TY - JOUR
T1 - Modeling tropical deforestation in the southern Yucatán peninsular region
T2 - Comparing survey and satellite data
AU - Geoghegan, Jacqueline
AU - Villar, Sergio Cortina
AU - Klepeis, Peter
AU - Mendoza, Pedro Mac Ario
AU - Ogneva-Himmelberger, Yelena
AU - Chowdhury, Rinku Roy
AU - Turner, B. L.
AU - Vance, Colin
N1 - Funding Information:
This work was undertaken through the auspices of the Southern Yucatán Peninsular Region project with core sponsorship from NASA’s LCLUC (Land-Cover and Land-Use Change) program (NAG 56406) and the Center for Integrated Studies on Global Change, Carnegie Mellon University (CIS-CMU; NSF-SBR 95-21914). Additional funding from NASA’s New Investigator Program (NAG5-8559) also supported the specific research in this paper. SYPR is a collaborative project of El Colegio de la Frontera Sur (ECOSUR), Harvard Forest, Harvard University, the George Perkins Marsh Institute, Clark University and CIS-CMU. The views expressed in this paper are those of the authors and do not necessarily represent those of the US Environmental Protection Agency. We thank Laura Schneider for her assistance with this paper.
PY - 2001/6
Y1 - 2001/6
N2 - This paper presents some initial modeling results from a large, interdisciplinary research project underway in the southern Yucatán peninsular region. The aims of the project are: to understand, through individual household survey work, the behavioral and structural dynamics that influence land managers' decisions to deforest and intensify land use; model these dynamics and link their outcomes directly to satellite imagery; model from the imagery itself; and, determine the robustness of modeling to and from the satellite imagery. Two complementary datasets, one from household survey data on agricultural practices including information on socio-economic factors and the second from satellite imagery linked with aggregate government census data, are used in two econometric modeling approaches. Both models test hypotheses concerning deforestation during different time periods in the recent past in the region. The first uses the satellite data, other spatial environmental variables, and aggregate socio-economic data (e.g., census data) in a discrete-choice (logit) model to estimate the probability that any particular pixel in the landscape will be deforested, as a function of explanatory variables. The second model uses the survey data in a cross-sectional regression (OLS) model to ask questions about the amount of deforestation associated with each individual farmer and to explain these choices as a function of individual socio-demographic, market, environmental, and geographic variables. In both cases, however, the choices of explanatory variables are informed by social science theory as to what are hypothesized to affect the deforestation decision (e.g., in a von Thünen model, accessibility is hypothesized to affect choice; in a Ricardian model, land quality; in a Chayanovian model, consumer-labor ratio). The models ask different questions using different data, but several broad comparisons seem useful. While most variables are statistically significant in the discrete choice model, none of the location variables are statistically significant in the continuous model. Therefore, while location affects the overall probability of deforestation, it does not appear to explain the total amount of deforestation on a given location by an individual.
AB - This paper presents some initial modeling results from a large, interdisciplinary research project underway in the southern Yucatán peninsular region. The aims of the project are: to understand, through individual household survey work, the behavioral and structural dynamics that influence land managers' decisions to deforest and intensify land use; model these dynamics and link their outcomes directly to satellite imagery; model from the imagery itself; and, determine the robustness of modeling to and from the satellite imagery. Two complementary datasets, one from household survey data on agricultural practices including information on socio-economic factors and the second from satellite imagery linked with aggregate government census data, are used in two econometric modeling approaches. Both models test hypotheses concerning deforestation during different time periods in the recent past in the region. The first uses the satellite data, other spatial environmental variables, and aggregate socio-economic data (e.g., census data) in a discrete-choice (logit) model to estimate the probability that any particular pixel in the landscape will be deforested, as a function of explanatory variables. The second model uses the survey data in a cross-sectional regression (OLS) model to ask questions about the amount of deforestation associated with each individual farmer and to explain these choices as a function of individual socio-demographic, market, environmental, and geographic variables. In both cases, however, the choices of explanatory variables are informed by social science theory as to what are hypothesized to affect the deforestation decision (e.g., in a von Thünen model, accessibility is hypothesized to affect choice; in a Ricardian model, land quality; in a Chayanovian model, consumer-labor ratio). The models ask different questions using different data, but several broad comparisons seem useful. While most variables are statistically significant in the discrete choice model, none of the location variables are statistically significant in the continuous model. Therefore, while location affects the overall probability of deforestation, it does not appear to explain the total amount of deforestation on a given location by an individual.
KW - Econometric models
KW - Land-use/cover change
KW - Mexico
KW - Survey and satellite data
KW - Tropical deforestation
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U2 - 10.1016/S0167-8809(01)00201-8
DO - 10.1016/S0167-8809(01)00201-8
M3 - Article
AN - SCOPUS:0034999030
SN - 0167-8809
VL - 85
SP - 25
EP - 46
JO - Agriculture, Ecosystems and Environment
JF - Agriculture, Ecosystems and Environment
IS - 1-3
ER -