USFS Mill Flat Creek Lidar, CA 2012

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Overview

USFS logo Lidar was collected over Mill Flat Creek, California between November 19th and November 20th 2012 for the USFS Pacific Region. This dataset was collected to aid in identifying ecological restoration opportunities by assessing forest structural complexity, mapping geomorphic features, and quantifying acreages of highly suitable wildlife habitat. This dataset covers over 32,000 acres ( over ~130 km2)
Platform: Airborne Lidar
Survey Area: 132.39 km2
Point Density: 15.40 pts/m2
Survey Date: 11/19/2012 - 11/20/2019
Full Metadata
Data Citation     Copied
Use License: CC0 1.0
Funder: USFS     Collector: WSI

Other Available Data Products: RasterPoint Cloud Bulk Download
1a. Select area of data to download or process  
  Usage Heat Map
 

1. Coordinates & Classification

Horizontal Coordinates: NAD83 (CORS96) / UTM Zone 11N Meters [EPSG: 26911]
Vertical Coordinates: NAVD88 (GEOID 03) [EPSG: 5703]
Units: meter
Data Selection Coordinates (Select an area on map)
  Manually enter selection coordinates (in the horizontal coordinate system listed above)  

  Choose Return Classification    
  Ground        
  Unclassified        
  Exclude Noise

2. Point Cloud Data Download

     Point cloud data in LAZ format
    Point cloud data in LAS format
     Point cloud data in ASCII format

3A. Custom DEM Generation (TIN)  

    Calculate TIN
Gridding Parameters
  Grid Resolution (Default = 1 meter)
  Max. triangle size (Default 50 units)
  Grid Format

3B. Custom DEM Generation (Local Gridding)  

Gridding Method
     Calculate Zmin grid
     Calculate Zmax grid
     Calculate Zmean grid
     Calculate Zidw grid
     Calculate standard deviation
     Calculate point count

4. Raster Visualization  

Visualization type:
    Hillshade
    Color-relief
    Color hillshade
    Slope
    Aspect
    Roughness

5. 3D Point Cloud Visualization  

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6. Contour Lines  

7. Hydrologic Terrain Analysis Products (tauDEM)  

This tauDEM is an advanced processing service. It is available for registered users, who understand these resource intensive algorithms and its products.
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Data Citation     |     Use License: CC0 1.0