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  Folha Amazônica


LC-21 (Asner / Bustamante / Silva)

LBA Dataset ID:



1. ASNER, G.P.
2. KNAPP, D.E.
      4. OLIVEIRA, P.J.C.
6. SILVA, J.N.M.

Point(s) of Contact:

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (

Dataset Abstract:

Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout the Amazon region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The results of the CLAS approach to coverage of the Amazon Basin is presented here.

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Data Center URL:

Distribution Contact(s):

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (

Access Instructions:


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.

LBA-ECO LC-21 Brazilian Amazon Fractional Land Cover Images: 1999-2002:

Documentation/Other Supporting Documents:

LBA-ECO LC-21 Brazilian Amazon Fractional Land Cover Images: 1999-2002:

Citation Information - Other Details:

Asner,G.P., D.E. Knapp, E.N. Broadbent, P.J.C. Oliveira, M. Keller, and J.N. Silva. 2013. LBA-ECO LC-21 Brazilian Amazon Fractional Land Cover Images: 1999-2002. Data set. Available on-line ( from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

Keywords - Theme:

Parameter Topic Term Source Sensor


Keywords - Place (with associated coordinates):

(click to view profile)
(click to view profile)
North South East West
  BRAZILIAN AMAZON 5.27220 -14.00000 -41.79550 -73.99060

Related Publication(s):

Asner, G.P., D.E. Knapp, A.N. Cooper, M.M.C. Bustamante, and L.P. Olander. 2005. Ecosystem Structure throughout the Brazilian Amazon from Landsat Observations and Automated Spectral Unmixing. Earth Interactions 9(7):1-31.

Asner, G.P., E.N. Broadbent, P.J.C. Oliveira, M. Keller, D.E. Knapp, and J.N.M. Silva. 2006. Condition and fate of logged forests in the Brazilian Amazon. Proceedings of the National Academy of Sciences of the United States of America 103(34):12947-12950.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

The images in this data set are in ENVI Standard format. An ENVI image consists of an image file and a corresponding ASCII header file (.hdr). The header file contains information about the structure and geolocation of the image.

File Naming


The files are named with the Brazilian state name, path/row, and date included. For example, para_p227r061_etm_081200.img.gz is from the State of Para, covering Path/Row 227/061 on August 12, 2000. This image is gzipped (*.gz). There is also a corresponding ENVI header named para_p227r061_etm_081200.img.hdr. ENVI header files are ASCII files that describe the structure of the image file.

Data Contents


Each image matching (*_mcuscaled.means) is a fractional cover image in ENVI format with a corresponding header (*_mcuscaled.hdr). These images have 7 bands which include:

1. The fractional cover for each pixel, ranging from 0 (0%) to 1000 (100%) of photosynthetic vegetation (PV).

2. The fractional cover for each pixel, ranging from 0 (0%) to 1000 (100%) of non-photosynthetic vegetation (NPV).

3. The fractional cover for each pixel, ranging from 0 (0%) to 1000 (100%) of bare substrate.

4. The standard deviation of the fractional cover for each pixel, ranging from 0 (0%) to 1000 (100%) of photosynthetic vegetation (PV).

5. The standard deviation of the fractional cover for each pixel, ranging from 0 (0%) to 1000 (100%) of non-photosynthetic vegetation (NPV).

6. The standard deviation of the fractional cover for each pixel, ranging from 0 (0%) to 1000 (100%) of bare substrate.

7. The root mean square (RMS) error between the spectra of the image pixel and the spectra produced by mixture of the end members. This represents the error or level of certainty in the modeled result. Higher values mean a less accurate modeling of the image pixel's spectrum.

The images matching (*MMDDYY.img) are 6 bands of the reflective bands of Landsat (Bands 1-5 and 7). These images are also in ENVI format and have corresponding ENVI header files.

The images matching (*_MMDDYY_therm.img) are single-band image of the second thermal band from Landsat-7. These images are also in ENVI format and have corresponding ENVI header files.

Most of the image files are compressed using gzip compression. The image file should first be gunzipped before reading the image into an image processing package.

The gzip and gunzip commands are freely available at the following website:

Data Projection


The images are in the UTM projection for the most appropriate UTM zone. The ENVI header file associated with each image contains a map info line that indicates the UTM zone, upper left corner coordinate, and resolution. For example:

map info = {UTM, 1.000, 1.000, 416299.500, 8661839.500, 3.0000000000e+001, 3.0000000000e+001, 20, South, WGS-84, units=Meters}

The file is in UTM, Zone 20 South, in the WGS-84 datum. It has a 30-meter pixel size (3.00e+001) with the upper left corner of the upper left pixel (1,1) at coordinate 416299.500, 8661839.500.

Data Application and Derivation:

These data can be used to identify areas of significant change in the fraction of photosynthetically active vegetation (PV), non-photosynthetically active vegetation (NPV), and bare substrate between the years 1999-2000,2000-2001, and 2001-2002.

These fractions were determined by using an automated Monte Carlo simulation algorithm that determined the fractions for each pixel given libraries of spectral end members for PV, NPV, and bare substrate.

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

We carefully quantified our uncertainty in two key areas: atmospheric correction (aerosol and water vapor and unobserved areas caused by persistent cloud cover.

Atmospheric Uncertainty

In the CLAS processing stream, Landsat ETM+ images are atmospherically corrected

using the 6S atmospheric correction algorithm, with monthly averages of aerosol and water vapor inputs from the MODIS satellite sensor. The CLAS AutoMCU algorithm was found to be minimally sensitive to uncertainties in aerosol and water vapor from MODIS (Asner et al. 2005 EI). To further understand the effect that the atmospheric correction has on the sensitivity of entire CLAS process, we atmospherically corrected five Landsat image pairs using randomly-selected, monthly aerosol and water vapor values from MODIS. The difference in the amount of automatically detected logging between the different atmospherically-corrected images was only 0.7%.

Unobserved Area Uncertainty

When cloud and cloud-shadow cover is greater than 50% in any 5,625 km squared area (2,500 x 2,500 pixels), the area of observed logging is used to estimate the amount of logging in the unobserved, cloudy areas. We assessed our sensitivity to this type of error by simply quantifying the fractional cover of clouds and cloud shadows in comparison to observed logging extent. The calculated absolute uncertainty caused by this step was approximately +5% over the five states.

Process Description:

Data Acquisition Materials and Methods:

Geographic Coverage

The Brazilian Amazon basin covers an area of approximately 4.1 million km squared. Analysis of the entire region with Landsat Enhanced Thematic Mapper-Plus (ETM+) imagery would require approximately 220 scenes per year or 880 images for the years 1999-2002, yet much of the northwestern Amazon still contains relatively little deforestation and logging (Nepstad et al. 1999). We therefore limited our study to the States of Acre, Para, Mato Grosso (northern 58% of the state containing most of the forested area), Rondonia, and Roraima. These five states contain ~90% of the deforestation reported by Brazil for all of the Legal Amazon (INPE 2005). This strategy reduced the number of required Landsat images to 480 scenes.

Our study covered the period 1999 to 2002, which is prior to the failure of the Scan Line Corrector (SLC) in the ETM+ instrument onboard Landsat 7. Following the SLC failure, roughly 40% of each acquired Landsat image is missing data. To seek out alternatives to Landsat 7, we conducted a satellite inter-comparison of logging detection capability based on our network of low- and high-intensity logging sites in Amazonia (Asner et al 2004 GCB). We compared the detection capabilities of 10 sensors hyperspectral and the only sensor to meet or exceed the performance of Landsat ETM+ was EO-1 Hyperion; all others failed to detect at least 80% of the logging damage in our field sites. However, the extremely limited spatial and temporal coverage of the EO-1 Hyperion makes its application to large-area analysis intractable.

Materials and Methods

Processing Methodology

The Carnegie Landsat Analysis System (CLAS) uses high spatial resolution satellite data for regional and global studies of forest disturbance. CLAS is an automated processing system that includes: (i) atmospheric correction of satellite data; (ii) deconvolution of spectral signatures into sub-pixel fractional cover of live forest canopy, forest debris and bare substrates;

(iii) cloud, water, and deforestation masking; and (iv) pattern recognition algorithms for forest disturbance mapping.

Image Preparation and Atmospheric Correction

The version of CLAS presented here ingests raw Landsat Enhanced Thematic Mapper

Plus (ETM+) satellite imagery and applies sensor gains and offsets to convert from digital number (DN) to exo-atmospheric radiance. The radiance data are passed to a fully automated version of the 6S atmospheric radiative transfer model. The 6S program is integrated into the CLAS processing stream and uses monthly averages of aerosol optical thickness (AOT) and water vapor (WV) values from the Moderate Resolution Imaging Spectrometer (MODIS) sensor onboard the NASA Terra spacecraft. Time-stamping of MODIS AOT and WV data with Landsat data is done on an automated basis.

Sub-pixel Analysis

The CLAS process relies upon the quantitative determination of fractional material cover at the sub-pixel scale (e.g., within each Landsat 30 x 30 m pixel). This core step employs a probabilistic spectral mixture sub-model. This process spectrally decomposes each image pixel into fractional cover estimates (0-100% cover) of photosynthetic vegetation (PV) canopy, non-photosynthetic vegetation (NPV), and bare substrate. This sub-model is based on an algorithm developed for forest, savanna, woodland and shrubland ecosystems (Asner and Lobel 2000 RSE, 2004 Ecol App, Asner and Heidebrecht 2002). It is fully automated and uses a Monte Carlo Unmixing (AutoMCU) approach to derive uncertainty estimates of the sub-pixel cover fraction values. The method uses three spectral endmember bundles, derived from extensive field databases and satellite imagery, to decompose each image pixel using a linear equation (see supporting information for full equation)

Until recently, there were a limited number of spectral signatures of green and senescent vegetation and bare substrates for tropical regions. Our mixture modeling technique requires spectral reflectance bundles (rho pv(lambda), rho npv(lambda), and rho substrate(lambda)) that encompass the common variation in canopy and soil properties. Asner (1998 RSE) and Asner et al. (2004 Ecol App, 2003 RSE) collected these spectral data using full optical range field spectroradiometers (Analytical Spectral Devices, Inc., Boulder, CO, USA) during field campaigns conducted from 1996 to 2000. The spectral endmember database encompasses the common variation in materials found throughout the Brazilian Amazon, with statistical variability well defined (Asner et al 2004 Ecol App). The bare substrate spectra have been collected across a diverse range of soil types, surface organic matter levels, and moisture conditions. Spectral collections for NPV have included surface litter, senescent grasslands, and deforestation residues (slash) from a wide range of species and decomposition stages. In contrast to the NPV and bare substrate spectra that can be collected via ground-based spectroscopic measurements, the photosynthetic vegetation (PV) spectra of forest species require overhead viewing conditions. This is very difficult in forest canopies with heights typically ranging from 10-50 m. Spectral measurements of individual leaves, stacks of foliage, or partial canopies (e.g., branches) introduce major errors in spectral mixture models and cannot be used (Asner 1998 ). Therefore, we collected canopy spectra using the Earth Observing-1 (EO-1) Hyperion sensor, the first spaceborne hyperspectral sensor for environmental applications (Ungar et al 2003). The PV spectral bundle was derived from more than 40,000 spectral observations made at 30 m spatial resolution with Hyperion (images taken throughout 1999), atmospherically corrected to apparent top-of-canopy reflectance using the ACORN-4 atmospheric correction algorithm for hyperspectral data (ImSpec Inc., Palmdale, CA USA), and convolved to Landsat ETM+ optical channels (Asner at al 2005 EI). These green vegetation spectra thus inherently included the variable effects of intra- and inter-crown shadowing, which are prevalent in tropical forests (Gastellu-Etchegorry et al 1999). In Amazonia, shade fractions average 25% cover in humid tropical forests, but the variance is high with standard deviations of 12% or more (Asner and Warner 2003). It is thus critically important to note that our PV results include shade, which varies substantially with forest structure. Using a separate shade endmember is attractive (Souza and Barreto 2000), but doing so with multi-spectral Landsat data and such high shadow fraction variability often results in an under-determined spectral and mathematical problem in linear mixture models. That is, there are many viable solutions to the mixture modeling problem in forests. Imaging spectroscopy (hyperspectral) data are needed to solve this problem (Roberts et al. 1993). We avoided this issue by accepting the limitations of incorporating variable shade directly into our PV bundle derived from the EO-1 Hyperion sampling of undisturbed forest canopies in Brazil. The PV bundle includes spectra from mature forest, late-stage forest regrowth, and logged forest of at least five years post-harvest. In the end, the total number of spectra retained in the endmember bundles for the AutoMCU sub-model was 252 for PV, 611 for NPV, and 434 for bare substrate. These spectra represent more than 130,000 field and spaceborne spectrometer observations collected over a five-year period of study (Asner et al 2005 EI).

Non-forest Masking and Atmospheric Compensation

A series of automated masks were designed to exclude clouds, water bodies, cloud shadows, non-image and non-forest areas (e.g., pasture, urban and agriculture) from the CLAS processing stream. Prior to execution of the AutoMCU sub-model, clouds are masked using the thermal channel (band 6) from the raw Landsat images. Asner et al. (2005 EI) found that a thermal band threshold DN value of 125 can conservatively detect cloudy pixels over Amazonia. Water bodies are masked by finding pixels in the calibrated Landsat reflectance data in which bands 1-4 (blue, green, red, and near-infrared) have a negative slope. Only water displays such a negative reflectance slope with increasing wavelength. Non-image areas containing zero values are also masked. Cloud shadows are identified using the root mean square error (RMSE) image that

results from the AutoMCU processing. Areas shadowed by clouds have large RMSE values and are masked by identifying pixels above a specific RMSE threshold (Asner et al 2005 EI). To limit the logging analysis to forested areas, Landsat thermal band 6, combined with the AutoMCU results, is used to identify pixels containing primarily forest and non-forest areas. Forests have a lower brightness temperature and a higher PV fractional cover than deforested

lands. We employ a conservative PV fractional cover threshold of 60% to delineate forest cover in the PV mask. The minimum and maximum thermal thresholds, which encompass forested areas in the thermal mask, are dynamically generated for each image by calculating the mean thermal value of all pixels having a PV fraction cover greater than 80% and then masking all pixels with values > 15 digital numbers (DN) from the mean thermal value. These final

masking steps have the added feature of removing residual clouds and cloud shadows that were missed in the masks applied earlier in the CLAS process.

Although atmospheric correction was performed on the raw imagery before processing through the AutoMCU sub-model, residual atmospheric effects can persist (Asner et al 2005 EI). These residual effects exist spatially within a scene and temporally between scenes. We greatly reduce these effects by calculating the average change in fractional forest cover in 55 km2 subsets of the imagery. These large geographic subsets are made at a spatial scale far greater than that of the most extensive logging activities, so temporal differences in the overall forest fractional cover at this scale are a result of atmospheric effects (e.g., haze) or forest phenology. These false fractional cover changes are normalized by adjusting the background forest temporal variation to zero. Since disturbances related to logging or other anthropogenic activities occur at a much smaller spatial scale than is considered in this processing step, normalization of the forest values across large areas does not affect the CLAS process in discriminating true disturbances from the surrounding forested areas.


Asner, G.P., 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ., 64, 234-253.

Asner, G.P. and D.B. Lobell, 2000. A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sens. Environ., 74, 99-112.

Asner, G.P. and K.B. Heidebrecht, 2002. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations. Int. J. Remote Sens., 23, 3939-3958.

Asner, G.P. and A.S. Warner, 2003. Canopy shadow in IKONOS satellite observations of tropical forests and savannas. Remote Sens. Environ., 87, 521-533.

Asner, G.P., M.M.C. Bustamante, and A.R. Townsend, 2003. Scale dependence of biophysical structure in deforested areas bordering the Tapajos National Forest, Central Amazon. Remote Sens. Environ., 87, 507-520.

Asner, G.P., M. Keller, R. Pereira, J.C. Zweede, and J.N.M. Silva, 2004. Canopy damage and recovery after selective logging in Amazonia: Field and satellite studies. Ecol. Appl., 14, S280-S298.

Asner GP, Knapp DE, Cooper AN, Bustamante MMC, Olander LP. 2005a. Ecosystem Structure throughout the Brazilian Amazon from Landsat Observations and Automated Spectral Unmixing. Earth Interactions, 9, 1-31

Asner, G.P., M. Keller, J.N M. Silva. 2005b Global Change Biol. 10, 7652005b

Gastellu-Etchegorry, J.P., P. Guillevic, F. Zagloski, V. Demarez, V. Trichon, D. Deering, and M. Leroy, 1999. Modeling BRF and radiation regime of boreal and tropical forests: I. BRF. Remote Sens. Environ., 68, 281-316.

INPE (Instituto Nacional de Pesquisas Espaciais (INPE),2005. PRODES: Assessment of deforestation in Brazilian Amazonia ( html)

Nepstad DC, Verissimo A, Alencar A, Nobre C, Lima E, Lefebvre P, Schlesinger P, Potter C, Moutinho P, Mendoza E, Cochrane M, Brooks V. (1999) Large-scale impoverishment of Amazonian forests by logging and fire. Nature, 398, 505-508.

Roberts, D.A., M.O. Smith, and J.B. Adams, 1993. Green vegetation, nonphotosynthetic vegetation,and soils in Aviris data. Remote Sens. Environ., 44, 255-269.

Souza and Barreto 2000 Int\'l J. Rem. Sens. 21, 173

Ungar, S.G. J.S. Pearlman, J.A. Mendenhall, D. Reuter, IEEE Trans. Geosci. Rem. Sens. 41, 1149


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