This data set provides course land cover classifications derived from Landsat TM images for 1986, 1988, and 1991 for the area surrounding the municipality of Uruara, Para, Brazil. Five land cover classes (Water, Clouds/Shadow, Forest, Not Forest, and Background) were derived (Aldrich et al. 2006). The Land Cover is in a compressed (*.zip) GeoTIFF file for each year.
Figure 1. The study area of Uruara, located along the Transamazon Highway (BR-230) in the state of Para, Brazil.
Cite this data set as follows:
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
The LBA Data and Publication Policy [http://daac.ornl.gov/LBA/lba_data_policy.html] is in effect for a period of five (5) years from the date of archiving and should be followed by data users who have obtained LBA data sets from the ORNL DAAC. Users who download LBA data in the five years after data have been archived must contact the investigators who collected the data, per provisions 6 and 7 in the Policy.
This data set was archived in December of 2011. Users who download the data between December 2011 and November 2016 must comply with the LBA Data and Publication Policy.
Data users should use the Investigator contact information in this document to communicate with the data provider. Alternatively, the LBA Web Site [http://lbaeco-archive.ornl.gov/] in Brazil will have current contact information.
Data users should use the Data Set Citation and other applicable references provided in this document to acknowledge use of the data.
Project: LBA (Large-Scale Biosphere-Atmosphere Experiment in the Amazon)
LBA Science Component: Human Dimensions
Team ID: LC-24 (Walker / Reis)
The investigators were Walker, Robert T.; Reis, Eustaquio J; Arima, Eugenio; Bohrer, Claudio Belmonte de Athayde; Caldas, Marcellus Marques; Perz, Stephen G; Pfaff, Alexander; Qi, Jiaguo and Souza Jr., Carlos Moreira de . You may contact Walker, Robert T. (email@example.com).
LBA Data Set Inventory ID: LC24_Land_Cover_Uruara_Para
This data set provides course land cover classifications derived from Landsat TM images for 1986, 1988, and 1991 for the area surrounding the municipality of Uruara, Para, Brazil. Five land cover classes (Water, Clouds/Shadow, Forest, Not Forest, and Background) were derived as described in Aldrich et al. 2006.
Related data Sets
The imagery were acquired from TRFIC (http://www.trfic.msu.edu/) and Landsat.org
Each year is a mosaic consisting of four images, so a total of 12
images were acquired. There are three compressed (*.zip) files with this data set.
Files are named as follows:
Projection: Universal Transverse Mercator, Zone 22S
Horizontal Datum Name: D_WGS_1984
Ellipsoid Name: WGS_1984
Cell size: 30 meters
1 = Forest
2 = Nonforest
3 = Water
4 = Cloud/Shadow
0 = Background
Westernmost Longitude -55.592774
Easternmost Longitude -51.669118
Northernmost Latitude -1.910215
Southernmost Latitude -5.258368
Westernmost Longitude -55.582046
Easternmost Longitude -52.015660
Northernmost Latitude -2.769178
Southernmost Latitude -4.671928
Westernmost Longitude -55.603709
Easternmost Longitude -51.778753
Northernmost Latitude -1.790539
Southernmost Latitude -5.294196
Site boundaries: (All latitude and longitude given in decimal degrees)
|Site (Region)||Westernmost Longitude||Easternmost Longitude||Northernmost Latitude||Southernmost Latitude||Geodetic Datum|
|Para Western (Santarem) - Uruara (Para Western (Santarem))||-55.6||-51.7||-1.8||-5.3||World Geodetic System 1984 (WGS-1984)|
Platform/Sensor/Parameters measured include:
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 3,000 ha in size (glebas) and those smaller (smallholder farms).
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.
Uruara is located on the Transamazon highway in the state of Para between Altamira and Ruropolis. Uruara is a midpoint of the transect from Altamira, through Uruara, to Santarem. The transect passes through various vegetation types and is used for remote sensing purposes as well as for field surveys of local farmers.
The imagery were acquired from TRFIC (http://www.trfic.msu.edu/) and Landsat.org (http://www.landsat.org). Uruara is centered close to the corner of four Worldwide Reference System II (WRSII) scenes (Path 226, Row 62; Path 226, Row 63; Path 227, Row 62; Path 227, Row 63), which necessitated the creation of a mosaic image for the area. Each year is a mosaic consisting of four images, so a total of 12 images were acquired.
Classification of rough land-cover
Each of the images for the years 1986, 1988, and 1991 were coregistered to 1999 imagery (not provided with this data set) with a resulting root mean square error (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 software. 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, by means of visual comparison, were then collapsed into the 5 final classes of Water, Clouds/Shadow, Forest, Not Forest, and Background (Aldrich et al. 2006).
This data is available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Contact for Data Center Access Information:
Telephone: +1 (865) 241-3952
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.