Geology topics

Barbara P. Buttenfield

Publications and source records attributed to Barbara P. Buttenfield.

24 records · Page 2Linked to original sources

USGS2016

High performance computing to support multiscale representation of hydrography for the conterminous United States

The National Hydrography Dataset (NHD) for the United States furnishes a comprehensive set of vector features representing the surface-waters in the country (U.S. Geological Survey 2000). The high-resolution (HR) layer of the NHD is largely comprised of hydrographic features originally derived from 1:24,000-scale (24K) U.S. Topographic maps. However, in recent years (2009 to present) densified hydrographic feature content, from sources as large as 1:2,400, have been incorporated into some watersheds of the HR NHD within the conterminous United States to better support the needs of various local and state organizations. As such, the HR NHD is a multiresolution dataset with obvious data density variations because of scale changes. In addition, data density variations exist within the HR NHD that are particularly evident in the surface-water flow network (NHD flowlines) because of natural variations of local geographic conditions; and also because of unintentional compilation inconsistencies due to variations in data collection standards and climate conditions over the many years of 24K hydrographic data collection (US Geological Survey 1955).

Conference Paper↗

USGS2015

An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.

Cartography and Geographic Information Science↗

USGS2015

A rapid approach for automated comparison of independently derived stream networks

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.

Cartography and Geographic Information Science↗

USGS2015

Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing

Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrologic and geomorphological analysis by indicating areas where too few headwater channels have been extracted. But natural drainage density patterns are not consistently available in existing hydrographic data for the United States because compilation and capture criteria historically varied, along with climate, during the period of data collection over the various terrain types throughout the country. This paper demonstrates an automated workflow that is being tested in a high-performance computing environment by the U.S. Geological Survey (USGS) to map natural drainage density patterns at the 1:24,000-scale (24K) for the conterminous United States. Hydrographic network drainage patterns may be extracted from elevation data to guide corrections for existing hydrographic network data. The paper describes three stages in this workflow including data pre-processing, natural channel extraction, and generation of drainage density patterns from extracted channels. The workflow is concurrently implemented by executing procedures on multiple subbasin watersheds within the U.S. National Hydrography Dataset (NHD). Pre-processing defines parameters that are needed for the extraction process. Extraction proceeds in standard fashion: filling sinks, developing flow direction and weighted flow accumulation rasters. Drainage channels with assigned Strahler stream order are extracted within a subbasin and simplified. Drainage density patterns are then estimated with 100-meter resolution and subsequently smoothed with a low-pass filter. The extraction process is found to be of better quality in higher slope terrains. Concurrent processing through the high performance computing environment is shown to facilitate and refine the choice of drainage density extraction parameters and more readily improve extraction procedures than conventional processing.

Conference Paper↗

USGS2015

Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry

Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features through cartographic generalization (Regnauld and McMaster 2008, Stanislawski et al. 2014). A variety of algorithms exist to simplify linear cartographic features, and all of the methods affect the positional accuracy of the features (Shahriari and Tao 2002, Regnauld and McMaster 2008, Stanislawski et al. 2012). In general, simplification operations are controlled by one or more tolerance parameters that limit the amount of positional change the operation can make to features. Using a single tolerance value can have varying levels of positional change on features; depending on local shape, texture, or geometric characteristics of the original features (McMaster and Shea 1992, Shahriari and Tao 2002, Buttenfield et al. 2010). Consequently, numerous researchers have advocated calibration of simplification parameters to control quantifiable properties of resulting changes to the features (Li and Openshaw 1990, Raposo 2013, Tobler 1988, Veregin 2000, and Buttenfield, 1986, 1989). This research identifies relations between local topographic conditions and geometric characteristics of linear features that are available in the National Hydrography Dataset (NHD). The NHD is a comprehensive vector dataset of surface 18 th ICA Workshop on Generalisation and Multiple Representation, Rio de Janiero, Brazil 2015 2 water features within the United States that is maintained by the U.S. Geological Survey (USGS). In this paper, geometric characteristics of cartographic representations for natural stream and river features are summarized for subbasin watersheds within entire regions of the conterminous United States and compared to topographic metrics. A concurrent processing workflow is implemented using a Linux high-performance computing cluster to simultaneously process multiple subbasins, and thereby complete the work in a fraction of the time required for a single-process environment. In addition, similar metrics are generated for several levels of simplification of the hydrographic features to quantify the effects of simplification over the various landscape conditions. Objectives of this exploratory investigation are to quantify geometric characteristics of linear hydrographic features over the various terrain conditions within the conterminous United States and thereby illuminate relations between stream geomorphological conditions and cartographic representation. The synoptic view of these characteristics over regional watersheds that is afforded through concurrent processing, in conjunction with terrain conditions, may reveal patterns for classifying cartographic stream features into stream geomorphological classes. Furthermore, the synoptic measurement of the amount of change in geometric characteristics caused by the several levels of simplification can enable estimation of tolerance values that appropriately control simplification-induced geometric change of the cartographic features within the various geomorphological classes in the country. Hence, these empirically derived rules or relations could help generate multiscale-representations of features through automated generalization that adequately maintain surface drainage variations and patterns reflective of the natural stream geomorphological conditions across the country.

Conference Paper↗

USGS2014

Generalisation operators

This chapter summarises cartographic generalisation operators used to generalise geospatial data. It includes a review of recent approaches that have been tested or implemented to generalise networks, points, or groups. Emphasis is placed on recent advances that permit additional flexibility to tailor generalisation processing in particular geographic contexts, and to permit more advanced types of reasoning about spatial conflicts, preservation of specific feature characteristics, and local variations in geometry, content and enriched attribution. Rather than an exhaustive review of generalisation operators, the chapter devotes more attention to operators associated with network generalisation, which illustrates well the logic behind map generalisation developments. Three case studies demonstrate the application of operators to road thinning, to river network and braid pruning, and to hierarchical point elimination. The chapter closes with some summary comments and future directions.

Book chapter↗