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Investigation:

LC-14 (Nepstad / Moutinho)

LBA Dataset ID:

LC14_AMAZON_SCENARIOS

Originator(s):

1. SOARES-FILHO, B.S.
2. NEPSTAD, D.C.
3. CURRAN, L.M.
4. VOLL, E.
5. GARCIA, R.A.
      6. RAMOS, C.A.
7. MCDONALD, A.J.
8. LEFEBVRE, P.A.
9. SCHLESINGER, P.

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 contains the results of the Amazon Scenarios project, which simulated monthly deforestation in the Brazilian Amazon from 2002 to 2050. We ran the model for two scenarios: The business-as-usual scenario considers the deforestation trends across the basin, projecting the rates by using historical figures and their variations from 1997 to 2002, and adding to them the effect of paving a set of major roads. The governance scenario also considers the current deforestation trends, but its projection assumes an asymptotic logistic curve. In this scenario, a 50% limit is imposed for deforested land within each basin\'s subregion and existing and proposed protected areas play a decisive role in hindering deforestation as well. The data sets presented here were assembled by The Woods Hole Research Center in collaboration with the following institutions: Instituto de Pesquisa Ambiental da Amazonia (Amazonian Institute for Environmental Research, IPAM), Conservation International, and Instituto Nacional de Pesquisas Espaciais (National Institute of Space Research, INPE), using their own data and contributions by many other organizations. Other datasets may be added in the future. Funding for the preparation of these data was provided by the Center for Applied Biodiversity Science of Conservation International to The Woods Hole Research Center.

Beginning Date:

2002-01-01

Ending Date:

2050-12-31

Metadata Last Updated on:

2013-04-05

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-14 Modeled Deforestation Scenarios, Amazon Basin: 2002-2050:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1153

Documentation/Other Supporting Documents:

LBA-ECO LC-14 Modeled Deforestation Scenarios, Amazon Basin: 2002-2050:  http://daac.ornl.gov/LBA/guides/LC14_Amazon_Scenarios.html

Citation Information - Other Details:

Soares-Filho B.S., D.C. Nepstad, L.M. Curran, E. Voll, G.C. Cerqueira, R.A. Garcia, C.A. Ramos, A. McDonald, P. Lefebvre, and P. Schlesinger. 2013.LBA-ECO LC-14 Modeled Deforestation Scenarios, Amazon Basin: 2002-2050. Data set. Available on-line (http:daac.ornl.gov) from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1153

Keywords - Theme:

Parameter Topic Term Source Sensor
DEFORESTATION LAND SURFACE LAND USE/LAND COVER COMPUTER MODEL MODEL ANALYSIS
LAND COVER LAND SURFACE LAND USE/LAND COVER COMPUTER MODEL MODEL ANALYSIS

Uncontrolled Theme Keyword(s):  AGRICULTURE , AGROECONOMIC STATISTICS , BRAZILIAN AMAZON, COMPUTER MODELS, DEFORESTATION, LAND USE, PROTECTED AREAS

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  AMAZON BASIN 12.98000 -55.77000 -34.29000 -81.86000

Related Publication(s):

Nepstad, D.C., G. Carvalho, A.C. Barros, A. Alencar, J.P. Capobianco, J. Bishop, P. Moutinho, P. Lefebvre, U.L. Silva, and E. Prins. 2001. Road paving, fire regime feedbacks, and the future of Amazon forests. Forest Ecology and Management 154(3):395-407.

Soares, B.S., D.C. Nepstad, L.M. Curran, G.C. Cerqueira, R.A. Garcia, C.A. Ramos, E. Voll, A. McDonald, P. Lefebvre, and P. Schlesinger. 2006. Modelling conservation in the Amazon basin. Nature 440(7083):520-523.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

The model, which we call SimAmazonia produces annual maps of simulated future deforestation under user-defined scenarios of highway paving, PA networks, PA effectiveness, deforestation rates and deforested land ceilings. We stratified the Amazon basin into 47 socioeconomic subregions for which individualized deforestation rates are forecast; these rates were estimated from historical trends (from 1997 to 2002) derived from satellite-based deforestation maps, the planned road paving schedule (see companion file: Amazon_Scenarios_Supplemental_Info.pdf, Table S1) and existing and proposed PAs. Proximity to paved highways is the major driver of deforestation rates in the model and this relationship was defined empirically from data on deforestation and paved roads for 432 counties of the Brazilian Amazon (See companion file, Fig. S5). Increasing proximity to paved highways accelerates deforestation within a subregion up to an inflection point when forests outside protected areas start becoming scarce. The spatial distribution of deforestation across the Amazon is simulated with a cellular automata model, with parameters customized for each subregion, that allocates deforestation on the basis of its empirical relationships with proximity to roads,rivers and towns, land use zoning, and biophysical features represented in raster grids of 1 km2 resolution. Spatial integrity across subregions is attained by employing spatial variables (for example, distance to previously deforested land and distance to all roads) that are updated annually over the entire basin. This latter variable is output of the road constructor model, a component that

simulates the expansion of the secondary road network and thereby incorporates the effect of endogenous roads on the evolving spatial patterns of deforestation. The spatial simulation model was calibrated and validated for 12 regional case studies (B.S.S.-F., unpublished material), each represented by a Landsat Thematic Mapper scene (180km x 180 km).



A detailed description of the model design is provided in Supplementary Information.

Data Application and Derivation:

Typical Application of Data:

The simulated patterns of deforestation can be employed in Regional-, national-, and sub-national-level forest and land use change assessments to evaluate future environmental impacts, such as habitat fragmentation, biodiversity loss, and hydrologic cycle alteration, to produce base lines of carbon emission, and used as inputs for Global Climate Models.

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

Not available.

Process Description:

Data Acquisition Materials and Methods:

The model, which we call SimAmazonia produces annual maps of simulated future deforestation under user-defined scenarios of highway paving, PA networks, PA effectiveness, deforestation rates and deforested land ceilings. We stratified the Amazon basin into 47 socioeconomic subregions for which individualized deforestation rates are forecast; these rates were estimated from historical trends (from 1997 to 2002) derived from satellite-based deforestation maps, the planned road paving schedule (see companion file: Amazon_Scenarios_Supplemental_Info.pdf, Table S1) and existing and proposed PAs. Proximity to paved highways is the major driver of deforestation rates in the model and this relationship was defined empirically from data on deforestation and paved roads for 432 counties of the Brazilian Amazon (See companion file, Fig. S5). Increasing proximity to paved highways accelerates deforestation within a subregion up to an inflection point when forests outside protected areas start becoming scarce. The spatial distribution of deforestation across the Amazon is simulated with a cellular automata model, with parameters customized for each subregion, that allocates deforestation on the basis of its empirical relationships with proximity to roads,rivers and towns, land use zoning, and biophysical features28 represented in raster grids of 1 km2 resolution. Spatial integrity across subregions is attained by employing spatial variables (for example, distance to previously deforested land and distance to all roads) that are updated annually over the entire basin. This latter variable is output of the road constructor model28, a component that

simulates the expansion of the secondary road network and thereby incorporates the effect of endogenous29 roads on the evolving spatial patterns of deforestation. The spatial simulation model was calibrated and validated for 12 regional case studies (B.S.S.-F., unpublished material), each represented by a Landsat Thematic Mapper scene (180km x 180 km). A detailed description of the model design is provided in Supplementary Information.

References:

Soares, B.S., Nepstad, D.C., Curran, L.M., Cerqueira, G.C., Garcia, R.A., Ramos, C.A., Voll, E., McDonald, A., Lefebvre, P. & Schlesinger, P. 2006 Modelling conservation in the Amazon basin. Nature 440, 520-523.

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