@article{fdi:010035696,
  title = {{A}ccuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density},
  author = {{C}haplot, {V}incent and {D}arboux, {F}. and {B}ourennane, {H}. and {L}eguedois, {S}. and {S}ilvera, {N}orbert and {P}hachomphon, {K}onngkeo},
  editor = {},
  language = {{ENG}},
  abstract = {{O}ne of the most important scientific challenges of digital elevation modeling is the development of numerical representations of large areas with a high resolution. {A}lthough there have been many studies on the accuracy of interpolation techniques for the generation of digital elevation models ({DEM}s) in relation to landform types and data quantity or density, there is still a need to evaluate the performance of these techniques on natural landscapes of differing morphologies and over a large range of scales. {T}o perform such an evaluation, we investigated a total of six sites, three in the mountainous region of northern {L}aos and three in the more gentle landscape of western {F}rance, with various surface areas from micro-plots, hillslopes, and catchments. {T}he techniques used for the interpolation of point height data with density values from 4 to 109 points/km(2) include: inverse distance weighting ({IDW}), ordinary kriging ({OK}), universal kriging ({UK}), multiquadratic radial basis function ({MRBF}), and regularized spline with tension ({RST}). {T}he study sites exhibited coefficients of variation ({CV}) of altitude between 12% and 78%, and isotropic to anisotropic spatial structures with strengths from weak (with a nugget/sill ratio of 0.8) to strong (0.01). {I}rrespective of the spatial scales or the variability and spatial structure of altitude, few differences existed between the interpolation methods if the sampling density was high, although {MRBF} performed slightly better. {H}owever, at lower sampling densities, kriging yielded the best estimations for landscapes with strong spatial structure, low {CV} and low anisotropy, while {RST} yielded the best estimations for landscapes with low {CV} and weak spatial structure. {U}nder conditions of high {CV}, strong spatial structure and strong anisotropy, {IDW} performed slightly better than the other method. {T}he prediction errors in height estimation are discussed in relation to the possible interactions with spatial scale, landform types, and data density. {T}hese results indicate that the accuracy of interpolation techniques for {DEM} generation should be tested not only in relation to landform types and data density but also to their applicability to multi-scales. (c) 2006 {E}lsevier {B}.{V}. {A}ll rights reserved.},
  keywords = {{DEM} ; interpolation method ; sampling density ; landform type ; spatial scale},
  booktitle = {},
  journal = {{G}eomorphology},
  volume = {77},
  numero = {1-2},
  pages = {126--141},
  ISSN = {0169-555{X}},
  year = {2006},
  DOI = {10.1016/j.geomorph.2005.12.010},
  URL = {https://www.documentation.ird.fr/hor/fdi:010035696},
}