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

Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, Andreas and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmullius, C. and Settele, Josef and Skidmore, Andrew and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael E. (2018) Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sensing, 10 (1120), pp. 1-52. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs10071120. ISSN 2072-4292.

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Official URL: http://www.mdpi.com/2072-4292/10/7/1120


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.

Item URL in elib:https://elib.dlr.de/124497/
Document Type:Article
Title:Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Lausch, AngelaUFZ LeipzigUNSPECIFIED
Borg, ErikErik.Borg (at) dlr.deUNSPECIFIED
Bumberger, JanUFZ LeipzigUNSPECIFIED
Dietrich, PeterUFZ LeipzigUNSPECIFIED
Heurich, MarcoMarco.Heurich (at) npv-bw.bayern.deUNSPECIFIED
Jung, AndreasTechnical Department, Szent István University, Villányi út 29–43, Budapest 1118, Hungary; jung.andrás (at) kertk.szie.huUNSPECIFIED
Klenke, ReinhardUFZ LeipzigUNSPECIFIED
Knapp, SonjaUFZ LeipzigUNSPECIFIED
Mollenhauer, HannesUFZ LeipzigUNSPECIFIED
Paasche, HendrikUFZ LeipzigUNSPECIFIED
Paulheim, HeikoData and Web Science Group, University of Mannheim, B6 26, D-68159 Mannheim, GermanyUNSPECIFIED
Schmullius, C.c.schmullius (at) uni-jena.deUNSPECIFIED
Settele, JosefUFZ LeipzigUNSPECIFIED
Skidmore, Andrewa.k.skidmore (at) utwente.nlUNSPECIFIED
Wegmann, Martinmartin.wegmann (at) uni-wuerzburg.deUNSPECIFIED
Zacharias, SteffenUFZ LeipzigUNSPECIFIED
Kirsten, ToralfFaculty of Applied Computer and Bio Sciences, University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, GermanyUNSPECIFIED
Schaepman, Michael E.michael.schaepman (at) geo.uzh.chUNSPECIFIED
Date:15 July 2018
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs10071120
Page Range:pp. 1-52
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
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
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Geoscientific remote sensing and GIS methods
Location: Neustrelitz , Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
German Remote Sensing Data Center > National Ground Segment
Deposited By: Wöhrl, Monika
Deposited On:06 Dec 2018 13:37
Last Modified:14 Dec 2019 04:26

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