@incollection{temmeGeostatisticalSimulationError2009,
  title = {Geostatistical Simulation and Error Propagation in Geomorphometry},
  booktitle = {Developments in {{Soil Science}}},
  author = {Temme, A. J. A. M. and Heuvelink, G. B. M. and Schoorl, J. M. and Claessens, L.},
  editor = {Hengl, Tomislav and Reuter, Hannes I.},
  date = {2009-01-01},
  volume = {33},
  pages = {121--140},
  publisher = {{Elsevier}},
  doi = {10.1016/S0166-2481(08)00005-6},
  url = {https://doi.org/10.1016/S0166-2481(08)00005-6},
  urldate = {2019-11-08},
  abstract = {This chapter aims to demonstrate how uncertainty in digital elevation model (DEM) attributes can be quantified using geostatistical methods and to show how the propagation of errors to DEM derived products may be computed. To attribute errors DEMs may have positional errors like a shift along one or both coordinate axes, rotational errors, scaling errors, projection errors, or a combination of these. In this chapter, only attribute errors are considered. It describe how propagation of attribute errors in spatial modeling can be computed using the Monte-Carlo method. This method is the most often used error propagation method because it is generic, flexible, and intuitively appealing. In order of increasing complexity, the chapter considers the propagation of error from DEMs to three derivatives, namely slope (a local land-surface parameter), topographic wetness index (a regional land-surface parameter), and soil redistribution resulting from water erosion (a complex model). It also describes the uncertainty propagation analysis in detail.},
  keywords = {~INRMM-MiD:z-BGZ4NZY6,elevation,monte-carlo,randomised-ensemble-uncertainty,slope,soil-erosion,soil-resources,topographic-wetness-index,uncertainty,uncertainty-propagation},
  langid = {english},
  series = {Geomorphometry}
}

{"_id":"3iNQTBDcnJuoM2crj","bibbaseid":"temme-heuvelink-schoorl-claessens-geostatisticalsimulationanderrorpropagationingeomorphometry","authorIDs":[],"author_short":["Temme, A. J. A. M.","Heuvelink, G. B. M.","Schoorl, J. M.","Claessens, L."],"bibdata":{"bibtype":"incollection","type":"incollection","title":"Geostatistical Simulation and Error Propagation in Geomorphometry","booktitle":"Developments in Soil Science","author":[{"propositions":[],"lastnames":["Temme"],"firstnames":["A.","J.","A.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Heuvelink"],"firstnames":["G.","B.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Schoorl"],"firstnames":["J.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Claessens"],"firstnames":["L."],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Hengl"],"firstnames":["Tomislav"],"suffixes":[]},{"propositions":[],"lastnames":["Reuter"],"firstnames":["Hannes","I."],"suffixes":[]}],"date":"2009-01-01","volume":"33","pages":"121–140","publisher":"Elsevier","doi":"10.1016/S0166-2481(08)00005-6","url":"https://doi.org/10.1016/S0166-2481(08)00005-6","urldate":"2019-11-08","abstract":"This chapter aims to demonstrate how uncertainty in digital elevation model (DEM) attributes can be quantified using geostatistical methods and to show how the propagation of errors to DEM derived products may be computed. To attribute errors DEMs may have positional errors like a shift along one or both coordinate axes, rotational errors, scaling errors, projection errors, or a combination of these. In this chapter, only attribute errors are considered. It describe how propagation of attribute errors in spatial modeling can be computed using the Monte-Carlo method. This method is the most often used error propagation method because it is generic, flexible, and intuitively appealing. In order of increasing complexity, the chapter considers the propagation of error from DEMs to three derivatives, namely slope (a local land-surface parameter), topographic wetness index (a regional land-surface parameter), and soil redistribution resulting from water erosion (a complex model). It also describes the uncertainty propagation analysis in detail.","keywords":"~INRMM-MiD:z-BGZ4NZY6,elevation,monte-carlo,randomised-ensemble-uncertainty,slope,soil-erosion,soil-resources,topographic-wetness-index,uncertainty,uncertainty-propagation","langid":"english","series":"Geomorphometry","bibtex":"@incollection{temmeGeostatisticalSimulationError2009,\n title = {Geostatistical Simulation and Error Propagation in Geomorphometry},\n booktitle = {Developments in {{Soil Science}}},\n author = {Temme, A. J. A. M. and Heuvelink, G. B. M. and Schoorl, J. M. and Claessens, L.},\n editor = {Hengl, Tomislav and Reuter, Hannes I.},\n date = {2009-01-01},\n volume = {33},\n pages = {121--140},\n publisher = {{Elsevier}},\n doi = {10.1016/S0166-2481(08)00005-6},\n url = {https://doi.org/10.1016/S0166-2481(08)00005-6},\n urldate = {2019-11-08},\n abstract = {This chapter aims to demonstrate how uncertainty in digital elevation model (DEM) attributes can be quantified using geostatistical methods and to show how the propagation of errors to DEM derived products may be computed. To attribute errors DEMs may have positional errors like a shift along one or both coordinate axes, rotational errors, scaling errors, projection errors, or a combination of these. In this chapter, only attribute errors are considered. It describe how propagation of attribute errors in spatial modeling can be computed using the Monte-Carlo method. This method is the most often used error propagation method because it is generic, flexible, and intuitively appealing. In order of increasing complexity, the chapter considers the propagation of error from DEMs to three derivatives, namely slope (a local land-surface parameter), topographic wetness index (a regional land-surface parameter), and soil redistribution resulting from water erosion (a complex model). It also describes the uncertainty propagation analysis in detail.},\n keywords = {~INRMM-MiD:z-BGZ4NZY6,elevation,monte-carlo,randomised-ensemble-uncertainty,slope,soil-erosion,soil-resources,topographic-wetness-index,uncertainty,uncertainty-propagation},\n langid = {english},\n series = {Geomorphometry}\n}\n\n","author_short":["Temme, A. J. A. M.","Heuvelink, G. B. M.","Schoorl, J. M.","Claessens, L."],"editor_short":["Hengl, T.","Reuter, H. I."],"key":"temmeGeostatisticalSimulationError2009","id":"temmeGeostatisticalSimulationError2009","bibbaseid":"temme-heuvelink-schoorl-claessens-geostatisticalsimulationanderrorpropagationingeomorphometry","role":"author","urls":{"Paper":"https://doi.org/10.1016/S0166-2481(08)00005-6"},"keyword":["~INRMM-MiD:z-BGZ4NZY6","elevation","monte-carlo","randomised-ensemble-uncertainty","slope","soil-erosion","soil-resources","topographic-wetness-index","uncertainty","uncertainty-propagation"],"downloads":0},"bibtype":"incollection","biburl":"https://tmpfiles.org/dl/58794/INRMM.bib","creationDate":"2020-07-02T22:41:30.810Z","downloads":0,"keywords":["~inrmm-mid:z-bgz4nzy6","elevation","monte-carlo","randomised-ensemble-uncertainty","slope","soil-erosion","soil-resources","topographic-wetness-index","uncertainty","uncertainty-propagation"],"search_terms":["geostatistical","simulation","error","propagation","geomorphometry","temme","heuvelink","schoorl","claessens"],"title":"Geostatistical Simulation and Error Propagation in Geomorphometry","year":null,"dataSources":["DXuKbcZTirdigFKPF"]}