Leveraging data-driven and procedural methods for generating high-fidelity visualizations of real forests

Abstract

Forests are indispensable in mitigating climate change due to their ability to sequester and store large amounts of carbon. Forest restoration and maintenance activities are crucial for retaining and enhancing this capacity. To promote sustainable practices, stakeholders need to assess the potential impacts of forestry operations. But in-depth comprehension of the forest dynamics is challenging due to the complex interactions between various ecosystem components. High-fidelity, immersive visualizations can support sense- and decision-making processes by facilitating the exploration of scientific and non-scientific data in the respective spatial, temporal, and semantic contexts. However, producing accurate representations of diverse ecosystems is complex and time-consuming. In this paper, we introduce a forest visualization prototype providing realistic representations of forest stands and different simulated scenarios. The utilized data-driven and procedural approach ensures efficiency, reproducibility, and accuracy. Furthermore, we present an array of requirements for realistic forest visualizations gathered through questionnaires and interviews with forestry experts.