A rapid approach for automated comparison of independently derived stream networks

Abstract

This paper presents an improved coefficient of line correspondence (CLC) metric for automatically assessing the similarity of two different sets of linear features. Elevation-derived channels at 1:24,000 scale (24K) are generated from a weighted flow-accumulation model and compared to 24K National Hydrography Dataset (NHD) flowlines. The CLC process conflates two vector datasets through a raster line-density differencing approach that is faster and more reliable than earlier methods. Methods are tested on 30 subbasins distributed across different terrain and climate conditions of the conterminous United States. CLC values for the 30 subbasins indicate 44-83% of the features match between the two datasets, with the majority of the mismatching features comprised of first-order features. Relatively lower CLC values result from subbasins with less than about 1.5 degrees of slope. The primary difference between the two datasets may be explained by different data capture criteria. First-order, headwater tributaries derived from the flow-accumulation model are captured more comprehensively through drainage area and terrain conditions, whereas capture of headwater features in the NHD is cartographically constrained by tributary length. The addition of missing headwaters to the NHD, as guided by the elevation-derived channels, can substantially improve the scientific value of the NHD.

Between the Lines: Measuring Areal Displacement in Line Simplification

“…In many situations, quantitative metrics of fidelity will be of secondary importance to shape recognition and simplicity, as illustrated for example by recent increased interest in schematization (Mackaness and Reimer 2014). On the other hand, cartographic generalization is also an integral part of data modeling and data dissemination by national mapping agencies (Weibel and Dutton 1999), and there has been increased interest in preserving line characteristics for specific analytical purposes, such as hydrologic analyses (Stanislawski et al 2015). When a single quantitative error measure is needed to assess the degree of distortion introduced by line simplification, areal displacement should be considered a foundational measure.…”

Streams Do Work: Measuring the Work of Low-Order Streams on the Landscape Using Point Clouds

“…The topographic data used for this study in the low relief areas are airborne lidar from NHD Hydrologic Unit Code (HUC) 10 watersheds in central Iowa, the Panther Creek watershed (PC), and northern Illinois, the Forked Creek (FC) watershed. These areas were selected because they likely include inaccurate NHD stream features as identified by Coefficient of Line Correspondence (CLC) analysis (Stanislawski et al, 2015). CLC analysis performs an automated conflation process to identify likely matching and mismatching linear features, in this case between NHD stream lines and lines derived from a weighted flow accumulation model (Stanislawski et al, 2015).…”

“…These areas were selected because they likely include inaccurate NHD stream features as identified by Coefficient of Line Correspondence (CLC) analysis (Stanislawski et al, 2015). CLC analysis performs an automated conflation process to identify likely matching and mismatching linear features, in this case between NHD stream lines and lines derived from a weighted flow accumulation model (Stanislawski et al, 2015). Data for the high relief study area are aerial lidar point clouds from a 6 square kilometer (km 2 ) catchment in Rowan County (RC), North Carolina, referred to as study area RC.…”

“…The NHD is used widely by researchers and governments yet historic collection methods and changes in the landscape and hydrologic conditions have led to inaccuracies in the NHD. The inaccuracies are pronounced among low-order features, especially in low topographic relief agricultural and high relief forested areas (Stanislawski et al, 2015). Given the importance of low-order features and the need for regular update of the NHD, this research seeks to identify techniques for automated validation of stream features using remotely sensed data.…”

Evaluation of River Network Generalization Methods for Preserving the Drainage Pattern

“…Methods must also maintain the topologic structure of the network, i.e., no gap must be created. It is important that a Coefficient of Line Correspondence (CLC) be calculated to compare generalized data with original data [5,6,34]. CLC is given based on length only, which cannot assess the generalized river network comprehensively.…”