This paper documents resolution dependencies in terrain analysis and describes how they vary across landform location. Six terrain attributes were evaluated as a function of DEM resolution-slope, plan curvature, profile curvature, northsouth slope orientation, east-west slope orientation, and topographic wetness index. The research highlights the effect of varying spatial resolution through a spatial sampling/resampling scheme while maintaining sets of indexed sample points at various resolutions. Tested sample points therefore coincide exactly between two directly compared resolutions in terms of their location and elevation value. An unsupervised landform classification procedure based on statistical clustering algorithms was employed to define landform classes in a reproducible manner. Correlation and regression analyses identified sensitive and consistent responses for each attribute as resolution was changed, although the tested terrain attributes responded in characteristically different ways. These responses displayed distinguishable patterns among various landform classes, a conclusion that was further verified by a series of two-sample, two-tailed t-tests.
This paper predicts the geographic distribution and size of gullies across central Lebanon using a geographic information system (GIS) and terrain analysis. Eleven primary (elevation; upslope contributing area; aspect; slope; plan, profile and tangential curvature; flow direction; flow width; flow path length; rate of change of specific catchment area along the direction of flow) and three secondary (steady-state; quasi-dynamic topographic wetness; sediment transport capacity) topographic variables were generated and used along with digital data collected from other sources (soil, geology) to statistically explain gully erosion field measurements. Three tree-based regression models were developed using (1) all variables, (2) primary topographic variables only and (3) different pairs of variables. The best regression tree model combined the steady-state topographic wetness and sediment transport capacity indices and explained 80% of the variability in field gully measurements. This model proved to be simple, quick, realistic and practical, and it can be applied to other areas of the Mediterranean region with similar environmental conditions, thereby providing a tool to help with the implementation of plans for soil conservation and sustainable management.The study area was chosen because it represents the environmental diversity of Lebanon in terms of geology, soil, hydrography, land cover and climate. It covers 676 km 2 , or 6·5% of the total area of Lebanon. It extends 33 km from west to east across the middle of Lebanon ( Figure 1) and can be divided into two major geomorphic units, Mount Lebanon and the Bekaa.Mount Lebanon, which comprised 76% of the study area, runs parallel to the shoreline, dipping steeply seaward, with an east-west gradient of 7·5-10%. It can be divided into three major parts: the lower slopes (100-500 m altitude), the upper sloping plateaus (500-1500 m altitude) and the elevated crests (>1500 m altitude). The lower slopes, consisting of clastic and oolithic limestone, sandstone and clayey rocks of the Lower Cretaceous and Upper Jurassic formations (Dubertret, 1945), are dominated by bare soils and residential/commercial urban areas. The upper sloping plateaus are covered with coniferous (mainly Pinus pinea), oak (mainly Quercus calliprinos) and broadleaf (Quercus infectoria) forests and shrub lands on dolomites, limestone and dolomitic limestone rocks with patches of basalts, sandstone and clay materials. The elevated crests are covered by grass and herbaceous vegetation on limestone and marly limestone Cenomanian rocks and dolomitic limestone Jurassic rocks. Mount Lebanon is structurally affected by faults running parallel to one another, cutting in a SW-NE direction and separated from the Bekaa by the shed line of the Dead Sea Fault Zone and the 'Yammounah Fault' with a NE-SW strike. The Bekaa comprises the hills (1000-1500 m altitude; 6% of total area) located between the crests of Mount Lebanon and the Bekaa valley, and the valley bottom (500-1000 m altitude; 18% of total area). The h...
This article provides an ontological as well as methodological evaluation of recent progress in terrain analysis. It focuses on six topographic factors, or existences, that are important in characterizing the biophysical functions of topography: elevation, surface shape, topographic position, topographic context, spatial scale, and landform object. Terrain analysis approaches are assessed according to what they really deal with, as well as how they work. Important trends are consequently identified, in which spatial scale plays critical but non-uniform roles. An index-based approach to the compound function of multiple topographic existences is recognized as successful in modelling surface/subsurface moisture and mass movement potential, but not mountain temperature. A classification scheme categorises defined landforms in the literature according to the way they exist in human knowledge rather than their morphological properties and derivation methods. Five categories are outlined: bona fide objects, prototypical objects, fiat objects, landform classes, and multiscale objects. Peak object delineation is lastly assessed as an example demonstrating some of the recent trends in terrain analysis. Representations of higher-scale landscape context are identified to have great potential of linking vastly different spatial scales, as well as bridging field- versus object-based treatments of the terrain surface.
Abstract:Flow direction and specific catchment area were calculated for different flow-routing algorithms using TAPES-G and TauDEM. A fuzzy classification was used along with eight topo-climatic attributes to delineate six landscape classes from a 10-m USGS DEM. A series of maps and tabular outputs were produced to compare flow-routing predictions in different parts of the study area in the Santa Monica Mountains of southern California. The matched pair t-test was used to compare the performance of pairs of specific catchment area grids across six user-defined fuzzy landscape classes. The results show that (1) the 'source' cells predicted with the D1, DEMON, and FD8 algorithms were confined to hilltops; (2) two single flow-routing algorithms (Rho8, D8) produced poor results; and (3) the choice of flow-routing algorithm has potentially important consequences for the calculation of upslope contributing areas, sediment transport capacity, topographic wetness, and several other topographic indices.
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