How can geodata integration and upscaling methods make responses to challenges in land management more sustainable?
Abstract der Sitzung
The session presents and discusses opportunities to use digital geospatial data for solving cross-sectoral land management challenges in different ecosystems, often exacerbated by climate change. These include complex problems that usually necessitate trade-offs among stakeholders with respect to management objectives and are of great importance to society and the environment, such as soil degradation, biodiversity loss, or groundwater depletion. Important human activities and environmental processes in this context are e.g. land use intensification, drought, fire, pest infestation, heavy rain, flooding, or soil erosion.
The session addresses in particular novel ways to detect, visualize, and analyze such processes as well as to monitor and assess mitigation and adaptation measures in the different affected ecosystems and landscapes. Against the background of a steadily differentiated spectrum of data and methods, geodata integration, modeling and synthesis as well as upscaling methodologies are becoming increasingly important in this field. Thus, the session sets a focus on novel research approaches and integrative methods, including in-situ monitoring (e.g., sensor constellations and networks), remote sensing (UAV and satellite systems) and modeling, but also the technical possibilities (cloud computing, deep learning) to better understand and interpret the processes and events outlined above.
However, management steps are often not easy to change in practice, and despite the high potential of technical advancements for more sustainability, a central question is the use and acceptance of the knowledge or derived recommendations gained from the new methods in practice, and potentially their continuous application by the stakeholders. The presentations should therefore briefly reflect on the potential contributions of the conducted research to knowledge and/or technology transfer.