Understanding forest health with remote sensing, Part III

Requirements for a scalable multi-source forest health monitoring network based on data science approaches

Angela Lausch*, Erik Borg, Jan Bumberger, Peter Dietrich, Marco Heurich, Andreas Huth, András Jung, Reinhard Klenke, Sonja Knapp, Hannes Mollenhauer, Hendrik Paasche, Heiko Paulheim, Marion Pause, Christian Schweitzer, Christiane Schmulius, Josef Settele, Andrew K. Skidmore, Martin Wegmann, Steffen Zacharias, Toralf Kirsten & 1 others Michael E. Schaepman

*Corresponding author for this work

    Research output: Contribution to journalReview article

    15 Citations (Scopus)
    7 Downloads (Pure)

    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.

    Original languageEnglish
    Article number1120
    Pages (from-to)1-52
    Number of pages52
    JournalRemote Sensing
    Volume10
    Issue number7
    DOIs
    Publication statusPublished - 1 Jul 2018

    Bibliographical note

    Copyright 2018 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • Big data
    • Data science
    • Digitalization
    • FAIR
    • Forest health
    • In situ forest monitoring
    • Linked open data
    • Multi-source forest health monitoring network
    • Remote sensing
    • Semantic web

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