Interpolation methods in ArcGIS_ESRI Geostatistical Analyst tool allow obtaining unknown values at unsampled points from observed data and generating continuous surfaces. In this paper, forest data variables as tree h...Interpolation methods in ArcGIS_ESRI Geostatistical Analyst tool allow obtaining unknown values at unsampled points from observed data and generating continuous surfaces. In this paper, forest data variables as tree height and diameter measured in two plots in Central Mountains in Spain. These data were georeferenced to obtain maps that can visualize the spatial variability of these forest variables. In order to evaluate the best interpolation method that could adequately explain the spatial variability of those variables, two interpolation methods were studied： inverse results was made by means of statistical methods to analyze distance weighted （IDW） and Ordinary Kriging （OK）. A comparison of residuals. Results with the kriging method were slightly better.展开更多
文摘Interpolation methods in ArcGIS_ESRI Geostatistical Analyst tool allow obtaining unknown values at unsampled points from observed data and generating continuous surfaces. In this paper, forest data variables as tree height and diameter measured in two plots in Central Mountains in Spain. These data were georeferenced to obtain maps that can visualize the spatial variability of these forest variables. In order to evaluate the best interpolation method that could adequately explain the spatial variability of those variables, two interpolation methods were studied： inverse results was made by means of statistical methods to analyze distance weighted （IDW） and Ordinary Kriging （OK）. A comparison of residuals. Results with the kriging method were slightly better.
