20231116 10260600 1.0 FGDC CSDGM Metadata Abridged2 FALSE BIO_VEG_MAP_CL BIO_VEG_RC Relationship BIO_VEG_RC Relationship 002 20210323 12224500 20210323 12224500 FGDC 150000000 5000 Allison Schichtel Sonoma County Agricultural Preservation & Open Space District Conservation GIS Analyst 747 Mendocino Ave, Suite 100 Santa Rosa CA 95401 Allison.Clark@sonoma-county.org 707.565.7353 Allison Schichtel The Sonoma County fine scale vegetation and habitat map is an 83-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD</SPAN></P><P><SPAN>Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (</SPAN><A href="https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8"><SPAN><SPAN>https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8</SPAN></SPAN></A><SPAN>). </SPAN></P><P STYLE="margin:0 0 14 0;"><SPAN><SPAN>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 18-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 (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. </SPAN></SPAN></P><P STYLE="margin:0 0 14 0;"><SPAN><SPAN>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.</SPAN></SPAN></P><P STYLE="margin:0 0 14 0;"><SPAN><SPAN>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. </SPAN></SPAN></P><P STYLE="margin:0 0 14 0;"><SPAN><SPAN>The fine scale vegetation and habitat map was developed 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 a refinement of the 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.</SPAN></SPAN></P><P STYLE="margin:0 0 14 0;"><SPAN><SPAN>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). </SPAN></SPAN></P><P /><P><SPAN /></P></DIV></DIV></DIV> Sonoma County Water Agency, Sonoma County Agricultural Preservation and Open Space District, Sonoma County Vegetation Mapping and LiDAR Program Allison Schichtel Sonoma County Agricultural Preservation & Open Space District Conservation GIS Analyst 747 Mendocino Ave, Suite 100 Santa Rosa CA 95401 Allison.Clark@sonoma-county.org 707.565.7353 Allison Schichtel 2017-05-19T00:00:00 2017-05-19T00:00:00 2017-05-19T00:00:00 Sonoma Veg Map - Map Classifications Sonoma County Vegetation Habitat <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>None, publicly available</SPAN></P></DIV></DIV></DIV> Allison Schichtel Sonoma County Agricultural Preservation & Open Space District Conservation GIS Analyst 747 Mendocino Ave, Suite 100 Santa Rosa CA 95401 Allison.Clark@sonoma-county.org 707.565.7353 Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595 BIO_VEG_MAP_CL Table 84 Lifeform Classes Sonoma Veg MAp OBJECTID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. OBJECTID OID 4 10 0 CLASS_CODE CLASS_CODE String 255 0 0 CLASS_NAME CLASS_NAME String 255 0 0 SUBCLASS_CODE SUBCLASS_CODE String 255 0 0 SUBCLASS_NAME SUBCLASS_NAME String 255 0 0 FORMATION_CODE FORMATION_CODE String 255 0 0 FORMATION_NAME FORMATION_NAME String 255 0 0 DIVISION_CODE DIVISION_CODE String 255 0 0 DIVISION_NAME DIVISION_NAME String 255 0 0 MACROGROUP_KEY MACROGROUP_KEY String 255 0 0 MACROGROUP_NAME MACROGROUP_NAME String 255 0 0 GROUP_NAME GROUP_NAME String 255 0 0 MAP_CLASS MAP_CLASS String 255 0 0 Table dataset 20210323