Exploring uncertainties in terrain feature extraction across multi-scale, multi-feature, and multi-method approaches for variable terrain

摘要

Terrain analysis uses different workflows to extract features from terrain models for the purpose of understanding topographic patterns and processes. However, the results of different workflows often conflict, leading to uncertainties about feature locations. Instead of relying upon a single workflow, we suggest that a fusion of information from multiple workflows better informs terrain analysis. From terrain data with different degrees of variability, we extracted terrain features related to the set of topographic surface network feature classes {peaks, pits, saddles, ridges, courses} using workflows from free, open-source, and commercial software. A multi-scale analysis produced terrain features with fuzzy membership values for various feature classes and revealed that terrain locations can exhibit characteristics of all classes. Multi-feature maps were created by determining at each location the dominant and second-ranked features, and an uncertainty value. Our multi-method approach incorporated all of the workflows' multi-scale results and again produced multi-feature maps that increased the confidence of some features and reduced the signal of dissimilar results. We also found that high variability terrain produced crisper features in both spatial extent and membership strength. Our overall conclusion is that multi-scale, multi-feature, and multi-method analyses clarify terrain feature uncertainty.