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Remote Sens. 2018, 10(7), 1120; https://doi.org/10.3390/rs10071120

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

1
Department Computational Landscape Ecology, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, D-04318 Leipzig, Germany
2
Lab for Landscape Ecology, Department of Geography, Humboldt University of Berlin, Rudower Chaussee 16, 12489 Berlin, Germany
3
German Aerospace Center—DLR, German Remote Sensing Data Center—DFD, Kalkhorstweg 53, D-17235 Neustrelitz, Germany
4
Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, D-04318 Leipzig, Germany
5
Dpartment of Geoscience, Centre of Applied Geosciences, Eberhard Karls University, Hölderlinstr. 12, D-72074 Tübingen, Germany
6
Department of Conservation and Research, Bavarian Forest National Park, Freyunger Straße 2, D-94481 Grafenau, Germany
7
Faculty of Environment and Natural Resources, University of Freiburg, Tennenbacher Straße 4, D-79106 Freiburg, Germany
8
Department of Ecological Modelling, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, D-04318 Leipzig, Germany
9
Technical Department, Szent István University, Villányi út 29–43, Budapest 1118, Hungary; jung.andrás@kertk.szie.hu
10
MTA-SZIE Plant Ecological Research Group, Szent István University, Páter Károly u.1., Gödöllő 2100, Hungary
11
Department of Conservation Biology, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, D-04318 Leipzig, Germany
12
Department of Community Ecology, Helmholtz Centre for Environmental Research—UFZ, Theodor-Lieser-Str. 4, D-06120 Halle, Germany
13
Data and Web Science Group, University of Mannheim, B6 26, D-68159 Mannheim, Germany
14
Institut of Photogrammetry and Remote Sensing, Technical University Dresden, Helmholtzstr. 10, D-01061 Dresden, Germany
15
German Environment Agency, Wörlitzer Platz 1, D-06844 Dessau-Rosslau, Germany
16
Department of Remote Sensing, University of Jena, Grietgasse 228B, Grietgasse 6, D-07743 Jena, Germany
17
iDiv, German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, Deutscher Platz 5e, D-04103 Leipzig, Germany
18
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, AE 7500 Enschede, The Netherlands
19
Department of Environmental Science, Macquarie University, NSW 2109, Sydney 2109, Australia
20
Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Oswald-Külpe Weg 86, 97074 Würzburg, Germany
21
Faculty of Applied Computer and Bio Sciences, University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany
22
Remote Sensing Laboratories, Department of Geography, University of Zurich Irchel, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Received: 13 April 2018 / Revised: 12 June 2018 / Accepted: 6 July 2018 / Published: 15 July 2018
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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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. View Full-Text
Keywords: forest health; in situ forest monitoring; remote sensing; data science; digitalization; big data; semantic web; linked open data; FAIR; multi-source forest health monitoring network forest health; in situ forest monitoring; remote sensing; data science; digitalization; big data; semantic web; linked open data; FAIR; multi-source forest health monitoring network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lausch, A.; Borg, E.; Bumberger, J.; Dietrich, P.; Heurich, M.; Huth, A.; Jung, A.; Klenke, R.; Knapp, S.; Mollenhauer, H.; Paasche, H.; Paulheim, H.; Pause, M.; Schweitzer, C.; Schmulius, C.; Settele, J.; Skidmore, A.K.; Wegmann, M.; Zacharias, S.; Kirsten, T.; Schaepman, M.E. Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sens. 2018, 10, 1120.

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