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Fig. 1.13: Comparison of spatial prediction techniques for mapping Zinc. T.Hengl Wednesday, February 17, 2010 - 18:46
Fig. 10.1: Stream network generated in SAGA GIS. T.Hengl Wednesday, December 9, 2009 - 15:05
Fig. 10.5: 100 simulations of DEM showing using a cross-section from West to East. T.Hengl Wednesday, December 9, 2009 - 15:12
Fig. 10.9: Drainage network derived using different grid cell sizes. T.Hengl Tuesday, December 8, 2009 - 13:19
Fig. 11.8. Interpolation of temperature visualized in Google Earth as time-series of maps. T.Hengl Tuesday, December 8, 2009 - 17:18
Fig. 2.11: Examples of (simulated) species distribution maps produced using common statistical models. T.Hengl Wednesday, February 17, 2010 - 18:34
Fig. 2.5. Best Combined Spatial Predictor. T.Hengl Tuesday, December 8, 2009 - 17:08
Fig. 2.6: Comparison of ordinary kriging and regression-kriging using a simple example with 5 points T.Hengl Wednesday, February 17, 2010 - 18:51
Fig. 3.13. Preparation of the image ground overlays. T.Hengl Monday, November 30, 2009 - 18:39
Fig. 4.13. Comparing results from SAGA (left) and gstat (right): regression-kriging. T.Hengl Monday, November 30, 2009 - 18:38
Fig. 4.3: MODIS HDF tiles. T.Hengl Tuesday, December 8, 2009 - 17:36
Fig. 4.4: A sample of downloaded and resampled MODIS LST images showing the average values of clear-sky LST. T.Hengl Wednesday, December 9, 2009 - 15:52
Fig. 5.13: Four simulations of liming requirements (indicator variable) using ordinary kriging. T.Hengl Tuesday, December 8, 2009 - 17:58
Fig. 5.15: Anisotropy (left) and variogram model fitted using the Maximum Likelihood (ML) method (right). T.Hengl Tuesday, December 8, 2009 - 18:01
Fig. 5.17: Zinc predicted using external trend kriging in geoR (left); simulations using the same model (right). T.Hengl Tuesday, December 8, 2009 - 18:03
Fig. 5.19. Mapping uncertainty for zinc visualized using whitening. T.Hengl Tuesday, December 8, 2009 - 17:01
Fig. 5.3: Meuse auxiliary predictors. T.Hengl Tuesday, December 8, 2009 - 17:40
Fig. 5.8: Variogram for original variable, and regression residuals. T.Hengl Tuesday, December 8, 2009 - 17:43
Fig. 6.11: First principal component derived using a stack of predicted maps of eight heavy metals. T.Hengl Tuesday, December 8, 2009 - 18:05
Fig. 6.12: Comparison of results of predicting values of Pb (ppm) using ordinary and regression-kriging. T.Hengl Thursday, December 10, 2009 - 13:05
Fig. 6.2. Sampling locations and values of Pb based on the NGS data set. T.Hengl Friday, May 21, 2010 - 23:02
Fig. 6.4. Examples of environmental predictors used to interpolate HMCs. T.Hengl Tuesday, December 8, 2009 - 16:57
Fig. 7.3: Predicted values of the target variable (log1p(SOC)) using the 20 most significant predictors. T.Hengl Tuesday, January 12, 2010 - 15:50
Fig. 7.5: Soil Organic Carbon stock (kg C m^2) for South America. T.Hengl Thursday, April 29, 2010 - 09:17
Fig. 9.2: Initial 5 m DEM (a) generated directly from the LiDAR points, and after filtering (b). T.Hengl Tuesday, December 8, 2009 - 15:13
Figure 3c from Hengl et al 2007, Computers and Geoscience T.Hengl Monday, November 29, 2010 - 16:43
Figure: Mean daily temperatures for four arbitrary dates predicted using spatio-temporal regression-kriging. T.Hengl Monday, July 11, 2011 - 09:14
First steps (meuse) T.Hengl Wednesday, February 17, 2010 - 13:39
First steps (meuse) T.Hengl Wednesday, December 9, 2009 - 15:16