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Investigation:

ND-30 (Davidson / Carvalho / Figueiredo / Vieira)

LBA Dataset ID:

ND30_PASTURE_DEGRADATION

Originator(s):

1. DAVIDSON, E.A.
2. ASNER, G.P.
3. STONE, T.A.
      4. NEILL, C.
5. FIGUEIREDO, R.D.O.

Point(s) of Contact:

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

Dataset Abstract:

Degradation of cattle pastures is a management concern that influences future land use in Amazonia. However, degradation is poorly defined and has different meanings for ranchers, ecologists, and policy makers. Here we analyze pasture degradation using objective scalars of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed soil (S) derived from Landsat imagery. A general, probabilistic spectral mixture model decomposed satellite spectral reflectance measurements into subpixel estimates of PV, NPV, and S covers at ranches in western and eastern Amazonia.

Beginning Date:

1996-07-06

Ending Date:

2002-05-22

Metadata Last Updated on:

2013-06-14

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 ND-30 Fractional Cover of Mixed Land Use Ranches, Para and Rondonia, Brazil:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1164

Documentation/Other Supporting Documents:

LBA-ECO ND-30 Fractional Cover of Mixed Land Use Ranches, Para and Rondonia, Brazil:  http://daac.ornl.gov/LBA/guides/ND30_Pasture_Degradation.html

Citation Information - Other Details:

Davidson, E.A., G.P. Asner, T.A. Stone, C.Neill, R.O. Figueiredo. 2013. LBA ECO ND-30 Fractional Cover of Mixed Land Use Ranches, Para and Rondonia, Brazil. 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/1164

Keywords - Theme:

Parameter Topic Term Source Sensor
LAND COVER HUMAN DIMENSIONS LAND USE/LAND COVER LANDSAT-5 TM
LAND MANAGEMENT HUMAN DIMENSIONS LAND USE/LAND COVER LANDSAT-7 ETM+ (ENHANCED THEMATIC MAPPER PLUS)

Uncontrolled Theme Keyword(s):  EXPOSED SOIL, LANDSAT, MANAGEMENT, NON-PHOTOSYNTHETIC VEGETATION, PASTURE DEGRADATION, PHOTOSYNTHETIC VEGETATION, SPECTRAL MIXTURE ANALYSIS

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  PARA EASTERN (BELEM) -1.99070 -11.03550 -45.69180 -63.13740

Related Publication(s):

Davidson, E. A., G. P. Asner, T. A. Stone, C. Neill, and R. O. Figueiredo. 2008. Objective indicators of pasture degradation from spectral mixture analysis of Landsat imagery. J. Geophys. Res., 113, G00B03, doi:10.1029/2007JG000622.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

Data are presented in ENVI format with 6 sets files available. Each sampling date is represented by four files with the Landsat path row and date included in each file name. There are four sets of files covering the Rondonia site and 2 sets of files covering the Para site (labeled maranhao in the file names).



Sample file names for a set:

rondonia_p231r067_etm052602_muscaled.means

rondonia_p231r067_etm052602_muscaled.aux

rondonia_p231r067_etm052602_muscaled.hdr

rondonia_p231r067_etm052602_muscaled.rrd

Data Application and Derivation:

These data can be used to identify areas of significant differences in the fraction of photosynthetically active vegetation (PV), non-photosynthetically active vegetation (NPV), and bare substrate between areas under different management regimes at two large ranches in the Amazonm Basin. At the western site (Rondonia, Fazenda Nova Vida) images from the wet and dry season also allow an analysis of seasonal effects on fractional cover. 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:

One challenge in assessing intact forests are the significant component of shadow in remotely sensed imagery which can be mistaken for water or bad data. Seasonal variation in forest and canopy structure is a concern of which were aware. In addition, interseasonal rainfall regimes can be highly variable and cause discrepancies. We did not have direct access to the digital GIS data from the ranch at Nova Vida and had to map it via a rubber sheeting technique which has inherent uncertainties particularly at the edges of the maps used. One of the images for the Fazenda Vitoria region had significant cloud cover but fortunately not directly over our area of interest. As mentioned in our paper (Davidson et al. 2008), we do not know how recently or frequently cattle were rotated through pasture management units and when other management activities occurred relative to the date of each scene. This potentially introduces additional temporal variation

Process Description:

Data Acquisition Materials and Methods:

Site descriptions:



Fazenda Vitoria is located 6.5 km northwest of the town of Paragominas, Para State, Brazil, in eastern Amazonia. The 3500 ha ranch is a mosaic of primary forest, logged forest, secondary forest, and pasture with moderately dissected topography. The areas that are now in pasture and secondary forest were originally cleared and burned in 1969. After 6 to 8 years of pasturing, some of the area was abandoned to fallow. Areas that continue to be actively managed for cattle pastures received management inputs such as disking, P fertilization (50 kg per ha), and planting with an African grass (Brachiaria brizantha) in the late 1980s.



Rainfall distribution in this region has a strong seasonality with less than 15% of the total annual precipitation falling between June and November. Average annual precipitation is 1800 plus or minus 550 mm (Jipp et al., 1998). Soils of this region are deeply weathered Oxisols (Haplustox) derived from the Belterra clay formation that developed on the top and upper slopes of a Pleistocene terrace. Belterra clays consist mainly of kaolinite, with minor fractions of quartz and hematite, and are widespread at elevations below 200 m in the Amazon Basin (Clapperton, 1993). The soils were classified by Sombroek [1966] as Kaolinitic Yellow Latosols (Haplustox, according to USDA Soil Taxonomy), and they contain 60 to 80% clay content. Forest soils are acidic with pHw less than 5.0 at all depths, whereas pasture soil pH in water was 5.2 to 5.7 in the top 10 cm (Markewitz et al., 2004).



Fazenda Nova Vida is located directly adjacent to BR-364, 50 km from the city of Ariquemes in central Rondonia in western Amazonia. Small amounts of forest on this 19,000 ha ranch were cleared for subsistence settlements as early as 1911, but large-scale land clearing at Nova Vida began in 1972 and continued with additional areas cleared in 1979, 1983, 1987 and 1989. Almost no new forest clearing has occurred since 1989. The ranch is now a mosaic of primary forest, logged forest and pastures. Nova Vida contains no abandoned pastures and almost no areas of second growth vegetation. Pastures at Nova Vida were planted directly from forest to Panicum maximum, Brachiaria humidicola and Brachiaria brizantha and managed by application of the herbicides picloram and 2,4-dinitrophenol and occasional burning through the 1990s. Management since 1998 has included mechanical clearing of woody debris, additional herbiciding, disking and application of lime (0.6 Mg per ha) and rock phosphate.



Terrain at Nova Vida is gently rolling at 200 to 500 m elevation and drained by low-gradient clear water streams. The region is underlain by Pre-Cambrian granitic bedrock of the Brazilian Shield (Projeto Radambrasil, 1978). The climate of central Rondonia is humid tropical. Annual precipitation is 2254 plus or minus 266 mm. Mean annual relative humidity is 89%, mean daily temperature is 25.6 C, and mean daily temperature for the warmest and coolest months varies less than 5 C (Bastos and Diniz, 1982). Rainfall has a distinct dry season from June to October. Rainfall averages more than 300 mm per month during the wettest four months (December through March) and less than 40 mm per month during the driest 3 months (June through August) (Bastos and Diniz, 1982). Soils at Nova Vida are predominantly weathered Ultisols (Kandiudults, Hapludults) with sandy loam or sandy clay loam texture and surface clay contents of 10 to 25% (Neill et al.,1997). Forest soils have a pHw of 4.7 to 4.9 at all depths and pastures have pHw that ranges from 6.0 to 7.2 at the surface to 4.8 to 6.5 at 50 cm.



Images:

For comparison between ranches, Landsat imagery was obtained for Fazenda Nova Vida in Rondonia (path/row 231/067; 11 August 2001) and Fazenda Vitoria in Para (path/row 222/62; 5 July 1996). Both dates are in the dry season. A subset of each image was extracted to focus on the region immediately surrounding the ranches. All images were geo-corrected to a common map projection (UTM zones 20 and 23, datum: Clark 1866), and were composed of 30 m resolution pixels. To compare the effects of seasonality at one site, an additional image for Nova Vida, acquired on 26 May 2002, was used to represent the end of the wet season. Images of Nova Vida acquired on 6 August 1999, 24 August 2000, and 11 August 2001, were also used to compute a multiyear mean image for purposes of comparison with a record of management treatments.



Spectral Mixture Analysis:

Prior to analysis, sensor gains and offsets were applied to the imagery to convert from digital number (DN) to exo-atmospheric radiance. The radiance data were then passed to a fully automated version of the 6S atmospheric radiative transfer model (Vermote et al., 1997).Aerosol optical thickness and water vapor data taken from the Moderate Resolution Imaging Spectrometer (MODIS) sensor onboard the NASA Terra spacecraft were used as inputs to 6S as described in detail by Asner et al. (2005). For the Landsat image collected prior to the launch of Terra, we used the long-term average MODIS aerosol and water vapor data from the same month of collection (July), a technique that has proven reliable for correcting Landsat imagery for the spectral mixture analysis approach that is used here (Oliveira et al., 2007). These steps provided estimates of apparent surface reflectance for Landsat bands 1 through 5 and 7.



We used the AutoMCU model [Asner and Heidebrecht, 2002] to decompose each Landsat pixel into fractional cover estimates (0 to 100% cover) of photosynthetic vegetation (PV) canopy, nonphotosynthetic vegetation (NPV), and bare substrate. AutoMCU is fully automated and uses a Monte Carlo unmixing approach to derive uncertainty estimates of the subpixel cover fraction values. The method uses three spectral end-member bundles derived from extensive field databases and satellite imagery (Asner et al., 2004, 2005), to decompose each image pixel.



Solving for the subpixel cover fractions requires that the observations (in this case, Landsat TM and ETM+ reflectance) contain sufficient spectral information to solve a set of linear equations, each of the form in equation (1) but at different wavelengths. The end-member reflectance bundles used in the AutoMCU code were compiled from field and satellite data as described in detail by Asner et al. (2005). Although no spectra were collected in the field in Rondonia, the AutoMCU method, which allows for uncertainty in end-member spectral properties as well as Landsat spectral calibration (Asner et al., 2005), has proven sufficiently generic to allow its use across a wide range of vegetation and soil conditions throughout Brazil and Peru (Asner et al., 2003, 2004; Oliveira et al., 2007). The output from the model included subpixel photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and exposed soil (S) along with standard deviations caused by uncertainty in both the reflectance imagery (following atmospheric correction) and the spectral end-members.

References:

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,

doi:10.1080/01431160110115960.



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, doi:10.1016/j.rse.2003.03.001.



Asner, G. P., A. R. Townsend, M. M. C. Bustamante, G. B. Nardoto, and

L. P. Olander (2004), Pasture degradation in the central Amazon: Linking

changes in carbon and nutrient cycling with remote sensing, Global

Change Biol., 10, 844-862, doi:10.1111/j.1529-8817.2003.00766.x.



Asner, G. P., D. E. Knapp, E. N. Broadbent, P. J. C. Oliveira, M. Keller, and

J. N. Silva (2005), Selective logging in the Brazilian Amazon, Science,

310, 480-482, doi:10.1126/science.1118051.



Bastos, T. X., and T. D. Diniz (1982), Avaliacao de clima do Estado de

Rondonia para desenvolvimento agricola, Bol. Pesquisa 44, Embrapa-

CPATU, Belem, Brazil.



Clapperton, C. (1993), Quaternary Geology of South America, Elsevier

Sci., New York.



Jipp, P., D. C. Nepstad, K. Cassel, and C. J. R. D. Carvalho (1998),

Deep soil moisture storage and transpiration in forests and pastures of seasonally- dry Amazonia, Clim. Change, 39(2/3), 395-412, doi:10.1023/

A:1005308930871.



Markewitz, D., E. Davidson, P. Moutinho, and D. Nepstad (2004), Nutrient

loss and redistribution after forest clearing on a highly weathered soil in

Amazonia, Ecol. Appl., 14(4), 177-199, doi:10.1890/01-6016.



Oliveira, P. J., G. P. Asner, D. E. Knapp, A. Almeyda, R. Galvan-

Gildemeister, S. Keene, R. F. Raybin, and R. C. Smith (2007),

Land-use allocation protects the Peruvian Amazon, Science, 317,

1233-1236, doi:10.1126/science.1146324.



Projeto Radambrasil (1978), Levantamento de recursos naturais, Dep. Nac.

de Prod. Miner., Min. das Minas e Energ., Rio de Janeiro.



Sombroek, W. G. (1966), Amazon Soils: A Reconnaissance of the Soils of

the Brazilian Amazon Region, 300 pp. Pudoc, Wageningen, Netherlands.



Vermote, E. F., D. Tanre, J. L. Deuze, M. Herman, and J. J. Morcrette

(1997), Second simulation of the satellite signal in the solar spectrum,

6S: An overview, IEEE Trans. Geosci. Remote Sens., 35, 675-686,

doi:10.1109/36.581987.

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