USGS Chesapeake Bay Hyper-Resolution Hydrography Database
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<p><b>Open the Data Resource:</b> <a href='https://doi.org/10.5066/P1GRAPEX' rel='nofollow ugc'>https://doi.org/10.5066/P1GRAPEX</a></p> <p>The Chesapeake Bay Hyper-Resolution Hydrography Database is intended to facilitate analysis of the landscape in the Chesapeake Bay watershed through identification of headwater and other low-order streams or drainage features (e.g. ditches) that, to date, may be absent from existing hydrography data products. A full description of the methodology and accuracy assessment is provided in the accompanying report titled: "Hydrography Mapping Supporting Modeling and Targeted Conservation: Project Overview and Lessons Learned". The data products were developed by the Chesapeake Conservancy and the University of Maryland Baltimore County (UMBC) as part of a 6-year Cooperative Agreement between the Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and the EPA to provide geospatial support to the Chesapeake Bay Program Office.</p> <p>The data release is structured by eight-digit level hydrologic unit codes (HUC8) for the Chesapeake Bay watershed. Each HUC8 contains seven files (see below) and uses the following nomenclature: where HUC_ID and WATERSHED_NAME are placeholders for HUC8 ID(s), and local watershed name(s) (e.g., "Hydrographic Datasets for Hydrologic Unit 02050101 - Upper Susquehanna")</p> <p>Data Release Structure: Project Overview and Lessons Learned.pdf (Project overview, methodology and accuracy assessment)<br /> huc_[HUC_ID]_streamLine.zip (Stream Lines)<br /> huc_[HUC_ID]_streamPoly.zip (Stream Polygons)<br /> huc_[HUC_ID]_agDitches.zip (Agricultural Ditches)<br /> huc_[HUC_ID]_rdDitches.zip (Road Ditches)<br /> huc_[HUC_ID]_geomorphon1m.tif (Geomorphon 1-meter)<br /> huc_[HUC_ID]_geomorphon10m.tif (Geomorphon 10-meter)<br /> metadata_[HUC_ID].xml (metadata xml)</p> <p>The Stream Line dataset is a polyline network connecting stream channels as identified from 1-m LiDAR-derived digital elevation models (DEMs). LiDAR quality and vintage varies across the Chesapeake Bay watershed and every effort was made to use the highest quality or most current elevation data available. Stream channels were identified from DEMs using a novel approach developed by the UMBC and the Chesapeake Conservancy that utilizes a multi-scalar computer vision algorithm to locate convergent terrain indicative of stream valleys and to further identify local depressions within or connected to valleys, indicative of stream channels. Additional steps were taken to remove non-fluvial features from the set of channel-like depressions and to isolate the remaining fluvial features that could be considered stream channels. Stream channels as visible in LiDAR DEMs may appear discontinuous in areas where they are crossed by culverts or bridges, in areas where streams are routed underground via pipes, in areas of dense vegetation where LiDAR cannot penetrate effectively, or in areas where the channels are naturally less defined or flow underground (e.g. karst topography). In these instances, the stream polylines contained in this dataset connect portions of visible stream channel following the surface topography as represented in the DEM. These portions of polyline segments that connect stream channels but do not directly overlap visible channels are considered "connectors" and their proportion is indicated in the attributes of each polyline. Some channel polygons were deliberately omitted from the polyline connection process (e.g. numerous small polygons corresponding to braided or remnant channels on floodplains, polygons that would generate an unrealistically short stream line, etc.) in an effort to generate a less cluttered polyline network. As a result, there may not always be a direct one-to-one match between stream polygons and stream centerlines.</p> <p>The Stream Polygon dataset is a two-dimensional polygon representation of stream channels as identified from 1-m LiDAR-derived digital elevation models (DEMs). LiDAR quality and vintage varies across the Chesapeake Bay watershed and every effort was made to use the highest quality or most current elevation data available. Stream channels were identified from DEMs using a novel approach developed by UMBC and Chesapeake Conservancy that utilizes a multi-scalar computer vision algorithm to locate convergent terrain indicative of stream valleys and to further identify local depressions within or connected to valleys, indicative of stream channels. Additional steps were taken to remove non-fluvial features from the set of channel-like depressions and to isolate the remaining fluvial features that could be considered stream channels. Stream channels as visible in LiDAR DEMs may appear discontinuous in areas where they are crossed by culverts or bridges, in areas where streams are routed underground via pipes, in areas of dense vegetation where LiDAR cannot penetrate effectively, or in areas where the channels are naturally less defined or flow underground (e.g. karst topography). In these instances, gaps will be present along the polygon stream channel network corresponding with the locations where a visible stream channel could not be perceived from the DEM.</p> <p>The Agricultural Ditches dataset is a two-dimensional polygon representation of agricultural ditches as identified from 1-m LiDAR-derived digital elevation models (DEMs). LiDAR quality and vintage varies across the Chesapeake Bay watershed and every effort was made to use the highest quality or most current elevation data available. Agricultural ditches were identified from DEMs using a novel approach developed by UMBC and Chesapeake Conservancy that utilizes a multi-scalar computer vision algorithm to identify locally convergent areas of terrain indicative of water conveyance features, including ditches. A supervised machine learning classification was applied to these features taking into account their physical characteristics (e.g. shape, size, uniformity) and their context within the landscape (e.g. surrounded by open fields) to identify the features most likely to be agricultural ditches.</p> <p>The Road Ditches dataset is a two-dimensional polygon representation of roadside ditches as identified from 1-m LiDAR-derived digital elevation models (DEMs). LiDAR quality and vintage varies across the Chesapeake Bay watershed and every effort was made to use the highest quality or most current elevation data available. Roadside ditches were identified from DEMs using a novel approach developed by UMBC and Chesapeake Conservancy that utilizes a multi-scalar computer vision algorithm to identify locally convergent areas of terrain indicative of water conveyance features, including ditches. A supervised machine learning classification was applied to these features taking into account their physical characteristics (e.g. shape, size, uniformity) and their context within the landscape (e.g. parallel to roads) to identify the features most likely to be roadside ditches.</p> <p>The geomorphon dataset is a raster representation of landforms interpreted from LiDAR-derived digital elevation models (DEMs) at a broad scale. The LiDAR quality and vintage varies across the Chesapeake Bay watershed and every effort was made to use the highest quality or most current elevation data available.</p> <p>To interpret 1-m geomorphon landforms at a local scale, 1-m LiDAR DEMs were denoised with an edge-preserving denoising algorithm (Sun et al. 2007) to enhance feature contiguity while reducing noise, then used as input to the geomorphon algorithm (Jasiewicz & Stepinski 2013). The geomorphon algorithm was configured with a 20-meter search radius and no skip radius to provide a localized interpretation of the terrain. This dataset is suitable for identifying stream channels, ditches, and other local-scale landform features.</p> <p>To interpret 10-m geomorphon landforms at a broad scale, 1-m LiDAR DEMs were denoised with an edge-preserving denoising algorithm (Sun et al. 2007) to enhance feature contiguity while reducing noise, then aggregated to 10-meter resolution and used as input to the geomorphon algorithm (Jasiewicz & Stepinski 2013). The geomorphon algorithm was configured with a 1000-meter search radius and a 20-meter skip radius to ignore local terrain and provide a broad interpretation of the terrain. This dataset is suitable for identifying stream valleys and other broad-scale landform features.</p> <p>Pixel values for 1-m and 10-m geomorphon landforms, and their corresponding landform type are as follows: 1 – flat 2 – peak 3 – ridge 4 – shoulder 5 – spur 6 – slope 7 – hollow 8 – footslope 9 – valley 10 – pit </p>
