Characterization and Modeling Agricultural and Forest Trajectories in the Northern Ecuadorian Amazon: Spatial Heterogeneity, Socioeconomic Drivers and Spatial Simulations Public Deposited

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  • March 20, 2019
  • Mena, Carlos F.
    • Affiliation: College of Arts and Sciences, Department of Geography
  • This research shows that agricultural frontier regions are heterogeneous and complex entities. This dissertation links four interconnected questions that seek to generate new insights into the processes of land use and land cover change in the Northern Ecuadorian Amazon (NEA). The research uses household survey data collected in the study area in 1990 and 1999 and a set of classified Landsat images for 1973, 1986, 1999, 1996, and 2002. This study, first, analyzes the composition and spatial configuration of the Land Use and Land Cover (LULC) trajectories in the NEA. Land trajectories are built using image algebra and stratified by deforestation stage and census sector. The analysis of LULC trajectories has suggested a core and periphery pattern of transitions in the NEA and shows the complexity of land changes in the region. Second, this research characterizes secondary forest succession, its extent and the socioeconomic, demographic, and biophysical factors that control forest generation. The analysis, using logistic regression, shows how improvements in accessibility and off-farm employment contribute positively to forest regeneration. Third, this research analyzes the spatial heterogeneity and spatial dependence of the relationships between socioeconomic, demographic, and biophysical drivers and LULC. The intent of this question is to find the spatial non-stationarity of the relationships between factors and LULC change using Geographically Weighted Regression and Spatial Lag Models. There is also an emphasis on new spatial representations of the parameters resulting from the regression analysis. This research component determined that the intensity of the drivers of LULC change is heterogeneous across space. Four, this research develops a cellular automata model that simulates LULC trajectories using pixels, neighborhoods, and spatial regimes that interact to produce broad LULC patterns. LULC patterns emerge from rules that control interactions among cells, cell neighborhoods and other spatial regimes created using GWR models. The aim of this research is to clarify the spatial and temporal nature of the relationship between population and land change and to predict positive and negative feedbacks between social, geographical, and biophysical factors that have implications for environmental management and policy.
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  • In Copyright
  • Walsh, Stephen J.
  • Open access

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