IDEAS home Printed from https://ideas.repec.org/a/spr/astaws/v10y2016i2d10.1007_s11943-016-0186-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11943-016-0186-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11943-016-0186-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, September.
    4. 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.
    5. 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.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salvati, Nicola & Chandra, Hukum & Giovanna Ranalli, M. & Chambers, Ray, 2010. "Small area estimation using a nonparametric model-based direct estimator," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2159-2171, September.
    2. Shonosuke Sugasawa & Tatsuya Kubokawa & J. N. K. Rao, 2018. "Small area estimation via unmatched sampling and linking models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 407-427, June.
    3. Julian Wagner & Ralf Münnich & Joachim Hill & Johannes Stoffels & Thomas Udelhoven, 2017. "Non‐parametric small area models using shape‐constrained penalized B‐splines," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1089-1109, October.
    4. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    5. Militino, A.F. & Goicoa, T. & Ugarte, M.D., 2012. "Estimating the percentage of food expenditure in small areas using bias-corrected P-spline based estimators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2934-2948.
    6. María José Lombardía & Esther López-Vizcaíno & Cristina Rueda, 2021. "Selection model for domains across time: application to labour force survey by economic activities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 228-254, March.
    7. Tang, Niansheng & Wu, Ying & Chen, Dan, 2018. "Semiparametric Bayesian analysis of transformation linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 225-240.
    8. Chandra, Hukum & Salvati, Nicola & Chambers, Ray, 2018. "Small area estimation under a spatially non-linear model," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 19-38.
    9. Lin, Fangzheng & Tang, Yanlin & Zhu, Huichen & Zhu, Zhongyi, 2022. "Spatially clustered varying coefficient model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    10. 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.
    11. María José Lombardía & Stefan Sperlich, 2008. "Semiparametric inference in generalized mixed effects models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 913-930, November.
    12. Rong Chen & Hua Liang & Jing Wang, 2011. "Determination of linear components in additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 367-383.
    13. Hongjian Yu & Yueyan Wang & Jean Opsomer & Pan Wang & Ninez A. Ponce, 2018. "A design‐based approach to small area estimation using a semiparametric generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1151-1167, October.
    14. Chiara Bocci & Emilia Rocco, 2014. "Estimates for geographical domains through geoadditive models in presence of incomplete geographical information," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 283-305, June.
    15. 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.
    16. Zanin, Luca & Marra, Giampiero, 2012. "Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach," Economic Modelling, Elsevier, vol. 29(4), pages 1328-1337.
    17. Ni, Xiao & Zhang, Hao Helen & Zhang, Daowen, 2009. "Automatic model selection for partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2100-2111, October.
    18. Proietti, Tommaso, 2010. "Trend Estimation," MPRA Paper 21607, University Library of Munich, Germany.
    19. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    20. Javier Parada Gómez Urquiza & Alejandro López-Feldman, 2013. "Poverty dynamics in rural Mexico: What does the future hold?," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 55-74, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:astaws:v:10:y:2016:i:2:d:10.1007_s11943-016-0186-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.