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 |
Recent comments
5 years 12 weeks ago
5 years 30 weeks ago
5 years 38 weeks ago
5 years 51 weeks ago