Dataset Selection: B04

Tateishi and Kojiwara Monthly Maximum Global Vegetation Index and Land Cover Classifications from NOAA-9
(JAN 1986 - DEC 1989)

Principal Investigators:

Ryutaro Tateishi and Koji Kajiwara
Remote Sensing and Image Research Center
Chiba University, Japan

Summary:

Dataset consits of four thematic coverages; a) Scaled Normalized Difference Vegetation Indices, b) Land cover Classification, c) Source Example: Scaled normalized Difference Gegetation Indices and d) Source Example: Land Cover Classification.  Data are gridded at a resolution of 10 and 5 minutes.
 
Dataset Description
(and File Lists)
Dataset Element Descriptions
(and Downloads)
Technical Report

Primary References:

Tateishi, R. and Kajiwara, K. 1991. Global Land-Cover Monitoring by NOAA GVI Data. International Geoscience and Remote Sensing Symposium Vol. II, No. 1 , pp 1637-1639.

Tateishi, R. and Kajiwara, K. 1991.Land Cover Monitoring in Asia by NOAA GVI Data. Geocarto International Vol. 6, No. 4, pp 53-64.

Tateishi, R., Kajiwara, K., and Odajima, T.1991. Global Land-Cover Classification by Phenological Methods Using NOAA GVI Data. Asian-Pacific Remote Sensing Journal Vol. 4, No. 1 , pp 41-50.

Odajima, T., Kajiwara, K.,  and Tateishi, R. 1990. Global Land-Cover Classification by NOAA AVHRR Data. Proceedings of the 11th Asian Conference on Remote Sensing Vol. II, , pp S-3-1 to S-3-6.

Tateishi and Kojiwara GVI and Land Cover Classification

DATASET DESCRIPTION

Dataset Description

INTEGRATED DATA­SET

Data­Set Citation:

Ryutaro Tateishi and Koji Kajiwara. 1993. Monthly Maximum Global Vegetation Index and Land Cover Classifications from NOAA-9 (JAN 1986 - DEC 1989). Digital Raster Data on a 10-minute Cartesian Orthonormal Geodetic (lat/long) 1080x2160 grid. In: Global Ecosystems Database Version 2.0. Boulder, CO: NOAA National Geophysical Data Center. Forty-eight independent and one dependent single-attribute spatial layers. 121,361,591 bytes in 105 fiiles.

Projection:

Global Cartesian Geodetic (Platte Carree - latitude/longitude)

Spatial Representation:

(1) (SNDVI) 10-minute grid produced from source grid using a "cell overlap" resampling technique
(2) (Land Cover Classification) 5-minute grid produced from source grid using a nearest neighbor cell overlap resampling technique

Temporal Representation:

Monthly maximum of weekly values

Data Representation:

(1) one byte unsigned integers representing Scaled Normalized Difference Vegetation Indices
(2) one byte unsigned integers representing land cover classes

Layers and Attributes:

Raster: Time series of 48 independent single-attribute spatial layers and one derived single-attribute spatial layer. 

Dataset Description

DESIGN

Variables:

(1) scaled normalized difference vegetation index (SNDVI)
(2) land cover classification derived from 1987 SNDVI data (see (1) above)

Origin:

NOAA-9 & NOAA-11 Polar Orbiting Satellites with Advanced Very High Resolution Radiometer sensors

Geographic Reference:

Cartesian Geodetic (Plate Carreé -- latitude/longitude)

Geographic Coverage: Global

Global
Maximum Latitude: +75 degrees (N)
Minimum Latitude: -55 degrees (S)
Maximum Longitude: +180 degrees (E)
Minimum Longitude: -180 degrees (W)

Geographic Sampling:

Last ("random") element of each 4x4 array of Global Area Coverage (GAC) 4 km values, mapped onto a 904x2500 global Plate Carreé (lat/long) grid. GAC values are 1x4 km averages (along scan-line) of sampled values within each 4x4 array of 1 km cells. Look-angle varies between pixels due to temporal compositing.

Time Period:

January 1986 to December 1989

Temporal Sampling:

(1) (SNDVI) monthly maximum of weekly values. Weekly values are maximum of 7 daily values. Time of day varies between pixels.
(2) (Land Cover Classification) static composite derived from 1987 SNDVI 

Dataset Description

SOURCE

Source Data Citation:

Ryutaro Tateishi and Koji Kajiwara. 1990. Monthly Maximum Global Vegetation Index and Land Cover Classifications from NOAA-9 (JAN 1986 - DEC 1989). Digital Raster Data on a 8.6-minute Plate Carreé (lat/long) 904x2500 grid. Chiba, Japan: Remote Sensing and Image Research Center, Chiba University. 112 MB in 52 files on one quarter-inch cartridge tape.

Contributor:

Ryutaro Tateish

Distributor:

NGDC, Global Ecosystems Database Project
National Geophysical Data Center
325 S. Broadway, E/GC1,
Boulder, CO  80303

Date of Production:

1990

Lineage & Contacts:

  1. Dr. Ryutaro Tateishi and Koji Kajiwara

    2. GIS/Remote Sensing and Land Cover Monitoring
    Chiba University
    1-33 Yayoi-cho, Inage-ku, Chiba 263
    Japan
     
  2. John J. Kineman and David C. Schoolcraft

    3. Global Ecosystems Database Project
    National Geophysical Data Center
    325 S. Broadway, E/GC1,)
    Boulder, CO 80303
    Email: dschoolcraft@ngdc.noaa.gov
    fax: (303) 497-6513
    Web: http://www.ngdc.noaa.gov/seg/eco

Dataset Description

ADDITIONAL REFERENCES

See references cited in Kineman, J.J., and Ohrenschall, M.A. et al. 1992. Global Ecosystems Database Version 1.0: Disc A, Documentation Manual. Key to Geophysical Records Documentation No. 27. USDOC/NOAA National Geophysical Data Center, Boulder, CO. 240p. 

Dataset Description

FILE LISTS

Tateishi and Kojiwara GVI and Land Cover Classification

DATASET ELEMENT DESCRIPTIONS

  • Scaled Normalized Difference Vegetation Indices
  • Land Cover Classification
  • Source Example: Scaled Normalized Difference Vegetation Indices
  • Source Example: Land Cover Classification
    •

    Scaled Normalized Difference Vegetation Indices

    Description:

    Normalized Difference Vegetation Index (NDVI) derived from the NOAA AVHRR Satellite with calibration corrections.

     Structure:

    10-minute Cartesian Geodetic (latitude / longitude) 1080x2160 grid

     Series:

    48 month time-series

     System Files:

    File type Metadata Data 
    Raster grid  tk8601.doc to tk8912.doc tk8601.img to tk8912.img
    File Series  tk.ts
    Vector Point 
    Vector Line
    Vector Polygon 
    Attribute Table 
    Color Palette  tk.pal tk.smp
    Projection latlong.ref
    Link

    Notes:

    (1) The minimum and maximum values for all files are set to the time series minimum and maximum of 0 (zero) and 255. 

    Land Cover Classification

    Description:

    Thirteen land cover classes derived from the Scaled Normalized Difference Vegetation Indices

     Structure:

    5-minute Cartesian Geodetic (latitude / longitude) 2160x4320 grid

     Series:

    None

     System Files:

    File type Metadata Data 
    Raster grid  tkcl13.doc tkcl13.img
    File Series 
    Vector Point 
    Vector Line
    Vector Polygon 
    Attribute Table 
    Color Palette  tkcl13.pal tkcl13.smp
    Projection latlong.ref
    Link

     Notes:

    (1) The original resolution of the source grid was 0.144, which was resampled by nearest neighbor to a .0833 cell size for comparability with the other datasets in the GED. The original resolution is indicated in the metadata file (TKCL13.doc), although it actually varies across the image due to the fractional difference in cell size between original and resampled grids. 
    Source Example: Scaled Normalized Difference Vegetation Indices

    Description:

    Source data example (before resampling for GED integration): Scaled Normalized Difference Vegetation Indices.

     Structure:

    8.6-minute Cartesian Geodetic (latitude / longitude) 904x2500 grid

     Series:

    None

     System Files:

    File type Metadata Data 
    Raster grid  tk8607.doc tk8607.img
    File Series  sample
    Vector Point 
    Vector Line
    Vector Polygon 
    Attribute Table 
    Color Palette  tk.pal tk.smp
    Projection latlong.ref
    Link

     Notes:

    None

    Source Example: Land Cover Classification

    Description:

    Source data example (before resampling for GED integration): Land Cover Classification

     Structure:

    8.6-minute Cartesian Geodetic (latitude / longitude) 904x2500 grid

     Series:

    None

     System Files:

    File type Metadata Data 
    Raster grid  tkcl13.doc tkcl13.img
    File Series 
    Vector Point 
    Vector Line
    Vector Polygon 
    Attribute Table 
    Color Palette  tkcl13.pal tkcl13.smp
    Projection latlong.ref
    Link

     Notes:

    None

    Tateishi and Kojiwara GVI and Land Cover Classification

    TECHNICAL REPORT

    DATA INTEGRATION

    John J. Kineman and David C. Schoolcraft
    National Geophysical Data Center
    Boulder, CO 80303 USA

    The data were received as 8.6 minute raster grids in Plate Carreé (lat/long) projection. To achieve integration with the GED grid structure, the Vegetation Index data were resampled to a 10-minute grid using an area-weighted cell overlap method (similar to distance weighted linear interpolation). The land cover classification data were resampled to a 5-minute grid using a nearest neighbor method to preserve the class identities. Nearest neighbor resampling to a coarser grid (e.g. 10-minutes) was shown to produce unacceptable artifacts owing to the loss of rows and columns inherent in this method. Source examples are provided for comparison and determination of error. Also, the original data are available separately from NGDC.

    Registration accuracy was assured by first determining the precise origin of the original grids, then resampling to the output grid using corresponding offsets in latitude and longitude. The origin was determined by a best fit of Micro World Data Bank II coastline data with coastlines derived from the Vegetation Index data by taking the first derivative (slope). This tends to be greatest at the transition between water (low GVI) and land, thus revealing coastlines at approximately one pixel resolution. Best fit was determined by maximizing the calculated slope values along the entire coastline, extracted globally along the MWDB-II coastline. A second method of determining the coordinates for small islands visible in the GVI images was also used, however this was found not to be a precise as the coastline comparisons. We then confirmed that the time-series was uniform in origin by determining the relative position of clearly visible features (e.g., the Nile river). These results were later confirmed with Dr. Tateishi.

    The original dataset was provided with three color separates for the classed image, to provide a preferred color scheme. These were converted into a palette file that accompanies the dataset.