSIRO applied to Sentinel-3 OLCI images was the most appropriate for the retrieval of chlorophyll-a in both seas (R² = 0.81). Based on 147 combined MERIS and OLCI synoptic satellite images for the Baltic Sea and 234 for the Black Sea, it was shown that the CyaBI corresponds to the eutrophication patterns and trends over the open, coastal and transitional waters. In the Baltic Sea, the cyanobacteria blooms mostly originated from the central part and the outflow of the Curonian Lagoon. In the Black Sea, they occurred in the coastal region and shelf zone. The recent decrease in bloom presence and its severity were revealed in the areas with riverine influence and coastal waters. Intensive blooms significantly enhanced the short-term increase in sea surface temperature (mean ≤ 0.7 °C and max ≤ 7.0 °C) compared to surrounding waters, suggesting that EO data originating from thermal infrared sensors could also be integrated for the ecological status assessment. Full article

Barren sites that lack soil are exposed to some of the harshest elements, which include high temperatures, solar radiation, wind, extreme temperature changes, and low soil moisture and nutrient conditions. An ecological restoration experiment was conducted using three site-preparation treatments, straw (S), Meri-Crusher (MC), and coarse woody debris (CWD), in a site-/no site-preparation 2 × 2 × 2 factorial on sites that had been barren for 25 years. In addition, four early successional deciduous species, white birch (WB, Betula papyrifera Marshall), gray birch (GB, Betula populifolia Marshall), green alder (GA, Alnus viridis Vill. subsp. crispa Ait), and speckled alder (SA, Alnus incana L. subsp. rugosa Du Roi), were examined. The two- and three-way interactions were almost all magnitude effects and not rank changes. Gray birch had the greatest overall first-year height growth, followed by GA, SA, and WB, with 12.1, 9.7, 9.6, and 5.6 cm, respectively. Straw doubled first-year growth, while CWD and MC increased first-year height growth by 43 and 31%, respectively. Straw’s ability to retain moisture in the dry summer provided the greatest benefit. In the second year, GA had the greatest height growth, followed by SA, GB, and WB, with 42.5, 30.5, 13.4, and 13.0 cm, respectively. Alders form symbiotic relationships with N-fixing bacteria and, although this was observed in some first-year roots, they did not fully express this advantage at these severely degraded sites until the second year, which allowed them to surpass birches in growth. Site-preparation treatments furthered their height growth affect, with S, and CWD doubling second-year height growth and MC, with an increase of 25%. Alders and birches had, on average, three and one stems, respectively, and the mean stem number of alders increased under S and CWD. After two years, overall stem dry mass had very large genus and species differences with GA, SA, GB, and WB, with 58.4, 30.3, 5.4, and 4.0 g, respectively. The N-fixing ability of alders under these conditions resulted in a 13-fold stem dry mass production increase compared with birches. Straw tripled, CWD doubled, and MC increased stem dry mass by 40%. For WB, site-preparation combinations had an additive effect, whereas GB, GA, and SA had several combined site-preparation treatments showing synergistic results, which were greater than the additive effects of single treatments. Under the control (no site prep.), second-year stem dry masses for WB, GB, GA, and SA were 0.7, 1.4, 17.8, and 0.5 g, respectively. Under the three combined treatments, MC × S × CWD, WB, GB, GA, and SA had 6.6, 12.3, 115.7, and 70.6 g stem dry masses, respectively. SA is ecologically a lowland species, hence the low 0.5 g under the control; however, the result under the three combined treatments demonstrates their combined effectiveness on these barren sites. Green alder seems to be the best adapted to the sites, having the greatest stem dry mass under control, although that was considerably magnified under the site-preparation treatments. This study using combinations of treatments with these early successional species introduces a novel research concept, and similar studies in the literature are currently lacking, creating an opportunity for future exploration. Full article

Barren, severely disturbed sites lacking soil, such as mine sites and waste deposit sites, present severe challenges to ecological service restoration because of high temperatures, solar radiation, and wind speeds; extreme temperature changes; and low soil moisture and nutrient availability. An ecological restoration experiment using three site preparation treatments was conducted. Straw (S), Meri-Crusher (MC), and coarse woody debris (CWD) were assessed in a site, no site preparation 2 × 2 × 2 factorial, including a control treatment, on sites barren for 25 years. In addition, four early-successional species: white birch (WB, Betula papyrifera Marsh), gray birch (GB, Betula populifolia Marsh), green alder (GA, Alnus viridis Vill. ssp. crispa Ait) and speckled alder (SA, Alnus incana L. ssp. rugosa Du Roi), were examined for mortality. Mortality was measured after three time periods, summer-related 2021, winter-related, and frost heave mortality (spring 2022). Summer-related mortality was predominantly influenced by S treatments (reduced mortality) and their interactions. Straw’s ability to retain moisture strongly suggests it mitigated summer-related drought mortality. S interactions were not rank changes but magnitude effects. The species × straw interaction showed that SA had the greatest magnitude difference, with 25% and 3.6% summer-related mortality for NS and S treatments, respectively. SA, a hydrophilic species, accounted for nearly half the total summer-related mortality, and there were strong species effects and species interactions. The full combination of site preparation treatments had the lowest summer-related mortality, at 1%. Winter-related mortality only affected 1.9% of the total sample size, and there were no species effects or interactions, but contrary to other results, S was the leading cause of mortality due to fungal presence found on expired seedlings. For frost heave mortality, it was clear that the S treatment was effective, with 1.2% and 20.7% overall mortality for S and NS, respectively. MC alone had the greatest negative effect, with 46.9% frost heave mortality; however, when interacting with S or CWD, the mortality decreased substantially. Frost heave had no species interactions and only a species effect, with SA having the greatest mortality. Over the first full year, MC alone and control had the greatest mortality, with 60% and 38%, respectively, after one year. Overall, one-year mortality showed S reduced mortality by 27% and CWD by 19%, while MC increased mortality by approximately 4%. When treatments were combined in any way, mortality dropped significantly, showing an additive effect, with the three-combination treatment resulting in the lowest one-year mortality, of only 3.1%. Straw provided the strongest effect, both as an effective barrier to moisture evaporation, providing up to 10% more soil moisture under dry conditions and provided an effective thermal barrier that substantially reduced the frost heave mortality. Even early-successional species such as WB, GB, GA, and SA need site preparation treatments to establish and survive the first year on long-term barren lands. Full article

This study focuses on deriving the total absorption coefficients based on field measurements and satellite data. An extended quasi−analytical algorithm (QAA−GRI) was developed based on the two in situ datasets collected from inland waters of Lake Qiandaohu (QDH) and oceanic waters of the East China Sea (ECS). The key model between absorption coefficients at 510 nm ($a(510)$) and green red index ($GRI$) was established using power function in the extended QAA−GRI algorithm. The results reveal that the extended QAA−GRI algorithm performs better than the original quasi−analytical algorithm (QAA−v5) and Garver–Siegel–Maritorena’s algorithm (GSM), and the red–green quasi−analytical algorithm (QAA−RGR), at least for the two in situ datasets from the ECS and QDH. For QAA−GRI, the averaged mean absolute percentage error (MAPE) value of retrieved versus in situ total absorption coefficients is approximately 20%. Subsequently, the extended QAA−GRI algorithm was applied to the OLCI satellite imagery, which is the new successor of MERIS with three specific bands (510, 560, and 620 nm). The implementation of the extended QAA−GRI algorithm on OLCI imagery yielded similar results comparable to that of the QAA−v5 in the ECS region. Furthermore, the application of the algorithm on seasonal and annual MERIS satellite imagery help clarify the combined influences from Yangtze River discharge and coastal currents on the distribution of total absorption in the ECS waters. This study suggests that the extended QAA−GRI algorithm is an alternative for retrieving total absorption coefficient, although it is not recommended for highly turbid waters. Full article