People and Pixels Linking Remote Sensing and Social Science (1998) / Chapter Skim
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4 Linking Satellite, Census, and Survey Data to Study Deforestation in the Brazilian Amazon
4 Linking Satellite, Census, and Survey Data to Study Deforestation in the Brazilian Amazon
Pages 70-93
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From page 70... ...
These considerations suggest the prospect of modeling the causes of deforestation by using a data set that links the estimates of land-cover change derived by satellite images to the social indicators generated by the various censuses. A regionwide research design based on satellite and census data was a natural vehicle for a productive collaboration between a systems ecologist with expertise in remote sensing technologies (Skole)
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From page 71... ...
-funded Pathfinder project achieved international recognition for its singular contribution to the production of accurate estimates of deforestation for the Amazon region as a whole (Skole and Tucker, 1993~. The estimates, which were years in the making, represented a timely contribution to a controversial and highly politicized debate regarding the amount of land clearing that had taken place in northern Brazil (e.g., World Bank, 1992~.
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From page 72... ...
The final two sections present the findings of our initial efforts to establish the covariates of deforestation in the Amazon and describe a proposed method for using field work to establish ground truth for the statistical models. THE BRAZILIAN AMAZON Defining the Region The geographic boundaries of Amazonia can be defined in various ways.
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From page 73... ...
Development policies designed to populate the region included credit and tax incentives to attract private capital to the region, construction of the Transamazon Highway, and the colonization of small farmers on 100-hectare plots along both sides of the new road (Fearnside, 1986; Moran, 1981; Smith, 1982~. Colonization projects organized by the Institute of Colonization and Agrarian Reform (INCRA)
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From page 74... ...
In a rural context characterized by violent competition for land and in the absence of clear property rights to guarantee ownership, individuals asserted their land claims by clearing the forest cover, often to a much greater degree than was economically necessary. The direct cause of deforestation in Amazonia was thus the change in land use that came about as a consequence of the decline of fishing, forest extraction, and shifting small-plot agriculture.
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From page 75... ...
, generally offer limited insights into the dynamics of landcover change as compared, for example, with regional analyses that are carried out within countries and make use of a wider range of independent variables (e.g., Reis and Guzman, 1992; Pfaff, 1997~. The most detailed results unsurprisingly come from case studies, which have been especially valuable in producing highly nuanced analyses of particular sites in the Brazilian Amazon.
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From page 76... ...
For example, the availability of satellite images for large areas in this case, the data for the entire Legal Amazon produced by the NASA Pathfinder project opens up the possibility of constructing regionwide models of the human dimensions of deforestation. Regional models, in turn, respond to the call for empirical results that are comparable from one country to another and can serve as inputs to improve the modeling and projection of various kinds of global dynamics.
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From page 77... ...
The satellite data were preprocessed at the EROS Data Center to a standard format and projection (Universal Transverse Mercator) and sent on 8 mm tape to the University of New Hampshire for analysis.
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From page 78... ...
It is important to eliminate the savanna regions in analyses of deforestation because these areas are not the outcome of deforestation, but were always naturally unforested. Census Estimates of Demographic and Economic Structure Indicators of demographic and economic structure presented here were derived from the 1980 population and agricultural censuses.
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From page 79... ...
The irregular shape of the geopolitical boundaries has important implications for the spatial correspondence between the satellite and census data. SPATIAL SCALES AND SPATIAL CORRESPONDENCE To merge the satellite and census data into a single data set, the land-cover data at 57 m resolution were aggregated to conform to the boundaries of each municipio, the smallest spatial unit for which economic and demographic data are available.
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From page 82... ...
. To interpret the correlations otherwise is to commit what is sometimes called the "ecological fallacy."4 In other words, the discontinuity introduced by the fact that the dependent and independent variables are not linked at the level of the decision unit necessitates important caveats in the interpretation of the empirical relationships observed in the data.
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From page 83... ...
NOTE: AREA, DEF, DEFPCT, CLOUDPCT, and SAVANPCT are from satellite images; the re mainder are from demographic and agricultural censuses; see Annex 4.1. The positive sign on the population density variable and the negative sign on its square are findings consistent with this expectation.
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From page 84... ...
As expected, the results indicate that the percentage of land deforested is strongly associated with the density of ranchers, but not with the density of farmers, miners, and fishermen. Additional indicators available in the 1980 agricultural census suggest the structure of land tenure in the region.
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From page 85... ...
At the same time, statistical models of real-world processes even when they do include a much wider range of independent variables will always contain error terms. The errors reflect the difference between the actual amount of deforestation measured in a particular municipio and the value predicted by the leastsquares regression model.
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From page 86... ...
In this situation, the associations produced by the statistical model are said to be "spurious." As in the case of specification error, the only way to be sure that the relationships depicted in the equation are faithful representations of real events is to visit the municipios in question in order to collect additional information.6 Note that the research design for the field component of the project is systematically derived from the results of the modeling exercise. Research sites will be selected by identifying a subset of municipios that are statistical outliers (to address the issue of specification error)
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From page 87... ...
In this sense, ground truthing is a procedure that is arguably more straightforward than what we have in mind. In the context of the present study, the objective of field work is to verify a relationship established in a statistical model.7 For example, when we determine that deforestation is highly associated with some variable in a regression equation, the question becomes whether that relationship is really what one observes on the ground.
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From page 88... ...
7 Similar efforts to go beyond traditional ground truthing through extensive field work include those that attempt to develop new age classes of secondary growth (Moran et al., 1994b) and to understand management practices and intensification (Brondizio, 1996)
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From page 89... ...
1997 From Pattern to Process. Presentation at the Open Meeting of the Human Dimensions of Global Environmental Change Research Community, IIASA, Laxenburg, Austria, June 12-14.
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From page 90... ...
Child Survival 7. Fertility Subregion State Microregion Municipio Deforestation Analysis Code Total Population Rural Population Economically Active Population Total Households Rural Households Total Population Aged 5+ Rural Population Aged 5+ Total Migrants Rural Area Migrants Northeast Origin Migrants Number of Farmers Number of Ranchers Number of Forest Product Extractors Number of Fishers Number of Miners Number of Day Laborers in Agriculture Males and Females, from Ages 0-4 to 75+ at 4-Year Intervals For Women Aged 15-19, 20-24, 25-29, and 30-34: Children Ever Born Children Surviving For Women Aged 15-19, ..., 45-49: Infants Born During the Previous Year
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From page 91... ...
Agricultural Inputs 15. Agricultural Outputs 91 For Total and Rural Heads of Households: Income in Minimum Wages: <1, 1-<2, 2-<3, 3+ For Total and Rural Households: Housing Units with Mud Walls Housing Units with Electricity For Total and Rural Populations Aged 5+: Literate Persons Number of Owners, Renters, Tenants, Occupants Total Number of Rural Properties Rural Land Area Claimed in Properties Land Area Under Different Uses: Annual and Perennial Crops Fallow Natural and Cultivated Pasture Natural and Cultivated Forest Land Not in Use Number of Properties and Land Area in Properties of <1 to 100,000+ ha Number of Properties with <1 to 1,000+ ha of Cultivated Land Use of Fertilizers Number of Tractors Value of Productive Goods Value of Investments During Previous Year Value of Credit During Previous Year Value of Fuels Consumed Amount of Various Fuels Consumed Amount of Electricity Produced and Consumed Land Area, Production Yields for Annuals: Sugar Cane Rice Beans Manioc
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From page 92... ...
Silviculture Products 18. Rural Industries A~cai Babassu Nuts Rubber Biomass Charcoal Babassu Nut Charcoal Castanha do Para Firewood Timber Palm Heart Firewood Timber Paper Pulp Plantation Trees: Andiroba, Cedro, Eucalyptus, Gmelina, Ipe, American Pine, Ucuubeira Sugar Cane Transformed: For Sugar (Production Volume and Value)
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From page 93... ...
For Tapioca (Production Volume and Value) Total Industrial Establishments: Number of Mineral Extraction Establishments Number of Mineral Processing Establishments Number of Metallurgy Establishments Number of Logging Establishments Number of Rubber Product Establishments Number of Rubber Processing Establishments Square Kilometers 1980 Demographic Census Estimate Square Kilometers Satellite-Based Estimate Land Area Under Forest Land Area Under Savanna Land Area Deforested Land Area Under Secondary Growth Land Area Under Water Land Area Under Clouds Land Area Under Shadow Kilometers of Roads
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