Christina River Basin Critical Zone Observatory April 2010 Lidar Survey

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Overview

High-resolution Lidar data (average first-return point density of 10 points m2 and 2-4 cm vertical accuracy) were obtained by NCALM for 121 km2 of the Christina River Basin Critical Zone Observatory (CRB-CZO) during both leaf-off (April 2010) and leaf-on (July 2010; see dataset CRB-10-Jul) periods. Data acquisition, ground-truthing, vegetation surveys and processing were funded and coordinated by NSF Award EAR-0922307 (PI. Qinghua Guo) to collect similar data at all six CZOs for a variety of cross-site analyses, including calibration of algorithms to extract vegetation characteristics from the LIDAR point cloud data. The CRB-CZO is particularly interested in using this LIDAR dataset for high-resolution analyses of stream channel and floodplain geomorphology.


Publications associated with this dataset can be found at NCALM's Data Tracking Center

Platform: Airborne Lidar
Survey Area: 133 km2
Raster Resolution: 1 meter
Survey Date: 04/07/2010 - 04/08/2010
Full Metadata
Data Citation     Copied
Use License: CC BY 4.0
Funder:   NSF     Partners:   UC Merced, CRB-CZO     Collector:   NCALM

Other Available Data Products:     Point Cloud,     Raster Bulk Download (Sign in required)
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1. Coordinates

Horizontal Coordinates: NAD83 / UTM Zone 18N Meters [EPSG: 26918]
Vertical Coordinates: NAVD88 [EPSG: 5703]
Units: meter

Data Selection Coordinates:       Manually enter selection coordinates (in the horizontal coordinate system listed above)  

2. Data Output Formats

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3. Layer Types & Additional SRTM Data

  Digital Elevation Models (DEMs)
Digital Terrain Model (DTM)
Digital Surface Model (DSM)
  Include Global 30m SRTM Data    

4. Raster Visualization  

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

5. Canopy Height Model (CHM)  

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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Job Description

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Data Citation     |     Use License: CC BY 4.0