Description: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
Description: The full datasheet for this product is available (https://vegmap.press/marin_vegmap_datasheet) . Map class definitions, as well as a dichotomous key for the map classes, can be found in the Marin Fine Scale Vegetation Map Key (https://vegmap.press/mapping_key). A key to map class abbreviations is also available (https://vegmap.press/marin_vegmap_abbrevs). A layer package containing the same spatial data as this download, but with the symbology seen in the thumbnail above can be found here: (https://vegmap.press/marin_vegmap_layer_package). The symbology is also available as a layer file (without spatial data) here: (https://vegmap.press/marin_vegmap_layer_file)Fine Scale Vegetation and Habitat Methods Overview: The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 27-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object-based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’18 and ’14), the 2019 LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The lifeform map was refined into the fine scale vegetation and habitat map using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in refinement and subdivision of the larger lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup). Related Datasets and Resources: Lifeform Products – Two lifeform products are available, the lifeform map (https://vegmap.press/marin_lifeform_map) and the forest lifeform map (https://vegmap.press/marin_forest_lifeform_map); both are derived from this Fine-Scale Vegetation and Habitat map. These products are simplifications of the vegetation map, with a fraction of the total map classes. The lifeform product provides more detail for agriculture and built up classes; the forest lifeform product provides more detail for forest and natural lands. Vector Tile Service - The Marin Fine Scale Vegetation and Habitat Map vector tile service (https://vegmap.press/marin_vegmap_vector_tiles_solid_colors) is intended to be used in web maps and GIS software packages. It should be used in conjunction with the vector tile service that provides labels for each polygon (https://vegmap.press/marin_vegmap_vector_tile_labels).Impervious Surfaces Map – The impervious surfaces map for all of Marin County will be released in summer 2021. Reports – A number of detailed documents will be published in summer and fall of 2021 related to the fine scale vegetation map. These include the following: - Detailed descriptions for alliances, with accompanying stand tables - A floristic key for Marin County (more detailed then the mapping key) - A report detailing fine scale vegetation mapping methods for Marin, accompanied by an accuracy assessment of the map
Copyright Text: Golden Gate National Parks Conservancy, Tukman Geospatial LLC