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Investigation:

LC-24 (Walker / Reis)

LBA Dataset ID:

LC24_LAND_COVER_URUARA_PARA

Originator(s):

1. ALDRICH, S.P.
      2. WALKER, R.T.

Point(s) of Contact:

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Dataset Abstract:

This data set is a classification of rough land-cover for Uruara, Para for the years 1986, 1988, and 1991 (Landsat TM). The classification shows 5 classes using visual comparison (Water, Clouds/Shadow, Forest, Not Forest, Background).

Beginning Date:

1986-08-03

Ending Date:

1991-08-03

Metadata Last Updated on:

2011-12-29

Data Status:

Archived

Access Constraints:

PUBLIC

Data Center URL:

http://daac.ornl.gov/

Distribution Contact(s):

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Access Instructions:

PUBLIC

Data Access:

IMPORTANT: The LBA-ECO Project website is no longer being supported. Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC. Please follow the fair use guidelines found in the dataset documentation when using or citing LBA data.
Datafile(s):

LBA-ECO LC-24 Land Cover Classes from Landsat TM, Uruara, Para: 1986-1991:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1053

Documentation/Other Supporting Documents:

LBA-ECO LC-24 Land Cover Classes from Landsat TM, Uruara, Para: 1986-1991:  http://daac.ornl.gov/LBA/guides/LC24_Land_Cover_Uruara_Para.html

Citation Information - Other Details:

Aldrich, S.P. and R.T. Walker. 2011. LBA-ECO LC-24 Land Cover Classes from Landsat TM, Uruara, Para: 1986-1991. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. doi:10.3334/ORNLDAAC/1053

Keywords - Theme:

Parameter Topic Term Source Sensor
LAND COVER HUMAN DIMENSIONS LAND USE/LAND COVER FIELD INVESTIGATION HUMAN OBSERVER
LAND COVER HUMAN DIMENSIONS LAND USE/LAND COVER LANDSAT-5 (LAND REMOTE-SENSING SATELLITE-5) TM (THEMATIC MAPPER)

Uncontrolled Theme Keyword(s):  LAND COVER

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  PARA WESTERN (SANTAREM) -1.80000 -5.30000 -51.70000 -55.60000

Related Publication(s):

Aldrich, S., R. Walker, E. Arima, M. Caldas, J. Browder, and S. Perz. 2006. Land-Cover and Land-Use Change in the Brazilian Amazon: Smallholders, Ranchers, and Frontier Stratification.. Economic Geography 82(3):265-288.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

The imagery were acquired from TRFIC (http://www.trfic.msu.edu/) and Landsat.org (http://www.landsat.org). Each year is a mosaic consisting of four images, so a total of 16 images were acquired.



Files are named as follows:


Uruara_LandCover_1986.e00


Uruara_LandCover_1988.e00


Uruara_LandCover_1991.e00



As mentioned above, the 5 classes include: Water, Clouds/Shadow, Forest, Not Forest, Background.

Data Application and Derivation:

These data were used in an analysis of the changing character of land-cover in the Uruara area for the years listed as divided between properties over 3000ha in size (glebas) and those smaller (smallholder farms).

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

No formal accuracy assessment has been undertaken with regards to attribution. It is possible, indeed, likely, that there are errors of commission and omission in the resulting classification. Spatial accuracy after georectification of the 1986, 1988, and 1991 scenes was good, RMSE less than 0.8.

Process Description:

Data Acquisition Materials and Methods:

Each of the images for the years 1986, 1988, and 1991 were coregistered to 1999 imagery with a resulting RMSE in all cases of less than 0.8. These images were then classified into 255 classes using transformed divergence in Erdas Imagine 8.1. These classes were then collapsed down to 20 by merging classes which, according to the measures developed in the transformed divergence procedure, were statistically spectrally similar across multiple bands. The resulting 20 classes were then collapsed into 5 final classes using visual comparison (Water, Clouds/Shadow, Forest, Not Forest, Background).

References:

None.

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