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RLC AVHRR-DERIVED LAND COVER, FORMER SOVIET UNION, 15-KM, 1984-1993
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Summary:

This data set consists of 15-km-resolution land cover data for the land area of the Former Soviet Union (FSU). There are sixty land cover classes distinguished in this data set, thirty-eight of which are forest cover classes. The data set is useful for stratification of the FSU into general sub-regions of land cover for subsequent study using higher resolution satellite data.

The data are provided in several different file formats, including binary raster data in Idrisi format, ASCIIGRID raster data in ASCII format (easily imported into ArcInfo), and a graphic map in JPEG format (see Figure 1). Please read all documentation before using these files.

Figure 1. Land cover map, 15-km-resolution, for the land area of the FSU

Data Citation:

Cite this data set as follows:

Stone, T. A., and P. Schlesinger. 2003. RLC AVHRR-Derived Land Cover, Former Soviet Union, 15-km, 1984-1993. Data set. Available on-line [http://www.daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/689.

Table of Contents:

1. Data Set Overview:

Data Set Contents:

This data set consists of 15-km-resolution land cover data for the land area of the FSU. Sixty land cover classes are distinguished.

Related Data Sets:

See other Russian Land Cover data sets.

Title of Investigation:

Measuring Changes to Russian Forest Over the Last 25 Years

Investigator(s) Name and E-mail:

Name Email
Stone, T.A. tstone@whrc.org
Schlesinger, P. pschles@whrc.org

Technical Contact(s) Name, Telephone, and E-mail:

Name Email Phone
Schlesinger, P. pschles@whrc.org (508)540-9900

Future Modifications and Plans:

Not available.

2. Data Characteristics:

Study Area:

Spatial Coverage:

Min. X: 19.816

Max. X: 190.096

Min. Y: 35.2389

Max. Y: 75.0

In degrees west longitude and north latitude

Spatial Resolution:

1000 meters

Projection:

Lambert Azimuthal Equal Area

Grid Description:

Cols: 626

Rows: 1637

Resolution: 1000 meters

Temporal Coverage:

Not applicable

Parameter or Variable:

Table for: dlcmap.img

Variable Description Units Instrument Range
LAND COVER land cover classesUnitlessAVHRRNot applicable

LAND COVER footnote: See companion file [ http://daac.ornl.gov/daacdata/russian_land_cover/land_cover/comp/dlcmap_legend.txt ] for text file of table contents.

CLASS
PRIMARY
SECONDARY
TERTIARY
1
"Tundra confused with desert"
Other desert
Sand desert
2
Tundra
Polar desert
Not present
3
Tundra
Northern/maritime taiga
Not present
4
Tundra
Polar desert
Wooded tundra
5
Tundra
Wooded tundra
Northern / maritime taiga
6
Tundra
Wooded tundra
-
7
Tundra
Northern/maritime taiga
Wooded tundra
8
Mild/hot warm grass/shrub
Sand desert
Other
9
Tundra
Wooded tundra
Northern / maritime taiga
10
Tundra
Northern/maritime taiga
Main boreal forest
11
Mild/hot grass/shrublands
Tundra
Sand desert
12
Northern/mar. taiga
Tundra
Not present
13
Tundra
Wooded tundra
Northern / maritime taiga
14
Mild/hot warm grass/shrub
Cool/cold shrublands
Sand desert
15
Northern/maritime taiga
Tundra
Main boreal conifers
16
Northern/maritime taiga
Tundra
Main boreal conifers
17
Main boreal conifers
Cool snowy grass/shrubs
Conifers
18
Northern/maritime taiga
Cool farmlands
Cool snowy grasslands
19
Northern/maritime taiga
Tundra
Wooded tundra
20
Tundra
Northern/maritime taiga
Wooded tundra
21
Northern/maritime taiga
Main boreal forest
Tundra
22
Cool/cold shrublands
Cool snowy grasslands
Mild hot warm grass/ shrub
23
Northern/maritime taiga
Tundra
Main boreal conifers
24
Northern/maritime taiga
Tundra
Main boreal forest
25
Cool/cold shrub
Mild/hot shrublands
Cool snowy grasslands
26
Northern/maritime taiga
Main boreal conifers
Tundra
27
Cool farmlands
Cool snowy grasslands
Main boreal forest
28
Northern/maritime taiga
Tundra
Wooded tundra
29
Main boreal conifers
Northern/maritime taiga
Tundra
30
Northern/maritime taiga
Main boreal conifers
Tundra
31
Wooded tundra
Tundra
Main boreal conifers.
32
Main boreal conifers
Tundra-taiga
Tie
33
Northern/maritime taiga
Main boreal conifers
Cold steppe
34
Cool snowy grasslands
Cool/cold shrublands
Cool
35
Main boreal conifers
Northern/maritime taiga
Tundra
36
Wooded tundra
Cold steppe
Tundra
37
Main boreal conifers
Cool snowy grasslands
Cool farmlands
38
Main boreal conifers
Cold steppe
Northern/maritime taiga
39
Main boreal conifers
Northern/maritime taiga
Tundra
40
Mild/hot warm grass/shrub
Mild/hot farmlands
Cool croplands
41
Cool farmlands
Cool snowy grasslands
Grasslands /croplands
42
Northern/mar. taiga
Tundra
Main boreal conifers
43
Main boreal conifers
Snowy non-boreal conifers
So dry taiga
44
Main boreal conifers
So dry taiga
Tundra
45
Main boreal conifers
Cool farms
Siberian boreal taiga
46
Main boreal conifers
Conif./decid. forests
Mires
47
Main boreal conifers
Conif./decid. forests
Mires
48
Mild/hot farmlands
Regrowing woods/crops
Conifers
49
Snowy nonboreal conifers
So dry taiga
Tundra
50
Main boreal conifers
Cold steppe
Northern/maritime taiga
51
Cool farmlands
Grassland / croplands
Cool snowy grasslands
52
Regrow. Woodlands/crops
So. dry taiga
Cool snowy grasslands
53
So dry taiga
Snowy non-boreal conifers
Main boreal forest
54
Mild/hot farmlands
Cool farms-regr woodlands
Tie
55
Main boreal conifers
Sib. Larch taiga
Snowy non-boreal conif. forest
56
Snowy non-boreal conifers
Main boreal conifer
Southern dry taiga
57
Conif/decid forest
Regrow wood/crops
Cool snowy grasslands
58
Regrow woodlands/crops
Cool farms
Grass/croplands
59
Cool farm
Regrw woods/crop
Conif./decid. forest
60
Snowy nonboreal conifers
Regrw woods/crop
Conif./decid. forest

Table for: dlcmap.dat

Variable Description Units Instrument Range
LAND COVER land cover classesUnitlessAVHRRNot applicable

LAND COVER footnote: The sixty land cover classes distinguished in this data set are listed above. See companion file [http://daac.ornl.gov/daacdata/russian_land_cover/land_cover/comp/dlcmap_legend.txt] for text file of table contents.

Data Organization

Please read all documentation before using these files: http://daac.ornl.gov/daacdata/russian_land_cover/land_cover/comp/dlcmap_readme.txt

Granule Name or Description:

dlcmap.img

Data Format:

A binary raster image depicting land cover in Idrisi format. Note: must be converted to an *rst file when using version 3.2 of Idrisi. Use the Idrisi File Conversion (16/32) utility available in the file pulldown menu to convert to the new format. Also note that the Idrisi v. 2 image documentation file, dlcmap.doc, MUST be present for the binary data to be read by Idrisi.

Sample Data Record:

Not applicable.

Granule Name or Description:

dlcmap.dat

Data Format:

ASCIIGRID version of the binary raster data (dlcmap.img). This data file was created by converting the Idrisi binary file (dlcmap.img) using Idrisi export utilities. Note: it was necessary to first convert the *.img file to a *rst file and the *doc file to a *rdc file to match the new file format and naming convention of raster data supported by Idrisi 3.2.

Sample Data Record:

Not applicable.

3. Application and Derivation:

Typical Application of Data

Regional-, national-, and sub-national-level forest and land use change assessments. Stratification of the FSU into general sub-regions of land cover for subsequent study using higher resolution satellite data.

Theory of Measurements

Not available

Derivation Techniques and Algorithms

Formulae:

Not available

Processing Steps:

We assembled a ten-year Global Vegetation Index (GVI) time series data set covering 1984 to 1993 for the region of the FSU. Data from the NOAA First Generation Weekly Composite products were re-mapped from a Polar Stereographic to a Platte Carree map projection. These data, with data from the NOAA second-generation weekly composite product, were read into both Idrisi and ERDAS software formats.

The inverse of the standard GVI equation (Kidwell 1990) was used to convert the scaled Normalized Difference Vegetation Index (NDVI) data values to the bright-land byte range. For instance, the transformation converted an NDVI value of 0.0 to a new value of 100, creating a minimum and maximum range from 90 to 163. Thus, a pixel with a value of 163 is equivalent to an NDVI of 0.63.

We then created a matrix with a common Platte Carree map projection and resolution (2,500 x 904 cells or pixels). Data from spring 1984 through the first week of April 1985 were re-projected using the algorithms in Kidwell (1990). The resulting data set had 48 to 53 weeks for each of the 10 years from 1984 to 1993. The data in these 510 weeks were then averaged into 120 months (12 months/yr for 10 years). The 120 monthly data were used to create a continuous FSU land surface, which crossed the international dateline (E. and W. longitude 180o) and had a size of 1183 columns by 277 rows.

A 12-month data set was created from the 10-year, 120-month data set by averaging together, for example, all of the January data to create a 10-year January average. Then, all of the February data and data for the following months were averaged to create an image with 12 bands. Averaging with maximum monthly GVI values reduces the effects of inter-satellite mis-calibration, sensor drift, orbital slippage, and noise in the data. These degrading effects upon AVHRR data have been thoroughly documented and are well known (Goward et al. 1991, 1993, Tateishi and Kajiwara 1991, Eastman and Fulk 1993). In general, using a long time series and a long averaging period reduces the amount of noise in the data.

Water pixels were deleted with a mask created from a re-projected hydrographic data set digitized from a 1:8,000,000 scale Russian base map (Main Geodesy and Cartography Organization for Ministries of the USSR 1990). This hydrographic data set, containing coastlines, islands, and major inland water bodies, was selected because of its size and ease of use. A comparison of coastlines between the Digital Chart of the World (DCW) by Environmental Systems Research Institute (ESRI) (1993) and the 1:8,000,000 scale map yielded an areal difference of less than 0.1 (10%) of a GVI pixel.

Because most of the degrading effects in the data were related to low winter sun angles for this boreal region, we chose to eliminate from the analysis December, January, and February. These months are the times of extreme cold for the vast majority of the region of the FSU, during which vegetation is inactive.

Processing Changes:

Not available

Special corrections/Adjustments:

Not available

Calculated Variables:

Not available

Graphs and Plots Used in Analysis:

Not available

4. Quality Assessment:

Data Usage Guidance:

Errors and Limitations:

Error Sources:

Not available

Limitations of the Data:

Not available

Known Problems with the Data:

Not available

Quality Assessment Activities:

Not available

Other Relevant Information about the Study:

Not available

5. Acquisition Materials and Methods:

Source or Platform Collection Environment:

NOAA satellites carrying AVHRR sensor

Source or Platform Program Management:

Not available

Source or Platform Mission Objectives:

Coverage Information:

Not available

Attitude Characteristics:

Not available

Data Collection System:

Not available

Communication Links:

Not available

List of Sensors or Instruments:

AVHRR

Ground Segment Information:

Data Acquisition and Processing:

Not available

Latitude Crossing Times:

Not available

Sensor or Instrument Descriptions

Key Variables:

Not available

Principles of Operation:

Not available

Sensor or Instrument Measurement Geometry:

Not available

Manufacturer of Sensor or Instrument:

Not available

Calibration:

Specifications:

Not applicable

Tolerance:

Not applicable

Frequency of Calibration:

Not applicable

Other Calibration Information:

Not applicable

Data Acquisition Methods:

The source satellite imagery data were acquired from the USGS/EROS Data Center, Sioux Falls, South Dakota, U.S.A. A USSR forest map was used to aid in classification of these data (Garsia 1990) (See section REFERENCES).

Observations

Data Notes:

Not available

6. Data Description and Access:

Data Access:

This data set is available from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Data Archive Center:

Contact for Data Center Access Information:

E-mail: uso@daac.ornl.gov
Telephone: +1 (865) 241-3952
FAX: +1 (865) 574-4665

Product Availability:

Data can be accessed electronically on the ORNL DAAC's anonymous HTTP site or from the DAAC's search and order system. Data files are also available by request from uso@daac.ornl.gov.

Reading the Media:

Not available

Software and Analyses Tools:

Idrisi

7. References:

The published reference for the source data is as follows:

Stone, T. A., R. A. Houghton, and P. Schlesinger. 2000. Map of the Former Soviet Union Based Upon a Time Series of 15 km Resolution NOAA AVHRR Data. In Disturbance in Boreal Forest Ecosystems: Human Impacts and Natural Processes, USDA Gen. Tech Report NC-209. International Boreal Forest Research Association 1997, Duluth, Symposium Proceedings. pp. 67-80.

Other references used here includa the followinge:

Eastman, R. J., and M. Fulk. 1993. Long Sequence Time Series Evaluation using Standardized Principal Components. Photogr. Eng. and Remote Sensing 49(8):1307-1312.

ESRI. 1993. Digital Chart of the World for use with Arc Info Software. Redlands, California.

Garsia, M. G (ed.). 1990. "Forests of the USSR," Scale 1: 2,500,000, Forest Cartography Department of All-Union State Planning - Research Institute "Sojuzgiprolezhoz" GUGK: Moscow, USSR.

Goward, S. N., B. Markham, D. G. Dye, W. Dulaney, and J. Yang. 1991. Normalized Difference Vegetation Index Measurements from the Advanced Very High Resolution Radiometer, Remote Sens. of the Environ. 35:257-277.

Goward, S. N., D. G. Dye, S. Turner, and J. Yang. 1993. Objective assessment of the NOAA Global Vegetation Index Data Product. International Journal of Remote Sensing 14(18):3365-3394.

Kidwell, K.D. (ed.). 1990. Global Vegetation Index Users Guide. NOAA/NESDIS, Nat. Climatic Data Center, Washington, D.C.

Main Geodesy and Cartography Organization for Ministries of the USSR. 1990. Physical Geography of the Soviet Union. Map at 1:8,000,000 scale.

Tateishi, R. and K. Kajiwara. 1991. Landcover Monitoring in Asia by NOAA GVI Data. Geocarto Intl. 6(4):53-64.

8. Glossary and Acronyms:

Glossary:

Not applicable

Acronyms:

Acronym Definition
ASCII American Standard Code for Information Interchange
AVHRR Advanced Very High Resolution Radiometer
DCW Digital Chart of the World
ESRI Environmental Systems Research Institute
GVI Global Vegetation Index
JPEG Joint Photographic Experts Group
NDVI Normalized Difference Vegetaion Index
NOAA National Oceanic and Atmospheric Administration

9. Document Information:

2003/11/26

Document Review Date:

2003/11/26

Document Curator:

webmaster@www.daac.ornl.gov

Document URL:

http://daac.ornl.gov