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Auflistung nach Schlagwort "climate change"

1 - 4 von 4
  • Textdokument
    Applying a deep learning-based approach for scaling vegetation dynamics to predict changing forest regimes under future climate and fire scenarios
    (INFORMATIK 2020, 2021) Rammer, Werner; Seidl, Rupert
    The ability to anticipate future changes in terrestrial ecosystems is key for their management. New tools are required that bridge the gap between a high level of process understanding at fine spatial grain, and the increasing relevance for management at larger extents. Such a tool is SVD (Scaling Vegetation Dynamics), a scaling framework that specifically uses deep learning to learn the behavior of detailed vegetation models in response to different environmental factors. This trained deep neural network (DNN) is then applied within the framework on large spatial scales. In addition, SVD includes also explicitly modelled processes such as fire disturbances. Here we use the framework to simulate forest regime change in the 3 Mio. ha landscape of the Greater Yellowstone Ecosystem. We used four climate change scenarios and pre-defined fire events from statistical modelling, and analyzed whether prevailing forest types are able to regenerate after fire. Our results show that up to 60% of the area may undergo regime change until the end of the 21st century.
  • Konferenzbeitrag
    Digital Transformation in Forestry - Stakeholders and Data Collection in German Forests
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Pleger, Michael; Schiering, Ina
    This paper examines the process of data collection within forestry and its individuals and organizations in Germany as a first step towards digitization. Germany has one of the largest forest areas in Europe and plays a significant role in mitigating the impacts of climate change and providing ecosystem services to society. To fulfill this role, data collection is required by law for most forests in Germany. This data collection has been historically labor intensive and time consuming. Data collection through Internet of Things (IoT) devices has the potential to improve the efficiency and accuracy of forestry operations while also providing valuable insights into forest health and productivity. Modern data collection through drones and satellite imagery already provide significant benefits to the economy of forestry. This could be further enhanced by low-cost IoT devices bundled as sensor networks in forests to gather data over a lifespan of a forest.
  • Konferenzbeitrag
    The InsightsNet Climate Change Corpus (ICCC)
    (BTW 2023, 2023) Bartsch, Sabine; Duan, Changxu; Tan, Sherry; Volkanovska, Elena; Stille, Wolfgang
    The discourse on climate change has become a centerpiece of public debate, thereby creating a pressing need to analyze the multitude of messages created by the participants in this communication process. In addition to text, messages on this topic are communicated through images, videos, tables and other data objects that are embedded within a document and accompany the text. This paper presents the process of building the InsightsNet Climate Change Corpus (ICCC), a multimodal corpus on the topic of climate change, using NLP tools to enrich corpus metadata, a dataset that lends itself to the exploration of the interplay between the various modalities that constitute the discourse on climate change.
  • Konferenzbeitrag
    Irrigation and Nitrogen Management for Sustainable Potato Production under Climate Change Scenario: A Simulation Study
    (39. GIL-Jahrestagung, Digitalisierung für landwirtschaftliche Betriebe in kleinstrukturierten Regionen - ein Widerspruch in sich?, 2019) Jaitawat, Ritu Raj; Swain, Dillip Kumar; Bernhardt, Heinz
    Potato growth and development is largely influenced by water and nutrient availability, light and temperature making it highly vulnerable to anticipated climate change. This study is undertaken to simulate the impact of climate change on potato production and to evaluate the various agro-adaptation strategies such as irrigation and fertilizer management. SUBSTOR- potato model was used to simulate the growth and development of potato crop. This study was carried out for Kharagpur conditions in India. Simulation results indicate that 140 kg N/ha applied in 3 split doses gives the highest sustainable yield for both automatic drip and conventional furrow irrigation schemes. Automatic drip irrigation gave 44 % higher yield than conventional furrow irrigation. The study has revealed yield reduction up to 27.81 % and 40.7 % for conventional furrow and automatic drip irrigation respectively in future climate scenario.