{ "culture": "en-US", "name": "Lidar_Geomorph_2022", "guid": "", "catalogPath": "", "snippet": "The geomorphon raster is calculated from the DTM and classifies the landscape into distinct geomorphic classes based on relief-independent, local spatial pattern and the magnitude of overall relief. Landform types are then generalized into 10 distinct categories (see Table 5). They were calculated on a smoothed DTM aimed at reducing noise with a 30-meter search radius.\n\nGeomorphon Code\nLandform Type \n1 = Flat\n2 = Summit\n3 = Ridge\n4 = Shoulder\n5 = Spur\n6 = Slope\n7 = Hollow\n8 = Footslope\n9 = Valley\n10 = Depression", "description": "

LiDAR Derivatives for 14 California Countieshttps://tukmangeospatial.egnyte.com/dl/ADWSBBL7acLiDAR Derivatives Datasheet Sonoma<\/span><\/p>

Original Coordinate System: NAD 1983 (2011) UTM Zone 10N<\/span><\/p>

Projected Coordinate System: State Plane NAD 1983 (2011), CA Zone II, US Survey Feet<\/span><\/p>

Horizontal Datum: NAD 1983 2011<\/span><\/span><\/p>

Vertical Coordinate System: NAVD88 (Geoid 18)<\/span><\/span><\/p>

Pixel size = 1-meter<\/span><\/span><\/p>

LiDAR Derivatives for 14 California Counties<\/span><\/span><\/a><\/p>

https://tukmangeospatial.egnyte.com/dl/ADWSBBL7ac<\/span><\/span><\/a><\/p>

LiDAR Derivatives Datasheet Sonoma<\/span><\/span><\/a><\/p><\/div><\/div><\/div>", "summary": "The geomorphon raster is calculated from the DTM and classifies the landscape into distinct geomorphic classes based on relief-independent, local spatial pattern and the magnitude of overall relief. Landform types are then generalized into 10 distinct categories (see Table 5). They were calculated on a smoothed DTM aimed at reducing noise with a 30-meter search radius.\n\nGeomorphon Code\nLandform Type \n1 = Flat\n2 = Summit\n3 = Ridge\n4 = Shoulder\n5 = Spur\n6 = Slope\n7 = Hollow\n8 = Footslope\n9 = Valley\n10 = Depression", "title": "Lidar_Geomorph_2022", "tags": [ "LiDAR", "Light Detection And Ranging", "imageryBaseMapsEarthCover", "Sonoma County" ], "type": "Image Service", "typeKeywords": [ "ArcGIS Server", "Data", "Image Service", "Service" ], "thumbnail": "thumbnail/thumbnail.png", "url": "", "minScale": 150000000, "maxScale": 5000, "spatialReference": "WGS_1984_Web_Mercator_Auxiliary_Sphere", "accessInformation": "Funders: California Natural Resources Agency, State Coastal Conservancy, NASA, USGS, Sonoma Water, University of California, San Diego, Sonoma Ag + Open Space, Humboldt Bay Municipal Water District\n\nGrantees & Coordinating Organizations: North Coast Resource Partnership, Solano County\n\nTechnical Team: NV5 Geospatial and Tukman Geospatial", "licenseInfo": "

Publicly available.<\/span><\/p>

This data has been reprojected from UTM Zone 10N. Horizontal units are in meters. Vertical units are in meters.Data Limitations and Missing Data: While it is great to have harmonized lidar data and county-by-county derivatives, there are numerous limitations to these data and some areas of missing data. Please be aware that these limitations can result in incorrect or misleading information. Limitations and areas of missing data include the following: 1. There is a 340,000 acre \u2018hole\u2019 (No Data) in the lidar data products between Del Norte and Humboldt Counties. 2. There are holes (No Data areas) over some tribal lands in Sonoma, Lake, and Mendocino Counties. 3. There are data quality issues, especially in the older Quality Level 2 datasets, leading to anomalies in the point clouds and the derivatives. These can manifest as \u2018pits\u2019 in the canopy height models, anomalous high returns in the canopy and forestry metrics that aren\u2019t vegetation, and missing data in places. We are limited to the data quality of the source point clouds and couldn\u2019t re-fly areas as part of this effort. 4. Note that because we have harmonized the point clouds and created products from a harmonized point cloud, there will be differences across collection boundaries in the characteristics of the data. These will stem from both numerous factors including variations in collection time of year, changes in ground condition between adjacent collection, point density of adjacent collections, and lidar collection specification differences of adjacent collections (e.g., point density, sensor characteristics, and processing specifications). Please be aware of these variations and the shortcomings that they impose when using these data for modeling and analysis. 5. There are particular issues with the 2017 FEMA R9 collection in Modoc County. As a result, some of the derivative rasters do not exist for Modoc County.<\/span><\/p><\/div><\/div><\/div>", "portalUrl": "" }