A cross-scale comparison of drainage basin characteristics derived from digital elevation models

The feasibility of using small-scale digital elevation models (DEMs) to extract various drainage basin characteristics was evaluated by comparing basin parameters derived from the 1:250 000 DEMs with those from the 1:24 000 DEMs. Twenty basins ranging approximately from 150 km² to 1000 km² in West Virginia, a geologically complex region, were examined in this study. The basin parameters examined included those commonly used in hydrology and geomorphology such as elevation, slope, stream length, drainage density, relief ratio and ruggedness number. Our results suggested that the 1:250 000 DEMs can provide accurate estimates for elevation-based and stream-length-based basin parameters, but not for slope-based parameters. After examining the differences between the DEM-derived basin parameters from the two different scales, we found that the performance of the 1:250 000 DEMs was not significantly influenced by basin size, while terrain complexity seems to be an important factor of accuracy of the estimated basin parameters. Copyright © 1999 John Wiley & Sons, Ltd.     

A comparison of drainage networks derived from digital elevation models at two scales

Automated generation of drainage networks has become increasingly popular with powerful analytical functions in geographic information systems (GIS) and with the increased availability of digital elevation models (DEMs). This paper compares drainage networks derived from DEMs at two scales, 1:250 000 (250K) and 1:24 000 (24K), using various drainage parameters common in hydrology and geomorphology. The comparison of parameters derived from the 250K DEMs with those from the 24K DEMs in 20 basins ranging from 150 to 1000 km² in West Virginia shows that the goodness-of-fit between parameter estimates based on the DEMs varies. Results clearly show that superior estimations are produced from the 24K DEMs. Better estimates can be obtained from the 250K DEMs for stream length and frequency parameters than for gradient parameters. However, the estimation of the mean gradient parameters based on the 250K DEMs seems to improve with increasing terrain complexity. Finally, basin size does not strongly affect the accuracy of parameter estimates based on the 250K DEMs.     

A comparision of interpolation methods for producing digital elevation models at the field scale

Digital elevation models have been used in many applications since they came into use in the late 1950s. It is an essential tool for applications that are concerned with the Earth's surface such as hydrology, geology, cartography, geomorphology, engineering applications, landscape architecture and so on. However, there are some differences in assessing the accuracy of digital elevation models for specific applications. Different applications require different levels of accuracy from digital elevation models. In this study, the magnitudes and spatial patterning of elevation errors were therefore examined, using different interpolation methods. Measurements were performed with theodolite and levelling. Previous research has demonstrated the effects of interpolation methods and the nature of errors in digital elevation models obtained with indirect survey methods for small‐scale areas. The purpose of this study was therefore to investigate the size and spatial patterning of errors in digital elevation models obtained with direct survey methods for large‐scale areas, comparing Inverse Distance Weighting, Radial Basis Functions and Kriging interpolation methods to generate digital elevation models. The study is important because it shows how the accuracy of the digital elevation model is related to data density and the interpolation algorithm used. Cross validation, split‐sample and jack‐knifing validation methods were used to evaluate the errors. Global and local spatial auto‐correlation indices were then used to examine the error clustering. Finally, slope and curvature parameters of the area were modelled depending on the error residuals using ordinary least regression analyses. In this case, the best results were obtained using the thin plate spline algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Problems in automated recognition of valley features from digital elevation models and a new method toward their resolution

Valley recognition methods given in the literature fail to produce valley networks suitable for locating valley heads for one of two main reasons: (a) they are based on the concept of [higher than] or (b) they are based on contributing area thresholds. In this paper a new method for recognizing valley bottoms is presented. This is based on two improvements of Carroll's (1983) method, and produces a network that reflects the topography well. The network is used to locate valley heads. Once located, the valley heads are delineated using criteria suggested by geomorphologists and hydrologists. The resultant valley heads are generally well recognized although two problems are evident: (a) there are a number of commission errors; and (b) the valley heads recognized by the method are too small.     

A combined algorithm for automated drainage network extraction from digital elevation models

Drainage networks are the basis for segmentation of watersheds, an essential component in hydrological modelling, biogeochemical applications, and resource management plans. With the rapidly increasing availability of topographic information as digital elevation models (DEMs), there have been many studies on DEM‐based drainage network extraction algorithms. Most of traditional drainage network extraction methods require preprocessing of the DEM in order to remove “spurious” sink, which can cause unrealistic results due to removal of real sinks as well. The least cost path (LCP) algorithm can deal with flow routing over sinks without altering data. However, the existing LCP implementations can only simulate either single flow direction or multiple flow direction over terrain surfaces. Nevertheless, terrain surfaces in the real world are usually very complicated including both convergent and divergent flow patterns. The triangular form‐based multiple flow (TFM) algorithm, one of the traditional drainage network extraction methods, can estimate both single flow and multiple flow patterns. Thus, in this paper, it is proposed to combine the advantages of the LCP algorithm and the TFM algorithm in order to improve the accuracy of drainage network extraction from the DEM. The proposed algorithm is evaluated by implementing a data‐independent assessment method based on four mathematical surfaces and validated against “true” stream networks from aerial photograph, respectively. The results show that when compared with other commonly used algorithms, the new algorithm provides better flow estimation and is able to estimate both convergent and divergent flow patterns well regarding the mathematical surfaces and the real‐world DEM.