20253 research outputs found
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Hosentaschenphotogrammetrie für die Waldinventur
Get PDFDie Erfassung des Baumbestands ist ein zentraler Bestandteil der Forstwissenschaft, -wirtschaft und -verwaltung. Sie spielt eine wesentliche Rolle bei der Quantifizierung der oberirdischen Biomasse, der Biodiversität sowie bei der Analyse der Auswirkun-gen von Abholzung und Klimawandel auf den Wald als Lebens- und Wirtschaftsraum. Fortschritte in der Sensortechnologie, die in Smartphones integriert sind, bieten viel-versprechende Perspektiven für die Waldinventur. Diese Technologien könnten die Effizienz der Messungen verbessern und es auch Laien ermöglichen, aktiv an der Waldinventur teilzunehmen.
Im Rahmen des Citizen-Science-Projekts C4C (https://iiasa.ac.at/projects/c4c) werden sowohl das Potenzial als auch die Grenzen von Smartphone-basierten Anwendungen zur Unterstützung der Waldinventur untersucht. Darüber hinaus werden Brücken ge-schlagen, um die gesammelten Smartphone-Daten mit Informationen von terrestri-schen und flugzeuggetragenen Laserscannern (TLS und ALS) sowie frei zugänglichen Satellitendaten (z.B. Sentinel-1 und -2) zu verknüpfen. Ziel ist es, die großflächige Kartierung von Baumarten und oberirdischer Biomasse zu verbessern.
Dieser Beitrag beleuchtet zunächst den aktuellen Stand der Waldinventurtechnologien und untersucht anschließend die Auswertung von Brusthöhendurchmesser- und Baumhöhenmessungen, die mittels verschiedener Smartphone-Apps in einem Testge-biet im Wiener Prater durchgeführt wurden. Als Referenz für die Smartphone-basierten Messungen dienen TLS-Messungen sowie traditionell aufgenommene Baum-parametermessungen. Ziel ist es, herauszufinden, welches Potenzial Smartphone-Anwendungen für die Waldinventur bieten und ob "Hosentaschenphotogrammetrie" in der Lage ist, etablierte Messprotokolle und Instrumente zu ergänzen oder sogar zu ersetzen
Border Gateway Protocol Hijacks and Anomalies Detection: A Graph-Based Deep Learning Approach
No full textThe Border Gateway Protocol (BGP) serves as the foundational routing protocol for the tens of thousands of Autonomous Systems (ASes) that constitute the backbone of the Internet. However, BGP is subject to a range of routing anomalies, including route hijacking, where ASes may falsely announce ownership or present a more favorable path to a prefix. The adoption of existing solutions has been limited, primarily due to high implementation costs and the intricate nature of the internet’s infrastructure. To address these challenges, we propose an approach leveraging the Deep Anomaly Detection on Attributed Networks (DOMINANT) model, which utilizes Graph Convolutional Networks (GCNs) and attributed networks to detect anomalous nodes within graph structures. Our dataset, comprising over 18,000 BGP updates related to Twitter’s AS and obtained via the RIPEstat Data API, spans from 2015 to 2022 and provides a robust foundation for anomaly detection. Given that DOMINANT generates anomaly scores at the node level, we refined this scoring methodology by aggregating scores across connections and pathways to yield comprehensive path-level anomaly metrics, facilitating efficient anomaly detection. This methodology accurately identified all known anomalous updates associated with the RT-Comm Twitter hijack in March 2022, as well as an additional, unexpected hijack in the dataset by a Colombian provider in 2019, confirmed to be an actual anomaly. Importantly, no false positives were detected, ensuring the precision of the approach. This approach is efficient, accessible, and cost-effective, providing a scalable solution that can be easily adapted for continuous anomaly detection across networks and expanded to address broader cybersecurity challenges
What are price mark-up shocks?
Get PDFUsing US data, we show that a large share of the variation in price mark-up shocks estimated from standard Dynamic Stochastic General Equilibrium (DSGE) models can be explained by energy and commodity price dynamics. We identify robust drivers of the price mark-up in the US and find that around 30% of the variation in their changes can be explained by variation in energy, metal and import prices. The explanatory power increases to over 60% if short-term fluctuations in price mark-ups are smoothed
Human-induced carbon stress power upon earth: integrated data set, rheological findings and consequences
Get PDFIn this study we take the position of an outer-space observer to understand Earth's planetary carbon-climate response to stress from a rheological perspective; with stress upon the Earth atmosphere–land and ocean system given by the uninterrupted increase in cumulative CO2 emissions caused by humankind between 1850 and 2021. This perspective complements the global carbon mass balance perspective applied by the carbon community. It gives reason to suspect that Earth is in an even worse environmental condition than commonly believed.
We apply a rheological (stress–strain) analogue model, a Maxwell body, consisting of elastic and damping (viscous) elements to reflect the overall behavior of the atmosphere–land and ocean system under the influence of global warming. For an observer it is the overall strain response of that system – expansion of the atmosphere by volume and uptake of CO2 by sinks – that is unknown.
Our rheological study addresses two important issues that had neither been mentioned previously nor elsewhere. Firstly, we quantify stress power exerted by humans upon Earth and its two subsystems, atmosphere and land-ocean, for 1850–2021.
Secondly, we compute the second derivative by time of the system's delay time, which indicates that Earth experienced a major change in its dynamics in the past by exhibiting a turning point which is when the deacceleration rate of the system's delay time goes through a maximum, some time between 1925 and 1945. After passing through the maximum, Earth's land-ocean subsystem does not respond characteristically to stress anymore; that is, outside its natural regime.
This finding suggests that the Earth system is on a slow end-of-life path since then, not necessarily collapsing as a whole; but, nonetheless, that it is becoming increasingly vulnerable to sub-global, threshold-transgressing incidents acting bottom-up which may cause the entire atmosphere–land and ocean system to ultimately collapse
Co-benefit or tradeoff? The impact of inter-provincial trade-embodied pollutants on air quality and public health under climate targets in China
No full textTrade-embodied pollutants significantly impact air quality and and public health across regions, particularly under climate policy constraints, yet their transboundary health impacts via inter-regional trade remain underexplored. To address this, this study integrates a general equilibrium model, an air quality model, and health impact model to assess how interprovincial trade redistributes PM₂.₅ and health burdens between China's Chuan-Yu zone (Sichuan-Chongqing) and their trade partner regions under business-as-usual (BaU) and 1.5 °C scenarios. The findings indicate that CO₂ emissions in the Chuan-Yu zone follow an inverse U-shape pattern, peaking earlier under the 1.5 °C target than the BaU scenario, with Chongqing (133.94 Mt, 2017) achieving peak emissions 5–8 years before Sichuan (318.79 Mt, 2025). While SO₂ shows the sharpest decline, agricultural NH₃ remains challenging to mitigate. Trade shifts redistribute PM2.5 pollution under the 1.5 °C target, with Shaanxi, Liaoning, and Hubei as inflow regions and Shanghai, Beijing, and Jiangsu as outflow regions. Notably, PM2.5 concentrations rise in Jiangsu, Zhejiang, and Guangdong as they absorb production originally from Chuan-Yu. Stricter climate targets exacerbate spatial and temporal inequalities in Chuan-Yu's morbidity and mortality rates. Strikingly, health inequalities intensify spatially duto to trade, with Chongqing benefiting from shifted pollution burdens while Sichuan facing higher mortality. Stroke and ischemic heart disease (IHD) remain leading mortality causes, with significant reductions projected by 2060 in developed regions like Shanghai, Jiangsu, and Beijing, though the decline is slower in Inner Mongolia and Heilongjiang. The findings underscore the necessity for spatially differentiated governance, advocating for province-level pollution offset mechanisms and health compensation funds to address trade-induced disparities
Warming of northern peatlands increases the global temperature overshoot challenge
Get PDFMeeting the Paris Agreement’s temperature goals requires limiting future carbon emissions, yet current policies make temporarily overshooting the 1.5°C target likely. The potential climate feedback from destabilizing peatlands, storing large amounts of carbon, remains poorly quantified. Using the reduced-complexity Earth System Model OSCAR with an integrated peat carbon module, we found that across various overshoot pathways that temporarily exceed 1.5°C–2.5°C, northern peatlands exhibit net positive feedback, amplifying the overshoot challenge. Warming increases peatlands’ net carbon uptake, but this is largely offset by higher methane emissions. We estimated that for each 1°C increase in peak warming, the positive feedback from peatlands decreases the remaining carbon budget by 37 GtCO2 (22–48 GtCO2). If the 1.5°C temperature target is exceeded, peatlands would increase carbon removal requirement by about 40 GtCO2 (16–60 GtCO2) (8.6%). Our findings highlight the importance of properly accounting for northern peatlands for estimating climate feedbacks, especially under overshoot scenarios
An interoperable and standardized protocol for reporting systematic conservation planning projects
Get PDFSystematic conservation planning ( SCP ) is an operational and scientific framework that assists in deciding where, how, and when to implement conservation intervention. Studies using SCP approaches have proliferated due to their immediate relevance for applied conservation. For example, they can help identify cost‐effective opportunities for expanding areas under conservation management to achieve high‐level policy goals such as those of the Global Biodiversity Framework. Yet SCP can be conducted in various ways, and results can vary depending on problem formulation, parameterizations, contexts, and prioritization approaches. There is a need to facilitate comparison of SCP studies to understand key criteria and assumptions made in the planning process. Here, we propose a standardized reporting protocol for SCP that is readily applicable across study aims, realms, and spatial scales. The new Overview and Design Protocol for Systematic Conservation Planning ( ODPSCP ) describes the key steps from the design to the computational stages of SCP . It enables researchers, scientific editors, and decision‐ and policymakers to assess the scope and comprehensiveness of SCP exercises. To facilitate uptake and ease of reporting, the protocol is openly available through an interactive web interface and which can be further enhanced following methodological advancements in conservation planning. We encourage the conservation community to adopt the reporting protocol to promote transparency and reproducibility, standardized reporting as well as facilitate peer review and independent evaluation
European sovereign debt control through reinforcement learning
Get PDFThe resilience of economic systems depends mainly on coordination among key stakeholders during macroeconomic or external shocks, while a lack of coordination can lead to financial and economic crises. The paper builds on the experience of global and regional shocks, such as the Eurozone crises of 2009–2012 and the economic disruption resulting from COVID-19, starting in 2020. The paper demonstrates the importance of cooperation in monetary and fiscal policies during emergencies to address macroeconomic non-resilience, particularly focusing on public debt management. The Euro area is chosen as the sample for testing the models presented in the paper, given that its resilience is heavily dependent on cooperation among different actors within the region. The shocks affecting nations within the European Union are asymmetric, and the responses to these shocks require coordination, considering heterogeneous economic structures, levels of economic development, and policies. We develop a macroeconomic modeling framework to simulate fiscal and monetary policy interactions under a cooperative regime. The approach builds on earlier nonlinear control models and incorporates modern reinforcement learning techniques. Specifically, we implement the Soft Actor-Critic algorithm to optimize policy responses across key variables including inflation, interest rates, output gaps, public debt, and government net lending. We demonstrate that the Soft Actor-Critic algorithm provides comparable or, in some cases, better solutions to multi-objective macroeconomic optimization problems, in comparison to Nonlinear Model Predictive Control (NMPC) algorithm
Realizing climate resilient development pathways in forestry: A focus on carbon management in Republic of Korea
Get PDFOvercoming the climate crisis and achieving the 1.5 °C target requires the exploration of climate-resilient development pathways (CRDPs), as emphasized in the intergovernmental panel on climate change (IPCC) AR6 report. Republic of Korea has aligned itself with the international context by setting nationally determined contributions (NDC) and long-term low greenhouse gas emission development strategies (LEDS) goals. In addition, the country has announced plans to enhance carbon sink in the forestry sector. This study explored the CRDP in the forestry sector using an advanced Korean forest dynamic growth model (AKO-G-Dynamic model) with refined management algorithms. We utilized this model and applied various options for forest management based on the available detailed data, including climate change scenarios and policies reflecting possible CRDPs in the Republic of Korea. As a result, CO2 sequestration in the 2050s was predicted to be 23.08 million tCO2 year−1 if climate change SSP 5–8.5 and the current forest management level are maintained and 28.49 million tCO2 year−1 if climate change SSP 1–2.6 and resilient level of forest management are applied. Furthermore, from the perspective of the age class of the forest, the proportion of over-matured forests decreased, leading to an improvement in the imbalance of age classes as climate change mitigation and sustainable forest management were implemented. Therefore, this study demonstrated realizable CRDPs and their implementation in decision-making concerning the NDC and LEDS. This comprehensive analysis of climate change and forest management, exploring the CRDP from various perspectives, can contribute to the development of forest management policies for climate adaptation strategies and carbon sink enhancement, thereby influencing the allocation of the carbon budget
Predicting Urban Traffic Under Extreme Weather by Deep Learning Method with Disaster Knowledge
Get PDFMeteorological and climatological trends are surely changing the way urban infrastructure systems need to be operated and maintained. Urban road traffic fluctuates more significantly under the interference of strong wind–rain weather, especially during tropical cyclones. Deep learning-based methods have significantly improved the accuracy of traffic prediction under extreme weather, but their robustness still has much room for improvement. As the frequency of extreme weather events increases due to climate change, accurately predicting spatiotemporal patterns of urban road traffic is crucial for a resilient transportation system. The compounding effects of the hazards, environments, and urban road network determine the spatiotemporal distribution of urban road traffic during an extreme weather event. In this paper, a novel Knowledge-driven Attribute-Augmented Attention Spatiotemporal Graph Convolutional Network (KA3STGCN) framework is proposed to predict urban road traffic under compound hazards. We design a disaster-knowledge attribute-augmented unit to enhance the model’s ability to perceive real-time hazard intensity and road vulnerability. The attribute-augmented unit includes the dynamic hazard attributes and static environment attributes besides the road traffic information. In addition, we improve feature extraction by combining Graph Convolutional Network, Gated Recurrent Unit, and the attention mechanism. A real-world dataset in Shenzhen City, China, was employed to validate the proposed framework. The findings show that the prediction accuracy of traffic speed can be significantly increased by 12.16%~31.67% with disaster information supplemented, and the framework performs robustly on different road vulnerabilities and hazard intensities. The framework can be migrated to other regions and disaster scenarios in order to strengthen city resilience