Dataset Listing

Shale Hills - Digital Elevation Model (DEM), GIS/Map Data, Land Cover, LiDAR, Soil Survey (2010)

Shaver's Creek Watershed

Variables:  Filtered (bare earth) DEM, Filtered (bare earth) hillshade, Unfiltered (first return) DEM, Unfiltered (first return) hillshade

Standard Variables:  Digital elevation model|Lidar

Date Range:  (2010-2010)    Dataset DOI: 10.5069/G9VM496T

Dataset Creators/Authors:  Guo, Qinghua

Contact:  Dr. Qinghua Guo. University of California-Merced. P.O. Box 2039. Merced, CA 95344. e-mail: phone: (209) 228-2911.

Field Area:   Susquehanna Shale Hills Critical Zone Observatory

Keywords & XML
  • Description

    High-resolution Lidar data (average 10 points/m2 with 2-4 cm vertical accuracy) were collected for the Susquehanna Shale Hills CZO (Area = 169.80901 km2) during leaf-on (7/14/2010-7/16/2010) and full leaf-off (snow clear) (12/3/2010-12/9/2010). Data acquisition, ground-truthing, vegetation surveys and processing were funded and coordinated by NSF Award EAR-0922307 (PI. Qinghua Guo). Data was collected with the Gemini 06SEN/CON195 and digitizer 08DIG017 system installed on the Cessna 337 tail number N337P. Total points: 2,840,000,000 pts. Area: Area = 169 km2. Shot density: 13.54 points/m2. Survey report, with details about data processing: All files are in ArcGRID format.
    Additional LiDAR data for the Commonwealth of Pennsylvania are available through the PA Department of Conservation and Natural Resources' PAMAP program, at the link provided in the sidebar. Shaver's Creek and Shale Hills watershed boundary DEM files contain 0.5 meter resolution DEM from LiDAR collected in February 2011. Grid cell dimension = 0.5 x 0.5 m, Projection = " +proj=utm +zone=18 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs" (UTM 18N), Gaussian Filter with square 4 x 4 m smoothing window applied to data to produce DEM. Boundary Delineation Algorithms consisted of three steps: 1) Calculate Upslope Contributing Area of catchment with DEM using multiple algorithms; 2) Model channel network using the DEM and upslope contributing area map(s); 3) Input channel network and DEM into a basin delineation algorithm. These steps were performed in SAGA GIS, which uses same algoritms as in TauDEM (ArcMap extension).
  • Keywords

    lidar, topography, digital elevation model, hillshade, density

    XML Metadata

    XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.

  • Citation for This Dataset

    LiDAR data acquisition and processing were completed by the National Center for Airborne Laser Mapping (NCALM), funded by the National Science Foundation Award EAR-0922307, and coordinated by Qinghua Guo for the Susquehanna Shale Hills Critical Zone Observatory funded by the National Science Foundation Award EAR-0725019.

    Citation for This Webpage

    Guo, Qinghua (2010). "CZO Dataset: Shale Hills - Digital Elevation Model (DEM), GIS/Map Data, Land Cover, LiDAR, Soil Survey (2010)." Retrieved 26 Feb 2020, from

  • Acknowledgements


    National Science Foundation EAR-0922307


Shaver's Creek - LiDAR Leaf-Off Flight 2010

(.zip)   Data Level 1,  Metadata

Shaver's Creek - LiDAR Leaf-On Flight 2010

(n.7z)   Data Level 1,  Metadata

Shaver's Creek - LiDAR Leaf-Off Digital Elevation Model 2010

(.zip)   Data Level 1,  Metadata

Shaver's Creek - LiDAR Leaf-Off Density 2010

(.zip)   Data Level 1,  Metadata

Shaver's Creek - LiDAR Leaf-Off Hillshade 2010

(.zip)   Data Level 1,  Metadata

Shale Hills - USGS 7.5' Digital Orthophoto - 2010

(.zip)   Data Level 1

Shaver's Creek - Watershed Boundary DEM - 2011

(.zip)   Data Level 1

Shale Hills - Watershed Boundary DEM - 2011

(.zip)   Data Level 1

Shaver's Creek - DEM

(.zip)   Data Level 1

Shale Hills - DEM

(.zip)   Data Level 1

Shaver's Creek - Geology

(.zip)   Data Level 1

Shale Hills - Geology

(.zip)   Data Level 1

Shaver's Creek - Land Cover - 2001

(.zip)   Data Level 1

Shale Hills - Land Cover - 2001

(.zip)   Data Level 1

Shaver's Creek - Soils

(.zip)   Data Level 1

Shale Hills - Soils

(.zip)   Data Level 1

Data Use Policy
Data Sharing Policy
  • Data Use Policy

    DRAFT v.0.4.0

    1. Use our data freely. All CZO Data Products* except those labelled Private** are released to the public and may be freely copied, distributed, edited, remixed, and built upon under the condition that you give acknowledgement as described below. Non-CZO data products — like those produced by USGS or NOAA — have their own use policies, which should be followed.

    2. Give proper citation and acknowledgement. Publications, models and data products that make use of these datasets must include proper citation and acknowledgement. Most importantly, provide a citation in a similar way as a journal article (i.e. author, title, year of publication, name of CZO “publisher”, edition or version, and URL or DOI access information. See Also include at least a brief acknowledgement such as: “Data were provided by the NSF-supported Southern Sierra Critical Zone Observatory” (replace with the appropriate observatory name).

    3. Let us know how you will use the data. The dataset creators would appreciate hearing of any plans to use the dataset. Consider consultation or collaboration with dataset creators.

    *CZO Data Products.  Defined as a data collected with any monetary or logistical support from a CZO.

    **Private. Most private data will be released to the public within 1-2 years, with some exceptionally challenging datasets up to 4 years. To inquire about potential earlier use, please contact us.

  • Data Sharing Policy

    DRAFT v.0.2.5

    All CZO investigators and collaborators who receive material or logistical support from a CZO agree to:

    1. Share data privately within 1 year. CZO investigators and collaborators agree to provide CZO Data Products* — including data files and metadata for raw, quality controlled and/or derived data — to CZO data managers within one year of collection of samples, in situ or experimental data. By default, data values will be held in a Private CZO Repository**, but metadata will be made public and will provide full attribution to the Dataset Creators†.

    2. Release data to public within 2 years. CZO Dataset Creators will be encouraged after one year to release data for public access. Dataset Creators may chose to publish or release data sooner.

    3. Request, in writing, data privacy up to 4 years. CZO PIs will review short written applications to extend data privacy beyond 2 years and up to 4 years from time of collection. Extensions beyond 3 years should not be the norm, and will be granted only for compelling cases.

    4. Consult with creators of private CZO datasets prior to use. In order to enable the collaborative vision of the CZO program, data in private CZO repositories will be available to other investigators and collaborators within that CZO. Releasing or publishing any derivative of such private data without explicit consent from the dataset creators will be considered a serious scientific ethics violation.

    * CZO Data Products. Defined as data collected with any monetary or logistical support from a CZO. Logistical support includes the use of any CZO sensors, sampling infrastructure, equipment, vehicles, or labor from a supported investigator, student or staff person. CZO Data Products can acknowledge multiple additional sources of support.

    ** Private CZO Repository. Defined as a password-protected directory on each CZO’s data server. Files will be accessible by all investigators and collaborators within the given CZO and logins will be maintained by that local CZO’s data managers. Although data values will not be accessible by the public or ingested into any central data system (i.e. CUAHSI HIS), metadata will be fully discoverable by the public. This provides the dual benefit of giving attribution and credit to dataset creators and the CZO in general, while maintaining protection of intellectual property while publications are pending.

    † Dataset Creators. Defined as the people who are responsible for designing, collecting, analyzing and providing quality assurance for a dataset. The creators of a dataset are analogous to the authors of a publication, and datasets should be cited in an analogous manner following the emerging international guidelines described at