NOTICE -- The LBA-ECO Project website is no longer being supported.  This archive is a snapshot, as it existed in 2013, of the LBA-ECO website, maintained by NASA Goddard Space Flight Center, and now archived at the ORNL DAAC.  Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC.
banner
banner banner banner banner banner banner
banner banner banner banner banner banner banner
home aboutlibrarynews archivecontacts banner

spacer
banner
Investigations
Overview
Abstracts & Profiles
Publications
Research Sites
Meetings
Synthesis Groups
LBA-HYDROMET
LBA-Air-ECO
Logistics
Overview
Field Support
Travel
Visa
Shipping
Data
  Overview
Find LBA Data
Investigator Checklist
Process & Policy
Documentation & Archive
Training & Education
  Overview
Activities Summary
T&E Goals
Student Opportunities
  Folha Amazônica
 
spacer

Investigation:

LC-05 (Laurance / Mesquita)

LBA Dataset ID:

LC05_Deforestation_Predictors

Originator(s):

1. Laurance, William F.
2. Albernaz, Ana Luisa K. M.
3. Mesquita, Rita Guimaraes
4. Silveira, Marcos
5. Andrade, Ana Cristina Segalin de
6. Bergen, Scott
7. Costa, Carlos Da
8. Delamonica, Patricia
9. Hepolito, Rosely Cavalcante
10. Luizao, Flavio de Jesus
      11. Monaco, Luciana Magalhaes
12. Moreira, Marcelo Paustein
13. Nascimento, Henrique Eduardo
14. Rittl, Carlos
15. Rubenstein, Adriana
16. Schroth, Goetz
17. Vasconcelos, Heraldo L.
18. Venticinque, Eduardo Martins
19. Laurance, Susan Gai Warriner
20. Williamson, G. Bruce

Point(s) of Contact:

Laurance, William F. (laurancew@tivoli.si.edu)
Nascimento, Dr. Henrique (nascimentoh@tivoli.si.edu)

Dataset Abstract:

Using a GIS, spatial data coverages were developed for deforestation and for three types of potential predictors: 1. human-demographic factors (rural-population density, urban-population size)
2. factors that affect physical accessibility to forests (linear distances to the nearest paved highway, unpaved road and navigable river) and 3. factors that may affect land-use suitability for human occupation and agriculture (annual rainfall, dry-season severity, soil fertility, soil waterlogging, soil depth). User beware -- data/metadata/documentation are missing or incomplete
Data Quality Statement: The Data Center has determined that this data set has missing or incomplete data, metadata, or other documentation resulting in diminished usability of this product. User beware.
Known Problems: Very little documentation was provided with these GIS data, however it is possible that sufficient metadata could be extracted from the GIS files to generate adequate documentation for long-term archive.

Beginning Date:

2001-01-20

Ending Date:

2001-06-20

Metadata Last Updated on:

2013-07-30

Data Status:

Archived at LBA-DIS Only

Access Constraints:

Public

Data Center URL:

http://daac.ornl.gov

Distribution Contact(s):

ORNL DAAC User Services (uso@daac.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):

Access data via LBA DIS ftp ftp://lba.cptec.inpe.br/lba_archives/LC/LC-05/Nascimento/:  ftp://lba.cptec.inpe.br/lba_archives/LC/LC-05/Nascimento/
Access data via ORNL DAAC:  Search at ORNL DAAC

Documentation/Other Supporting Documents:

the Biological Dynamics of Forest Fragments Project (BDFFP) Web Site:  http://www.inpa.gov.br/~pdbff/

Citation Information - Other Details:

Laurance, W.F., A.K.M. Albernaz, G. Schroth, P.M. Fearnside, S. Bergen, E.M. Venticinque, and C. Da Costa. 2008. LBA-ECO LC-05 Human and Geophysical Predictors of Amazon Deforestation (GIS). Data set. Available on-line [http://lba.cptec.inpe.br/] from LBA Data and Information System, National Institute for Space Research (INPE/CPTEC), Cachoeira Paulista, Sao Paulo, Brazil. However, please do not use these data without formal permission from the data provider: William F. Laurance, laurancew@tivoli.si.edu, (507)-212-8252.

Keywords - Theme:

Parameter Topic Term Source Sensor
LAND COVER LAND SURFACE LAND USE/LAND COVER LANDSAT-5 (LAND REMOTE-SENSING SATELLITE-5) TM (THEMATIC MAPPER)

Uncontrolled Theme Keyword(s):  Amazon , Brazil , deforestation , government policy , habitat fragmentation , tropical rain forest

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
Amazonas (Manaus) ZF3 Biological Dynamics of Forest Fragments Project (BDFFP) -2.51800 -2.51800 -60.03000 -60.03000

Related Publication(s):

Laurance, W.F., A.K.M. Albernaz, and C. Da Costa. 2001. Is deforestation accelerating in the Brazilian Amazon? Environmental Conservation 28(4):305-311.

Laurance, W.F., A.K.M. Albernaz, G. Schroth, P.M. Fearnside, S. Bergen, E.M. Venticinque, and C. Da Costa. 2002. Predictors of deforestation in the Brazilian Amazon. Journal of Biogeography 29(5-6):737-748.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

Data are presented as Arc/Info Binary Grid format files. Each raster data file is labelled w001001.adf and has 7 associated files:

a header file hdr.adf

a boundary file dblbound.adf

a value attribute file vat.adf

a raster statistics file sta.adf

an index file for the raster file w001001x.adf

and a log file



Data for the following layers are available:



Layer Folder name

allrdist

Deforestation deforest

Annual rainfall chuva

Severity of the dry season dryseas

Distance to paved highway estr_asf

Distance to unpaved road nasf_dist

Distance to navigable river rios_dist

Soil fertility class soilf

Soil depth and stoniness soilst

Soil water logging soilw

Data Application and Derivation:

These data can be used as a baseline for comparison for future studies. Deforestation maps are based on imagery from 1999 and population from the 2000 census.

Process Description:

Data Acquisition Materials and Methods:

Most data processing was carried out at the GIS laboratory of the Biological Dynamics of Forest Fragments Project in Manaus, Brazil, using ArcView 3.2, Spatial Analyst 2.0, and IDRISI (1996a, b) software. Analyses were conducted using quadrats of 2500 km2 (50 by 50 km). There were 1927 quadrats of 2500-km2 in the Brazilian Legal Amazon, a region of 4.0 million km2 that encompasses all Amazon-basin forests within Brazil and some adjoining woodlands and savannas. The quadrats in our analysis do not include those along the margins of the Legal Amazon that had less than 80 percent of their area within the region�s boundaries, which were excluded from analyses. For each quadrat, deforestation and predictor variables were

extracted into data tables to permit statistical analysis.



Deforestation

Data on forest cover in the Brazilian Amazon were derived from 1999 imagery produced by the National Oceanographic and Atmospheric Administration using 1992 Pathfinder and 1998 AVHRR satellite data. Separate georeferenced images

for the individual states in the Brazilian Amazon (e.g. Rondonia, Acre) were image-mosaiced using Imagine 8.4 software, and the composite file was georeferenced with a second-order polynomial transformation to digital maps

provided by the Brazilian Socio-Environmental Institute [Instituto Socioambiental (ISA), 1999].



The final imagery included four categories of coverage: forest, water, areas of persistent light at night (cities), and non-forested areas (see the website above for the elaborate procedure used to discriminate these four cover-classes). Non-forested areas included both deforested lands and open

vegetation (principally cerrado savanna and open woodland). To discriminate deforested areas from open vegetation, the latter was converted to raster format and subtracted from the original image using digital vegetation

maps (ISA, 1999). In addition, cities and areas flooded by hydroelectric reservoirs (e.g. Balbina & Tucuruı reservoirs) were classified as being deforested. The final result was an image, based on 1 square km pixels, in which forested areas, deforested lands and natural bodies of water were discriminated. Per cent deforestation data were calculated by determining the proportion of deforested pixels within each 2500 km2 quadrat.



Highways

Data on existing highways (defined as being paved) were derived from digitized and georeferenced maps from ISA (1999), augmented with extensive personal knowledge of the region. Using IDRISI, the distance to the nearest highway

was determined for each 1-km2 pixel within the Brazilian Amazon, and the mean distance was then calculated for all 2500 pixels in each quadrat. This mean provided an effective index of highway density that also included the potentially important effects of highways in adjoining quadrats.



Roads

Data on roads (defined as being unpaved) were quantified separately from paved highways, because roads provide less efficient transportation than highways and may not be usable during the wet season. Data for roads were generated

in the same way as those for paved highways, using data from ISA (1999).



Navigable rivers

The distribution of major navigable rivers in the Brazilian Amazon was estimated using georeferenced data from ISA (1999). We excluded from the analysis any rivers of less than 1 km in width and any river stretches that were isolated from the main stem of the Amazon or Rio Negro Rivers by cascades

or waterfalls. For each quadrat, the distance to the nearest river was calculated for each 1 km2 pixel and the mean value for all pixels within each quadrat was used to provide an overall index of river accessibility.





Dry-season severity

Data on average duration of the dry season were also produced from the digitized map derived from Sombroek (2001), who generated isoclines of the number of months with less than 100-mm rainfall. Again, to avoid abrupt boundaries between isoclines, data were interpolated using the TIN mode of IDRISI, yielding a continuous surface of dry-season severity at a scale of 1 km2 pixels.





Soil factors

Data on soils were based on a 1 to 3,000,000-scale digital soil map of Brazilian Amazonia that was produced in the 1970s by the Soils Division of the Brazilian Institute for Agricultural Research (EMBRAPA, Rio de Janeiro), which is regarded as the best available soils map for the Amazon (W. G. Sombroek, pers. comm.). The map contains seventeen major soil types that are further subdivided into over 100 subtypes, using the Brazilian soil taxonomy (cf. Beinroth, 1975). The map was used to generate data layers for three indices of soil suitability for agriculture: (1) a general index of soil fertility (see below), (2) waterlogging and hydromorphy, and (3) soil shallowness/stoniness. Information for classifying the different soil subtypes was derived from published sources (especially Sombroek, 1984, 2000; Oliveira et al., 1992).



Soil fertility

This parameter is a composite index of soil suitability for agriculture that ranges from 1 (poorest soils) to 10 (best soils), and that incorporates data on soil chemistry, texture, depth, waterlogging, stoniness, and other features. Soil fertility classes 8�10 have the highest agricultural potential. These include alluvial soils in varzea forests (seasonally inundated by whitewater rivers that carry nutrient-rich sediments from the Andes Mountains), terra roxa soils(nutrient-rich, well-structured upland soils that have formed on base-rich rock and are in high demand for cocoa and other nutrient-demanding crops), eutrophic Cambisols (young, relatively unweathered soils with high-activity clay

and high nutrient status), and Vertisols (clay soils with high activity clay minerals and high nutrient contents). All of these soil types have very limited distributions, collectively encompassing just 1.8 percent of the Brazilian Amazon, according to the EMBRAPA map. Soil classes 5�7 have some agricultural potential but also important limitations, such as high acidity, low nutrient

availability, shallowness, waterlogging, and concretionary soils. These soil types are very extensive, comprising 53.4 percent of the Brazilian Amazon according to the EMBRAPA map. Soil classes 2�4 have restricted potential for certain lowdemand uses, such as cattle pasture or undemanding tree crops. These include the intensively weathered Xanthic Ferralsols of central Amazonia, very stony and shallow soils, nutrient-poor waterlogged soils and Plinthosols (soils that form into hardened laterite when exposed to wetting and drying cycles). According to the EMBRAPA map, these soils encompass 34.8 percent of the Brazilian Amazon. Soil class 1 has no potential for agriculture. These include

very sandy soils (podzols and quartz sands, some of which are waterlogged) and a small area of salt-affected soils along the ocean shore. This class encompasses 7.8 percent of the Brazilian Amazon, according to the EMBRAPA map.



Soil waterlogging

This index quantifies waterlogging, poor drainage and flooding risk, and has four classes. A value of 0 indicates soils with no waterlogging or flooding (77.9 percent of the Brazilian Amazon). Soils with a value of 1 are at risk of

seasonal flooding but are not hydromorphic (anoxic), such as varzea soils (0.7 percent of the Brazilian Amazon). A value of 2 indicates soils that are hydromorphic at greater depth or periodically waterlogged (4.6 percent of the Brazilian Amazon), whereas 3 indicates soils that are hydromorphic near the soil

surface and often permanently waterlogged (such as gley soils; 14.6 percent of the Brazilian Amazon).



Soil shallowness and stoniness

This index has three classes: 0 (not shallow or stony; 91.5 percent of the Brazilian Amazon), 1 (somewhat shallow or stony, including relatively young soils in mountainous regions and concetionary soils; 1.9 percent of the Brazilian Amazon), and 2 (very shallow or stony soils, including very young soils in mountain regions and a small area of Planosols that have a compact subsoil; 4.4 percent of the Brazilian Amazon). This index is important because certain young soils are chemically rich but too shallow for agricultural



Statistics

To ensure that a wide range of deforestation values were included, chosen quadrats were stratified on deforestation intensity by randomly selecting forty plots within each of three deforestation categories (0�33.33, 33.33�66.67 and more than 66.67 percent deforestation).

References:

Beinroth, F.H. (1975) Relationships between US Soil Taxonomy, the Brazilian system and FAO/UNESCO units. Soil management in tropical America (eds E. Bornemisza and A. Alvarado), pp. 97�108. North Carolina State University,

Raleigh, NC.



ISA (1999) PROBIO Databases. Instituto Socioambiental (ISA), Sao Paulo, Brazil.



Oliveira, J.B., Jacomine, P.K.T. & Camargo, M.N. (1992) Classes gerais de solos do Brasil. FUNEP, Jaboticabal, Brazil.



Sombroek, W.G. (1984) Soils of the Amazon region. The Amazon � limnology and landscape ecology of a mighty tropical river and its basin (ed. H. Sioli), pp. 521�535. Dr W. Junk, Dordrecht, the Netherlands.



Sombroek, W.G. (2000) Amazon landforms and soils in relation to biological diversity. Acta Amazonica, 30, 81�100.



Sombroek, W.G. (2001) Spatial and temporal patterns of Amazon rainfall: consequences for planning of agricultural occupation and the protection of primary forests. Ambio, 30, 388�396.

Skip navigation linksHOME | ABOUT | LIBRARY | NEWS ARCHIVE | CONTACTS | INVESTIGATIONS | LOGISTICS | DATA |TRAINING & EDUCATION

NASA logo
ORNL DAAC
Get Acrobat Reader