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

Outline Introduction Methods Results Conclusions

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

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.

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

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

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

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

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

Results: Comparison of the AGREE DEMs

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

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.

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

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