This data set provides fractional land cover type images for shade, green vegetation (GV), non-photosynthetic vegetation (NPV), and soil for the regions of JiParana, PortoVelho, Luiza, Ariquemes, and Cacoal in the state of Rondonia, Brazil, for the period 1984 to 2000. The images were derived with a spectral mixture analysis (SMA) of Landsat Thematic Mapper (TM) time series scenes for each of these areas. There were 249 TM scenes and one Landsat Multispectral Scanner (MSS) scene acquired for these analyses. The images are 30-m Landsat resolution and were georectified to the Brazilian space agency 1998 and 1999 PRODES imagery.
There are 250 GeoTIIF image files (*.tif) in this data set. Files are grouped by region and year/month/day scene was taken.
Cite this data set as follows:
Roberts, D.A., I. Numata, K.W. Holmes, G.T. Batista, T. Krug, A.L. Monteiro, R.L. Powell, and O.A. Chadwick. 2013. LBA-ECO ND-01 Fractional Land Cover Images, Rondonia, Brazil: 1984-2000. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1188
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 Users who download LBA data in the five years after data have been archived must contact the investigators who collected the data, per provisions 6 and 7 in the Policy.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 September 2013. Users who download the data between September 2013 and August 2018 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.
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)
Activity: LBA-ECO
LBA Science Component: Land Use and Land Cover
Team ID: ND-01 (Roberts / Barreto / Soares)
The investigators were Roberts, Dar A.; Batista, Getulio T.; Soares, Joao Vianei; Barreto, Paulo Goncalves; Biggs, Trent W.; Chadwick, Oliver A.; Gessler, Paul E.; Holmes, Karen W.; Monteiro, Andre L.; Numata, Izaya; Nunes, Samia Serra; Powell, Rebecca Lynn; Renno, Camilo Daleles; Souza Jr., Carlos Moreira de and Tomasella, Javier . You may contact Roberts, Dar A. (dar@geog.ucsb.edu).
LBA Data Set Inventory ID: ND01_Spectra_Mixture_Models
This data set provides fractional land cover type images for shade, green vegetation (GV), non-photosynthetic vegetation (NPV), and soil for the regions of JiParana, PortoVelho, Luiza, Ariquemes, and Cacoal in the state of Rondonia, Brazil, for the period 1984 to 2000. The images were derived with a spectral mixture analysis (SMA) of Landsat Thematic Mapper (TM) time series scenes for each of these areas. There were 249 TM scenes and one Landsat Multispectral Scanner (MSS) scene acquired for these analyses. The images are 30-m Landsat resolution and were georectified to the Brazilian space agency 1998 and 1999 PRODES imagery.
Related Data Set:
LBA-ECO ND-01 Primary Forests Land Cover Transition Maps, Rondonia, Brazil: 1975-1999 (images from this data set were classified into age transition maps)
There are 250 GeoTIIF image files (*.tif) in this data set. Files are grouped by region and year/month/day scene was taken. The images are of JiParana, PortoVelho, Luiza, Ariquemes and Cacoal, Rondonia, Brazil, for the period 1984 to 2000.
There are five files for each Landsat TM scene including end-member information for shade, NPV, GV, soil, and a Root Means Squared Error (RMS) error image file. Note: there are two 1986 scenes for Luiza, one from Landsat TM, and one from the Landsat MSS.
Each scene has four files named according to the following:
XXYYMMDD_em#.tif
where:
XX= one of the five the regions:
lu=Luiza
ar=Ariquemes
ca=Cacoal
ji=JiParana
pv=PortoVel
YY=year, MM=month, and DD=day
em=endmember
# =
0 -- fraction image for endmember 0 (shade)
1 -- fraction image for endmember 1 (non-photosynthetic vegetation, NPV)
2 -- fraction image for endmember 2 (green vegetation, GV)
3 -- fraction image for endmember 3 (soil)
The is also one file of the following type with each scene:
XXYYMMDD_rms.pic -- RMS error image
Example file names for the Ariquemes location: ar000628_em0.tif and ar000628_rms.tif
Image Projection Information:
Locations and Landsat path/row utilized:
Porto Velho (P232, R66)
Ariquemes (P232, R67)
Ji-Parana (P231, R67)
Luiza (P231,
R68)
Cacoal (P230, R68)
| Site (Region) | Westernmost Longitude | Easternmost Longitude | Northernmost Latitude | Southernmost Latitude | Geodetic Datum |
|---|---|---|---|---|---|
| Rondonia - Ariquemes (Rondonia) | -64.64056 | -62.69722 | -9.18083 | -11.07528 | South-American Datum, 1969 (SAD-69) |
| Rondonia - Ji Parana (Rondonia) | -63.06833 | -61.18167 | -9.36861 | -10.93583 | South-American Datum, 1969 (SAD-69) |
| Rondonia - Luiza (Rondonia) | -63.40667 | -61.48278 | -10.6975 | -12.43111 | South-American Datum, 1969 (SAD-69) |
| Rondonia - PortoVelho (Rondonia) | -64.28306 | -62.44778 | -7.8275 | -9.51889 | South-American Datum, 1969 (SAD-69) |
| Rondonia - Cacoal (Rondonia) | -61.88907 | -59.85667 | -10.65794 | -12.49684 | South-American Datum, 1969 (SAD-69) |
Time period:
Platform/Sensor/Parameters measured include:
Many of these images were used to develop land cover transition age maps, determination of rates of deforestation, changes in forest area, forest edge, and spatial and temporal patterns in land-cover.
From Roberts et al., 2002:
Ideally, an image end-member can be represented as a mixture of one or more spectrally pure, identifiable reference end-members. When selecting candidate reference end-members for each image end-member, the objective is to locate library spectra that are more extreme than the image end-members, provide a good fit (as measured by RMS), and have fractions that match expected values based on field measurements or aerial photo interpretation (Roberts et al., 1998). Reference end-members were selected from the same spectral library used to retrieve surface reflectance.
Site Description
The images covered five areas of Rondonia, Brazil: JiParana, PortoVelho, Luiza, Ariquemes, and Cacoal. The regions provide a gradient in deforestation rates and pasture ages from low rates and young pastures in Ariquemes to higher initial deforestation and older pastures in Ji-Paraná and Luiza. Natural vegetation is dominated by dense tropical forests with locally abundant savanna, transitional forest, and infrequent patches of more open, shorter forests (RADAMBRASIL, 1978). Anthropogenic land cover includes pastures, second-growth forest, annual crops, perennial crops, bare soil, and urbanized areas (Browder, 1994).
Landsat TM Images
Fifty Landsat TM scenes were assembled into a comprehensive time series:
All Landsat images were coregistered to 1998 or 1999 digital PRODES.
Derivation of Fraction Images (Roberts et al. (1998, 2002):
1. Image End-Member Selection: Using SMA, a spectrum consisting of radiance reflected off of multiple materials within the field of view was decomposed into fractions of several unmixed spectra, called end-members (Adams et al., 1993). The final product was five images, one for each end-member and an RMSE image.
2. Reflectance Retrieval: Encoded radiance was converted to apparent surface reflectance through absolute calibration (Kaufman, 1989) or relative reflectance retrievals (Elvidge and Portigal, 1990) to identify surface materials and compare satellite observations to laboratory or field measured spectra. The quality of retrieved surface reflectance for Rondonia was evaluated by comparison to surface reflectance measured over similar targets in Manaus, retrieved using multiple ground reflectance measured in the field during the summer the Manaus Landsat TM data were acquired.
3. Reference End-Member Selection: Reference end-members are spectra of known materials (Adams et al., 1993). Reference end-members were selected from the same spectral library used to retrieve surface reflectance.
4. Intercalibration: Following step three, remaining data sets were standardized to the reference scene using relative radiometric calibration techniques (Schott et al., 1988; Hall et al., 1991).
5. Spectral Mixture Analysis: Once the entire time series was intercalibrated and reference end-members selected, the same model was applied to the entire data set. This generated four fraction images for each scene (GV, NPV, soil, and shade) and an RMSE image.
These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Contact for Data Center Access Information:
E-mail: uso@daac.ornl.gov
Telephone: +1 (865) 241-3952
Adams, J.B., M.O. Smith and A.R. Gillespie. 1993. Imaging spectroscopy:Interpretation based on spectral mixture analysis, in Remote Geochemical Analysis: Elemental and Mineralogical Composition, edited by C. M.Pieters and P. Englert, pp. 145-166, Cambridge Univ. Press., New York.
Browder, J. O., Surviving in Rondonia: The dynamics of colonist farming strategies in Brazil’s northwest frontier, Stud. Comp. Int. Dev., 29(3), 45– 69, 1994.
Elvidge, C.D. and F.P. Portigal. 1990 Change detection in vegetation using 1989 AVIRIS data, in Proc. SPIE Imaging Spectroscopy of the Terrestrial Environment, Orlando, Fla., 16-17 April, edited by G. Vane, pp. 178-189, Int. Soc. for Opt. Eng., Bellingham, Wash.
Friedl, M.A. and C.E. Brodley. 1997. Decision tree classification of land cover from remotely sensed data, Remote Sens. Environ., 61, 399-409. Furby, S. L., & Campbell, N. A. (2001), Calibrating images from different dates to like-value digital counts, Remote Sens. Environ., 77, 186-196.
Hall, F.G., D.E. Strebel, J.E. Nickeson and S.J. Goetz. 1991. Radiometric rectification, toward a common radiometric response among multidate, multisensor images, Remote Sens. Environ., 35, 11-27.
Hess, L.L., et al. 2001. Geocoded digital videography for validation of land cover mapping in the Amazon Basin, Int. J. Remote Sens., 23(7), 1527- 1555.
Kaufman, Y.J. 1989. The atmospheric effect on remote sensing and its correction,in Theory and Applications of Optical Remote Sensing, edited by G. Asnar, pp. 336-428, John Wiley, New York.
RADAMBRASIL. 1978. Mapa Exploratório de solos. Projeto Radambrasil: Programa de Integração Nacioal, 1:1,000,000, Minits. Das Minas e Energ., De. Nac. De Prod., Rio de Janeiro.
Richards, J.A. 1999. Remote Sensing Digital Image Analysis: An Introduction, Springer-Verlag, New York.
Roberts, D.A., I. Numata, K. Holmes, G. Batista, T. Krug, A. Monteiro, B. Powell, and O.A. Chadwick. 2002. Large area mapping of land-cover change in Rondonia using multitemporal spectral mixture analysis and decision tree classifiers. Journal of Geophysical Research-Atmospheres, Vol. 107, No. D20, p. 8073.
Roberts, D.A., Batista, G., Pereira, J., Waller, E., and Nelson, B. 1998. Change Identification using Multitemporal Spectral Mixture Analysis: Applications in Eastern Amazonia, Chapter 9 in Remote Sensing Change Detection: Environmental Monitoring Applications and Methods, (Elvidge, C. and Lunetta R., Eds.), Ann Arbor Press, Ann Arbor, MI, pp. 137-161.
Schott, J., C. Salvaggio and W. Volchok. 1988. Radiometric scene normalization using pseudoinvariant features, Remote Sens. Environ., 26, 1-16.
Smith, M.O., S.L. Ustin, J.B. Adams and A.R. Gillespie. 1990. Vegetation in deserts, I, A regional measure of abundance from multispectral images, Remote Sens. Environ., 31, 1-26.