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LIDAR/MARIN_2019_CHM_3FT (ImageServer)

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View Footprint In:   ArcGIS Online Map Viewer

Service Description:

Methods:

This LiDAR derivative provides information about vegetation height. The 3-foot resolution raster was produced from the winter, 2019 Quality Level 1 LiDAR point cloud (already ground classified) using Lastools in a multistep process. The processing steps were as follows:

  • Create Tiles (lastile)

  • Height Normalize the Point Cloud (lasheight)

  • Classify Buildings (lasclassify)

  • Clamp Building to 0 Ft. Normalized Height (las2las)

  • Thin the Point Cloud (lasthin)

  • Convert the Point Cloud to a DEM (las2dem)

Uses and Limitations:

The canopy height model provides a raster depiction of the highest vegetation returns for each 3x3 foot raster cell across Marin County. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Marin County fine scale vegetation map. However, this data product was produced based on a rapid, fully automated point cloud classification and was not manually edited. As such, it includes some ‘false positives’ – pixels with a canopy height in the raster that aren’t vegetation. These false positives include noise from water aboveground non-vegetation returns from bridge decks, powerlines, and edges of buildings.



Name: LIDAR/MARIN_2019_CHM_3FT

Description:

Methods:

This LiDAR derivative provides information about vegetation height. The 3-foot resolution raster was produced from the winter, 2019 Quality Level 1 LiDAR point cloud (already ground classified) using Lastools in a multistep process. The processing steps were as follows:

  • Create Tiles (lastile)

  • Height Normalize the Point Cloud (lasheight)

  • Classify Buildings (lasclassify)

  • Clamp Building to 0 Ft. Normalized Height (las2las)

  • Thin the Point Cloud (lasthin)

  • Convert the Point Cloud to a DEM (las2dem)

Uses and Limitations:

The canopy height model provides a raster depiction of the highest vegetation returns for each 3x3 foot raster cell across Marin County. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Marin County fine scale vegetation map. However, this data product was produced based on a rapid, fully automated point cloud classification and was not manually edited. As such, it includes some ‘false positives’ – pixels with a canopy height in the raster that aren’t vegetation. These false positives include noise from water aboveground non-vegetation returns from bridge decks, powerlines, and edges of buildings.



Single Fused Map Cache: false

Extent: Initial Extent: Full Extent: Pixel Size X: 3.0

Pixel Size Y: 3.0

Band Count: 1

Pixel Type: F32

RasterFunction Infos: {"rasterFunctionInfos": [{ "name": "None", "description": "", "help": "" }]}

Mensuration Capabilities: Basic

Has Histograms: true

Has Colormap: false

Has Multi Dimensions : false

Rendering Rule:

Min Scale: 0

Max Scale: 0

Copyright Text: Golden Gate National Parks Conservancy, Tukman Geospatial LLC

Service Data Type: esriImageServiceDataTypeGeneric

Min Values: 0

Max Values: 319.97998046875

Mean Values: 14.520779744969413

Standard Deviation Values: 29.17988803358033

Object ID Field:

Fields: None

Default Mosaic Method: Center

Allowed Mosaic Methods:

SortField:

SortValue: null

Mosaic Operator: First

Default Compression Quality: 75

Default Resampling Method: Nearest

Max Record Count: null

Max Image Height: 4100

Max Image Width: 15000

Max Download Image Count: null

Max Mosaic Image Count: null

Allow Raster Function: true

Allow Copy: true

Allow Analysis: true

Allow Compute TiePoints: false

Supports Statistics: false

Supports Advanced Queries: false

Use StandardizedQueries: true

Raster Type Infos: Has Raster Attribute Table: false

Edit Fields Info: null

Ownership Based AccessControl For Rasters: null

Child Resources:   Info   Histograms   Statistics   Key Properties   Legend   Raster Function Infos

Supported Operations:   Export Image   Identify   Measure   Compute Histograms   Compute Statistics Histograms   Get Samples   Compute Class Statistics   Query Boundary   Compute Pixel Location   Validate   Project