1. Influence of DEM interpolation methods in Drainage Analysis
By: Manuel Peralvo

2. Outline
Introduction
Methods
Results
Conclusions

3. Introduction Topography as a key landscape control
Availability of elevation information
Objective
Evaluate the accuracy and influence of interpolation methods in the results of automated drainage analysis

4. Study Area
The surface is 5700 ha, elevation goes from 2,700 to 4,500 masl
Topography is gentler in the west part of the study area, and is dominated by steep slopes and V-shaped valleys as we move to the east
The two sectors were chosen to facilitate the interpretation of the results as significant examples of different topographic conditions.

5. Methods Data preparation
Four interpolation techniques: IDW, RBF, KRG, TOPOGRID
Modeled stream networks
Qualitative comparison
Quantitative comparison: Kappa coefficient
Cleaning errors in elevation of the curves
Assigning the right directions to the river networks
Converting contour lines to points

6. Methods IDW and Kriging Thin-plate spline RBF TOPOGRID Iterative
Drainage enforcement
Roughness penalty
IDW, KRG, and RBF are local interpolators: they use a neighborhood of points to make the predictions
Iterative: goes from a coarse DEM to progressively finer resolutions
Ridges and streamlines are modeled in each iteration
Roughness penalty which is locally influenced to the slope of the terrain
Source: Johnstotn et al. 2001

7. Methods Kappa coefficient of agreement:
Khat represents the proportion of agreement between two categorical maps after chance agreement is removed

8. IDW
RBF
KRG
TOPOGRID (DE)
Results:
Comparison
Sector a
TOPOGRID (WDE)

9. IDW
RBF
KRG
TOPOGRID (DE)
Results:
Comparison
Sector b
TOPOGRID (WDE)

10. Results:
Comparison
of the AGREE
DEMs

11. Results: Modeled and original contour lines
IDW
RBF
KRG
TOPOGRID (WDE)
Results: Modeled and original contour lines

12. Quantitative level of agreement
Results
Quantitative level of agreement
Raw DEMs
Reconditioned DEMs
Kappa is used to compare. The absolute values are low due to the difference in the derivation of models.

13. Conclusions
Geostatistical methods: estimation of the accuracy of the prediction
Deterministic methods: time efficiency
TOPOGRID: adaptation to local topography
Quantitative evaluation of accuracy
RBF actually outperformed KRG
Quantitative evaluation is important when the differences between two models are subtle

14. Thank you