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

Abstract

In the field of terrain analysis, a primary goal is to effectively identify topographic features for a better understanding of their associated processes. The relationships among features are, therefore, of particular importance. The concept of the surface network, involving and defined by such features as peaks, pits, various saddles, ridge lines, and the opposite course lines, can be a beneficial construct for describing and modeling any mathematical surface and, perhaps, topographic surfaces, as well. However, limitations of terrain data collection, storage, and computational processing have presented difficulties when attempting to make the jump from such logical constructs and their supporting mathematical theories to the development of tools and mapped products representing the measured topography of a landscape. Compared to feature extraction, less attention has been given to the topological relationships among topographic features. This article provides a chronological review of the development of surface network and critical point theory, the study of topography, and the progression of terrain analysis with particular consideration given to the application of surface network theory to represent the topology of topography. Any possible true computed surface network is concluded to be scaledependent, fuzzy, and vague and its undisputed calculation elusive.

Analysis and Exploitation of Landforms for Improved Optimisation of Camera-Based Wildfire Detection Systems

“…Various classification approaches were recently investigated and compared by Romero and Clarke [38] for open-source and commercial software, and Schillaci et al [26] provide additional comparisons of classification methods and software. The methods that they investigated require separate workflows in order to identify different landform types, and generally require workflows comprising multiple steps for each.…”

“…The strategy of selecting candidate sites based upon landform classes should be approached carefully. The various classification techniques applied to the same area can return different landform classifications for individual sites, and results returned by a specific technique may also vary with different parameter settings [38,42,47]. One example of an important parameter in geomorphon classification is the lookup distance, which is the distance between the centre-point being classified and the surrounding points that are compared to the pre-defined landform patterns-effectively the scale over which landforms are identified [42].…”

Decision support for the selection of optimal tower site locations for early‐warning wildfire detection systems in South Africa

“…The strategy of selecting candidate sites according to landforms such as peaks and ridges should, however, be approached with caution because a level of uncertainty is introduced which may result in good sites being discarded (Romero and Clarke, 2018). Therefore, to avoid the unsubstantiated implementation of geopmorphons it was decided to first analyse the terrain feature classes at 165 ForestWatch towers from systems in Mpumalanga Province in South Africa (93 towers), Douglas County in the state of Oregon, USA (31 towers) and the central region of Saskatchewan Province in Canada (41 towers).…”