Search Results (1,042)

The South Caspian Basin of Iran (SCBI), a vital ecosystem for unique and valuable fish species, is under severe threats due to anthropogenic activities that are rapidly deteriorating its fish biodiversity. The initial step to effectively combat or mitigate threats to biodiversity is to precisely identify these threats. While such threats are often categorized qualitatively, there is a lack of a comparative quantitative assessment of their severity. This means that although we may have a general understanding of the threats, we do not have a clear picture of how serious they are relative to one another. This study aimed to quantify and prioritize these threats using a modified quantitative “SWOT” (Strengths, Weaknesses, Opportunities, Threats) analysis. Twenty multidisciplinary experts identified and evaluated 26 threats, and we used multivariate cluster analysis to categorize them as “High”, “Medium”, and “Low” based on their quantitative contributions to overall threat. Invasive non-native species and global warming emerged as the most significant threats, followed by resource exploitation, habitat destruction, and pollution. We then used this information to develop a “Situation Model” and “Results Chains” to guide responses to the threats. According to the Situation Model, these threats are interconnected, driven by factors such as population growth, unsustainable resource use, and climate change. To address these challenges, we propose the Results Chains, including two strategies focused on scientific research, land-use planning, public awareness, and community engagement. Prioritizing these actions is crucial for conserving the Caspian Sea’s unique fish fauna and ensuring the region’s ecological and economic sustainability. Full article

In the current context of tight constraints on land resources in major Chinese cities, the development of underground space in green spaces (USGSs) has become an important approach to exploit land use potential and alleviate the contradiction between human and land resources. Evaluating USGS development potential scientifically is crucial for project site selection and improving underground space utilization. However, most studies have focused on underground space as a whole, with limited attention to single land use types, and research on USGSs has mainly concentrated on planning and design. This study proposes a two-stage evaluation framework for urban green spaces, identifying suitable development areas while safeguarding ecological functions. The framework evaluates from “restrictiveness” and “suitability”: first extracting developable green spaces by restrictiveness evaluation and then assessing development potential by suitability evaluation. This approach overcomes traditional methods that disregard prerequisite relationships among factors. A case study in Binjiang District, Hangzhou, showed that small green spaces and connectivity were key limiting factors for the development of USGSs. The proposed framework could provide some degree of reference for future development potential evaluation of USGSs, and the results could provide actionable guidance for high-density built environments similar to Binjiang District. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

Reclaiming coastal wetlands for agricultural purposes has led to intensified farming activities, which are anticipated to affect greenhouse gas (GHG) flux processes within coastal wetland ecosystems. However, how greenhouse gas exchanges respond to variations in agricultural reclamation activities across different years remains uncertain. To address this knowledge gap, this study characterized dynamic exchanges within the soil–plant–atmosphere continuum by employing continuous monitoring across four representative coastal wetland soil–vegetation systems in Jiangsu, China. The results show the carbon dioxide (CO₂) and nitrous oxide (N₂O) flux exchanges between the system and the atmosphere and soil–vegetation carbon pools, which revealed the drivers of carbon dynamics in the coastal wetland system. The four study sites, converted from coastal wetlands to agricultural lands at different times (years), generally act as CO₂ sinks and N₂O sources. Higher levels of CO₂ sequestration occur as the age of reclamation rises. In terms of time scale, crops lands were found to be CO₂ sinks during the growing period but became CO₂ sources during the crop fallow period. Although the temporal trend of the N₂O flux was generally smooth, reclaimed farmlands acted as net sources of N₂O, particularly during the crop-growing period. The RDA and PLS-PM models illustrate that soil salinity, acidity, and hydrothermal conditions were the key drivers affecting the magnitude of the GHG flux exchanges under reclamation. This study demonstrates that GHG emissions from reclaimed wetlands can be effectively regulated through science-based land management, calling for prioritized attention to post-development practices rather than blanket restrictions on coastal exploitation. Full article

Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal data fusion strategies. This study focuses on the Keriya Oasis, Xinjiang, China, utilizing RS data, including Sentinel-2 multispectral and GF-3 full-polarimetric SAR (PolSAR) images, to conduct soil salinization classification. We propose a Dual-Modal deep learning network for Soil Salinization named DMSSNet, which aims to improve the mapping accuracy of salinization soils by effectively fusing spectral and polarimetric features. DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. Polarimetric decomposition features and spectral indices are jointly exploited to characterize diverse land surface conditions. Comprehensive field surveys and expert interpretation were employed to construct a high-quality training and validation dataset. Experimental results indicate that DMSSNet achieves an overall accuracy of 92.94%, a Kappa coefficient of 79.12%, and a macro F1-score of 86.52%, positively outperforming conventional DL models (ResUNet, SegNet, DeepLabv3+). The results confirm the superiority of attention-guided dual-branch fusion networks for distinguishing varying degrees of soil salinization across heterogeneous landscapes and highlight the value of integrating Sentinel-2 optical and GF-3 PolSAR data for complex land surface classification tasks. Full article

Gonghe Basin is an important frontier of resource and energy development and environmental protection on the Qinghai–Tibetan Plateau and upper sections of the Yellow River. As a characteristic ecotone, this area exhibits complex and diverse ecosystem types while demonstrating marked ecological vulnerability. The response of ecosystem services (ESs) to human activities (HAs) is directly related to the sustainable construction of an ecological civilization highland and the decision-making and implementation of high-quality development. However, this response relationship is unclear in the Gonghe Basin. Based on remote sensing data, land use, meteorological, soil, and digital elevation model data, the current research determined the human activity intensity (HAI) in the Gonghe Basin by reclassifying HAs and modifying the intensity coefficient. Employing the InVEST model and bivariate spatial autocorrelation methods, the spatiotemporal evolution characteristics of HAI and ESs and responses of ESs to HAs in Gonghe Basin from 2000 to 2020 were quantitatively analyzed. The results demonstrate that: From 2000 to 2020, the HAI in the Gonghe Basin mainly reflected low-intensity HA, although the spatial range of HAI continued to expand. Single plantation and town construction activities exhibited high-intensity areas that spread along the northwest-southeast axis; composite activities such as tourism services and energy development showed medium-intensity areas of local growth, while the environmental supervision activity maintained a low-intensity wide-area distribution pattern. Over the past two decades, the four key ESs of water yield, soil conservation, carbon sequestration, and habitat quality exhibited distinct yet interconnected characteristics. From 2000 to 2020, HAs were significantly negatively correlated with ESs in Gonghe Basin. The spatial aggregation of HAs and ESs was mainly low-high and high-low, while the aggregation of HAs and individual services differed. These findings offer valuable insights for balancing and coordinating socio-economic development with resource exploitation in Gonghe Basin. Full article

Remote sensing tasks, such as land cover classification, are increasingly becoming multimodal problems, where information from multiple imaging devices, complementing each other, can be fused. In particular, synergies between optical and synthetic aperture radar (SAR) are widely recognized to be beneficial in a variety of tasks. At the same time, archival of multimodal imagery for global coverage poses significant storage requirements due to the multitude of available sensors, and their increasingly higher resolutions. In this paper, we exploit redundancies between SAR and optical imaging modalities to create a joint encoding that improves storage efficiency. A novel neural network design with progressive attentive fusion modules is proposed for joint compression. The model is also promptable at test time with a desired tradeoff between the input modalities, to enable flexibility in the fidelity of the joint representation to each of them. Moreover, we show how end-to-end optimization of the joint compression model, including its modality tradeoff prompt, allows for better accuracy on downstream tasks leveraging multimodal inference when a constraint on the rate is to be met. Full article

Occupational exposures in the agricultural industry globally have been associated with heightened risk for several diseases. Reports written in South Africa in the last decade have raised awareness of the harsh occupational conditions and human rights abuses suffered by farmworker communities in the wine industry. Despite receiving “fair trade” labels upon reentry into the global market in the 1990s, the working conditions on wine farms in South Africa have remained unchanged and exploitative for centuries. Farmworkers remain dependent on substandard farm housing, have insecure land tenure rights, are exposed to toxic pesticides, are denied access to benefits and unionization, and endure long working hours in harsh environmental conditions with low pay. These occupational conditions are linked to interacting disease clusters: metabolic syndrome, problematic drinking, and communicable diseases including tuberculosis, HIV, and COVID-19. This milieu of interacting diseases with deleterious outcomes is an under-considered occupational syndemic that will likely worsen given both the lasting impacts of COVID-19 and more recent shifts in global public health funding. Full article

As fundamental geophysical information, the high-precision detection of shallow water bathymetry is critical data support for the utilization of island resources and coral reef protection delimitation. In recent years, the combination of active and passive remote sensing technologies has led to a revolutionary breakthrough in satellite-derived bathymetry (SDB). Optical SDB extracts bathymetry by quantifying light–water–bottom interactions. Therefore, the apparent differences in the reflectance of different bottom types in specific wavelength bands are a core component of SDB. In this study, refined classification was performed for complex seafloor sediment and geomorphic features in coral reef habitats. A multi-model synergistic SDB fusion approach constrained by coral reef habitat classification based on the deep learning framework Mamba was constructed. The dual error of the global single model was suppressed by exploiting sediment and geomorphic partitions, as well as the accuracy complementarity of different models. Based on multispectral remote sensing imagery Sentinel-2 and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) active spaceborne lidar bathymetry data, wide-range and high-accuracy coral reef habitat classification results and bathymetry information were obtained for the Yuya Shoal (0–23 m) and Niihau Island (0–40 m). The results showed that the overall Mean Absolute Errors (MAEs) in the two study areas were 0.2 m and 0.5 m and the Mean Absolute Percentage Errors (MAPEs) were 9.77% and 6.47%, respectively. And R² reached 0.98 in both areas. The estimated error of the SDB fusion strategy based on coral reef habitat classification was reduced by more than 90% compared with classical SDB models and a single machine learning method, thereby improving the capability of SDB in complex geomorphic ocean areas. Full article

Soil salinization hinders plant growth and agricultural production, so breeding salt-tolerant crops is an economical way to exploit saline–alkali soils. However, the specific metabolites and associated pathways involved in salt tolerance of the dandelion have not been clearly elucidated so far. Here, we compared the transcriptome and metabolome responses of 0.7% NaCl-stressed dandelion ‘BINPU2’ (variety A) and ‘TANGHAI’ (variety B). Our results showed that 222 significantly altered metabolites mainly enriched in arginine biosynthesis and pyruvate metabolism according to a KEGG database analysis in variety A, while 147 differential metabolites were predominantly enriched in galactose metabolism and the pentose phosphate pathway in variety B. The transcriptome data indicated that the differentially expressed genes (DEGs) in variety A were linked to secondary metabolite biosynthesis, phenylpropanoid biosynthesis, and photosynthesis–antenna proteins. Additionally, KEGG annotations revealed the DEGs had functions assigned to general function prediction only, post-translation modification, protein turnover, chaperones, and signal transduction mechanisms in variety A. By contrast, the DEGs had functions assigned to variety B as plant–pathogen interactions, phenylpropanoid biosynthesis, and photosynthesis–antenna proteins, including general function prediction, signal transduction mechanisms, and secondary metabolite biosynthesis from the KOG database functional annotation. Furthermore, 181 and 162 transcription factors (TFs) expressed under saline stress conditions specifically were detected between varieties A and B, respectively, representing 36 and 37 TF families. Metabolomics combined with transcriptomics revealed that salt stress induced substantial changes in terpenoid metabolites, ubiquinone biosynthesis metabolites, and pyruvate metabolites, mediated by key enzymes from the glycoside hydrolase family, adenylate esterases family, and P450 cytochrome family at the mRNA and/or metabolite levels. These results may uncover the potential salt-response mechanisms in different dandelion varieties, providing insights for breeding salt-tolerant crop plants suitable for saline–alkali land cultivation. Full article

Aircraft and fixed-wing drones, designed to perform vertical take-off and landing (VTOL), often incorporate unconventional configurations that offer unique capabilities but simultaneously pose significant challenges in flight mechanics modeling, whose reliability strongly depends on the correct tuning of the inertial and aerodynamic parameters. Having a good characterization of the aerodynamics represents a critical issue, especially in the design and optimization of unconventional aircraft configurations, when, indeed, one is bound to employ empirical or semi-empirical methods, devised for conventional geometries, that struggle to capture complex aerodynamic interactions. Alternatives such as high-fidelity computational fluid dynamics (CFD) simulations, although more accurate, are typically expensive and impractical for both preliminary design and lofting optimization. This work introduces a procedure that exploits multiple analyses conducted through semi-empirical methodologies implemented in the USAF Digital DATCOM to develop a flight mechanics model for fixed-wing unmanned aerial vehicles (UAVs). The reference UAV chosen to test the proposed procedure is the Dragonfly DS-1, an electric VTOL UAV developed by Overspace Aviation, featuring a three-surface configuration. The accuracy of the polar data, i.e., the lift and drag coefficients, is assessed through comparisons with computational fluid dynamics simulations and flight data. The main discrepancies are found in the drag estimation. The present work represents a preliminary investigation into the possible extension of semi-empirical methods, consolidated for traditional configurations, to unconventional aircraft so as to support early-stage UAV design. Full article

Castor (Ricinus communis L.) is a plant belonging to the Euphorbiaceae family originated from Asia or Africa and well adapted to the Mediterranean environment. As an oilseed crop with a high oil content (35–65%), it is nowadays used for biofuels production, with a large potential for applications in chemical and pharmaceutical sectors as well. As for other oilseed crops, the interest towards this crop has grown exponentially in the past decades because of the necessity of limiting fossil fuels, obtaining clean energy, and use of a renewable energy source as required by RED (Renewable Energy Directive) within the European Union. Moreover, castor has a great adaptability in different soil and climate conditions, and it is known as a low-key maintenance crop. These characteristics, together with the necessity of increasing renewable energy sources, with the possibility of re-evaluating marginal lands, make castor the ideal plant to be exploited in the years to come. This review aims at giving useful information regarding its cultivation and soil and climate requirements, providing an overview on its spread on the market. Full article

Increasing pressures on groundwater in the last decades have led to a deterioration in the quality of groundwater for human consumption around the world. Beyond the essential evaluation of groundwater dynamics and quality, analyzing the situation from the perspective of the Ecosystem Services Assessment (ESA) approach can be useful to support aquifer management plans aiming to recover aquifers’ capacity to provide good quality water. This work illustrates how to implement the ESA using groundwater flow and nitrate transport modelling for evaluating future trends of the provisioning service Groundwater of Good Quality for Human Supply. It has been applied to the Medina del Campo Groundwater Body (Spain), where the intensification of agricultural activities and groundwater exploitation since the 1970s caused severe nitrate pollution. Nitrate status and future trends under different fertilizer and aquifer exploitation scenarios were modelled with MT3DMS coupled to a MODFLOW model calibrated with piezometric time series. Historical land use and fertilizer data were compiled to assess nitrogen loadings. Besides the uncertainties of the model, the results clearly show that: (i) managing fertilizer loads is more effective than managing aquifer exploitation; and (ii) only the cessation of nitrogen application by the year 2030 would improve the evaluated provisioning service in the long term. The study illustrates how the ESA can be incorporated to evaluate the expected relative impact of different management actions aimed at improving significant groundwater services to humans. Full article

This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. For an effective comparison of the flood detection performance between dual-pol parameters, we presented a simple fuzzy-based flood detection algorithm. Scattering characteristics of water surface and non-water land can vary depending on the region and flood conditions. Therefore, the flood detection performance of the dual-pol parameters was evaluated across three datasets with different geographic, climatic, and land cover conditions. The results demonstrated that accurate and stable performance in the detection of inundated areas under different surface conditions can be achieved by combining water body information from dual-pol channels in a disjunctive way. It also suggests that synergy in flood detection can be expected when using polarization observation data by considering each polarimetric channel as an independent information source and combining them rather than deriving the most relevant polarization parameter. Furthermore, combining common information from two dual-pol channels in a conjunctive way could provide the most reliable SAR flood detection results with minimum false alarms from the user’s perspective. Based on these experimental results, a two-class flood classification scheme was proposed for improving the applicability of SAR remote sensing in identifying flooded areas. Full article

The exploitation of the Amazon biome in search of net profit, specifically in the production of cocoa (Theobroma cacao) and açaí (Euterpe oleracea), has caused deforestation, degradation of natural resources, and high greenhouse gas (GHG) emissions, highlighting the urgency of improving the environmental, economic and social sustainability of these crops. These species were selected for their rapid expansion in the Amazon, driven by global demand, their local economic relevance, and their potential to either promote conservation or drive deforestation, depending on the production system. This study analyzes the pillars of environmental, social, and economic sustainability of cocoa and açaí production systems in the Amazon, comparing monoculture, agroforestry, and extractivism to support forest conservation strategies in the biome. Analysis of the environmental life cycle, social life cycle, and economic performance were used to determine the carbon footprint, the final point of workers, and the net profit of the activities. According to the results found in this study, cocoa monoculture had the largest carbon footprint (1.35 tCO₂eq/ha), followed by agroforestry (1.20 tCO₂eq/ha), açaí monoculture (0.84 tCO₂eq/ha) and extractivism (0.25 tCO₂eq/ha). In the carbon balance, only the areas outside indigenous lands presented positive carbon. Regarding the economic aspect, the net profit of açaí monoculture was USD 6783.44/ha, extractivism USD 6059.42/ha, agroforestry USD 4505.55/ha, and cocoa monoculture USD 3937.32/ha. In the social sphere, in cocoa and açaí production, the most relevant negative impacts are the subcategories of child labor and gender discrimination, and the positive impacts are related to the sub-category of forced labor. These results suggest that açaí and cocoa extractivism, under responsible management plans, offer a promising balance between profitability and environmental conservation. Furthermore, agroforestry systems have also demonstrated favorable outcomes, providing additional benefits such as biodiversity conservation and system resilience, which make them a promising sustainable alternative. Full article