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Layer: GOGA Marin County Fine Scale Vegetation (ID:8)

View In:   Map Viewer

Name: GOGA Marin County Fine Scale Vegetation

Display Field: OID_COPY

Type: Feature Layer

Geometry Type: esriGeometryPolygon

Description: The Marin County fine scale vegetation map (v. 9/30/21)) is an 106-class vegetation map of Marin County with 96,638 polygons. The fine scale vegetation map represents the state of the landscape in 2018 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here (https://vegmap.press/marin_vegmap_datasheet).A web map for online viewing of the Marin County Fine Scale Vegetation Map can be found here (https://vegmap.press/marin_vegmap_webmap).Symbology for this FileGDB is available as a layer file (without spatial data) here: (https://vegmap.press/marin_vegmap_layer_file)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 feature service with symbology included can be found here (https://vegmap.press/marin_vegmap_feature_service). 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).Fine Scale Vegetation Methods Overview:The fine scale vegetation map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation 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 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 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 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 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 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 tablesA 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

Min. Scale: 0

Max. Scale: 0

Default Visibility: true

Max Record Count: 2000

Supported query Formats: JSON, geoJSON, PBF

Use Standardized Queries: True

Extent:

Drawing Info:

HasZ: false

HasM: false

Has Attachments: false

Has Geometry Properties: true

HTML Popup Type: esriServerHTMLPopupTypeAsHTMLText

Object ID Field: OBJECTID

Unique ID Field:

Global ID Field: GlobalID

Type ID Field: MAP_CLASS_18

Fields:
Types:

Is Data Versioned: false

Has Contingent Values: false

Supports Rollback On Failure Parameter: true

Last Edit Date: 10/30/2024 4:36:08 PM

Schema Last Edit Date: 10/30/2024 4:36:08 PM

Data Last Edit Date: 10/30/2024 4:36:08 PM

Supported Operations:   Query   Query Top Features   Query Analytic   Query Bins   Generate Renderer   Validate SQL   Get Estimates   ConvertFormat