Priority_Grazing_Land_archive_20210618 (Map Service)
View In:
ArcGIS JavaScript
ArcGIS.com
WMTS
Current Version: 10.81
Service Description: The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tDClass definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8). 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. 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 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.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).
Map Name: Publish
Legend
All Layers and Tables
Layers:
Tables:
Description: The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tDClass definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8). 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. 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 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.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).
Copyright Text: Sonoma County Water Agency, Sonoma County Agricultural Preservation and Open Space District, Sonoma County Vegetation Mapping and LiDAR Program
Spatial Reference: 102100
Single Fused Map Cache: true
Capabilities: Map,TilesOnly,Tilemap
Tile Info:
- Height: 256
- Width: 256
- DPI: 96
- Levels of Detail: (# Levels: 24)
- Level ID: 0 [Start Tile, End Tile]
Resolution: 156543.033928
Scale: 5.91657527591555E8
- Level ID: 1 [Start Tile, End Tile]
Resolution: 78271.5169639999
Scale: 2.95828763795777E8
- Level ID: 2 [Start Tile, End Tile]
Resolution: 39135.7584820001
Scale: 1.47914381897889E8
- Level ID: 3 [Start Tile, End Tile]
Resolution: 19567.8792409999
Scale: 7.3957190948944E7
- Level ID: 4 [Start Tile, End Tile]
Resolution: 9783.93962049996
Scale: 3.6978595474472E7
- Level ID: 5 [Start Tile, End Tile]
Resolution: 4891.96981024998
Scale: 1.8489297737236E7
- Level ID: 6 [Start Tile, End Tile]
Resolution: 2445.98490512499
Scale: 9244648.868618
- Level ID: 7 [Start Tile, End Tile]
Resolution: 1222.99245256249
Scale: 4622324.434309
- Level ID: 8 [Start Tile, End Tile]
Resolution: 611.49622628138
Scale: 2311162.217155
- Level ID: 9 [Start Tile, End Tile]
Resolution: 305.748113140558
Scale: 1155581.108577
- Level ID: 10 [Start Tile, End Tile]
Resolution: 152.874056570411
Scale: 577790.554289
- Level ID: 11 [Start Tile, End Tile]
Resolution: 76.4370282850732
Scale: 288895.277144
- Level ID: 12 [Start Tile, End Tile]
Resolution: 38.2185141425366
Scale: 144447.638572
- Level ID: 13 [Start Tile, End Tile]
Resolution: 19.1092570712683
Scale: 72223.819286
- Level ID: 14 [Start Tile, End Tile]
Resolution: 9.55462853563415
Scale: 36111.909643
- Level ID: 15 [Start Tile, End Tile]
Resolution: 4.77731426794937
Scale: 18055.954822
- Level ID: 16 [Start Tile, End Tile]
Resolution: 2.38865713397468
Scale: 9027.977411
- Level ID: 17 [Start Tile, End Tile]
Resolution: 1.19432856685505
Scale: 4513.988705
- Level ID: 18 [Start Tile, End Tile]
Resolution: 0.597164283559817
Scale: 2256.994353
- Level ID: 19 [Start Tile, End Tile]
Resolution: 0.298582141647617
Scale: 1128.497176
- Level ID: 20 [Start Tile, End Tile]
Resolution: 0.14929107082380833
Scale: 564.248588
- Level ID: 21 [Start Tile, End Tile]
Resolution: 0.07464553541190416
Scale: 282.124294
- Level ID: 22 [Start Tile, End Tile]
Resolution: 0.03732276770595208
Scale: 141.062147
- Level ID: 23 [Start Tile, End Tile]
Resolution: 0.01866138385297604
Scale: 70.5310735
- Format: PNG
- Compression Quality: 0
- Origin:
X: -2.0037508342787E7
Y: 2.0037508342787E7
- Spatial Reference:
102100
Initial Extent: XMin: -1.36824973755121E7
YMin: 4605467.508089471
XMax: -1.3646112887013044E7
YMax: 4653727.89363462
Spatial Reference:
102100
Full Extent: XMin: -1.3738992147419814E7
YMin: 4594573.366794024
XMax: -1.362028407448649E7
YMax: 4699427.4228431415
Spatial Reference:
102100
Min Scale: 1155581.108577
Max Scale: 18055.954822
Min LOD: 9
Max LOD: 15
Units: esriMeters
Supported Image Format Types: PNG
Export Tiles Allowed: false
Max Export Tiles Count: 100000
Document Info: - Title: Priority_Grazing_Land_archive_20210618
- Author: SonomaOpenSpace
- Comments:
- Subject: VLI Priority Grazing Land
- Category:
- Keywords: