A computational-grid based system for continental drainage network extraction using SRTM digital elevation models

Remote sensing of floodplain geomorphology as a surrogate for biodiversity in a tropical river system (Madre de Dios, Peru)

“…A raster digital elevation model dataset is available at a spacing of 3″ (∼ 90 m) for South America. Performance evaluations show an absolute vertical error of ± 5-6 m, and relative errors are estimated to be smaller (Curkendall et al, 2003;Smith and Sandwell, 2003). In forested landscapes, however, the elevation estimates are affected by scattering from woody biomass, and, thus, elevations lie somewhere between the height of the ground surface and the height of the forest canopy (Kellndorfer et al, 2004), which often reaches 20-30 m in the study area.…”

HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia

“…Some of these features can be real properties of the relief, but often they represent artifacts in the data. SRTM data, perhaps because of radar speckle (noise) or vegetation effects, have more spurious points than other DEMs (Curkendall et al, 2003). Besides, forests have characteristic sylvigenetic dynamics with a relatively high occurrence of tree gaps (Oldeman, 1990), which will appear in the SRTM-DEM as depressions.…”

“…Jenson and Domingue, 1988), and drainage network and catchment area (e.g. Curkendall et al, 2003) are a few classical descriptors. A range of hydrological parameters such as superficial runoff trajectories, accumulated contributing area and groundwater related variables (e.g.…”

Comparison of distribution strategies in uncertainty-aware catchment delineation

“…In addition, the methods' localised data access patterns made the use of data level parallelisation techniques possible. In Method 2 we used the tiling approach [19,25], where DEM is divided into rectangular subareas each of which is assigned to different nodes according to their available memory capacity. This division of data between K nodes reduces each nodes' memory requirements for N-cell DEM from O(N) to O(N/K).…”

“…With the increasing popularity of grid computing making use of loosely-coupled computers, the situation is starting to change [24]. The uncertainty-aware distributed computational framework is based on earlier research with deterministic distributed drainage basin delineation methods [19]. In our previous working papers [25,26] we represented the basic idea of applying distributed computing in the uncertaintyaware catchment delineation and showed intermediate results of the prototype implementations and their limitations.…”