Gradient Nearest Neighbor (GNN) Vegetation Classes - 1996

Metadata also available as - [Outline]

Frequently-anticipated questions:

What does this data set describe?
    Title: Gradient Nearest Neighbor (GNN) Vegetation Classes - 1996
    Abstract:
    Vegetation classes developed from the Gradient Nearest Neighbor (GNN) method to be used in initializing vegetation conditions for the Coastal Landscape Analysis and Modeling Study (CLAMS)
    Supplemental_Information:
    Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground attributes of vegetation to each pixel in a digital landscape map. The Gradient Nearest Neighbor method integrates vegetation measurements from regional grids of field plots, mapped environmental data, and Landsat TM imagery. In the Oregon coastal province, species gradients were most strongly associated with regional climate and geographic location, whereas variation in forest structure was best explained by Landsat TM variables. At the regional level, mapped predictions represented the range of variability in the sample data, and predicted area by vegetation type closely matched sample-based estimates. At the site level, mapped predictions maintained the covariance structure among multiple response variables. Prediction accuracy for tree species occurrence and several measures of vegetation structure and composition was good to moderate. Vegetation maps produced with the Gradient Nearest Neighbor method are appropriately used for regional-level planning, policy analysis, and research, not to guide local management decisions.

  1. How should this data set be cited?

    Janet L. Ohmann (USFS), Matthew J. Gregory (Oregon State Univer, Unpublished Material, Gradient Nearest Neighbor (GNN) Vegetation Classes - 1996.

    Online Links:

    Other_Citation_Details:
    Ohmann, J.L.; Gregory, M.J. In press. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, USA. Canadian Journal of Forest Research.

  2. What geographic area does the data set cover?

    West_Bounding_Coordinate: -124.661867
    East_Bounding_Coordinate: -122.595926
    North_Bounding_Coordinate: 46.2694
    South_Bounding_Coordinate: 42.613265

  3. What does it look like?

  4. Does the data set describe conditions during a particular time period?

    Calendar_Date: 1996
    Currentness_Reference: ground condition

  5. What is the general form of this data set?

    Geospatial_Data_Presentation_Form: raster digital data

  6. How does the data set represent geographic features?

    1. How are geographic features stored in the data set?

      This is a Raster data set. It contains the following raster data types:

      • Dimensions 16197 x 6369 x 1, type Grid Cell

    2. What coordinate system is used to represent geographic features?

      The map projection used is Transverse Mercator.

      Projection parameters:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -123
      Latitude_of_Projection_Origin: 0
      False_Easting: 500000
      False_Northing: 0

      Planar coordinates are encoded using Coordinate Pair
      Abscissae (x-coordinates) are specified to the nearest 25
      Ordinates (y-coordinates) are specified to the nearest 25
      Planar coordinates are specified in meters

      The horizontal datum used is D_Clarke_1866.
      The ellipsoid used is Clarke 1866.
      The semi-major axis of the ellipsoid used is 6378206.4.
      The flattening of the ellipsoid used is 1/294.978698.

  7. How does the data set describe geographic features?

    veg96.vat
    Value attribute table (Source: ArcInfo 7.2.1)

    Count
    Number of pixels in each class (Source: Dataset developer)

    Range of values
    Minimum:1
    Maximum:no limit

    Value
    Vegetation class code (Source: Dataset developer)

    ValueDefinition
    2Water
    3Open Forest : 0.0-1.4 sq m/ha basal area
    6Broadleaf : >1.4 sq m/ha basal area, >65% basal area hardwood
    7Small mixed : >1.4 sq m/ha basal area, 20-65% basal area hardwood, <25cm DBH
    8Medium mixed : >1.4 sq m/ha basal area, 20-65% basal area hardwood, 25-50cm DBH
    9Large mixed : >1.4 sq m/ha basal area, 20-65% basal area hardwood, 50-75cm DBH
    10Very large mixed : >1.4 sq m/ha basal area, 20-65% basal area hardwood, >75cm DBH
    11Small conifer : >1.4 sq m/ha basal area, <20% basal area hardwood, <25cm DBH
    12Medium conifer : >1.4 sq m/ha basal area, <20% basal area hardwood, 25-50cm DBH
    13Large conifer : >1.4 sq m/ha basal area, <20% basal area hardwood, 50-75cm DBH
    14Very large conifer : >1.4 sq m/ha basal area, <20% basal area hardwood, >75cm DBH
    21Open nonforest
    22Woodlands and other vegetation

    Vegclass
    Vegetation class description (Source: Dataset developer)

    Class descriptions

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)

  2. Who also contributed to the data set?

    Funding provided by USDA Forest Service; data processing by Alissa Moses and Tom Maiersperger

  3. To whom should users address questions about the data?

    Matt Gregory
    Oregon State University
    Faculty Research Assistant
    3200 Jefferson Way
    Corvallis, OR 97331
    USA

    (541) 750-7285 (voice)
    (541) 750-7760 (FAX)
    matt.gregory@orst.edu

    Hours_of_Service: 8 am - 5 pm

Why was the data set created?
Initializing vegetation conditions for CLAMS simulation modeling.

How was the data set created?
  1. From what previous works were the data drawn?

  2. How were the data generated, processed, and modified?

    Date: Unknown (process 1 of 1)
    See Supplemental_Information

How reliable are the data; what problems remain in the data set?
  1. How well have the observations been checked?

    See <http://www.fsl.orst.edu/lemma/method/>

  2. How accurate are the geographic locations?

    Not assessed

  3. How accurate are the heights or depths?

  4. Where are the gaps in the data? What is missing?

    No exclusions

  5. How consistent are the relationships among the observations, including topology?

    Not applicable

How can someone get a copy of the data set?
Are there legal restrictions on access or use of the data?

Access_Constraints:
Registration required at CLAMS web site (<http://www.fsl.orst.edu/clams/>)
Use_Constraints:
Please cite the CLAMS project when using this data.

  1. Who distributes the data set? (Distributor 1 of 1)

    CLAMS Webmaster
    CLAMS
    3200 Jefferson Way
    Corvallis, OR 97331
    USA

    (541) 750-7279 (voice)
    (541) 750-7760 (FAX)
    clamsweb@fsl.orst.edu

    Hours_of_Service: 8 am - 5 pm
  2. What's the catalog number I need to order this data set?

    veg96, gnn96

  3. What legal disclaimers am I supposed to read?

    While substantial efforts are made to ensure the accuracy of data and documentation, complete accuracy of data sets cannot be guaranteed. CLAMS shall not be liable for damages resulting from any use or misinterpretation of data sets.

  4. How can I download or order the data?

Who wrote the metadata?
Dates:
Last modified: 21-Mar-2002

Metadata author:
CLAMS
c/o CLAMS Webmaster
3200 Jefferson Way
Corvallis, OR 97331
USA

(541) 750-7279 (voice)
(541) 758-7760 (FAX)
clamsweb@fsl.orst.edu

Hours_of_Service: 8 am - 5 pm
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)

Metadata extensions used:

Generated by mp version 2.6.2 on Thu Mar 21 15:21:36 2002