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.
Highlights
Anthropogenic and natural stressors and disturbances affect all levels of biotic organization in forest ecosystems, potentially affecting their 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
Healthy forest ecosystems can be defined as vigorous, diverse systems that are characterized by a high resilience on different levels of biotic organization from the gene, molecular, individual, and community level to that of forest ecosystems, with the ability to quickly return to an initial state following external stressors, disturbances, or resource limitations, and withstand negative impacts from external influences [34]
Summary
Anthropogenic and natural stressors and disturbances affect all levels of biotic organization in forest ecosystems, potentially affecting their resilience. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded on a low-cost basis Such information should be comparable among regions and based on harmonized methods. Even though appropriate data and methods are available, no uniform, comparable, multidimensional, and multi-source forest health monitoring network (MUSO-FH-MN) exists, which corresponds to the future requirements of data science in the 21st century We think that such a network is necessary to enable timely data and process-based decisions in order to guarantee better understanding, modeling, prediction, and assessing of healthy and resilient forest ecosystems. (1) What is required to bridge the gaps in information, data, models, and tools needed by forest managers and scientists for a better understanding of the complexity and multidimensionality of forest health drivers, stressors, disturbances, effects, and related processes?. (2) Why are these requirements essential for a better understanding of forest health? (3) Which requirements are imperative to establish a multidimensional, multi-source forest health monitoring network in the future?
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