Lawrence V Stanislawski

Biographic Data

ID6745993
NAMELawrence V Stanislawski
GIVEN NAMESLawrence V
FAMILY NAMEStanislawski
SIGNATURESTANISLAWSKI L V
AFFILIATIONSUnited States Geological Survey
ORCID0000-0002-9437-0576
VERIFIEDYes
TOTAL WORKS6
TOTAL CITATIONS7
AUTHOR COUNT6
EDITOR COUNT0
FIRST PUBLICATION YEAR2011
LATEST PUBLICATION YEAR2017
H-INDEX2

  • An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions

    Lawrence V Stanislawski, Kornelijus Survila et al.ARTICLECartography and Geographic…2017Cited by: 1

    This paper describes a workflow for automating the extraction of elevation-derived stream lines using open source tools with parallel computing support and testing the effectiveness of procedures in various terrain conditions within the conterminous United States. Drainage networks are extracted from the US Geological Survey 1/3 arc-second 3D Elevation Program elevation data having a nominal cell size of 10 m. This research demonstrates the utili…

  • A rapid approach for automated comparison of independently derived stream networks

    Lawrence V Stanislawski, Barbara Buttenfield et al.ARTICLECartography and Geographic…2015References: 2

    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 a…

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

    Jochen Wendel, Barbara Buttenfield et al.ARTICLECartography and Geographic…2015

    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 …

  • Automated thinning of road networks and road labels for multiscale design of The National Map of the United States

    Cynthia A Brewer, Lawrence V Stanislawski et al.ARTICLECartography and Geographic…2013Cited by: 3

    This paper reports on progress in generalization and selective feature removal for a subset of fundamental base map layers that enables competent mapping through scales ranging from 1:24,000 to 1:1,000,000. Thinning and partitioning methods are applied to road features and labels for The National Map of the United States. Roads are thinned adaptively using the ArcGIS Thin Road Network geoprocessing tool, which removes features by feature hierarch…

  • Hydrographic Generalization Tailored to Dry Mountainous Regions

    Lawrence V Stanislawski, Barbara ButtenfieldARTICLECartography and Geographic…2011Cited by: 1

    A wide variety of climate and terrain conditions exist in the United States and optimal cartographic generalization techniques for one area of the country may not be suitable for another, particularly when working with surface hydrographic data. This paper presents generalization and data modeling to produce reduced scale versions of hydrographic data for a multi-resolution national data set, The National Map, of the United States Geological Surv…

  • Adapting Generalization Tools to Physiographic Diversity for the United States National Hydrography Dataset

    Barbara Buttenfield, Lawrence V Stanislawski et al.ARTICLECartography and Geographic…2011Cited by: 2

    This paper reports on generalization and data modeling to create reduced scale versions of the National Hydrographic Dataset (NHD) for dissemination through The National Map, the primary data delivery portal for USGS. Our approach distinguishes local differences in physiographic factors, to demonstrate that knowledge about varying terrain (mountainous, hilly or flat) and varying climate (dry or humid) can support decisions about algorithms, param…