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Schätzung von Holzvorräten unter Verwendung von Fernerkundungsdaten
[Estimation of timber reserves using remote sensing data]

Author

Listed:
  • Ralf Münnich

    (Universität Trier, FB IV, VWL)

  • Julian Wagner

    (Universität Trier, FB IV, VWL)

  • Joachim Hill

    (Universität Trier, FB VI)

  • Johannes Stoffels

    (Universität Trier, FB VI)

  • Henning Buddenbaum

    (Universität Trier, FB VI)

  • Thomas Udelhoven

    (Universität Trier, FB VI)

Abstract

Zusammenfassung Die Effizienz moderner Verfahren der Datenerhebung sowie deren zugehörige Auswertung hängen immer mehr von der Güte der Vor- oder Zusatzinformationen ab. Die Verfügbarkeit von Big Data liefert heutzutage ganz neue und andersartige Möglichkeiten, Schätzungen in der amtlichen und institutionellen Statistik zu verbessern, stellt aber auch Herausforderungen an die Qualität der Resultate auf, die diskutiert werden müssen. In der Forstinventur wird schon seit einiger Zeit die Verwendung von Fernerkundungsdaten diskutiert und sogar umgesetzt. Im Rahmen dieser Arbeit werden die aktuell diskutierten Verfahren vorgestellt und konkrete Schätzungen für Rheinland-Pfalz durchgeführt. Abschließend werden die Herausforderungen an zukünftige Anwendungen vorgestellt, die sich im Rahmen von Big Data durch die allgemeine Verfügbarkeit von Satellitendaten ergeben.

Suggested Citation

  • Ralf Münnich & Julian Wagner & Joachim Hill & Johannes Stoffels & Henning Buddenbaum & Thomas Udelhoven, 2016. "Schätzung von Holzvorräten unter Verwendung von Fernerkundungsdaten [Estimation of timber reserves using remote sensing data]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 95-112, October.
    • Handle: RePEc:spr:astaws:v:10:y:2016:i:2:d:10.1007_s11943-016-0186-0
    DOI: 10.1007/s11943-016-0186-0
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    References listed on IDEAS

    as
    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    2. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    3. Ralf Münnich & Jan Burgard & Martin Vogt, 2013. "Small Area-Statistik: Methoden und Anwendungen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 149-191, March.
    4. Ugarte, M.D. & Goicoa, T. & Militino, A.F. & Durbán, M., 2009. "Spline smoothing in small area trend estimation and forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3616-3629, August.
    5. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    6. J. D. Opsomer & G. Claeskens & M. G. Ranalli & G. Kauermann & F. J. Breidt, 2008. "Non‐parametric small area estimation using penalized spline regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 265-286, February.
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    Cited by:

    1. Ralf Thomas Münnich & Markus Zwick, 2016. "Big Data und was nun? Neue Datenbestände und ihre Auswirkungen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 73-77, October.

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