Zusammenfassung
Abstract: Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on
our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience
and stability of our forest ecosystems as well as their ecosystem functions. The relationships between
drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often
non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid
decisions that are data-driven and based on short and long-term monitoring information, complex
modeling, and analysis approaches. A huge number of long-standing and standardized forest
health inventory approaches already exist, and are increasingly integrating remote-sensing based
monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis,
prognosis, and assessment still do not satisfy the future requirements of information and digital
knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail
five sets of requirements, including their relevance, necessity, and the possible solutions that would
be necessary for establishing a feasible multi-source forest health monitoring network for the 21st
century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest
health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different
monitoring approaches; (4) using data science as a bridge between complex and multidimensional
big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became
apparent that no existing monitoring approach, technique, model, or platform is sufficient on its
own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the
development of a multi-source forest health monitoring network, we argue that in order to gain a
better understanding of forest health in our complex world, it would be conducive to implement the
concepts of data science with the components: (i) digitalization; (ii) standardization with metadata
management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles;
(iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy
tools for scientists, data managers, and stakeholders for decision-making support.
Nutzer