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Well-posedness of a variable-exponent telegraph equation applied to image despeckling
Get PDFIn this paper, we present a telegraph diffusion model with variable exponents for image despeckling. Moving beyond the traditional assumption of a constant exponent in the telegraph diffusion framework, we explore three distinct variable exponents for edge detection. All of these depend on the gray level of the image or its gradient. We rigorously prove the existence and uniqueness of weak solutions of our model in a functional setting and perform numerical experiments to assess how well it can despeckle noisy gray-level images. We consider both a range of natural images contaminated by varying degrees of artificial speckle noise and synthetic aperture radar (SAR) images. We finally compare our method with the nonlocal speckle removal technique and find that our model outperforms the latter at speckle elimination and edge preservation
Integrated Solutions and Distributed Models’ Linkage Procedures for Food–Energy–Water–Environmental Nexus Security Modeling and Management
No full textThis paper discusses a new modelling approach enabling the linkage of distributed individual food, energy, water optimization models under joint (e.g., water, land) resource constraints, uncertainty, and asymmetric information. The approach is based on an iterative stochastic quasigradeint (SQG) solution procedure of, in general, nonsmooth nondifferentiable optimization. The SQG procedure organizes an iterative computerized negotiation between individual FEW systems (models) representing Intelligent Agents. The convergence of the procedure to the socially optimal solution is based on the results of nondifferentiable optimization providing a new type of machine learning algorithms. The linkage problem can be viewed as a general endogenous reinforced learning problem. The models act as “agents” that communicate with a “central hub” (a regulator) and take decisions in order to maximize the “cumulative reward". In this way, they continue to be the same individual models and different modeling teams do not need to exchange information about their models – instead, they only need to harmonize the inputs and outputs that are part of the joint resource constraints. In this way, the agents link their models into an integrated model under asymmetric information. The convergence of the solution of the linked models to the solution of the hard-integrated model is discussed. Application of the approach is illustrated with a case study linking distributed agricultural, water and energy sector models. The approach can be effectively used for decentralized deregulated planning of interdependent agricultural, energy, water systems
Spatiotemporal assessment of land use land cover dynamics in Mödling district, Austria, using remote sensing techniques
Get PDFRemotely sensed imagery plays a crucial role in analyzing and monitoring land cover and urban growth. The accuracy and applicability of European CORINE Land Cover (CLC) maps in Land Use and Land Cover (LULC) monitoring across European regions, especially at local scales, have been critiqued and remain limited due to temporal methodological variations. This study aims to understand the dynamics of LULC, assess the effectiveness of vegetation indices in estimating forest cover, and validate the local applicability of CORINE maps in the Lower Austrian district of Mödling in the neighbourhood of Vienna from 1999 to 2022. We employed a supervised maximum likelihood classifier and class-based change detection to analyze multi-decadal multispectral imagery for mapping and quantifying vegetation and land use changes across the district, in comparison with satellite indices and CORINE data. The study identified changing patterns and assessed the accuracy of the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) in estimating Mödling's forest cover, determining optimal thresholds for improved assessment. Our findings reveal a slight reduction in Mödling's forest area – decreasing from 39.11 % in 1999 to 36.5 % in 2022 – with an overall reduction of 2.61 %. Agriculture primarily caused forest loss in the early period, expanding by over 37 %. In the most recent decade, settlement expansion, with built-up areas gaining approximately 650 ha, has exacerbated the loss of forest and agricultural lands. Our classification achieved high overall accuracy (92 %–94 %) and Kappa accuracy (0.90–0.93). The supervised classification exhibited a consistent reduction, aligning with CORINE outputs and refuting reports of its limited local applicability and accuracy. Although NDVI and SAVI estimates revealed a non-monotonic trend in forest cover across different years, NDVI performed better than SAVI. The results of this study are vital, providing evidence and recommending effective measures for enhancing monitoring, policy development, and decision-making regarding vegetation conservation, urban development, and overall land management. This research contributes to the limited body of core studies employing spectral imagery and GIS tools to monitor changes in land cover or assess CORINE maps in Austria and across Europe, with a special focus on the peri-urban interface
Shaping Tomorrow's Citizen Science Research Infrastructure: A Co-Design Experience – CAPS 2025 Symposium Report
Get PDFThis symposium represents the inaugural event of its kind within the RIECS-concept project, as a first in-person event engaging the community. RIECS-concept "Towards a Pan-European Research Infrastructure for Excellent Citizen Science” is funded by Horizon Europe under the Horizon Research Infrastructures main programme, specifically under the topic consolidating and developing research infrastructures of European
interest. The project aims to conceptualize a pan-European infrastructure for citizen science through activities planned until December 31, 2027. During this period, the consortium will organize multiple events designed to understand the challenges, opportunities, and needs of the citizen science community and other stakeholders while developing a comprehensive infrastructure model
A global dataset of public agricultural R&D investment: 1960-2022
Get PDFInvestment in public agricultural R&D is a key driver of agricultural productivity, which is regarded as one of the major solutions to enhance global food security, reduce the environmental impact of agricultural production and enhance crop resilience to climate change. Detailed information on public agricultural R&D investment is required to inform national science and technology policies that aim to improve the performance of the agricultural sector and monitor whether countries are on track to reach (inter)national targets. Due to the long lead-time, it requires exceptionally long time-series to assess the impact of public agricultural R&D spending on present and future agricultural productivity. To address these issues, this paper presents GRAPE - the WUR-ERS Global Research on Agriculture: Personnel & Expenditures dataset, which contains data on the number of public agricultural researchers and R&D expenditures for 190 countries, broadly covering the period 1960–2022. To construct the dataset, information from a large number of sources was combined, different R&D classifications were harmonized and missing data were imputed
The effect of community resilience and disaster risk management cycle stages on morbi-mortality following floods: an empirical assessment
No full textPractice and policy have emphasized the need for building resilience to climate-related events in a further warming world. Scholarship has studied resilience largely in terms of process, latent capacity informing vulnerability, or the outcome of risk management interventions, with little work integrating these perspectives. Implementation science work by the Climate Resilience Alliance has developed the Flood Resilience Measurement for Communities (FRMC) process and tool to measure resilience as an outcome (post-flood mortality and morbidity reduction) and as capacity (pre- and post-intervention levels). This article builds on FRMC analytics to investigate the effect of resilience capacity, represented by five forms of capital (5Cs) and five stages of the disaster risk management (DRM) cycle, on injury and mortality outcomes across 66 flood-affected communities in seven Global South countries. Data were collected using household surveys, community focus groups, key informant interviews, and secondary sources. We applied a quasi-experimental regression design, controlling for demographic and flood hazard/exposure variables, to estimate the effect of 5Cs and DRM stages on health outcomes. Results show that social and human capital help reduce injuries after floods, and preparedness lowers both deaths and injuries. Some results were unexpected, such as the positive association between natural capital and delayed deaths, where limited gains in natural capital may not yield meaningful protection in communities with degraded ecosystems. This study finds that preparedness is the most consistent predictor of positive health outcomes, while forms of 5Cs may not translate into reduced mortality. By combining 5Cs, DRM stages, and health indicators, this paper contributes to bridging a gap in the literature and offers policy-relevant insights for improving community-level disaster response
The Blue Carbon Cost Tool - understanding market potential and investment requirements for high-quality coastal wetland projects
No full textBlue carbon ecosystems, such as mangroves, tidal marshes, and seagrasses, are important for climate mitigation. As carbon sinks, they often exhibit higher per hectare carbon storage capacity and sequestration rates than terrestrial systems. These ecosystems provide additional benefits, including enhancing water quality, sustaining biodiversity, and maintaining coastal resilience to climate change impacts. The widespread loss of blue carbon ecosystems due to anthropogenic activities can contribute to increasing carbon emissions globally. Monetizing blue carbon through carbon credits offers an avenue to generate revenue and incentivize conservation and restoration efforts. However, limited data on project costs and carbon benefits make prioritization of blue carbon projects challenging. To address these challenges, we have developed, in collaboration with blue carbon experts, the Blue Carbon Cost Tool. This is a user-friendly interface enabling comparison of three core market project components – 1) carbon credit estimation, 2) project cost estimation, and 3) a qualitative, non-economic feasibility assessment – to assess and compare potential for blue carbon projects. Tool simulations with data available from nine countries demonstrate (a) how factors such as country, ecosystem type and project scale drive variability, (b) the need for local or project-specific data to enhance accuracy and reduce uncertainty, particularly in tidal marsh and seagrass systems, and (c) that higher price tolerance or upfront capital is needed to bridge implementation and maintenance cost gaps. The Blue Carbon Cost Tool can aid project developers and investors to better understand market opportunity and the resources needed to develop high quality blue carbon market projects
Sufficiency
No full textThe concept of Consumption Corridors (CC) provides a framework for sustainable consumption governance. It suggests achieving sustainability by developing and implementing corridors of consumption, defined by consumption minima and maxima. The lower boundaries are meant to allow every individual to satisfy their needs, and thus to live a life they value by determining what every individual must have access to. The upper boundaries are meant to prevent the consumption by individuals or groups from inhibiting or affecting the well-being of other individuals, living now or in the future. To that end, they determine thresholds that if (quantitatively or qualitatively) trespassed adversely impact the quality of life of other individuals by putting others’ minima at risk. The space between these boundaries is what is referred to as a consumption corridor (Figure 30.1). This space leaves room for individual life plans and choices, for individual freedom (see Freedom of Choice). The concept of CC posits quality of life and justice as criteria to define minima and maxima of consumption. Both the lower and the upper limits refer to satisfiers (see Box 30.1)
Maximising the benefits of sustainable development target interactions: An integrated priority analytical model applied to China
No full textAchieving the Sustainable Development Goals (SDGs) requires effective prioritisation tools that account for synergies and trade-offs across targets. Existing multicriteria models often overlook negative and higher-order interactions, limiting their capacity to guide systemic transformation. In this study, a three-dimensional priority model that integrates (1) the cascading impacts of each target across the SDG network, (2) the influence of interactions on individual target progress, and (3) current performance trends is proposed. This framework distinguishes between temporal priority (urgency and direction of intervention) and resource priority (required allocation intensity), enabling more targeted and cost-effective strategies. We apply the model to China's SDG targets using network and time series data from the IGES SDG Interlinkages database. The results show that only 11.9 % of the targets require immediate action, especially in terms of biodiversity preservation and forest management. Furthermore, 27.4 % of targets face systemic obstacles that demand greater resource investment, concentrated in SDGs 12 (responsible consumption and production), 15 (life on land), and 16 (peace, justice, and strong institutions). In contrast, the targets of SDG 13 (climate action) are bolstered by synergistic effects and can progress with more modest inputs. This integrated approach offers a practical and transferable tool for policy-makers to prioritise SDG targets on the basis of systemic influence, feasibility, and urgency, especially under resource constraints
Towards annual updating of forced warming to date and constrained climate projections
No full textIn the context of rapid human-caused climate change, regular updates of the state of knowledge of current and future climate are needed. New statistical methods using observational constraints underpinned estimates of present-day human-induced warming and projected future warming in the most recent IPCC report. As time goes by, and new updated observational records become available, how should estimates of the current and projected human-caused climate change be updated? Here, we use a perfect model framework and show that incorporating observations from every new year in observationally constrained projections improves their accuracy, without causing major year-to-year spurious variability on outcomes. The forced warming estimated for the current year also exhibits high enough stability to be considered as a robust indicator of the state of the climate system